{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b82b23ee-1fbf-4762-83f1-70b610432bbc",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5190e22c-85c4-47b6-97d1-c16977fbd1a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:03.199471Z",
     "iopub.status.busy": "2024-04-01T18:27:03.198471Z",
     "iopub.status.idle": "2024-04-01T18:27:03.393515Z",
     "shell.execute_reply": "2024-04-01T18:27:03.392514Z",
     "shell.execute_reply.started": "2024-04-01T18:27:03.199471Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Default colour cycle: ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import importlib\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time as time\n",
    "import datetime\n",
    "from scipy.optimize import curve_fit # This causes conflicts when everyone imports every scipy module as sp!!\n",
    "import shutil\n",
    "    \n",
    "from Config import *\n",
    "from PlottingLibrary import *\n",
    "from InstrumentsLibrary import *\n",
    "import glob, re\n",
    "    \n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "from matplotlib import cm\n",
    "\n",
    "time_stamp_old=None\n",
    "\n",
    "import concurrent.futures as futures\n",
    "\n",
    "import grpc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import matplotlib as mpl\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 15  ## to get the customized size of the plot \n",
    "plt.rcParams['figure.figsize'] = [10,10]\n",
    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "colors = prop_cycle.by_key()['color']\n",
    "print(\"Default colour cycle: \" +str([color for color in colors]))\n",
    "\n",
    "from qm.logger import logger\n",
    "logger.setLevel(\"WARNING\")\n",
    "\n",
    "import pickle\n",
    "\n",
    "kmeans_standard = pickle.load(open(f'Z:\\SMPD3-8\\SpinRun3-1\\standard_kmean.model', 'rb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61b08b1d-a345-42cd-9de6-b0b9d6ee9d0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:03.448515Z",
     "iopub.status.busy": "2024-04-01T18:27:03.447517Z",
     "iopub.status.idle": "2024-04-01T18:27:03.657770Z",
     "shell.execute_reply": "2024-04-01T18:27:03.656770Z",
     "shell.execute_reply.started": "2024-04-01T18:27:03.448515Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "kmeans_standard = pickle.load(open(f'Z:\\SMPD3-8\\SpinRun3-1\\standard_kmean.model', 'rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe9c0ae-7e7c-446a-b981-6be3bfbbc5b3",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "800182b1-d8c3-4cb9-948b-0b1a06918076",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:19.188066Z",
     "iopub.status.busy": "2024-04-01T18:27:19.188066Z",
     "iopub.status.idle": "2024-04-01T18:27:20.113766Z",
     "shell.execute_reply": "2024-04-01T18:27:20.112765Z",
     "shell.execute_reply.started": "2024-04-01T18:27:19.188066Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "from Config import *\n",
    "\n",
    "def PrintStatic(s):\n",
    "    sys.stdout.flush()\n",
    "    sys.stdout.write(s + \" \" * (78 - len(s)) + \"\\r\")\n",
    "    \n",
    "def better_sleep(t_sleep):\n",
    "    \"\"\"Better way of pausing a cell and annoying manu\n",
    "    Prints progress and allows interrupt\"\"\"\n",
    "    if t_sleep<=0: return\n",
    "    PrintStatic((\"Annoying Yui for %i s of %s s\")%(0, t_sleep))\n",
    "    for counter in range(int(t_sleep)):\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Yui for %i s of %s s\")%(counter+1, t_sleep))\n",
    "    time.sleep(t_sleep%int(t_sleep))\n",
    "    print((\"Annoyed Manu for %g s                     \\n\")%(t_sleep))  \n",
    "\n",
    "def fit_function(guess,func,xdata,ydata,lb = -np.inf,ub = np.inf, extend = False):\n",
    "    \"\"\"Wrapper around curve_fit to conveniently do all the stuff you wish it already did\n",
    "    returns est,std,fine,data_fit\"\"\"\n",
    "    \n",
    "    est,cov = curve_fit(func, xdata, ydata,p0 = guess, bounds = (lb, ub))\n",
    "    std = np.sqrt(np.diag(cov))\n",
    "    #print (est,std)\n",
    "    #\n",
    "    if not extend:\n",
    "        fine = np.linspace(min(xdata),max(xdata),len(xdata)*20)\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "    else:\n",
    "        delta = max(xdata) - min(xdata)\n",
    "        fine = np.linspace(min(xdata) - extend*delta,max(xdata) + extend*delta,len(xdata)*10*(2*extend+1))\n",
    "        data_fit=func(fine,*est)\n",
    "        return (est,std,fine,data_fit)\n",
    "\n",
    "def better_qua_wait(t):\n",
    "    \"\"\"\n",
    "    Adaptative waiting function, the input t should be in cycle units.\n",
    "    Works for any input time including t > 1 s\n",
    "    \"\"\"\n",
    "    k = declare(int)\n",
    "    with if_(t>10e6):\n",
    "        with for_(k, 0, k < 4*t/1e6, k + 1):\n",
    "            wait(int(1e6//4))\n",
    "        wait(t - k * int(1e6//4))\n",
    "    with else_():\n",
    "         wait(t)    \n",
    "\n",
    "def sinhspace(start,stop,npoints, nonlinearity = 2*np.e): \n",
    "    \"\"\"\n",
    "    Returns a hyperbolic sinh range with the same syntax as numpy.linspace\n",
    "    points will be symmetrically spaced about the middle of the range\n",
    "    nolinearity=0 gives a linear sweep.\n",
    "    Larger values enhance the number of points in the middle.\n",
    "    Useful for quickly sweeping over a resonance peak at a known location!!\n",
    "    \"\"\"\n",
    "    if start==stop:\n",
    "        return(np.ones(npoints)*start)\n",
    "\n",
    "    elif nonlinearity!=0:\n",
    "        centre = (start+stop)/2\n",
    "        sweeprange = stop-start\n",
    "        sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(-1,1,npoints))\n",
    "        sweep = centre+0.5*abs(sweeprange)*sweep/max(sweep)\n",
    "        return(sweep)\n",
    "    else:\n",
    "        return(np.linspace(start,stop,npoints))\n",
    "\n",
    "def PrintStatic(s):\n",
    "    sys.stdout.flush()\n",
    "    sys.stdout.write(s + \" \" * (78 - len(s)) + \"\\r\")\n",
    "    \n",
    "def better_sleep(t_sleep):\n",
    "    \"\"\"Better way of pausing a cell and annoying manu\n",
    "    Prints progress and allows interrupt\"\"\"\n",
    "    if t_sleep<=0: return\n",
    "    PrintStatic((\"Annoying Manu for %i s of %s s\")%(0, t_sleep))\n",
    "    for counter in range(int(t_sleep)):\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Manu for %i s of %s s\")%(counter+1, t_sleep))\n",
    "    time.sleep(t_sleep%int(t_sleep))\n",
    "    print((\"Annoyed Manu for %g s                     \\n\")%(t_sleep))  \n",
    "       \n",
    "def calculate_field_xyz(field_value_mT,theta,phi,psi):\n",
    "\n",
    "    # components of the field in the magnet referential\n",
    "    Bx = field_value_mT * (np.sin(theta) * np.sin(phi) - np.cos(theta) * np.sin(psi) * np.cos(phi))\n",
    "    By = field_value_mT * (np.sin(theta) * np.cos(phi) + np.cos(theta) * np.sin(psi) * np.sin(phi))\n",
    "    Bz = field_value_mT * np.cos(theta) * np.cos(psi)\n",
    "    return(Bx,By,Bz)\n",
    "    \n",
    "def go_to_field_xyz(field_value_mT,theta,phi,psi):\n",
    "\n",
    "    # components of the field in the magnet referential\n",
    "    Bx = field_value_mT * (np.sin(theta) * np.sin(phi) - np.cos(theta) * np.sin(psi) * np.cos(phi))\n",
    "    By = field_value_mT * (np.sin(theta) * np.cos(phi) + np.cos(theta) * np.sin(psi) * np.sin(phi))\n",
    "    Bz = field_value_mT * np.cos(theta) * np.cos(psi)\n",
    "    \n",
    "    B_list=(Bx,By,Bz)\n",
    "    FieldtoCurrentRatio_list=(18.49,18.7,24.58)\n",
    "    # change the magnetic field\n",
    "    print('B0 set to %.3f mT' %(field_value_mT))\n",
    "    for Magnet,B,FieldtoCurrentRatio in zip(Magnet_list,B_list,FieldtoCurrentRatio_list):\n",
    "        output=Magnet.set_and_check_current(B / FieldtoCurrentRatio)\n",
    "        print('current set to %.3f A (%.4f mT) in %s'%(output,output*FieldtoCurrentRatio,Magnet.name()))\n",
    "\n",
    "    t0=time.time()\n",
    "    t2=time.time()\n",
    "    Ramping_state=[Magnet.get_ramping_state(verbose=False) for Magnet in Magnet_list]\n",
    "    while Ramping_state!=[2,2,2]:\n",
    "        time.sleep(0.1)\n",
    "        t1=time.time()\n",
    "        Ramping_state=[Magnet.get_ramping_state(verbose=False) for Magnet in Magnet_list]\n",
    "        if (t1-t2)>10:\n",
    "            t2=time.time()\n",
    "            print('ramping_state = ',Ramping_state)\n",
    "    t1=time.time()\n",
    "    print(\"Ramping time = {:.1f} s\".format(t1 - t0))\n",
    "\n",
    "    for Magnet,FieldtoCurrentRatio in zip(Magnet_list,FieldtoCurrentRatio_list):\n",
    "        current=Magnet.get_supply_current()\n",
    "        print('current ramped to %.3f A (%.4f mT) in %s'%(current,current*FieldtoCurrentRatio,Magnet.name()))\n",
    "\n",
    "def go_to_field_x(field_value_mT):\n",
    "    Bx = field_value_mT\n",
    "    FieldtoCurrentRatioX=18.49\n",
    "    Ix = Bx/FieldtoCurrentRatioX\n",
    "    output=MagnetX.set_and_check_current(Ix)\n",
    "    print('current set to %.3f A (%.3f mT) in %s'%(output,output*FieldtoCurrentRatioX,MagnetX.name()))\n",
    "    time.sleep(0.2)\n",
    "    \n",
    "    t0=time.time()\n",
    "    t2=time.time()\n",
    "    Ramping_state=1\n",
    "    while Ramping_state!=2:\n",
    "        time.sleep(0.1)\n",
    "        t1=time.time()\n",
    "        Ramping_state=MagnetX.get_ramping_state(verbose=False)\n",
    "        if (t1-t2)>10:\n",
    "            t2=time.time()\n",
    "            print('ramping_state = ',Ramping_state)\n",
    "    t1=time.time()\n",
    "    print(\"Ramping time = {:.1f} s\".format(t1 - t0))\n",
    "\n",
    "    current=MagnetX.get_supply_current()\n",
    "    print('current ramped to %.3f A (%.3f mT) in %s'%(current,current*FieldtoCurrentRatioX,MagnetX.name()))         \n",
    "        \n",
    "def go_to_field_y(field_value_mT):\n",
    "    By = field_value_mT\n",
    "    FieldtoCurrentRatioY=18.7\n",
    "    Iy = By/FieldtoCurrentRatioY\n",
    "    output=MagnetY.set_and_check_current(Iy)\n",
    "    print('current set to %.3f A (%.4f mT) in %s'%(output,output*FieldtoCurrentRatioY,MagnetY.name()))\n",
    "    \n",
    "    t0=time.time()\n",
    "    time.sleep(2)\n",
    "    \n",
    "    t2=time.time()\n",
    "    Ramping_state=1\n",
    "    while Ramping_state!=2:\n",
    "        time.sleep(0.1)\n",
    "        t1=time.time()\n",
    "        Ramping_state=MagnetY.get_ramping_state(verbose=False)\n",
    "        if (t1-t2)>10:\n",
    "            t2=time.time()\n",
    "            print('ramping_state = ',Ramping_state)\n",
    "    t1=time.time()\n",
    "    print(\"Ramping time = {:.1f} s\".format(t1 - t0))\n",
    "\n",
    "    current=MagnetY.get_supply_current()\n",
    "    print('current ramped to %.3f A (%.4f mT) in %s'%(current,current*FieldtoCurrentRatioY,MagnetY.name()))   \n",
    "    \n",
    "    return current\n",
    "\n",
    "def go_to_field_z(field_value_mT):\n",
    "    Bz = field_value_mT\n",
    "    FieldtoCurrentRatioZ=24.58\n",
    "    Iz = Bz/FieldtoCurrentRatioZ\n",
    "    output=MagnetZ.set_and_check_current(Iz)\n",
    "    print('current set to %.5f A (%.4f mT) in %s'%(output,output*FieldtoCurrentRatioZ,MagnetZ.name()))\n",
    "    time.sleep(0.2)\n",
    "    \n",
    "    t0=time.time()\n",
    "    t2=time.time()\n",
    "    Ramping_state=1\n",
    "    while Ramping_state!=2:\n",
    "        time.sleep(0.1)\n",
    "        t1=time.time()\n",
    "        Ramping_state=MagnetZ.get_ramping_state(verbose=False)\n",
    "        if (t1-t2)>10:\n",
    "            t2=time.time()\n",
    "            print('ramping_state = ',Ramping_state)\n",
    "    t1=time.time()\n",
    "    print(\"Ramping time = {:.1f} s\".format(t1 - t0))\n",
    "\n",
    "    current=MagnetZ.get_supply_current()\n",
    "    print('current ramped to %.5f A (%.4f mT) in %s'%(current,current*FieldtoCurrentRatioZ,MagnetZ.name()))       \n",
    "    \n",
    "def print_field_current_xyz(field_value_mT,theta,phi,psi):\n",
    "\n",
    "    # components of the field in the magnet referential\n",
    "    Bx = field_value_mT * (np.sin(theta) * np.sin(phi) - np.cos(theta) * np.sin(psi) * np.cos(phi))\n",
    "    By = field_value_mT * (np.sin(theta) * np.cos(phi) + np.cos(theta) * np.sin(psi) * np.sin(phi))\n",
    "    Bz = field_value_mT * np.cos(theta) * np.cos(psi)\n",
    "\n",
    "    B_list=np.array([Bx,By,Bz])\n",
    "    FieldtoCurrentRatio_list=np.array([18.49,18.7,24.58])\n",
    "    current_list = B_list/FieldtoCurrentRatio_list\n",
    "    print('Current (Ix, Iy, Iz) = (%.4f, %.4f, %.4f)' %tuple(current_list))\n",
    "    print('Field (Bx, By, Bz) = (%.3f, %.3f, %.3f)' %tuple(B_list))\n",
    "    \n",
    "def go_to_field_xyz_changingPS(field_value_mT,theta,phi,psi):\n",
    "    # Turn on PSwitch to exit persistent mode\n",
    "    MagnetZ.set_and_check_PSwitch(1)\n",
    "    MagnetX.set_and_check_PSwitch(1)\n",
    "    print('Heating persistent switch...')\n",
    "    time.sleep(35)\n",
    "    print('Persistent switch ON')\n",
    "    # Changing B0\n",
    "    go_to_field_xyz(field_value_mT,theta,phi,psi)\n",
    "    # Switich to persistent mode\n",
    "    MagnetZ.set_and_check_PSwitch(0)\n",
    "    MagnetX.set_and_check_PSwitch(0)\n",
    "    print('Cooling persistent switch...')\n",
    "    time.sleep(205)\n",
    "    print('Persistent switch OFF')\n",
    "    \n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "def plot_spectroscopy_fieldsweep(path,timestamp,xlabel = \"B [mT]\",integration_max = -1):\n",
    "    file = getfiles(path+timestamp,'.hdf5')[0]\n",
    "    \n",
    "    data = load_h5_to_dic(path+timestamp+file)[0]\n",
    "    \n",
    "    B0_list   = list(data.keys())\n",
    "    click     = np.array([data[B0]['click_hist'] for B0 in B0_list])\n",
    "    time_axis = np.array([data[B0]['time_axis'] for B0 in B0_list])\n",
    "    \n",
    "    \n",
    "    # global variable for one B0 spectroscopy --> taken with the first B0\n",
    "    theta_list = data[B0_list[0]]['theta']*180/np.pi\n",
    "    amplitude_list = data[B0_list[0]]['amplitude_pulse']\n",
    "    \n",
    "    B0list_list = np.array([float(string) for string in B0_list])\n",
    "    \n",
    "    bins = 20\n",
    "    \n",
    "    time_hist = time_axis.reshape(time_axis.shape[0], bins, int(time_axis.shape[1]/bins)).mean(-1)*1e-6\n",
    "    click_hist = click.reshape(click.shape[0], bins, int(time_axis.shape[1]/bins))[:,:integration_max].mean(-1)\n",
    "    \n",
    "    if False:\n",
    "        for ii, B in enumerate(B0_list):\n",
    "            plt.figure()\n",
    "            plt.plot(time_hist[ii], click_hist[ii])\n",
    "            plt.xlabel(\"Time [us]\")\n",
    "            plt.ylabel(\"Counts / us\")\n",
    "            plt.title(f\"Bz = {B} mT\")\n",
    "            plt.grid()\n",
    "    clickshape=click_hist.shape[1]\n",
    "    time_step=(time_hist.mean(0)[1:]-time_hist.mean(0)[:-1]).mean(0)\n",
    "        \n",
    "    number_of_counts = click_hist[:,:].sum(-1)*time_step\n",
    "    number_of_counts_subs = (click_hist[:,:10].sum(-1)-click_hist[:,-1:].sum(-1))*time_step\n",
    "    \n",
    "    fig, (ax0, ax1) = plt.subplots(2, 1, tight_layout=True)\n",
    "    fig.supxlabel(xlabel)\n",
    "    ax0.plot(B0list_list, number_of_counts)\n",
    "    ax0.set_ylabel(\"Number of counts\")\n",
    "    ax0.grid()\n",
    "    ax1.plot(B0list_list, number_of_counts_subs)\n",
    "    ax1.set_ylabel(\"Number of counts subtracted\")\n",
    "    ax1.grid()\n",
    "    \n",
    "def downsample(data,window):\n",
    "    return(np.mean(data[0:len(data)-int(len(data)%window)].reshape(-1,window),axis=1))\n",
    "\n",
    "def plot_2d_sweep(data,x=[],y=[],xlabel = '',ylabel = '',clabel = '',title = '',xtick = 'auto',ytick = 'auto',\n",
    "                  centre = None,vmin = None,vmax = None,cmap = sns.diverging_palette(240, 10, n=361),\n",
    "                  horizontal_ticks = False, fontsize = None,annot=False):\n",
    "    \"\"\"\n",
    "    Generic plotting function for 2D datasets\n",
    "    \"\"\"\n",
    "    \n",
    "    if len(x)==0: x = np.linspace(0, data.shape[1]-1, data.shape[1], dtype = int)\n",
    "    if len(y)==0: y = np.linspace(0, data.shape[0]-1, data.shape[0], dtype = int)\n",
    "    fieldsweep_df = pd.DataFrame(data=np.flip(data,axis = 0),index=np.flip(y,axis = 0),columns=x)\n",
    "    #else: fieldsweep_df = pd.DataFrame(data=data)\n",
    "    ax = sns.heatmap(fieldsweep_df, xticklabels = xtick, yticklabels = ytick,cmap = cmap,center = centre,vmin = vmin, vmax = vmax,annot=annot,annot_kws={\"fontsize\":10})#,center = -100,cmap = sns.diverging_palette(240, 10, n=361))\n",
    "    ax.collections[0].colorbar.set_label(clabel, fontsize = fontsize)\n",
    "    ax.collections[0].colorbar.ax.tick_params(labelsize=fontsize)\n",
    "    plt.tick_params(labelsize = fontsize)\n",
    "    plt.xlabel(xlabel, fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    if horizontal_ticks == True:\n",
    "        plt.xticks(rotation=0)\n",
    "        plt.yticks(rotation=0)\n",
    "    plt.title(title, fontsize = fontsize)\n",
    "\n",
    "def field_angle_to_cartesian(field, angle_degrees):\n",
    "    angle_radians = angle_degrees * np.pi / 180\n",
    "    field_y = np.sin(angle_radians) * field\n",
    "    field_z = np.cos(angle_radians) * field\n",
    "    return [0, field_y, field_z]\n",
    "\n",
    "def run_state_disc_old(centre_freq,spacing):\n",
    "    param_type=int\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 60000\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_readout_pulse = 0.0067\n",
    "    gauss_duration = 60000\n",
    "    \n",
    "    amplitude_sideband_pulse = 0.0001\n",
    "    t_wait = 5000\n",
    "    \n",
    "    span1=0.1e6\n",
    "    freq_final1 = Photon_IF+0.8e6+offset+span1/2\n",
    "    freq_init1 = Photon_IF+0.8e6+offset-span1/2\n",
    "    freq_step1 = 20e3\n",
    "    n_steps1 = int((freq_final1-freq_init1)/freq_step1)+1\n",
    "    freq_range1 = np.linspace(freq_init1,freq_final1,n_steps1)\n",
    "    \n",
    "    span2=0.1e6\n",
    "    freq_final2 = Photon_IF-0.8e6+offset+span2/2\n",
    "    freq_init2 = Photon_IF-0.8e6+offset-span2/2\n",
    "    freq_step2 = 20e3\n",
    "    n_steps2 = int((freq_final2-freq_init2)/freq_step2)+1\n",
    "    freq_range2 = np.linspace(freq_init2,freq_final2,n_steps2)\n",
    "    \n",
    "    freq_sidebands = np.concatenate((freq_range1,freq_range2))\n",
    "    \n",
    "    span=spacing\n",
    "    freq_final = Photon_IF + centre_freq*1e3 + 1e3*spacing//2\n",
    "    freq_init = Photon_IF +  centre_freq*1e3 - 1e3*spacing//2\n",
    "    freq_step = freq_final - freq_init\n",
    "    \n",
    "    print((freq_final-Photon_IF)/1e3,(freq_init-Photon_IF)/1e3)\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "    \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    print(N_iterations)\n",
    "    Measurement_time= 20 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    print(N_repetition)\n",
    "\n",
    "    for shot,freq_sideband in enumerate(freq_sidebands[:1]):\n",
    "        \n",
    "        # print(\"Shot %i of %i, Sideband frequency offset = %i kHz\"%(shot,len(freq_sidebands[:1]),(freq_sideband-Photon_IF)/1e3))\n",
    "    \n",
    "        with program() as Spin_detection_linewidth_angle:\n",
    "            I = declare(fixed)\n",
    "            I1 = declare(fixed)\n",
    "            Q2 = declare(fixed)\n",
    "            click=declare(bool)\n",
    "        \n",
    "            i = declare(int)\n",
    "            j = declare(int)\n",
    "            \n",
    "            freq_set = declare(int)\n",
    "            \n",
    "        \n",
    "            if stream_I:\n",
    "                I_stream = declare_stream()\n",
    "        \n",
    "            p_stream = declare_stream()\n",
    "            index_stream = declare_stream()\n",
    "        \n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "          \n",
    "                with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "                \n",
    "                    update_frequency(spin_element, freq_sideband)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_sideband_pulse), spin_element, duration = gauss_duration//4) \n",
    "                    \n",
    "                    wait(t_wait, spin_element)\n",
    "        \n",
    "                    update_frequency(spin_element, freq_set)\n",
    "                    \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration//4) \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    wait(int(waiting_time_spin/4), readout_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "        \n",
    "                    save(j, index_stream)\n",
    "        \n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    with for_(i, 0, i < N_iterations, i + 1):\n",
    "        \n",
    "                        with while_(I>I_threshold_reset):\n",
    "                            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                            play(qubit_pulse, qubit_element)\n",
    "                            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                        wait(int(waiting_time/4), readout_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "        \n",
    "        \n",
    "                        play(pump_pulse, pump_element) \n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                        assign(click, I>I_threshold)\n",
    "        \n",
    "                        if stream_I:\n",
    "                            save(I, I_stream)  # I is saved into I_stream\n",
    "        \n",
    "                        save(click, p_stream)  # I is saved into I_stream\n",
    "        \n",
    "            with stream_processing():\n",
    "                if stream_I:\n",
    "                    I_stream.buffer(N_iterations).save_all('I')\n",
    "                #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "                p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "                p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "                index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    return(res,freq_range,directory,filename)\n",
    "\n",
    "def run_state_disc(centre_freq,spacing, freq_sideband = None, N_repetition = None):\n",
    "    param_type=int\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 60000\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_readout_pulse = 0.00825\n",
    "    gauss_duration = 80000\n",
    "    \n",
    "    gauss_sideband_duration = 40000\n",
    "    amplitude_sideband_pulse = 0.02\n",
    "    t_wait = 5000\n",
    "    \n",
    "    span=spacing\n",
    "    freq_final = Photon_IF + centre_freq*1e3 + 1e3*spacing//2\n",
    "    freq_init = Photon_IF +  centre_freq*1e3 - 1e3*spacing//2\n",
    "    freq_step = freq_final - freq_init\n",
    "    \n",
    "    # print((freq_final-Photon_IF)/1e3,(freq_init-Photon_IF)/1e3)\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "    \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=5000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "    Measurement_time= 40 * 1000000 # in seconds * us /s\n",
    "    \n",
    "    if N_repetition == None:\n",
    "        N_repetition = Measurement_time//Integration_time\n",
    "\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        \n",
    "    \n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "      \n",
    "            with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "                \n",
    "                if freq_sideband is not None:\n",
    "                    update_frequency(spin_element, freq_sideband)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_sideband_pulse), spin_element, duration = gauss_sideband_duration//4) \n",
    "                    wait(t_wait, spin_element)\n",
    "                    \n",
    "                update_frequency(spin_element, freq_set)\n",
    "                \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration//4) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                wait(int(waiting_time_spin/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "    \n",
    "                save(j, index_stream)\n",
    "    \n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                    with while_(I>I_threshold_reset):\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "    \n",
    "    \n",
    "                    play(pump_pulse, pump_element) \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    assign(click, I>I_threshold)\n",
    "    \n",
    "                    if stream_I:\n",
    "                        save(I, I_stream)  # I is saved into I_stream\n",
    "    \n",
    "                    save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    return(res,freq_range,directory,filename)   \n",
    "\n",
    "def run_state_disc(centre_freq,spacing, freq_sideband = None,amp_sideband = 0.01, prep_freq = None, N_repetition = None):\n",
    "    param_type=int\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 60000\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_readout_pulse = 0.00825\n",
    "    gauss_duration = 80000\n",
    "    \n",
    "    gauss_sideband_duration = 40000\n",
    "    t_wait = 2000000\n",
    "    \n",
    "    amplitude_prep_pulse = 0.01\n",
    "    gauss_prep_duration = 40000\n",
    "    t_wait_prep = 2000000\n",
    "    \n",
    "    span=spacing\n",
    "    freq_final = Photon_IF + centre_freq*1e3 + 1e3*spacing//2\n",
    "    freq_init = Photon_IF +  centre_freq*1e3 - 1e3*spacing//2\n",
    "    freq_step = freq_final - freq_init\n",
    "    \n",
    "    # print((freq_final-Photon_IF)/1e3,(freq_init-Photon_IF)/1e3)\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "    \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "    Measurement_time= 40 * 1000000 # in seconds * us /s\n",
    "    \n",
    "    if N_repetition == None:\n",
    "        N_repetition = Measurement_time//Integration_time\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        \n",
    "    \n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "      \n",
    "            with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "                \n",
    "                # Preparation pulses\n",
    "                if prep_freq is not None:\n",
    "                    update_frequency(spin_element, prep_freq)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_prep_pulse), spin_element, duration = gauss_prep_duration//4)\n",
    "                    wait(t_wait_prep//4, spin_element)\n",
    "                \n",
    "                if freq_sideband is not None:\n",
    "                    update_frequency(spin_element, freq_sideband)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amp_sideband), spin_element, duration = gauss_sideband_duration//4) \n",
    "                    wait(t_wait//4, spin_element)\n",
    "                    \n",
    "                update_frequency(spin_element, freq_set)\n",
    "                \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration//4) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                wait(int(waiting_time_spin/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "    \n",
    "                save(j, index_stream)\n",
    "    \n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                    with while_(I>I_threshold_reset):\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "    \n",
    "    \n",
    "                    play(pump_pulse, pump_element) \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    assign(click, I>I_threshold)\n",
    "    \n",
    "                    if stream_I:\n",
    "                        save(I, I_stream)  # I is saved into I_stream\n",
    "    \n",
    "                    save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    return(res,freq_range,directory,filename)   \n",
    "\n",
    "def run_state_disc2(centre_freq,spacing, freq_sideband = None,amp_sideband = 0.2, prep_freq = None, N_repetition = None):\n",
    "    param_type=int\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 60000\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_readout_pulse = 0.00825\n",
    "    gauss_duration = 80000\n",
    "    \n",
    "    gauss_sideband_duration = 40000\n",
    "    t_wait = 4000000\n",
    "    \n",
    "    amplitude_prep_pulse = 0.2\n",
    "    gauss_prep_duration = 40000\n",
    "    t_wait_prep = 4000000\n",
    "    \n",
    "    span=spacing\n",
    "    freq_final = Photon_IF + centre_freq*1e3 + 1e3*spacing//2\n",
    "    freq_init = Photon_IF +  centre_freq*1e3 - 1e3*spacing//2\n",
    "    freq_step = freq_final - freq_init\n",
    "    \n",
    "    # print((freq_final-Photon_IF)/1e3,(freq_init-Photon_IF)/1e3)\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "    \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "    Measurement_time= 10 * 1000000 # in seconds * us /s\n",
    "    \n",
    "    if N_repetition == None:\n",
    "        N_repetition = Measurement_time//Integration_time\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        \n",
    "    \n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "        \n",
    "      \n",
    "        with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "            # Preparation pulses\n",
    "            if prep_freq is not None:\n",
    "                update_frequency(spin_element, prep_freq)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_prep_pulse), spin_element, duration = gauss_prep_duration//4)\n",
    "                wait(t_wait_prep//4, spin_element)\n",
    "            \n",
    "            if freq_sideband is not None:\n",
    "                update_frequency(spin_element, freq_sideband)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amp_sideband), spin_element, duration = gauss_sideband_duration//4) \n",
    "                wait(t_wait//4, spin_element)\n",
    "                \n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "                    \n",
    "                update_frequency(spin_element, freq_set)\n",
    "                \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration//4) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                wait(int(waiting_time_spin/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "    \n",
    "                save(j, index_stream)\n",
    "    \n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                    with while_(I>I_threshold_reset):\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "    \n",
    "    \n",
    "                    play(pump_pulse, pump_element) \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    assign(click, I>I_threshold)\n",
    "    \n",
    "                    if stream_I:\n",
    "                        save(I, I_stream)  # I is saved into I_stream\n",
    "    \n",
    "                    save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    return(res,freq_range,directory,filename)   \n",
    "\n",
    "def run_state_prep(centre_freq,spacing, freq_sideband = None):\n",
    "    \"\"\"prepares |g> then |e> on the nuclear spin state, alternating, then does readout on the electron\"\"\"\n",
    "    param_type=int\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 60000\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_readout_pulse = 0.0067\n",
    "    gauss_duration = 60000\n",
    "    \n",
    "    amplitude_sideband_pulse = 0.00001\n",
    "    sideband_duration = gauss_duration*10\n",
    "    t_wait = 5000\n",
    "    \n",
    "    span_sideband=770+800\n",
    "    freq_sideband_final = Photon_IF + centre_freq*1e3 + 1e3*span_sideband//2\n",
    "    freq_sideband_init = Photon_IF +  centre_freq*1e3 - 1e3*span_sideband//2\n",
    "    freq_sideband_step = freq_sideband_final - freq_sideband_init\n",
    "    \n",
    "    span=spacing\n",
    "    freq_final = Photon_IF + centre_freq*1e3 + 1e3*spacing//2\n",
    "    freq_init = Photon_IF +  centre_freq*1e3 - 1e3*spacing//2\n",
    "    freq_step = freq_final - freq_init\n",
    "    \n",
    "    # print((freq_final-Photon_IF)/1e3,(freq_init-Photon_IF)/1e3)\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "    \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "    Measurement_time= 600 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        \n",
    "    \n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "\n",
    "    with for_(freq_sideband, freq_sideband_init, freq_sideband < freq_sideband_final+freq_sideband_step//2, freq_sideband + freq_sideband_step):\n",
    "        update_frequency(spin_element, freq_sideband)\n",
    "        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "        play(spin_pulse*amp(amplitude_sideband_pulse), spin_element, duration = sideband_duration//4) \n",
    "        wait(t_wait, spin_element)  \n",
    "        \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "        \n",
    "            with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "                update_frequency(spin_element, freq_set)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration//4) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                wait(int(waiting_time_spin/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "        \n",
    "                save(j, index_stream)\n",
    "        \n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                with for_(i, 0, i < N_iterations, i + 1):\n",
    "        \n",
    "                    with while_(I>I_threshold_reset):\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "        \n",
    "        \n",
    "                    play(pump_pulse, pump_element) \n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    assign(click, I>I_threshold)\n",
    "        \n",
    "                    if stream_I:\n",
    "                        save(I, I_stream)  # I is saved into I_stream\n",
    "        \n",
    "                    save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).buffer(N_repetition).map(FUNCTIONS.average(3)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    return(res,freq_range,directory,filename)   \n",
    " \n",
    "def run_freq_sweep(centre_freq,freq_step = 0.008e6,meas_time_secs = 1):\n",
    "    sim_and_plot=True\n",
    "    excecute=True\n",
    "    \n",
    "    BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "    BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "    BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "    \n",
    "    param_type=int\n",
    "    waiting_time= 1000//4\n",
    "    waiting_time_spin= 60000//4\n",
    "    N = 30\n",
    "    stream_I = False\n",
    "    \n",
    "    amplitude_pulse = 0.0045\n",
    "    \n",
    "    gauss_duration = 80000//4\n",
    "\n",
    "    span=0.04e6\n",
    "    freq_final = 1e3*centre_freq+Photon_IF+span/2+20e3\n",
    "    freq_init  = 1e3*centre_freq+Photon_IF-span/2+20e3\n",
    "    \n",
    "    n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "    \n",
    "    prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "    flattop_prep_duration = 200_000//4\n",
    "    t_wait_prep = int(3e6//4)\n",
    "    amplitude_prep_pulse = 0.1\n",
    "\n",
    "    N_preparation = 5\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 \n",
    "    \n",
    "    ###################### hardcoded nonlinear sweep - need to change later!!! ######################\n",
    "    spacing = 60\n",
    "    centre1 = centre_freq-spacing/2\n",
    "    centre2 = centre_freq+spacing/2\n",
    "    span = 40\n",
    "    nonlinear_space = sinhspace(-span/2,span/2, n_steps, nonlinearity = 2)\n",
    "    freq_range = (1e3*(nonlinear_space+centre2)+Photon_IF).astype(int).tolist()\n",
    "\n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=1500 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "    Measurement_time= meas_time_secs * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        \n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "\n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "        \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Preparation #################\n",
    "            # Prepare the nucleus in desired state using N_preparation sideband pulses followed by a wait time\n",
    "            update_frequency(spin_sticky_element, prep_freq)\n",
    "            play('ON',fsv_trigger)\n",
    "            with for_(n, 0, n < N_preparation, n + 1):\n",
    "                align()\n",
    "                flattop_pulse(amplitude_prep_pulse, flattop_prep_duration, switch_duration_extra)\n",
    "                wait(t_wait_prep, spin_element)\n",
    "            # wait(t_wait_prep, spin_element) \n",
    "            with for_each_(freq_set, freq_range):\n",
    "                \n",
    "                ################# Frequency sweep #################\n",
    "                update_frequency(spin_element, freq_set)\n",
    "                \n",
    "                align()\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "                align()\n",
    "                wait(waiting_time_spin)\n",
    "                align()\n",
    "    \n",
    "                save(j, index_stream)\n",
    "                ################# Readout #################\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                    with while_(I>I_threshold_reset):\n",
    "                        align()\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align()\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    wait(int(waiting_time))\n",
    "                    align()\n",
    "\n",
    "                    play(pump_pulse, pump_element) \n",
    "                    align()\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    assign(click, I>I_threshold)\n",
    "    \n",
    "                    if stream_I:\n",
    "                        save(I, I_stream)  # I is saved into I_stream\n",
    "    \n",
    "                    save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    \n",
    "    return(res,np.array(freq_range))\n",
    "    \n",
    "def analyse_state_disc(res,freq_range,directory,filename,run, centre_freq, plot = False):\n",
    "    average_number=res.clicks.count_so_far()\n",
    "   \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "    integration_index=5\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    \n",
    "    fullpath=directory+filename+'_state_disc.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BX_mT':Field_list[0],\n",
    "                'BY_mT':Field_list[1],\n",
    "                'BZ_mT':Field_list[2],\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'freq_range':freq_range,\n",
    "                'centre_freq': centre_freq\n",
    "                }\n",
    "    save_h5(fullpath,datasets, group=str(run), overwrite=False)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    chunk = 200\n",
    "    npts = np.round(click_array.shape[0],-2)\n",
    "    offset = 0\n",
    "    excess_array = []\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "\n",
    "        stop = start+chunk\n",
    "        \n",
    "        integration_index=10\n",
    "        integration_index_bg=5\n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        \n",
    "        background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "\n",
    "    if plot: plt.figure()\n",
    "\n",
    "    plt.plot(chunk/2+np.linspace(0,npts-chunk,npts//chunk,dtype = int),excess_array[:,0],label = r\"$|\\uparrow \\rangle$\")\n",
    "    plt.plot(chunk/2+np.linspace(0,npts-chunk,npts//chunk,dtype = int),excess_array[:,1],label = r\"$|\\downarrow \\rangle$\")\n",
    "    plt.xlabel('Shot')\n",
    "    plt.ylabel('Integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'state_disc_time_resolved_%i.pdf'%run)\n",
    "    if plot: plt.show()\n",
    "    else: plt.clf()\n",
    "    return(chunk/2+np.linspace(0,npts-chunk,npts//chunk,dtype = int),excess_array[:,0],excess_array[:,1])\n",
    "    \n",
    "def analyse_freq_sweep(res,freq_range,run, save=False):\n",
    "    average_number=res.clicks.count_so_far()\n",
    "\n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    \n",
    "    integration_index=10\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    if save:\n",
    "        fullpath=directory+filename+'_freq_sweep.hdf5'\n",
    "        datasets= {\n",
    "                    'click_array': click_array,\n",
    "                    'time_axis': time_axis,\n",
    "                    'BX_mT':Field_list[0],\n",
    "                    'BY_mT':Field_list[1],\n",
    "                    'BZ_mT':Field_list[2],\n",
    "                    'amplitude_pulse':amplitude_pulse,\n",
    "                    'gauss_duration': gauss_duration,\n",
    "                    'freq_range':freq_range,\n",
    "                    }\n",
    "        save_h5(fullpath,datasets, group=str(run), overwrite=False)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "\n",
    "    npts = click_array.shape[0]\n",
    "    offset = 0\n",
    "\n",
    "    \n",
    "    integration_index=10\n",
    "    integration_index_bg=5\n",
    "\n",
    "    \n",
    "    excess=click_array.mean(0)[:,:integration_index].sum(-1)*(time_hist[1]-time_hist[0])\n",
    "    \n",
    "\n",
    "    \n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    \n",
    "    return(np.array(excess))\n",
    "\n",
    "def flattop_pulse(amplitude, duration, switch_duration_extra, element=spin_sticky_element):\n",
    "    \"\"\"\n",
    "    Send flattop pulse through the spin line. \n",
    "    The pulse is sent a combination of two ErfRising and a separate trigger pulse\n",
    "    \n",
    "    \"\"\"\n",
    "    play(\"ON\", spin_switch_element, duration=duration+switch_duration_extra)\n",
    "    \n",
    "    play(spin_sticky_pulse*amp(amplitude), element) \n",
    "    \n",
    "    wait(duration, element)\n",
    "    \n",
    "    play(spin_sticky_pulse*amp(-amplitude), element) \n",
    "    \n",
    "    ramp_to_zero(element, duration=4)\n",
    "\n",
    "def flattop_pulse2(amplitude, duration, switch_duration_extra, element=spin_sticky_element):\n",
    "    \"\"\"\n",
    "    Send flattop pulse through the spin line. \n",
    "    The pulse is sent a combination of two ErfRising and a separate trigger pulse\n",
    "    \n",
    "    \"\"\"\n",
    "    play(spin_sticky_pulse*amp(amplitude), element) \n",
    "    wait(duration, element)\n",
    "    play(spin_sticky_pulse*amp(-amplitude), element) \n",
    "    \n",
    "def flattop_pulse3(amplitude, duration, switch_duration_extra, ramp_time, element=spin_sticky_element):\n",
    "    \"\"\"\n",
    "    Send flattop pulse through the spin line. \n",
    "    The pulse is sent a combination of two ErfRising and a separate trigger pulse\n",
    "    \n",
    "    \"\"\"\n",
    "    play(spin_sticky_pulse*amp(amplitude), element, duration = ramp_time) \n",
    "    wait(duration, element)\n",
    "    play(spin_sticky_pulse*amp(-amplitude), element, duration = ramp_time) \n",
    "\n",
    "def flattop_pulse3_cos(amplitude, duration, switch_duration_extra, ramp_time, element=spin_sticky_element):\n",
    "    \"\"\"\n",
    "    Send flattop pulse through the spin line. \n",
    "    The pulse is sent a combination of two ErfRising and a separate trigger pulse\n",
    "    \n",
    "    \"\"\"\n",
    "    play(spin_cos_sticky_pulse*amp(amplitude), element, duration = ramp_time) \n",
    "    wait(duration, element)\n",
    "    play(spin_cos_sticky_pulse*amp(-amplitude), element, duration = ramp_time) \n",
    "    \n",
    "def flattop_pulse3_chirped(amplitude, duration, switch_duration_extra, ramp_time, element=spin_sticky_element):\n",
    "    \"\"\"\n",
    "    Send flattop pulse through the spin line. \n",
    "    The pulse is sent a combination of two ErfRising and a separate trigger pulse\n",
    "    \"\"\"\n",
    "    play(spin_chirp_sticky_pulse*amp(amplitude), element, duration = ramp_time) \n",
    "    wait(duration, element)\n",
    "    play(spin_chirp_sticky_pulse_down*amp(amplitude), element, duration = ramp_time) \n",
    "    ramp_to_zero(element, duration=4)\n",
    "\n",
    "def lorentz(delta,delta0,kappa,a,b):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n",
    "\n",
    "def fit_resonance_chunked(click_array,chunk,freq_range,Photon_IF, plot = False):\n",
    "    \"\"\"\n",
    "    Returns an updated estimate of electron spin transition centre frequency.\n",
    "    'Centre' refers to the midpoint between two lines corresponding to the two nuclear spin states.\n",
    "    Spacing is the estimated difference between the lines\n",
    "    By default, the function fits the peak, but can be made to just use the maximum point instead\n",
    "    \"\"\"\n",
    "    \n",
    "    npts = np.round(click_array.shape[0],-3)\n",
    "    offset = 0\n",
    "    integration_index=10\n",
    "    t_step = time_hist[1]-time_hist[0]\n",
    "    \n",
    "    index_array = []\n",
    "    maxfreq_array = []\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "    \n",
    "        stop = start+chunk\n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "\n",
    "        ####################################\n",
    "        # Find max point\n",
    "        x = (freq_range-Photon_IF)/1e3\n",
    "        maxfreq = x[np.argmax(excess)]\n",
    "\n",
    "        # Estimate for linewidth is half the spacing for now. Might not be the best guess in general, beware!\n",
    "        guess=[maxfreq, (x[-1]-x[0])/5, max(excess),0]\n",
    "\n",
    "\n",
    "        ####################################\n",
    "        # Try fitting\n",
    "\n",
    "        try:\n",
    "            popt, pcov = sp.curve_fit(lorentz,x,excess,guess)\n",
    "            index_array.append(start)\n",
    "            maxfreq_array.append(popt[0])\n",
    "        except Exception as e:\n",
    "            print('fit failed : ', e)\n",
    "            popt=guess\n",
    "            linestyle = '-'\n",
    "    \n",
    "    if plot:plt.figure()\n",
    "    plt.plot(index_array, maxfreq_array)\n",
    "    plt.title('Frequency tracking')\n",
    "    plt.xlabel('Index')\n",
    "    plt.ylabel('Frequency offset (kHz)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_%i_frequency_track.pdf')\n",
    "    if plot: plt.show()\n",
    "    else:plt.clf()\n",
    "    \n",
    "\n",
    "    return(np.array([index_array, maxfreq_array]))\n",
    "\n",
    "def lorentz_back(delta,delta0,kappa,a,b,c):\n",
    "    return a/(1+(delta-delta0)**2/(kappa/2)**2) + b + c*delta\n",
    "\n",
    "def bootstrap(fit_function, x, y, guess, n_sampling=100, plot=False):\n",
    "    \n",
    "    if plot:\n",
    "        plt.figure()\n",
    "    fit, _ = sp.curve_fit(fit_function, x, y, guess)\n",
    "    \n",
    "    popt_list = []\n",
    "    for i in range(n_sampling):\n",
    "        mask = np.random.randint(0,len(x),len(x))\n",
    "        try:\n",
    "            boot_x = np.array(x)[mask]\n",
    "            boot_y = np.array(y)[mask]\n",
    "            \n",
    "            popt, pcov = sp.curve_fit(fit_function, boot_x, boot_y, guess)\n",
    "            popt_list.append(popt)\n",
    "            \n",
    "            x_fit = np.linspace(x.min(), x.max(), 100)\n",
    "            \n",
    "            if plot:\n",
    "                plt.plot(x_fit, fit_function(x_fit, *popt), 'r', alpha=0.1)\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(x, y, 'ok')\n",
    "        plt.plot(x_fit, fit_function(x_fit, *fit), 'k', alpha=0.7, linewidth=5)\n",
    "    plt.xlim([min(x), max(x)])\n",
    "    plt.ylim([min(y), max(y)])\n",
    "    return np.std(np.array(popt_list), axis=0)\n",
    "\n",
    "def measure_cavity_point(photon_if_param, N_iterations=200):\n",
    "\n",
    "    waiting_time=1000\n",
    "    photon_if_param = int(photon_if_param)\n",
    "    \n",
    "    with program() as Pump_calibration:\n",
    "        I = declare(fixed)\n",
    "        Q = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q1 = declare(fixed)\n",
    "        I2 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "\n",
    "        click=declare(bool)\n",
    "\n",
    "        i = declare(int)\n",
    "\n",
    "        click_stream_on=declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "\n",
    "        with for_(i, 0, i < N_iterations, i + 1):\n",
    "            save(i, index_stream)\n",
    "            update_frequency(photon_element, photon_if_param)\n",
    "            align( readout_element,pump_element)\n",
    "\n",
    "            play(pump_pulse*amp(1.), pump_element)  # Qubit pi pulse\n",
    "            play(photon_pulse*amp(1.4), photon_element)  # Qubit pi pulse\n",
    "            align(  readout_element,pump_element)\n",
    "\n",
    "            I,Q=readout_block(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q1,I2,Q2,I,Q)\n",
    "            assign(click, I>I_threshold)\n",
    "\n",
    "            save(click, click_stream_on)\n",
    "\n",
    "            with while_(I>I_threshold):\n",
    "                align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                play(qubit_pulse, qubit_element)\n",
    "                align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                I=readout_block_I(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q2,I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element, readout_element, pump_element)\n",
    "\n",
    "            wait(int(waiting_time/4), readout_element)\n",
    "\n",
    "        with stream_processing():\n",
    "            index_stream.save('interation')\n",
    "            click_stream_on.boolean_to_int().average().save('p_ON')\n",
    "    \n",
    "    start = time.time()\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Pump_calibration)\n",
    "    end = time.time()\n",
    "    #print('Compilation time: ', end - start)\n",
    "    \n",
    "    start = time.time()\n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    end = time.time()\n",
    "    #print('Run time: ', end - start)\n",
    "    \n",
    "    return np.array(res.p_ON.fetch_all())\n",
    "\n",
    "def measure_cavity_spectra(span, param_step2, directory='', N_iterations=200, central_freq=Photon_IF):\n",
    "    \n",
    "    timestamp = get_timestamp()\n",
    "    directory = make_exp_directory(path,f\"{directory}/measure_cavity_spectra/\")\n",
    "\n",
    "    waiting_time=1000\n",
    "\n",
    "    param_final2 = central_freq + span/2\n",
    "    param_init2  = central_freq - span/2\n",
    "    n_steps2 = int((param_final2-param_init2)/param_step2)\n",
    "    param_range2 = np.linspace(param_init2,param_final2,n_steps2)\n",
    "\n",
    "    with program() as Pump_calibration:\n",
    "        I = declare(fixed)\n",
    "        Q = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q1 = declare(fixed)\n",
    "        I2 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "\n",
    "        click=declare(bool)\n",
    "\n",
    "        i = declare(int)\n",
    "        param_set2 = declare(int)\n",
    "\n",
    "        click_stream_on=declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "\n",
    "        with for_(i, 0, i < N_iterations, i + 1):\n",
    "            save(i, index_stream)\n",
    "\n",
    "            with for_(param_set2, param_init2, param_set2 < param_final2, param_set2 + param_step2):\n",
    "\n",
    "                update_frequency(photon_element, param_set2)\n",
    "                align( readout_element,pump_element)\n",
    "\n",
    "                play(pump_pulse*amp(1.), pump_element)  # Qubit pi pulse\n",
    "                play(photon_pulse*amp(1.4), photon_element)  # Qubit pi pulse\n",
    "                align(  readout_element,pump_element)\n",
    "\n",
    "                I,Q=readout_block(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q1,I2,Q2,I,Q)\n",
    "                assign(click, I>I_threshold)\n",
    "\n",
    "                save(click, click_stream_on)\n",
    "\n",
    "                with while_(I>I_threshold):\n",
    "                    align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                    I=readout_block_I(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q2,I)\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "                    align(qubit_element, readout_element, pump_element)\n",
    "\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "\n",
    "        with stream_processing():\n",
    "            index_stream.save('interation')\n",
    "            click_stream_on.boolean_to_int().buffer(n_steps2).average().save('p_ON')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Pump_calibration)\n",
    "    res = job.result_handles\n",
    "\n",
    "    filename=timestamp+'cavity_spec_%.1f_%.1f_step%.1f'%(param_init2,param_final2,param_step2)\n",
    "\n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    p_ON = np.array(res.p_ON.fetch_all())\n",
    "\n",
    "    x=(Photon_LO - param_range2)\n",
    "    y=p_ON\n",
    "    guess=[x[np.argmax(y)],(x[-1]-x[0])/10,max(y),0,0]\n",
    "\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(lorentz_back,x,y,guess)\n",
    "    except Exception as e:\n",
    "        print('fit failed: ', e)\n",
    "        popt=guess\n",
    "\n",
    "    return x, y, popt\n",
    "\n",
    "def get_buffer_yoko_rate(yoko, central_voltage, n_sample=2, N_iterations=200):\n",
    "    \n",
    "    timestamp = get_timestamp()\n",
    "    directory = make_exp_directory(path,f\"get_buffer_yoko_rate/{timestamp}\")\n",
    "    \n",
    "    cavity_frequencies = []\n",
    "    voltages = central_voltage + np.linspace(-0.4, 0.4, n_sample)\n",
    "    plt.figure()\n",
    "    plt.xlabel('Photon Frequency [GHz]')\n",
    "    plt.ylabel('Signal (counts)')\n",
    "    \n",
    "    for i in range(n_sample):\n",
    "        yoko.setVoltage(voltages[i],slewrate=0.5)\n",
    "        span=5e6\n",
    "        param_step2 = 0.05e6\n",
    "        x, y, popt = measure_cavity_spectra(span, param_step2,  N_iterations=N_iterations)\n",
    "        x -= Photon_freq\n",
    "        plt.plot((x+Photon_freq)*1e-9, y, '.')\n",
    "        plt.plot((x+Photon_freq)*1e-9, lorentz_back(x+Photon_freq, *popt), label='V = %.2f | df = %.3f MHz'%(voltages[i], (popt[0]-Photon_freq)*1e-6), alpha=0.5)\n",
    "        cavity_frequencies.append(popt[0])\n",
    "    \n",
    "    \n",
    "    out = np.polyfit(voltages, cavity_frequencies, 2)\n",
    "    plt.legend()\n",
    "    return out\n",
    "\n",
    "def autocalibrate_sb(yoko, voltages, N_iterations=200):\n",
    "    \n",
    "    timestamp = get_timestamp()\n",
    "    directory = make_exp_directory(path,f\"yoko_autocalibration_sb/{timestamp}\")\n",
    "    \n",
    "    heights = []\n",
    "    buffer_freqs = []\n",
    "    print(\"Autocalibration started \\n##################################################################################\")\n",
    "    \n",
    "    original_voltage = yoko.outputValue()\n",
    "    \n",
    "    ######Find SPMD frequencies on a range of yoko voltages\n",
    "    p_vals = get_buffer_yoko_rate(yoko, np.mean(voltages), n_sample=4, N_iterations=200)\n",
    "    print(p_vals)\n",
    "    for v in voltages:\n",
    "        yoko.setVoltage(v,slewrate=1)\n",
    "        if_frequency_center = Photon_LO - np.polyval(p_vals, v)\n",
    "        \n",
    "        counts = measure_cavity_point(if_frequency_center, N_iterations=N_iterations)\n",
    "        print('Max signal strength at %.2f V :: %.3f'%(v, counts))\n",
    "        heights.append(counts)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(voltages, heights,\"o\")\n",
    "    plt.xlabel(\"Yoko Voltage [V]\")\n",
    "    plt.ylabel(\"Max Signal strength [a.u.]\")\n",
    "    \n",
    "\n",
    "    guess=[\n",
    "        voltages[np.argmin(heights)],    \n",
    "        (voltages[-1]-voltages[0])/4,\n",
    "        np.min(heights)-np.max(heights),\n",
    "        np.max(heights), \n",
    "        (heights[-1]-heights[0])/(voltages[-1]-voltages[0])\n",
    "    ]\n",
    "        \n",
    "    popt, pcov = sp.curve_fit(lorentz_back,voltages,heights,guess)\n",
    "\n",
    "    optimal_v = popt[0]\n",
    "    \n",
    "    if not optimal_v:\n",
    "        print('Error during calibration fitting, returning to original state')\n",
    "        yokoBuffer.setVoltage(original_voltage,slewrate=0.5) \n",
    "        return config, heights\n",
    "    \n",
    "    # Interpolate the optimal voltage to obtain the cavity frequency\n",
    "    cavity_freq = np.polyval(p_vals, optimal_v)\n",
    "\n",
    "    v_plot = np.linspace(voltages[0], voltages[-1], 201)\n",
    "    plt.plot(v_plot, lorentz_back(v_plot, *popt))\n",
    "    plt.title(f\"Yoko optimal = {optimal_v} V \\n Cavity freq = {cavity_freq} Hz\")\n",
    "        \n",
    "    filename='calibration'\n",
    "    plt.savefig(directory+filename+'.pdf')\n",
    "    np.save(directory+filename+'.npy', np.array([np.polyval(p_vals, voltages),heights]))\n",
    "\n",
    "    if optimal_v:\n",
    "        yokoBuffer.setVoltage(optimal_v,slewrate=0.5) \n",
    "        Photon_IF=int(Photon_LO-cavity_freq)\n",
    "        config['elements']['Spin']['intermediate_frequency']=Photon_IF\n",
    "        config['elements']['Spin_sticky']['intermediate_frequency']=Photon_IF\n",
    "        config['elements']['Spin_sticky_extra']['intermediate_frequency']=Photon_IF\n",
    "        config['elements']['Photon']['intermediate_frequency']=Photon_IF\n",
    "        config['elements']['Photon_sticky']['intermediate_frequency']=Photon_IF\n",
    "        config['mixers']['Photon_mixer'][0]['intermediate_frequency']=Photon_IF\n",
    "\n",
    "\n",
    "        print('New cavity frequency: %d'%cavity_freq)\n",
    "        pump_detuning=-(cavity_freq-Photon_freq)\n",
    "        Pump_IF=int(Pump_LO-(Pump_freq+pump_detuning))\n",
    "        config['mixers']['Pump_mixer'][0]['intermediate_frequency']=Pump_IF\n",
    "        config['elements']['Pump']['intermediate_frequency']=Pump_IF\n",
    "        config['elements']['Pump_sticky']['intermediate_frequency']=Pump_IF\n",
    "        print(\"Change the photon freequency to %d\"%cavity_freq)\n",
    "        \n",
    "    return config, heights\n",
    "\n",
    "def autocalibrate_sb_old(yoko, span, param_step2, voltages, N_iterations=200):\n",
    "    \n",
    "    timestamp = get_timestamp()\n",
    "    directory = make_exp_directory(path,f\"yoko_autocalibration_sb/{timestamp}\")\n",
    "    \n",
    "    waiting_time=1000\n",
    "    \n",
    "    param_final1 = Pump_IF + span/2 \n",
    "    param_init1  = Pump_IF - span/2\n",
    "    param_step1  = span / 5\n",
    "\n",
    "    param_final2 = Photon_IF + span/2\n",
    "    param_init2  = Photon_IF - span/2\n",
    "\n",
    "    n_steps1 = int((param_final1-param_init1)/param_step1)\n",
    "    n_steps2 = int((param_final2-param_init2)/param_step2)\n",
    "\n",
    "    param_range1 = np.linspace(param_init1,param_final1,n_steps1)\n",
    "    param_range2 = np.linspace(param_init2,param_final2,n_steps2)\n",
    "\n",
    "    param_type=fixed\n",
    "\n",
    "    plot=False\n",
    "    sim_and_plot=False\n",
    "\n",
    "    param_name1='Pump freq'\n",
    "    param_name2='Photon freq'\n",
    "\n",
    "    with program() as Pump_calibration:\n",
    "        I = declare(fixed)\n",
    "        Q = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q1 = declare(fixed)\n",
    "        I2 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "\n",
    "        click=declare(bool)\n",
    "\n",
    "        i = declare(int)\n",
    "        param_set1 = declare(int)\n",
    "        param_set2 = declare(int)\n",
    "\n",
    "        click_stream_on=declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "\n",
    "        with for_(i, 0, i < N_iterations, i + 1):\n",
    "            save(i, index_stream)\n",
    "\n",
    "            with for_(param_set1, param_init1, param_set1 < param_final1, param_set1 + param_step1):\n",
    "                update_frequency(pump_element, param_set1)\n",
    "\n",
    "                with for_(param_set2, param_init2, param_set2 < param_final2, param_set2 + param_step2):\n",
    "\n",
    "                    update_frequency(photon_element, param_set2)\n",
    "                    align( readout_element,pump_element)\n",
    "\n",
    "                    play(pump_pulse*amp(1.), pump_element)  # Qubit pi pulse\n",
    "                    play(photon_pulse*amp(1.), photon_element)  # Qubit pi pulse\n",
    "                    align(  readout_element,pump_element)\n",
    "\n",
    "                    I,Q=readout_block(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q1,I2,Q2,I,Q)\n",
    "                    assign(click, I>I_threshold)\n",
    "\n",
    "                    save(click, click_stream_on)\n",
    "\n",
    "                    with while_(I>I_threshold):\n",
    "                        align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                        play(qubit_pulse, qubit_element)\n",
    "                        align(qubit_element, readout_element, pump_element, photon_element)\n",
    "                        I=readout_block_I(readout_element,JPA_element,readout_pulse,JPA_pulse,I1,Q2,I)\n",
    "                        wait(int(waiting_time/4), readout_element)\n",
    "                        align(qubit_element, readout_element, pump_element)\n",
    "\n",
    "                    wait(int(waiting_time/4), readout_element)\n",
    "\n",
    "        with stream_processing():\n",
    "            index_stream.save('interation')\n",
    "            click_stream_on.boolean_to_int().buffer(n_steps1*n_steps2).average().save('p_ON')\n",
    "    \n",
    "    heights = []\n",
    "    buffer_freqs = []\n",
    "    print(\"Autocalibration started \\n##################################################################################\")\n",
    "    \n",
    "    plt.ioff() # Only show last plot\n",
    "    \n",
    "    for v in voltages:\n",
    "        yoko.setVoltage(v,slewrate=0.5)\n",
    "\n",
    "        experiment_name='autocalibration_sb'\n",
    "        time_stamp=get_timestamp()\n",
    "\n",
    "        qmm = QuantumMachinesManager()\n",
    "        qm = qmm.open_qm(config)\n",
    "        job = qm.execute(Pump_calibration)\n",
    "        res = job.result_handles\n",
    "\n",
    "        filename=time_stamp+'%s_%.1f_%.1f_step%.1f'%(experiment_name,param_init2,param_final2,param_step2)\n",
    "        \n",
    "        res = job.result_handles\n",
    "        res.wait_for_all_values()\n",
    "        p_ON = np.reshape(np.array(res.p_ON.fetch_all()),(n_steps1,n_steps2))\n",
    "        \n",
    "        x=(Photon_LO - param_range2)\n",
    "        y=p_ON.mean(0)\n",
    "        def lorentz(delta,delta0,kappa,a,b):\n",
    "            return a/(1+(delta-delta0)**2/(kappa/2)**2)+b\n",
    "        guess=[x[np.argmax(y)],(x[-1]-x[0])/10,max(y),0]\n",
    "            \n",
    "        try:\n",
    "            popt, pcov = sp.curve_fit(lorentz,x,y,guess)\n",
    "        except:\n",
    "            print('fit failed')\n",
    "            popt=guess\n",
    "        \n",
    "        print('Max signal strength at %.2f V :: %.3f'%(v, popt[2]))\n",
    "        buffer_freqs.append(popt[0])\n",
    "        heights.append(popt[2])\n",
    "\n",
    "        plt.figure()\n",
    "        plt.plot(x, y)\n",
    "        plt.plot(x, lorentz(x, *popt))\n",
    "        plt.xlabel(\"Frequency [V]\")\n",
    "        plt.ylabel(\"Signal strength [a.u.]\")\n",
    "        plt.title(f\"Voltage = {v} \\n height= {popt[2]} V \\n buffer freq = {popt[0]} Hz\")\n",
    "\n",
    "        filename = f'V_{v}'\n",
    "        plt.savefig(directory+filename+'.pdf')\n",
    "        np.savetxt(directory+filename+'.txt', np.transpose([x, y]))\n",
    "        plt.close()\n",
    "\n",
    "    def lorentz(delta,delta0,kappa,a,b,c):\n",
    "        return -a/(1+(delta-delta0)**2/(kappa/2)**2) + b + c*delta\n",
    "    \n",
    "    plt.ion()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(voltages, heights,\"o\")\n",
    "    plt.xlabel(\"Yoko Voltage [V]\")\n",
    "    plt.ylabel(\"Max Signal strength [a.u.]\")\n",
    "    \n",
    "    try:\n",
    "        guess=[voltages[np.argmin(heights)],(voltages[-1]-voltages[0])/4,max(y),np.max(heights), (heights[-1]-heights[0])/(voltages[-1]-voltages[0])]\n",
    "        popt, pcov = sp.curve_fit(lorentz,voltages,heights,guess)\n",
    "        \n",
    "        optimal_v = popt[0]\n",
    "        # Interpolate the optimal voltage to obtain the cavity frequency\n",
    "        cavity_freq = np.interp(optimal_v, voltages, buffer_freqs)\n",
    "        \n",
    "        v_plot = np.linspace(voltages[0], voltages[-1], 201)\n",
    "        plt.plot(v_plot, lorentz(v_plot, *popt))\n",
    "        plt.title(f\"Yoko optimal = {optimal_v} V \\n Cavity freq = {cavity_freq} Hz\")\n",
    "        \n",
    "        filename='calibration'\n",
    "        plt.savefig(directory+filename+'.pdf')\n",
    "        \n",
    "        return optimal_v, cavity_freq\n",
    "    \n",
    "    \n",
    "    except Exception as e:\n",
    "        print(\"Error found during calibration :\", e)\n",
    "        return None, None\n",
    "\n",
    "def run_B0_theta_point(B0_field, theta, optimal_voltage, calibration = False):\n",
    "    integration_index = 10\n",
    "    go_to_field_xyz(B0_field, theta, phi, psi)\n",
    "    time.sleep(0.1)\n",
    "    currentX = MagnetX.get_supply_current()\n",
    "    currentY = MagnetY.get_supply_current()\n",
    "    currentZ = MagnetZ.get_supply_current()\n",
    "    time.sleep(0.1)\n",
    "    # directoryname1 = 'B0_%.2f_mT_amp%.3f'%(B0_field, amplitude_pulse)\n",
    "    # filename = time_stamp+'%s_B0_%.2f_mT_amp%.3f'%(experiment_name, B0_field, amplitude_pulse)\n",
    "    # directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp+\"/\"+directoryname1)\n",
    "    filename = time_stamp+'%s_B0_%.4f_mT_theta%.4f'%(experiment_name, B0_field, theta)\n",
    "    directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "      \n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    Measurement_time=8 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "\n",
    "    ##################################################\n",
    "    # Calibration\n",
    "    if calibration:\n",
    "        ###Calibration\n",
    "        span=5e6\n",
    "        param_step2 = 0.04e6\n",
    "\n",
    "        voltages = sinhspace(-0.4, 0.4, 11, nonlinearity=3) + optimal_voltage\n",
    "    \n",
    "        v, cavity_freq = autocalibrate_sb(yokoBuffer, span, param_step2, voltages, N_iterations=25)\n",
    "        if v:\n",
    "            optimal_voltage = v\n",
    "            yokoBuffer.setVoltage(optimal_voltage,slewrate=0.05)\n",
    "            \n",
    "            Photon_IF=int(Photon_LO-cavity_freq)\n",
    "            config['elements']['Spin']['intermediate_frequency']=Photon_IF\n",
    "            config['elements']['Photon']['intermediate_frequency']=Photon_IF\n",
    "            config['elements']['Photon_sticky']['intermediate_frequency']=Photon_IF\n",
    "            config['mixers']['Photon_mixer'][0]['intermediate_frequency']=Photon_IF\n",
    "    \n",
    "            pump_detuning=-(cavity_freq-Photon_freq)\n",
    "            Pump_IF=int(Pump_LO-(Pump_freq+pump_detuning))\n",
    "            config['mixers']['Pump_mixer'][0]['intermediate_frequency']=Pump_IF\n",
    "            config['elements']['Pump']['intermediate_frequency']=Pump_IF\n",
    "            config['elements']['Pump_sticky']['intermediate_frequency']=Pump_IF\n",
    "        else:\n",
    "            yokoBuffer.setVoltage(optimal_voltage,slewrate=0.05)\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "    \n",
    "        if stream_I:\n",
    "            I_stream = declare_stream()\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "    \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            play(spin_pulse*amp(amplitude_pulse), spin_element) \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "    \n",
    "            save(j, index_stream)\n",
    "    \n",
    "            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "            with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                with while_(I>I_threshold_reset):\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "    \n",
    "    \n",
    "                play(pump_pulse, pump_element) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                assign(click, I>I_threshold)\n",
    "    \n",
    "                if stream_I:\n",
    "                    save(I, I_stream)  # I is saved into I_stream\n",
    "    \n",
    "                save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "    \n",
    "    \n",
    "        with stream_processing():\n",
    "            if stream_I:\n",
    "                I_stream.buffer(N_iterations).save_all('I')\n",
    "            #p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).map(FUNCTIONS.average(1)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    \n",
    "    average_number=res.clicks.count_so_far()\n",
    "    powerdBm = 5.5+10*np.log10((Spin_const_amp/0.15)**2)\n",
    "    \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    \n",
    "    # N=50\n",
    "    res.clicks.count_so_far()\n",
    "    #click_hist=click_array.reshape(click_array.shape[0],int(click_array.shape[1]/N),N).sum(-1)\n",
    "    click_hist=(click_array.mean(0))\n",
    "    click_hist_bin = click_hist.reshape(N, int(click_hist.shape[0]/N)).mean(-1)\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    \n",
    "    integrated_signal = click_hist[:integration_index].mean()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(time_hist,click_hist_bin,'o-')\n",
    "    plt.ylabel(\"count/ms\")\n",
    "    plt.xlabel(\"time (ms)\")\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_hist_bin,[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    \n",
    "    \n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n B0 = %.3f mT'%(average_number, cycle_time*1e3, B0_field))\n",
    "    \n",
    "    fullpath=directory+time_stamp+experiment_name+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_hist': click_hist,\n",
    "                'time_axis': time_axis,\n",
    "                'theta': theta,\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'currentX_in_A':currentX,\n",
    "                'currentY_in_A':currentY,\n",
    "                'currentZ_in_A':currentZ,\n",
    "                'B0_mT':B0_field,\n",
    "                'B0_array': B0_array,\n",
    "                'theta_array': theta_array\n",
    "                }\n",
    "    save_h5(fullpath,datasets,group=str(B0_field)+\"_%.8f\"%(theta))\n",
    "    \n",
    "    plt.savefig(directory+filename+'.pdf')\n",
    "    \n",
    "    \n",
    "    \n",
    "    return(integrated_signal)\n",
    "\n",
    "def measure_SMPD(click_stream,N_SMPDcycle,waiting_time, accumulate=False, return_click=False, timestamp=False, amplitude_correction=1.):\n",
    "    \"\"\"Measures number of clicks using the SMPD for N_SMPDcycle cycles\n",
    "     if accumulate True: streams the number of clicks detected during the N_SMPDcycle cycles as an int\n",
    "     if accumulate False: streams an array of bools of length N_SMPDcycle\n",
    "    doesn't return anything\"\"\"\n",
    "\n",
    "    if accumulate:\n",
    "        click=declare(int)\n",
    "        assign(click, 0)   # Clicks will be saved as a single inetger\n",
    "    else:\n",
    "        click=declare(bool)# Clicks will be saved as a bool\n",
    "        \n",
    "    i = declare(int)  # index of repetitions of SMPD cycles\n",
    "    I = declare(fixed)  # I Quadrature of SMPD readout\n",
    "    \n",
    "\n",
    "    I = readout_block_Idual(readout_element,  readout_pulse,  I)\n",
    "    with for_(i, 0, i < N_SMPDcycle, i + 1):\n",
    "\n",
    "        with while_(I>I_threshold_reset):\n",
    "            align(qubit_element,  readout_element, pump_element)\n",
    "            play(qubit_pulse, qubit_element)\n",
    "            align(qubit_element,  readout_element, pump_element)\n",
    "            I = readout_block_Idual(readout_element,  readout_pulse, I)\n",
    "            \n",
    "        wait(int(waiting_time/4), readout_element)\n",
    "        align(qubit_element,  readout_element, pump_element)\n",
    "\n",
    "        play(pump_pulse*amp(amplitude_correction), pump_element)\n",
    "        align(qubit_element,  readout_element, pump_element)\n",
    "        I = readout_block_Idual(readout_element,  readout_pulse,  I)\n",
    "        \n",
    "        if accumulate:\n",
    "            with if_(I>I_threshold):\n",
    "                assign(click, click+1)\n",
    "        elif timestamp:\n",
    "            with if_(I>I_threshold):\n",
    "                save(1, click_stream)\n",
    "        else:\n",
    "            assign(click, I>I_threshold)\n",
    "            save(click, click_stream)\n",
    "            \n",
    "    if accumulate:\n",
    "        save(click, click_stream)\n",
    "        if return_click:\n",
    "            return click\n",
    "                     \n",
    "def measure_SMPD_nostream(click, N_SMPDcycle,waiting_time):\n",
    "    \"\"\"Measures number of clicks using the SMPD for N_SMPDcycle cycles\n",
    "     if accumulate True: streams the number of clicks detected during the N_SMPDcycle cycles as an int\n",
    "     if accumulate False: streams an array of bools of length N_SMPDcycle\n",
    "    doesn't return anything\"\"\"\n",
    "\n",
    "    assign(click, 0)   # Clicks will be saved as a single inetger\n",
    "        \n",
    "    i = declare(int)  # index of repetitions of SMPD cycles\n",
    "    I = declare(fixed)  # I Quadrature of SMPD readout\n",
    "    \n",
    "\n",
    "    I = readout_block_Idual(readout_element,  readout_pulse,  I)\n",
    "    with for_(i, 0, i < N_SMPDcycle, i + 1):\n",
    "\n",
    "        with while_(I>I_threshold_reset):\n",
    "            align(qubit_element,  readout_element, pump_element)\n",
    "            play(qubit_pulse, qubit_element)\n",
    "            align(qubit_element,  readout_element, pump_element)\n",
    "            I = readout_block_Idual(readout_element,  readout_pulse, I)\n",
    "            \n",
    "        wait(int(waiting_time/4), readout_element)\n",
    "        align(qubit_element,  readout_element, pump_element)\n",
    "\n",
    "\n",
    "        play(pump_pulse, pump_element) \n",
    "        align(qubit_element,  readout_element, pump_element)\n",
    "        I = readout_block_Idual(readout_element,  readout_pulse,  I)\n",
    "        \n",
    "        with if_(I>I_threshold):\n",
    "            assign(click, click+1)\n",
    "          \n",
    "    return click        \n",
    "   \n",
    "def run_BZ_BY_point(BZ_field, BY_field, optimal_voltage, calibration = False, plot=False, directory=None):\n",
    "    integration_index = 10\n",
    "    go_to_field_y(BY_field)\n",
    "    go_to_field_z(BZ_field)\n",
    "    time.sleep(0.1)\n",
    "    currentX = MagnetX.get_supply_current()\n",
    "    currentY = MagnetY.get_supply_current()\n",
    "    currentZ = MagnetZ.get_supply_current()\n",
    "    time.sleep(0.1)\n",
    "    \n",
    "    time_stamp=get_timestamp()\n",
    "    filename = time_stamp+'%s_Bz_%.4f_mT_By_%.4f_mT'%(experiment_name, BZ_field, BY_field)\n",
    "    if not directory:\n",
    "        directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "    \n",
    "    N = 30\n",
    "    cycle_time_estimated=22  #in us\n",
    "    Integration_time=2000 #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    Measurement_time=5 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    ##################################################\n",
    "    # Calibration\n",
    "    if calibration:\n",
    "        ###Calibration\n",
    "        new_config, heights = autocalibrate_sb(yokoBuffer, voltages, N_iterations=2000)\n",
    "        if True:\n",
    "            config = new_config\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "    \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "    \n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "    \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            play(spin_pulse*amp(amplitude_pulse), spin_element) \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "    \n",
    "            save(j, index_stream)\n",
    "    \n",
    "            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "            with for_(i, 0, i < N_iterations, i + 1):\n",
    "    \n",
    "                with while_(I>I_threshold_reset):\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "    \n",
    "    \n",
    "                play(pump_pulse, pump_element) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                assign(click, I>I_threshold)\n",
    "    \n",
    "                save(click, p_stream)  # I is saved into I_stream\n",
    "    \n",
    "    \n",
    "    \n",
    "        with stream_processing():\n",
    "            #p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).map(FUNCTIONS.average(1)).save_all('clicks')\n",
    "            p_stream.boolean_to_int().buffer(N_iterations).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "    \n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle)\n",
    "    \n",
    "    res = job.result_handles\n",
    "    res.wait_for_all_values()\n",
    "    \n",
    "    average_number=res.clicks.count_so_far()\n",
    "    powerdBm = 5.5+10*np.log10((Spin_const_amp/0.15)**2)\n",
    "    \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    \n",
    "    # N=50\n",
    "    res.clicks.count_so_far()\n",
    "    #click_hist=click_array.reshape(click_array.shape[0],int(click_array.shape[1]/N),N).sum(-1)\n",
    "    click_hist=(click_array.mean(0))\n",
    "    click_hist_bin = click_hist.reshape(N, int(click_hist.shape[0]/N)).mean(-1)\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    \n",
    "    integrated_signal = click_hist[:integration_index].mean()\n",
    "    \n",
    "    if plot:\n",
    "        plt.figure()\n",
    "        plt.plot(time_hist,click_hist_bin,'o-')\n",
    "        plt.ylabel(\"count/ms\")\n",
    "        plt.xlabel(\"time (ms)\")\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_hist_bin,[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        if plot:\n",
    "            plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "            plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    \n",
    "    \n",
    "    if plot:\n",
    "        plt.title('average number %s \\n cycle time %.1f us \\n By = %.4f mT, Bz = %.4f mT'%(average_number, cycle_time*1e3, BY_field, BZ_field))\n",
    "    \n",
    "    fullpath=directory+time_stamp+experiment_name+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_hist': click_hist,\n",
    "                'time_axis': time_axis,\n",
    "                'BY_field': BY_field,\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'currentX_in_A':currentX,\n",
    "                'currentY_in_A':currentY,\n",
    "                'currentZ_in_A':currentZ,\n",
    "                'BZ_field':BZ_field,\n",
    "                }\n",
    "    save_h5(fullpath,datasets,group=\"%.6fZ_%.6fY\"%(BZ_field,BY_field))\n",
    "    \n",
    "    if plot:\n",
    "        plt.savefig(directory+filename+'.pdf')\n",
    "    \n",
    "    \n",
    "    \n",
    "    return(integrated_signal)\n",
    "\n",
    "def spin_v_Bfield(BY_array,BZ_array, directory=None):\n",
    "    counts_array = []\n",
    "\n",
    "    time_stamp=get_timestamp()\n",
    "    directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "    for index_y, BY_field in enumerate(BY_array):\n",
    "        for index, BZ_field in enumerate(BZ_array):\n",
    "            shot = index_y*len(BZ_array)+index\n",
    "            print(r\"Shot %i of %i, By = %.3f mT, Bz = %.3f mT\"%(shot,len(BY_array)*len(BZ_array),BY_field,BZ_field))\n",
    "            counts_array.append(run_BZ_BY_point(BZ_field, BY_field, optimal_voltage, calibration = False, directory=directory))\n",
    "            \n",
    "    return np.array(counts_array)\n",
    "\n",
    "def double_lorentz(delta,delta_a,delta_b,kappa,a,b,c):\n",
    "    return a/(1+(delta-delta_a)**2/(kappa/2)**2)+b/(1+(delta-delta_b)**2/(kappa/2)**2)+c\n",
    "\n",
    "def calc_new_centre_freq(excess_array,centre_freq,spacing,tolerance,run, freq_range,Photon_IF, max_shift = 15, fit = True, plot = False, fit_double_lorentz = False):\n",
    "    \"\"\"\n",
    "    Returns an updated estimate of electron spin transition centre frequency.\n",
    "    'Centre' refers to the midpoint between two lines corresponding to the two nuclear spin states.\n",
    "    Spacing is the estimated difference between the lines\n",
    "    By default, the function fits the peak, but can be made to just use the maximum point instead\n",
    "    x, centre_freq, tolerance, maxfreq, spacing and max_shift are all in kHz\n",
    "    \"\"\"\n",
    "    print(\"Current centre frequency = %.1f kHz\"%centre_freq)\n",
    "    ####################################\n",
    "    # Find max point\n",
    "    x = (freq_range-Photon_IF)/1e3\n",
    "    maxfreq = x[np.argmax(excess_array)]\n",
    "\n",
    "    \n",
    "    if plot:plt.figure()\n",
    "        \n",
    "    ####################################\n",
    "    # Try fitting\n",
    "    if fit_double_lorentz:\n",
    "        # Estimate for linewidth is half the spacing for now. Might not be the best guess in general, beware!\n",
    "        guess=[centre_freq-spacing/2,centre_freq+spacing/2,spacing/2,max(excess_array),max(excess_array),0]\n",
    "        try:\n",
    "            popt, pcov = sp.curve_fit(double_lorentz,x,excess_array,guess)\n",
    "            linestyle = 'o'\n",
    "            x_fit = np.linspace(x[0],x[-1],201)\n",
    "            y_fit = double_lorentz(x_fit,*popt)\n",
    "            plt.plot(x_fit,y_fit, label = 'fit')\n",
    "            maxfreq = popt[0]\n",
    "            other_line = popt[1]\n",
    "            centre_freq = (popt[0]+popt[1])/2\n",
    "        except:\n",
    "            print('fit failed')\n",
    "            popt=guess\n",
    "            x_fit = np.linspace(x[0],x[-1],201)\n",
    "            y_fit = double_lorentz(x_fit,*popt)\n",
    "            plt.plot(x_fit,y_fit, label = 'guess')\n",
    "            linestyle = '-'\n",
    "            other_line = None\n",
    "            \n",
    "    else:\n",
    "        # Estimate for linewidth is half the spacing for now. Might not be the best guess in general, beware!\n",
    "        guess=[maxfreq,spacing/2,max(excess_array),0]\n",
    "        try:\n",
    "            popt, pcov = sp.curve_fit(lorentz,x,excess_array,guess)\n",
    "            linestyle = 'o'\n",
    "            x_fit = np.linspace(x[0],x[-1],201)\n",
    "            y_fit = lorentz(x_fit,*popt)\n",
    "            plt.plot(x_fit,y_fit, label = 'fit')\n",
    "            # plt.plot(x_fit,lorentz(x_fit,*guess), label = 'guess')\n",
    "            maxfreq = popt[0]\n",
    "        except:\n",
    "            print('fit failed')\n",
    "            popt=guess\n",
    "            x_fit = np.linspace(x[0],x[-1],201)\n",
    "            plt.plot(x_fit,lorentz(x_fit,*guess), label = 'guess')\n",
    "            linestyle = '-'\n",
    "    \n",
    "        ####################################\n",
    "        # Guess which side of the threshold we're on to determine where the other line is \n",
    "        if maxfreq>centre_freq+tolerance and abs(maxfreq-spacing/2-centre_freq)<max_shift:\n",
    "            centre_freq = maxfreq-spacing/2\n",
    "            other_line  = maxfreq-spacing\n",
    "            print(\"New centre frequency offset: %.1f kHz\"%centre_freq)\n",
    "        elif maxfreq<centre_freq-tolerance and abs(maxfreq+spacing/2-centre_freq)<max_shift:\n",
    "            centre_freq = maxfreq+spacing/2\n",
    "            other_line  = maxfreq+spacing\n",
    "            print(\"New centre frequency offset: %.1f kHz\"%centre_freq)\n",
    "        else:\n",
    "            print(maxfreq,centre_freq,tolerance,max_shift,abs(maxfreq-centre_freq)<max_shift)\n",
    "            print(\"Keeping previous centre frequency offset: %.1f kHz\"%centre_freq)\n",
    "            other_line = None\n",
    "        \n",
    "    ####################################\n",
    "    # Plot\n",
    "    \n",
    "    plt.plot((freq_range-Photon_IF)/1e3,excess_array,linestyle, label = 'data')\n",
    "    plt.axvline(maxfreq,linestyle = \"--\", label = \"line maximum\", color = \"tab:green\")\n",
    "    if other_line is not None: plt.axvline(other_line,linestyle = \"--\", label = \"line 2 maximum\", color = \"tab:purple\")\n",
    "    plt.axvline(centre_freq,linestyle = \"--\", label = \"centre\", color = \"tab:red\")\n",
    "    \n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('Integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[1],3))+'mT_%i_'%run+'frequency_sweep.pdf')\n",
    "    if plot: plt.show()\n",
    "    else:plt.clf()\n",
    "\n",
    "    return(centre_freq)\n",
    "\n",
    "def wait_for_results(res,one_measurement_time):\n",
    "    counter = 0\n",
    "    while res.is_processing():\n",
    "        PrintStatic(\"Waiting %i of %i s for measurment (estimated)...\"%(counter,one_measurement_time))\n",
    "        time.sleep(1)\n",
    "        counter+=1\n",
    "    res.wait_for_all_values()\n",
    "    print()\n",
    "    \n",
    "def Complex_osc_decay(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T)**2-1j*(2*np.pi*f*t+2*np.pi*phi))+beta+1j*beta+(a+1j*a)*t\n",
    "\n",
    "def loss(arg):\n",
    "    data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "    return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "def extract_populations_4state(click_NRO, frequency_domain = False, accumulated = False, kmeans = None):\n",
    "    \"\"\"takes an array of ramsey readout clicks and, assuming there are 4 possible states, extracts the population probabilities of each\n",
    "    frequency_domain = False assumes time-domain readout and discriminates in phase\n",
    "    frequency_domain = True assumes frequency seletcive readout, discriminating along a single frequency axis\"\"\"\n",
    "    \n",
    "    # We need to do the mean or the sum over two axes for this to work, here we do the first\n",
    "    if accumulated: XY_NRO = click_NRO\n",
    "    else: XY_NRO = click_NRO.sum(-2)\n",
    "        \n",
    "    # Now we find two differential quantities allowing us to define two axes along which we will discriminate states\n",
    "    if frequency_domain:\n",
    "        delta_x = np.take(XY_NRO, 0, -1)+np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 2, -1)-np.take(XY_NRO, 3, -1)\n",
    "        delta_y = np.take(XY_NRO, 0, -1)+np.take(XY_NRO, 2, -1)-np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 3, -1)\n",
    "    else: \n",
    "        delta_x = np.take(XY_NRO, 0, -1)-np.take(XY_NRO, 2, -1)\n",
    "        delta_y = np.take(XY_NRO, 1, -1)-np.take(XY_NRO, 3, -1)\n",
    "    \n",
    "    # Now we take the 4 possible quadrants of the two differential quantities defined above to get 4 populations\n",
    "    q0 = np.logical_and(delta_x>0,delta_y>0).mean(0)\n",
    "    q1 = np.logical_and(delta_x>0,delta_y<0).mean(0)\n",
    "    q2 = np.logical_and(delta_x<0,delta_y>0).mean(0)\n",
    "    q3 = np.logical_and(delta_x<0,delta_y<0).mean(0)\n",
    "    \n",
    "    # If a kmeans object is passed as an argument, we use this to classify points in a 2D space instead of quadrants\n",
    "    if kmeans is not None:\n",
    "        if kmeans.n_features_in_ == 2:\n",
    "            original_shape = delta_x.shape\n",
    "\n",
    "            flatten_x = delta_x.flatten()\n",
    "            flatten_y = delta_y.flatten()\n",
    "\n",
    "            pop = kmeans.predict(np.transpose([flatten_x, flatten_y]))\n",
    "        elif  kmeans.n_features_in_ == 4:\n",
    "            original_shape = data.shape[:-1]\n",
    "            Z = data.reshape(np.cumprod(original_shape)[-1], 4)\n",
    "            pop = kmeans.predict(Z)\n",
    "            \n",
    "        pop = pop.reshape(original_shape)\n",
    "        \n",
    "        q0 = (pop==0).mean(0)\n",
    "        q1 = (pop==1).mean(0)\n",
    "        q2 = (pop==2).mean(0)\n",
    "        q3 = (pop==3).mean(0)\n",
    "        \n",
    "    return(q0,q1,q2,q3,delta_x,delta_y)\n",
    "\n",
    "def plot_populations(x,click_NRO_prep,click_NRO,directory,filename,save = True, xlabel = 'Frequency (kHz)'):\n",
    "    \n",
    "    \"\"\"Plots nucelar spin populations for preparation and readout.\n",
    "    Assumes 4 state ramsey readout.\n",
    "    x is the x axis sweep variable against which the 4 populations will be plotted\n",
    "    Saving is optional.\"\"\"\n",
    "    \n",
    "    (q0,q1,q2,q3,delta_x_prep, delta_y_prep,)=extract_populations_4state(click_NRO_prep)\n",
    "    plt.figure(figsize = (14,14))\n",
    "    \n",
    "    plt.subplot(2,2,1)\n",
    "    plt.title('Preparation')\n",
    "    \n",
    "    plt.plot(x, q0, label=r\"$|\\downarrow\\uparrow  \\rangle$\")\n",
    "    plt.plot(x, q1, label=r\"$|\\downarrow\\downarrow\\rangle$\")\n",
    "    plt.plot(x, q2, label=r\"$|\\uparrow  \\downarrow\\rangle$\")\n",
    "    plt.plot(x, q3, label=r\"$|\\uparrow  \\uparrow  \\rangle$\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel('Probability')\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "    (q0,q1,q2,q3,delta_x,delta_y)=extract_populations_4state(click_NRO)\n",
    "    plt.subplot(2,2,2)\n",
    "    plt.title('Spectroscopy')\n",
    "    plt.plot(x, q0, label=r\"$|\\downarrow\\uparrow  \\rangle$\")\n",
    "    plt.plot(x, q1, label=r\"$|\\downarrow\\downarrow\\rangle$\")\n",
    "    plt.plot(x, q2, label=r\"$|\\uparrow  \\downarrow\\rangle$\")\n",
    "    plt.plot(x, q3, label=r\"$|\\uparrow  \\uparrow  \\rangle$\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel('Probability')\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "    plt.subplot(2,2,3)\n",
    "    plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    plt.xlabel(\"Readout iteration\")\n",
    "    plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.subplot(2,2,4)\n",
    "    plt.scatter(delta_x, delta_y, s=10, alpha = 0.1)\n",
    "    plt.scatter(delta_x_prep, delta_y_prep, s=10, alpha = 0.1, color = 'red')\n",
    "\n",
    "    # plt.text( 0.2,  0.35, 'P=%.3f'%q0.mean())\n",
    "    # plt.text( 0.2, -0.40, 'P=%.3f'%q1.mean())\n",
    "    # plt.text(-0.4,  0.35, 'P=%.3f'%q2.mean())\n",
    "    # plt.text(-0.4, -0.40, 'P=%.3f'%q3.mean())\n",
    "    plt.xlabel(r'$\\Delta_\\mathrm{clicks}~x$')\n",
    "    plt.ylabel(r'$\\Delta_\\mathrm{clicks}~y$')\n",
    "    # plt.xlim([-0.5, 0.5])\n",
    "    # plt.ylim([-0.5, 0.5])\n",
    "    plt.axvline(0, linestyle = \"--\",color = 'red', alpha = 0.7)\n",
    "    plt.axhline(0, linestyle = \"--\",color = 'red', alpha = 0.7)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    if save: \n",
    "        try: \n",
    "            plt.savefig(directory+filename+'_spectrum.pdf')\n",
    "        except:\n",
    "            print(\"saving failed\")\n",
    "    plt.show()\n",
    "    \n",
    "def Raman_pulse(nuclear_spin_freq, freq_electron, raman_detuning, detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration,ramp_time):\n",
    "    \"\"\"\n",
    "    Plays simultaneous flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    align()\n",
    "\n",
    "    update_frequency(spin_sticky_element      , freq_electron + raman_detuning + nuclear_spin_freq )                   # Detuned Sideband frequency\n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + raman_detuning)                                        # Detuned Electron frequency\n",
    "    \n",
    "    reset_phase(spin_sticky_element)\n",
    "    reset_phase(spin_sticky_extra_element)\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_element,       ramp_time=ramp_time)\n",
    "    flattop_pulse3(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_extra_element, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_element, duration=4)\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "\n",
    "def Raman_pulse_cos(nuclear_spin_freq, freq_electron, raman_detuning, detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration,ramp_time):\n",
    "    \"\"\"\n",
    "    Plays simultaneous cosine rise flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    align()\n",
    "\n",
    "    update_frequency(spin_sticky_element      , freq_electron + raman_detuning + nuclear_spin_freq )                   # Detuned Sideband frequency\n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + raman_detuning)                                        # Detuned Electron frequency\n",
    "    \n",
    "    reset_phase(spin_sticky_element)\n",
    "    reset_phase(spin_sticky_extra_element)\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3_cos(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_element,       ramp_time=ramp_time)\n",
    "    flattop_pulse3_cos(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_extra_element, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_element, duration=4)\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "\n",
    "def Raman_pulse_cos_no_phase_reset(nuclear_spin_freq, freq_electron, raman_detuning, detuned_electron_amplitude, detuned_sideband_amplitude, \n",
    "                                   Raman_pulse_duration,ramp_time, element1 = spin_sticky_element, element2 = spin_sticky_extra_element,\n",
    "                                   keep_phase = False):\n",
    "    \"\"\"\n",
    "    Plays simultaneous cosine rise flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    align()\n",
    "\n",
    "    update_frequency(element1, freq_electron + raman_detuning + nuclear_spin_freq, keep_phase = keep_phase) # Detuned Sideband frequency\n",
    "    update_frequency(element2, freq_electron + raman_detuning                    , keep_phase = keep_phase) # Detuned Electron frequency\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3_cos(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = element1, ramp_time=ramp_time)\n",
    "    flattop_pulse3_cos(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = element2, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(element1, duration=4)\n",
    "    ramp_to_zero(element2, duration=4)\n",
    "    \n",
    "def Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 =spin_sticky_element, element2 = spin_sticky_extra_element):\n",
    "    \"\"\"\n",
    "    Plays simultaneous cosine rise flattop pulses\n",
    "    Does not update reference frame or pulse frequency, these should be set correctly outside this function\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3_cos(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = element1, ramp_time=ramp_time)\n",
    "    flattop_pulse3_cos(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = element2, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(element1, duration=4)\n",
    "    ramp_to_zero(element2, duration=4)\n",
    "    \n",
    "def Raman_pulse_chirped(nuclear_spin_freq, freq_electron, raman_detuning, detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration,ramp_time):\n",
    "    \"\"\"\n",
    "    Plays simultaneous flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    align()\n",
    "\n",
    "    update_frequency(spin_sticky_element      , freq_electron + raman_detuning + nuclear_spin_freq )                   # Detuned Sideband frequency\n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + raman_detuning)                                        # Detuned Electron frequency\n",
    "    \n",
    "    reset_phase(spin_sticky_element)\n",
    "    reset_phase(spin_sticky_extra_element)\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3_chirped(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_element,       ramp_time=ramp_time)\n",
    "    flattop_pulse3_cos(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_extra_element, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_element, duration=4)\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "\n",
    "def Raman_pulse_no_phase_reset(nuclear_spin_freq, freq_electron, raman_detuning, detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration,ramp_time, keep_phase = False):\n",
    "    \"\"\"\n",
    "    Plays simultaneous flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    can activate keep_phase = True on one of the pulses if necessary\n",
    "    \"\"\"\n",
    "    align()\n",
    "\n",
    "    update_frequency(spin_sticky_element      , freq_electron + raman_detuning + nuclear_spin_freq , keep_phase = keep_phase)                   # Detuned Sideband frequency\n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + raman_detuning)                                        # Detuned Electron frequency\n",
    "    \n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_element,       ramp_time=ramp_time)\n",
    "    flattop_pulse3(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = spin_sticky_extra_element, ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_element, duration=4)\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "\n",
    "def Raman_pulse_no_phase_reset_no_frequpdate( detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 =spin_sticky_element, element2 = spin_sticky_extra_element):\n",
    "    \"\"\"\n",
    "    Plays simultaneous flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "\n",
    "    align()\n",
    "    play(\"ON\", spin_switch_element, duration=Raman_pulse_duration+int(2.1*ramp_time))\n",
    "    flattop_pulse3(detuned_sideband_amplitude, Raman_pulse_duration, switch_duration_extra, element = element1,       ramp_time=ramp_time)\n",
    "    flattop_pulse3(detuned_electron_amplitude, Raman_pulse_duration, switch_duration_extra, element = element2,       ramp_time=ramp_time)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(element1, duration=4)\n",
    "    ramp_to_zero(element2, duration=4)\n",
    "    \n",
    "def Raman_pulse_ms(k,detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration_ms, ramp_time):\n",
    "    \"\"\"\n",
    "    Plays simultaneous flattop pulses\n",
    "    Raman_detuning is the detuning from the excited state in the lambda system\n",
    "    detuned_electron_amplitude: electron pulse amp\n",
    "    detuned_sideband_amplitude: sideband (diagonal) pulse amp\n",
    "    \"\"\"\n",
    "    align()\n",
    "    play(\"ON\", 'Spin_switch', duration=ramp_time+switch_duration_extra)\n",
    "    play(spin_sticky_pulse*amp(detuned_sideband_amplitude), spin_sticky_element,       duration=ramp_time) \n",
    "    play(spin_sticky_pulse*amp(detuned_electron_amplitude), spin_sticky_extra_element, duration=ramp_time) \n",
    "    align()\n",
    "    with for_(k,0,k<Raman_pulse_duration_ms,k+1):\n",
    "        play(\"ON\", 'Spin_switch', duration=int(1_000_000//4))\n",
    "        wait(int(1_000_000//4),spin_sticky_element)\n",
    "    align(spin_sticky_element, spin_sticky_extra_element)\n",
    "    play(\"ON\", 'Spin_switch', duration=ramp_time+switch_duration_extra)\n",
    "    play(spin_sticky_pulse*amp(-detuned_sideband_amplitude), spin_sticky_element,       duration=ramp_time) \n",
    "    play(spin_sticky_pulse*amp(-detuned_electron_amplitude), spin_sticky_extra_element, duration=ramp_time) \n",
    "    align()\n",
    "\n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_element, duration=4)\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "    wait(int(5_000//4))\n",
    "    align()\n",
    "    \n",
    "def nuclear_spin_RO(stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, spin_freq, waiting_time_after_spin, N_readout,\n",
    "                    waiting_time_SMPD, wait_time_after_spin_readout=1e6//4, enable_fsv_trigger = False):\n",
    "    click=declare(int)\n",
    "    assign(click_acc, 0)\n",
    "    with for_(n, 0, n < N_ROcycle, n + 1):\n",
    "        if enable_fsv_trigger:\n",
    "            with if_(n==N_ROcycle-10):\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "        \n",
    "        # Then excite the spin and readout in each electron spin frequency\n",
    "        update_frequency(spin_element, spin_freq)\n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(amplitude_readout_pulse), spin_element, duration = gauss_duration) \n",
    "        align()\n",
    "        wait(int(waiting_time_after_spin), readout_element)\n",
    "        align()\n",
    "        click=measure_SMPD_nostream(click, N_readout, waiting_time_SMPD)\n",
    "        assign(click_acc, click_acc + click)\n",
    "        wait(int(wait_time_after_spin_readout))\n",
    "\n",
    "    save(click_acc, stream)\n",
    "    \n",
    "    return click_acc\n",
    "\n",
    "def estimate_fwhm(x_data, y_data, invert = False):\n",
    "    # Baseline Correction\n",
    "    if invert: y_data=-y_data\n",
    "    \n",
    "    peak_index = np.argmax(y_data)\n",
    "    baseline = np.mean(np.concatenate([y_data[:int(0.1 * len(y_data))], y_data[int(0.9 * len(y_data)):]]))\n",
    "    corrected_y = y_data - baseline\n",
    "\n",
    "    # Find Peak and Half Maximum\n",
    "    peak_value = corrected_y[peak_index]\n",
    "    half_max = peak_value / 2\n",
    "\n",
    "    # Find Nearest Points to Half Maximum\n",
    "    left_idx = np.where(corrected_y[:peak_index] <= half_max)[0][-1]\n",
    "    right_idx = np.where(corrected_y[peak_index:] <= half_max)[0][0] + peak_index\n",
    "\n",
    "    # Calculate FWHM\n",
    "    fwhm = x_data[right_idx] - x_data[left_idx]\n",
    "    return fwhm\n",
    "\n",
    "def raman_preparation_no_sideband(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, freq):\n",
    "    \n",
    "    with while_(click_acc<threshold):\n",
    "\n",
    "        ################# Preparation #################\n",
    "        # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "        align()\n",
    "        # If we're not in the right state, try flipping nuclear spin a\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "\n",
    "        # If we're still not right after this, we try flipping nuclear spin b\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "\n",
    "        # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "    \n",
    "def raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, freq, freq_sb):\n",
    "    \n",
    "    with while_(click_acc<threshold):\n",
    "\n",
    "        ################# Preparation #################\n",
    "        # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "        align()\n",
    "        # If we're not in the right state, try flipping nuclear spin a\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "\n",
    "        # If we're still not right after this, we try flipping nuclear spin b\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "\n",
    "        # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "        \n",
    "        # play('ON',fsv_trigger)\n",
    "        # align()\n",
    "        \n",
    "        # Extra unconditional sideband reset pulses on nuclear spin a should ensure any unwanted cross relaxation during readout is corrected for\n",
    "        update_frequency(spin_sticky_element, freq_sb)\n",
    "\n",
    "        #for i in range(10):\n",
    "        #    align()\n",
    "        #    wait(int(5e6//4))\n",
    "        #    flattop_pulse(sb_pi_amplitude_prep, sb_pi_duration_prep, switch_duration_extra)\n",
    "        wait(int(10e6//4))\n",
    "            \n",
    "def raman_preparation_b(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, freq, freq_sb):\n",
    "\n",
    "    with while_(click_acc<threshold):\n",
    "        align()\n",
    "        ################# Preparation #################\n",
    "        # Prepare the nucleus in desired state using coherent raman pulses\n",
    "        \n",
    "\n",
    "        \n",
    "        # If we're not in the right state, try flipping nuclear spin b\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "\n",
    "        # If we're still not right after this, we try flipping nuclear spin a\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "         \n",
    "        # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "        with if_(click_acc<threshold):\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep)\n",
    "        \n",
    "        # play('ON',fsv_trigger)\n",
    "        # align()\n",
    "        \n",
    "        # Extra unconditional sideband reset pulses on nuclear spin a should ensure any unwanted cross relaxation during readout is corrected for\n",
    "        update_frequency(spin_sticky_element, freq_sb)\n",
    "        for i in range(10):\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            flattop_pulse(sb_pi_amplitude_prep, sb_pi_duration_prep, switch_duration_extra)\n",
    "        wait(int(10e6//4))       \n",
    "\n",
    "def kmeans_plot(delta, h=1, ax=None, title=''):\n",
    "    init = np.array([\n",
    "        [ 100, 100],\n",
    "        [ 100,-100],\n",
    "        [-100, 100],\n",
    "        [-100,-100]\n",
    "    ])\n",
    "    \n",
    "    # Reshape into a good shape\n",
    "    X = np.array([delta[0].flatten(), delta[1].flatten()]).astype(int).T\n",
    "    kmeans = KMeans(n_clusters=4, random_state=0, init=init, n_init=1)\n",
    "    y_pred = kmeans.fit_predict(X)\n",
    "    \n",
    "    # construct mesh\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "    # obtain labels per mesh point (reuse stored model)\n",
    "    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    \n",
    "    # change the definition of the clusters \n",
    "    \n",
    "    if not ax:\n",
    "        plt.figure(figsize=(5,5))\n",
    "        ax = plt.gca()\n",
    "    \n",
    "    cmap = mpl.colors.ListedColormap(colors[:4])\n",
    "    norm = mpl.colors.BoundaryNorm(np.arange(4), cmap.N)\n",
    "    \n",
    "    ax.plot(delta[0],delta[1],\".\", alpha = 0.5)\n",
    "    ax.imshow(\n",
    "        Z, interpolation='nearest', cmap=cmap, alpha=0.1,\n",
    "        extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "        aspect='auto', origin='lower',\n",
    "    )\n",
    "    ax.set_xlabel('Counts $\\Delta_x$')\n",
    "    ax.set_ylabel('Counts $\\Delta_y$')\n",
    "    \n",
    "    probs = []\n",
    "    for i in range(4):\n",
    "        pred = kmeans.predict(np.array(delta)[:,:,i].T)\n",
    "        P = []\n",
    "        for j in range(4): P.append((pred==j).sum()/len(pred))\n",
    "        probs.append(P)\n",
    "        # title += f\"$P_{i:d}$={P[i]:.2f},  \"\n",
    "        # if i == 1: title += '\\n'\n",
    "    # ax.set_title(title[:-2], color='k', ha ='center', fontsize='medium')\n",
    "    ax.set_title(title, color='k', ha ='center', fontsize='medium')\n",
    "    \n",
    "    return probs, kmeans\n",
    "\n",
    "def kmeans_4d(data):\n",
    "    '''\n",
    "    the dimensions of data should be [N, 4, 4]. N being the number of averages, 4 the preparation states and 4 the readout counts.\n",
    "    '''\n",
    "    init = np.array([\n",
    "            [ 100, 50, 50, 50],\n",
    "            [ 50, 100, 50, 50],\n",
    "            [ 50, 50, 100, 50],\n",
    "            [ 50, 50, 50, 100]\n",
    "    ])\n",
    "    original_shape = data.shape[:-1]\n",
    "    X=data.reshape(np.cumprod(original_shape)[-1], 4)\n",
    "    kmeans = KMeans(n_clusters=4, random_state=0, init=init, n_init=1)\n",
    "\n",
    "    Z = kmeans.fit_predict(X).reshape(original_shape)\n",
    "    probs=np.array([(Z==j).mean(0) for j in range(4)])\n",
    "    return probs, kmeans\n",
    "\n",
    "def save_fig_manustyle(filename):\n",
    "    \"\"\"same as plt.savefig but swears at you if you have the pdf open and try to overwrite it\"\"\"\n",
    "    try:\n",
    "        plt.savefig(filename)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "        \n",
    "def sinhspace_asymm(start,stop,npoints, nonlinearity = None): \n",
    "    \"\"\"\n",
    "    Returns a hyperbolic sinh range with the same syntax as numpy.linspace\n",
    "    points will be asymmetrically spaced, with more points at smaller values\n",
    "    nolinearity=0 gives a linear sweep.\n",
    "    Larger values enhance the number of points at values closer to zero.\n",
    "    For default nonlinearity this behaves very similar to a logarithmic sweep\n",
    "    When start==0 default nonlinearity acts similar to a lograithmic sweep over 2 orders of magnitude\n",
    "    Useful for T1, T2 and similar measurements\n",
    "    \"\"\"\n",
    "    if nonlinearity == None and start !=0: nonlinearity = np.log(abs(stop/start))\n",
    "    elif nonlinearity == None: nonlinearity = np.log(100)\n",
    "    if start==stop:\n",
    "        return(np.ones(npoints)*start)\n",
    "    elif nonlinearity!=0:\n",
    "        centre = (start+stop)/2\n",
    "        sweeprange = stop-start\n",
    "        if abs(stop)>abs(start):sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(0,1,npoints))\n",
    "        else: sweep = np.sinh(nonlinearity*sweeprange/abs(sweeprange)*np.linspace(1,0,npoints))\n",
    "        sweep = sweep-sweep[0]\n",
    "        sweep = sweep/sweep[-1]\n",
    "        sweep = start+sweeprange*sweep\n",
    "        return(sweep)\n",
    "    else:\n",
    "        return(np.linspace(start,stop,npoints))\n",
    "    \n",
    "def nuclear_readout_block(click, click_acc_N, spin_freq, measure=True):\n",
    "    \"\"\"updates spin element frequency, pulses the spin at that frequency and reads it out once\n",
    "    increments the variable click_acc if a click is detected\"\"\"\n",
    "    \n",
    "    update_frequency(spin_element, spin_freq)\n",
    "    align()\n",
    "    \n",
    "    play(spin_gauss_pulse*amp(amplitude_readout_pulse_prep), spin_element, duration = gauss_duration_prep) \n",
    "    align()\n",
    "    \n",
    "    if measure:\n",
    "        wait(int(waiting_time_spin_prep))\n",
    "        align()\n",
    "\n",
    "        click=measure_SMPD_nostream(click, N_readout_prep, waiting_time_SMPD_prep)\n",
    "        assign(click_acc_N, click_acc_N + click)\n",
    "    \n",
    "    wait(int(waiting_after_spinreadout_prep))\n",
    "    \n",
    "def nuclear_spin_RO_4freq_interleaved(stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False):\n",
    "    \"\"\"reads nuclear spin at 4 readout frequencies given by readout_freqs\n",
    "    readout pulses are interleaved and repeated N_ROcycle times each\n",
    "    4 accumulator variables are incremented and returned one after another\"\"\"\n",
    "    \n",
    "    n_ro_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    click=declare(int)\n",
    "    click_acc1=declare(int)\n",
    "    click_acc2=declare(int)\n",
    "    click_acc3=declare(int)\n",
    "    click_acc4=declare(int)\n",
    "\n",
    "    assign(click_acc1, 0)\n",
    "    assign(click_acc2, 0)\n",
    "    assign(click_acc3, 0)\n",
    "    assign(click_acc4, 0)\n",
    "\n",
    "    # Loop over N_ROcycle\n",
    "    with for_(n_ro_set, 0, n_ro_set < N_ROcycle, n_ro_set + 1):\n",
    "        # Trigger before last 40 (10x4) readout pulses can be enabled for diagnostic purposes\n",
    "        if enable_fsv_trigger:\n",
    "            with if_(n_ro_set==N_ROcycle-10):\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                \n",
    "        # Runs nuclear_readout_block function once for each frequency\n",
    "        assign(freq_set,readout_freqs[0])\n",
    "        nuclear_readout_block(click, click_acc1, freq_set+delta_freq)\n",
    "        assign(freq_set,readout_freqs[1])\n",
    "        nuclear_readout_block(click, click_acc2, freq_set+delta_freq)\n",
    "        assign(freq_set,readout_freqs[2])\n",
    "        nuclear_readout_block(click, click_acc3, freq_set+delta_freq)\n",
    "        assign(freq_set,readout_freqs[3])\n",
    "        nuclear_readout_block(click, click_acc4, freq_set+delta_freq)\n",
    "\n",
    "    save(click_acc1, stream)\n",
    "    save(click_acc2, stream)\n",
    "    save(click_acc3, stream)\n",
    "    save(click_acc4, stream)\n",
    "    \n",
    "    return (click_acc4)\n",
    "\n",
    "def nuclear_spin_RO_Nfreq_interleaved(stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[True, True, True, True]):\n",
    "    \"\"\"reads nuclear spin at 4 readout frequencies given by readout_freqs\n",
    "    readout pulses are interleaved and repeated N_ROcycle times each\n",
    "    4 accumulator variables are incremented and returned one after another\"\"\"\n",
    "    \n",
    "    n_ro_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    click=declare(int)\n",
    "    \n",
    "    click_acc1=declare(int)\n",
    "    assign(click_acc1, 0)\n",
    "\n",
    "    click_acc2=declare(int)\n",
    "    assign(click_acc2, 0)\n",
    "\n",
    "    click_acc3=declare(int)\n",
    "    assign(click_acc3, 0)\n",
    "\n",
    "    click_acc4=declare(int)\n",
    "    assign(click_acc4, 0)\n",
    "\n",
    "\n",
    "    # Loop over N_ROcycle\n",
    "    with for_(n_ro_set, 0, n_ro_set < N_ROcycle, n_ro_set + 1):\n",
    "        \n",
    "        # Trigger before last 40 (10x4) readout pulses can be enabled for diagnostic purposes\n",
    "        if enable_fsv_trigger:\n",
    "            with if_(n_ro_set==N_ROcycle-10):\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                \n",
    "        # Runs nuclear_readout_block function once for each frequency\n",
    "        if state_list[0]:\n",
    "            assign(freq_set,readout_freqs[0])\n",
    "            nuclear_readout_block(click, click_acc1, freq_set+delta_freq)\n",
    "        if state_list[1]:\n",
    "            assign(freq_set,readout_freqs[1])\n",
    "            nuclear_readout_block(click, click_acc2, freq_set+delta_freq)\n",
    "        if state_list[2]:\n",
    "            assign(freq_set,readout_freqs[2])\n",
    "            nuclear_readout_block(click, click_acc3, freq_set+delta_freq)\n",
    "        if state_list[3]:\n",
    "            assign(freq_set,readout_freqs[3])\n",
    "            nuclear_readout_block(click, click_acc4, freq_set+delta_freq)\n",
    "\n",
    "        \n",
    "    save(click_acc1, stream)\n",
    "    save(click_acc2, stream)\n",
    "    save(click_acc3, stream)\n",
    "    save(click_acc4, stream)\n",
    "        \n",
    "    \n",
    "    return (click_acc4)\n",
    "\n",
    "def nuclear_spin_RO_1freq(stream, N_ROcycle, readout_freq, delta_freq, enable_fsv_trigger = False):\n",
    "    \"\"\"reads nuclear spin at 4 readout frequencies given by readout_freqs\n",
    "    readout pulses are interleaved and repeated N_ROcycle times each\n",
    "    4 accumulator variables are incremented and returned one after another\"\"\"\n",
    "    \n",
    "    n_ro_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    click=declare(int)\n",
    "    click_acc1=declare(int)\n",
    "\n",
    "    assign(click_acc1, 0)\n",
    "\n",
    "    # Loop over N_ROcycle\n",
    "    with for_(n_ro_set, 0, n_ro_set < N_ROcycle, n_ro_set + 1):\n",
    "        # Trigger before last 40 (10x4) readout pulses can be enabled for diagnostic purposes\n",
    "        if enable_fsv_trigger:\n",
    "            with if_(n_ro_set==N_ROcycle-10):\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                \n",
    "        # Runs nuclear_readout_block function once for each frequency\n",
    "        assign(freq_set,readout_freqs[0])\n",
    "        nuclear_readout_block(click, click_acc1, freq_set+delta_freq)\n",
    "\n",
    "    save(click_acc1, stream)\n",
    "    \n",
    "    return (click_acc1)\n",
    "\n",
    "def nuclear_spin_RO_downdown(stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False):\n",
    "    \"\"\"reads nuclear spin at 4 readout frequencies given by readout_freqs\n",
    "    readout pulses are interleaved and repeated N_ROcycle times each\n",
    "    4 accumulator variables are incremented and returned one after another\"\"\"\n",
    "    \n",
    "    n_ro_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    click=declare(int)\n",
    "    click_acc1=declare(int)\n",
    "    click_acc4=declare(int)\n",
    "\n",
    "    assign(click_acc1, 0)\n",
    "    assign(click_acc4, 0)\n",
    "\n",
    "    # Loop over N_ROcycle\n",
    "    with for_(n_ro_set, 0, n_ro_set < N_ROcycle, n_ro_set + 1):\n",
    "        # Trigger before last 40 (10x4) readout pulses can be enabled for diagnostic purposes\n",
    "        if enable_fsv_trigger:\n",
    "            with if_(n_ro_set==N_ROcycle-10):\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                \n",
    "        # Runs nuclear_readout_block function once for each frequency\n",
    "        assign(freq_set,readout_freqs[0])\n",
    "        nuclear_readout_block(click, click_acc1, freq_set+delta_freq, measure=False)\n",
    "        assign(freq_set,readout_freqs[1])\n",
    "        nuclear_readout_block(click, click_acc1, freq_set+delta_freq, measure=False)\n",
    "        assign(freq_set,readout_freqs[2])\n",
    "        nuclear_readout_block(click, click_acc1, freq_set+delta_freq)\n",
    "        assign(freq_set,readout_freqs[3])\n",
    "        nuclear_readout_block(click, click_acc4, freq_set+delta_freq)\n",
    "\n",
    "    save(click_acc1, stream)\n",
    "    save(click_acc4, stream)\n",
    "    \n",
    "    return (click_acc4)\n",
    "\n",
    "def basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans = None):\n",
    "    \n",
    "    fig,ax=plt.subplots(4,1,figsize=(10,15),tight_layout=True)\n",
    "\n",
    "    p_data = (data[:,:,-1]>100).mean(0)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = [p_data]\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ### Plot 1 - Number of accumulated counts ###\n",
    "    for i in range(len(readout_freqs)): \n",
    "        y = data.mean(0)[:,i]\n",
    "        dy = data.std(0)[:,i]/np.sqrt(len(data))\n",
    "        ax[0].errorbar(x,  y, dy, label = labels[i], fmt = \"o-\", color = colors[i])\n",
    "        \n",
    "    ax[0].set_xlabel(xlabel)\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    \n",
    "    ### Plot 2 - Probability of the state ###\n",
    "    for l, pop in zip(labels, pops): \n",
    "        ax[1].plot(x, pop, \"o-\", label = l)\n",
    "\n",
    "    ax[1].set_xlabel(xlabel)\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "    ### Plot 3 - Histogram ###\n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep, bins=bins, label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "\n",
    "    ### Plot 4 - FFT ###\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(pops[-1] - pops[-1].mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "    ax[3].plot(fft_x, fft_y)\n",
    "    ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[3].set_ylabel(\"FFT\")\n",
    "    ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return ax, pops\n",
    "\n",
    "def calc_chirp_rate(start_freq,stop_freq,pulse_duration):\n",
    "    \"\"\"\n",
    "    Converts a frequency interval (Hz) to a chirp rate, based on a pulse duration (given in ns!)\n",
    "    returns an integer with the chirp rate in Hz/sec\n",
    "    \"\"\"\n",
    "    return int((stop_freq-start_freq)*1e-3/(pulse_duration*1e-9))\n",
    "\n",
    "def chirped_square_pulse(amplitude, element, duration, start_freq, chirp_rate = 0):\n",
    "    \"\"\"\n",
    "    Plays a chirped square pulse with defined amplitude, duration and chirp rate (defined in KHz/sec)\n",
    "    Afterwards resets to original frequency\n",
    "    \"\"\"\n",
    "    update_frequency(element, start_freq)\n",
    "    play(spin_pulse*amp(amplitude), element, duration = duration, chirp = (chirp_rate,'KHz/sec'))\n",
    "    update_frequency(element, start_freq)\n",
    "\n",
    "def chirped_pumping(centre_freq, delta_freq,element = spin_element,pump_steps = pump_steps_chirped, pump_pulse_delay = pump_delay_chirped, enable_fsv_trigger=False):\n",
    "    \n",
    "    align()\n",
    "    reset_frame(element)\n",
    "    \n",
    "    if enable_fsv_trigger: play('ON',fsv_trigger)\n",
    "    with for_(m, 0, m<pump_steps, m+1):\n",
    "        align()\n",
    "        chirped_square_pulse(chirped_pump_amplitude, element, chirped_pump_duration, chirped_pump_freq+delta_freq+centre_freq*1e3 , chirp_rate = chirp_rate)\n",
    "        align()\n",
    "        wait(pump_pulse_delay)\n",
    "        \n",
    "def knill_pulse(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time = ramp_time_prep, element1 =spin_sticky_element, element2 = spin_sticky_extra_element):\n",
    "    \"\"\"\n",
    "    Plays a pulse sequence for a field/frequency robust Pi rotation, leaves a Pi/3 z phase rotation on the qubit.\n",
    "    Pi(pi/6) Pi(0) Pi(pi/2) Pi(0) Pi(pi/6)\n",
    "    Doesn't update the phase or frequency of either elements\n",
    "    \"\"\"\n",
    "    frame_rotation_2pi(0.0833, element1)\n",
    "    Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 = element1, element2 = element2)\n",
    "    align()\n",
    "    wait(100)\n",
    "    align()\n",
    "    frame_rotation_2pi(-0.0833, element1)\n",
    "    Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 = element1, element2 = element2)\n",
    "    align()\n",
    "    wait(100)\n",
    "    align()\n",
    "    frame_rotation_2pi(0.25, element1)\n",
    "    Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 = element1, element2 = element2)\n",
    "    align()\n",
    "    wait(100)\n",
    "    align()\n",
    "    frame_rotation_2pi(-0.25, element1)\n",
    "    Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 = element1, element2 = element2)\n",
    "    align()\n",
    "    wait(100)\n",
    "    align()\n",
    "    frame_rotation_2pi(0.0833, element1)\n",
    "    Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude, detuned_sideband_amplitude, Raman_pulse_duration, ramp_time, element1 = element1, element2 = element2)\n",
    "    align()\n",
    "    wait(100)\n",
    "    frame_rotation_2pi(-0.0833, element1)\n",
    "    align()\n",
    "    \n",
    "def plot_flattop_ft(pulse_duration, center_freq, ramp_time = ramp_time_prep):\n",
    "    \"\"\"\n",
    "    Returns an x and y array with the fourier transform of a flattop pulse (height = 1)\n",
    "    Pulse duration is given in clock cycles, frequency is in Hz\n",
    "    \"\"\"\n",
    "    pulse_bandwidth = 1/(pulse_duration*4*1e-9)\n",
    "    around_pulse = 10e6\n",
    "    vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(ramp_time,1,0)[0], [1]*pulse_duration, chirp_cos_raise(ramp_time,-1,0)[0]+1,[0]*int(around_pulse/4)))\n",
    "    times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "    fft_y = np.abs(np.fft.rfft(vals))\n",
    "    full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "    full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "    return full_freq+center_freq, full_pulse/max(full_pulse)\n",
    "\n",
    "def Pauli_swept(pulse_identifier, delta_freq, pulse_duration_adj = 0, phi_adj = 0, phi2_adj=0, ramp_time_adj = 0, electron_amp_adj = 0, sideband_amp_adj = 0,\n",
    "                electron_freq_adj = 0, sideband_freq_adj = 0, starkshift_adj = 0):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", \"aY-\", etc.\n",
    "    will only do plus or minus pi or pi/2 along x or y for nuclear spin a or b\n",
    "    you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "    phi is the additional Z rotation applied after the pulse in units of 2pi\n",
    "    \"\"\"\n",
    "    \n",
    "    if pulse_identifier[0] == \"I\": return()\n",
    "        \n",
    "    spin = pulse_identifier[0]    \n",
    "    axis = pulse_identifier[1]   \n",
    "    if pulse_identifier[-2:] == '90':\n",
    "        angle = 'pihalf'\n",
    "        if pulse_identifier[-3] == '-':\n",
    "            pulse_phase = 'pi'\n",
    "        else:\n",
    "            pulse_phase = 'zero'\n",
    "    elif len(pulse_identifier) <= 3:\n",
    "        angle = 'pi'\n",
    "        if pulse_identifier[-1] == '-':\n",
    "            pulse_phase = 'pi'\n",
    "        else:\n",
    "            pulse_phase = 'zero'\n",
    "    else:\n",
    "        print(\"error: must specify angle 90 wait (w) or leave blank for pi rotation\")\n",
    "        return()\n",
    "    \n",
    "    if spin!=\"a\" and spin!=\"b\": \n",
    "        print(\"error: must specify spin a or b\")\n",
    "        return()\n",
    "        \n",
    "    if axis!=\"X\" and axis!=\"Y\" and axis!='W': \n",
    "        print(\"error: must specify axis X or Y, or wait (W)\")\n",
    "        return()\n",
    "    \n",
    "    \n",
    "    \n",
    "    if spin=='a':\n",
    "        phi = z_phase_rotation_a_prep\n",
    "        \n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        element3 = spin_sticky4_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "            phi2 = z_phase_rotation_a_on_b_prep\n",
    "\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "            phi2 = z_phase_rotation_a_half_on_b_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if axis!='W':\n",
    "            update_frequency(element1, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep + sideband_freq_adj, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep + electron_freq_adj,                            keep_phase=True)  # Detuned Electron frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "        \n",
    "    elif spin=='b':\n",
    "        phi = z_phase_rotation_b_prep\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        element3 = spin_sticky_extra_element\n",
    "\n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "            phi2 = z_phase_rotation_b_on_a_prep\n",
    "\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "            phi2 = z_phase_rotation_b_half_on_a_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if axis!='W':\n",
    "            update_frequency(element1, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep + sideband_freq_adj, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep + electron_freq_adj,                            keep_phase=True)  # Detuned Electron frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "\n",
    "    if axis!='W':\n",
    "        \n",
    "        wait(100,element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element2)\n",
    "        wait(100,element2)\n",
    "        if pulse_phase =='pi':\n",
    "            frame_rotation_2pi(0.5, element2)\n",
    "        wait(100,element2)\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude + electron_amp_adj,\n",
    "                                                     detuned_sideband_amplitude + sideband_amp_adj,\n",
    "                                                     raman_duration             + pulse_duration_adj,\n",
    "                                                     ramp_time_prep             + ramp_time_adj,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element2)\n",
    "        wait(100,element2)\n",
    "        if pulse_phase =='pi':\n",
    "            frame_rotation_2pi(-0.5, element2)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        frame_rotation_2pi(phi + phi_adj, element2)\n",
    "        frame_rotation_2pi(phi2 + phi2_adj, element3) # Phase due to applying a gate on the other element\n",
    "\n",
    "        wait(100,element2)\n",
    "     \n",
    "    elif axis=='W':\n",
    "        wait(100,element2)\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(0,\n",
    "                                                     0,\n",
    "                                                     raman_duration             + pulse_duration_adj,\n",
    "                                                     ramp_time_prep             + ramp_time_adj,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "\n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        wait(100,element2)\n",
    "     \n",
    "    \n",
    "    align()\n",
    "    \n",
    "    ############# Another dummy pulse to enforce timings #############\n",
    "\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "    wait(100,element2)\n",
    "\n",
    "    ############# Now update element 2 such that the nuclear spin frame evolves at the bare undriven frequency #############\n",
    "    if spin == 'a':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift + starkshift_adj, keep_phase=True)  # Detuned Electron frequency\n",
    "\n",
    "\n",
    "    elif spin == 'b':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_starckshift + starkshift_adj, keep_phase=True)  # Detuned Electron frequency\n",
    "    \n",
    "    ############# Last dummy pulse #############\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100)\n",
    "    wait(100,element2)\n",
    "\"\"\"\n",
    "def CZ_gate(delta_freq, CZ_duration=CZ_duration_prep, CZ_detuning=CZ_detuning_prep, CZ_amp=CZ_amp_prep, CZ_phase_correction_b=CZ_phase_correction_b_prep, CZ_phase_correction_a=CZ_phase_correction_a_prep):\n",
    "    \n",
    "    # Play a dummy pulse to keep OPX happy\n",
    "    wait(100,spin_sticky_extra_element)\n",
    "    play(spin_sticky_pulse*amp(0.0), spin_sticky_extra_element, duration = 100)\n",
    "    wait(100,spin_sticky_extra_element)\n",
    "    align()\n",
    "    \n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + CZ_detuning, keep_phase = True) #\n",
    "    play(\"ON\", spin_switch_element, duration=CZ_duration+int(2.1*ramp_time_prep))\n",
    "    flattop_pulse3_cos(CZ_amp, CZ_duration, switch_duration_extra, element = spin_sticky_extra_element, ramp_time=ramp_time_prep)\n",
    "    \n",
    "    align()\n",
    "    ramp_to_zero(spin_sticky_extra_element, duration=4)\n",
    "    \n",
    "    frame_rotation_2pi(CZ_phase_correction_b, spin_sticky4_element)\n",
    "    frame_rotation_2pi(CZ_phase_correction_a, spin_sticky_extra_element)\n",
    "    update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift, keep_phase = True)\n",
    "    \n",
    "    # Play a dummy pulse to keep OPX happy\n",
    "    wait(100,spin_sticky_extra_element)\n",
    "    play(spin_sticky_pulse*amp(0.0), spin_sticky_extra_element, duration = 100)\n",
    "    wait(100,spin_sticky_extra_element)\n",
    "    align()\n",
    "\"\"\"\n",
    "p_decay = lambda x,p, A,B: A*p**x+B"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb6d14d-37b8-4df1-8d87-a15570dc2539",
   "metadata": {},
   "source": [
    "## Packaged tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00b03f91-3797-4b7c-9951-a2ab3da4fabc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:22.707416Z",
     "iopub.status.busy": "2024-04-01T18:27:22.706417Z",
     "iopub.status.idle": "2024-04-01T18:27:22.918435Z",
     "shell.execute_reply": "2024-04-01T18:27:22.917439Z",
     "shell.execute_reply.started": "2024-04-01T18:27:22.707416Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "centre_freq = 30\n",
    "\n",
    "N_Freq_tracking=10                                   # Number of nuclear readout cycles per point\n",
    "interrogation_time=20_000//4                         # Length of the ramsey tracking sequence\n",
    "\n",
    "P_coeff = int(1/(4*np.pi*interrogation_time*1e-9*4)) # integrator coefficient; approx frequency shift per unit of Sy (defined as the interrogation time)\n",
    "I_coeff=int(P_coeff//50)                             # proportional coefficient; must be an integer\n",
    "\n",
    "Y_filter_length=2000                                 # Memory of Y (low pass filter)\n",
    "Y_filter_scaling_factor=0.1 * (2000/Y_filter_length) # Contrast of the signal, must result in a signal smaller than 8 approx\n",
    "\n",
    "track_prep_freq = Photon_IF-0.785e6#+delta_freq*1e3\n",
    "\n",
    "def track_frequency(\n",
    "        delta_freq,\n",
    "        delta_freq_acc,\n",
    "        Y,\n",
    "        centre_freq,\n",
    "        I_coeff=I_coeff, \n",
    "        P_coeff=P_coeff, \n",
    "        interrogation_time=interrogation_time, \n",
    "        N_Freq_tracking=N_Freq_tracking,\n",
    "        Y_filter_length=Y_filter_length, \n",
    "        Y_filter_scaling_factor=Y_filter_scaling_factor\n",
    "        ):\n",
    "    \n",
    "    angle = declare(fixed)\n",
    "    \n",
    "    # Prepare the nuclear spin in the red sideband, the correct values for a preparation have been hardcoded\n",
    "    update_frequency(spin_sticky_element, Photon_IF - 0.755e6 + delta_freq + centre_freq*1e3)\n",
    "    with for_(n, 0, n < 10, n + 1):\n",
    "        align()\n",
    "        flattop_pulse(0.31, 21_000//4, switch_duration_extra)\n",
    "        wait(int(3e6//4))\n",
    "\n",
    "    ################# Frequency tracking  #################\n",
    "    #  N_Freq_tracking x 2 Ramsey spin + SMPD acumulation\n",
    "\n",
    "    update_frequency(spin_element, Photon_IF + 20e3  + delta_freq + centre_freq*1e3)\n",
    "   \n",
    "\n",
    "    with for_(n, 0, n < N_Freq_tracking, n + 1):\n",
    "        with for_each_(angle,[-0.25,0.25]):\n",
    "\n",
    "            align()\n",
    "            play(spin_gauss_pulse*amp(amplitude_readout_pulse_ramsey*0.5), spin_element, duration = gauss_duration_ramsey)\n",
    "            frame_rotation_2pi(angle, spin_element)\n",
    "            wait(int(interrogation_time))\n",
    "            play(spin_gauss_pulse*amp(amplitude_readout_pulse_ramsey*0.5), spin_element, duration = gauss_duration_ramsey)\n",
    "\n",
    "            align()\n",
    "            click_track = measure_SMPD(prep_stream,N_readout,waiting_time, accumulate=True, return_click=True)\n",
    "            assign(Y,(1-1/Y_filter_length)*Y+Cast.mul_fixed_by_int(angle*Y_filter_scaling_factor, click_track))\n",
    "            \n",
    "            wait(int(5e5//4))\n",
    "\n",
    "    assign(delta_freq_acc, delta_freq_acc+Cast.mul_int_by_fixed(I_coeff,Y))\n",
    "    assign(delta_freq, Cast.mul_int_by_fixed(P_coeff,Y)+delta_freq_acc) \n",
    "    return delta_freq\n",
    "\n",
    "N_Freq_tracking=10                                   # Number of nuclear readout cycles per point\n",
    "interrogation_time=20_000//4                         # Length of the ramsey tracking sequence\n",
    "\n",
    "P_coeff = int(1/(4*np.pi*interrogation_time*1e-9*4)) # integrator coefficient; approx frequency shift per unit of Sy (defined as the interrogation time)\n",
    "I_coeff=int(P_coeff//50)                                # proportional coefficient; must be an integer\n",
    "\n",
    "Y_filter_length=2000                                 # Memory of Y (low pass filter)\n",
    "Y_filter_scaling_factor=0.1 * (2000/Y_filter_length) # Contrast of the signal, must result in a signal smaller than 8 approx\n",
    "\n",
    "track_prep_freq = Photon_IF-0.785e6 #+delta_freq*1e3\n",
    "\n",
    "cycle_time_estimated_track = 17  #in us Need to change this to something better\n",
    "Integration_time_track = 2000 #in us\n",
    "N_readout_track = int(Integration_time_track/cycle_time_estimated_track)\n",
    "waiting_time_track = 1000\n",
    "\n",
    "def track_frequency_2spins(\n",
    "        delta_freq,\n",
    "        delta_freq_acc,\n",
    "        Y,\n",
    "        tracking_peak_freq,\n",
    "        amplitude_readout_pulse_ramsey, # Pi pulse amplitude\n",
    "        gauss_duration_ramsey,\n",
    "        I_coeff=I_coeff, \n",
    "        P_coeff=P_coeff, \n",
    "        interrogation_time=interrogation_time, \n",
    "        N_Freq_tracking=N_Freq_tracking,\n",
    "        Y_filter_length=Y_filter_length, \n",
    "        Y_filter_scaling_factor=Y_filter_scaling_factor,\n",
    "        waiting_time = waiting_time_track\n",
    "    \n",
    "        ):\n",
    "    \n",
    "    angle = declare(fixed)\n",
    "    n = declare(int)\n",
    "    click_track = declare(int)\n",
    "    # Prepare the nuclear spin in the red sideband, the correct values for a preparation have been hardcoded\n",
    "    # update_frequency(spin_sticky_element, Photon_IF - 0.755e6 + delta_freq + centre_freq*1e3)\n",
    "    # with for_(n, 0, n < 10, n + 1):\n",
    "    #     align()\n",
    "    #     flattop_pulse(0.31, 21_000//4, switch_duration_extra)\n",
    "    #     wait(int(3e6//4))\n",
    "\n",
    "    ################# Frequency tracking  #################\n",
    "    #  N_Freq_tracking x 2 Ramsey spin + SMPD acumulation\n",
    "\n",
    "    update_frequency(spin_element, Photon_IF   + delta_freq + tracking_peak_freq*1e3)\n",
    "   \n",
    "\n",
    "    with for_(n, 0, n < N_Freq_tracking, n + 1):\n",
    "        with for_each_(angle,[-0.25,0.25]):\n",
    "\n",
    "            align()\n",
    "            play(spin_gauss_pulse*amp(amplitude_readout_pulse_ramsey*0.5), spin_element, duration = gauss_duration_ramsey)\n",
    "            frame_rotation_2pi(angle, spin_element)\n",
    "            wait(int(interrogation_time-gauss_duration_ramsey))\n",
    "            play(spin_gauss_pulse*amp(amplitude_readout_pulse_ramsey*0.5), spin_element, duration = gauss_duration_ramsey)\n",
    "            align()\n",
    "            click_track = measure_SMPD_nostream(click_track, N_readout_track,waiting_time)\n",
    "            # click_track = measure_SMPD(prep_stream,N_readout,waiting_time, accumulate=True, return_click=True)\n",
    "            #Low pass filter on the click_track with an accumulation constant of Y_filter_length that returns the filtered value Y\n",
    "            assign(Y,(1-1/Y_filter_length)*Y+Cast.mul_fixed_by_int(angle*Y_filter_scaling_factor, click_track))\n",
    "            \n",
    "            wait(int(5e5//4))\n",
    "\n",
    "    assign(delta_freq_acc, delta_freq_acc+Cast.mul_int_by_fixed(I_coeff,Y))\n",
    "    assign(delta_freq, Cast.mul_int_by_fixed(P_coeff,Y)+delta_freq_acc)\n",
    "    \n",
    "    \n",
    "    return delta_freq\n",
    "\n",
    "def Jaimsey_RO(p_stream_NRO, N_NRO,amplitude_pulse, gaussian_pulse_length, interrogation_time = 12500//4):\n",
    "\n",
    "    meas_angle = declare(fixed)\n",
    "    with for_(j, 0, j < N_NRO//4, j + 1):\n",
    "        with for_each_(meas_angle, [0, 0.25, 0.5, 0.75]):\n",
    "            align()\n",
    "            reset_frame(spin_element)\n",
    "            play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "            wait(interrogation_time-gaussian_pulse_length, spin_element)\n",
    "            frame_rotation_2pi(meas_angle, spin_element)\n",
    "            play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "            align()\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align()\n",
    "            measure_SMPD(p_stream_NRO,N_iterations,waiting_time, accumulate=True, return_click=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98780ea-d6b4-45f2-8fdb-81f5894cd562",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Magnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14b6ae97-0f11-46ee-8e85-81d0de8b165f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:28.096276Z",
     "iopub.status.busy": "2024-04-01T18:27:28.095277Z",
     "iopub.status.idle": "2024-04-01T18:27:28.324256Z",
     "shell.execute_reply": "2024-04-01T18:27:28.323258Z",
     "shell.execute_reply.started": "2024-04-01T18:27:28.096276Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "from quantrolab.instruments.all import *\n",
    "from quantrolab.instruments.keithley.smu_keithley import Keithley_SMU_temp\n",
    "from quantrolab.helpers.instrumentmanager.instrument_manager import InstrumentManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0e86d95-bd91-4efe-acfb-7784662ddb1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:28.681420Z",
     "iopub.status.busy": "2024-04-01T18:27:28.680420Z",
     "iopub.status.idle": "2024-04-01T18:27:28.874423Z",
     "shell.execute_reply": "2024-04-01T18:27:28.873423Z",
     "shell.execute_reply.started": "2024-04-01T18:27:28.681420Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "MagnetX = AmericanMagneticsProgrammer430(name='MagnetX')\n",
    "# MagnetY = MilosMagnet(name='MagnetY')\n",
    "MagnetY = AmericanMagneticsProgrammer430(name='MagnetY')\n",
    "#MagnetZ = MilosMagnet(name='MagnetZ')\n",
    "MagnetZ = AmericanMagneticsProgrammer430(name='MagnetZ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d85e326-6cf2-4b43-9aa0-76f21f398dde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:29.142012Z",
     "iopub.status.busy": "2024-04-01T18:27:29.142012Z",
     "iopub.status.idle": "2024-04-01T18:27:36.822200Z",
     "shell.execute_reply": "2024-04-01T18:27:36.821199Z",
     "shell.execute_reply.started": "2024-04-01T18:27:29.142012Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "MagnetX.initialize(visaAddress='TCPIP0::192.168.0.99::7180::SOCKET') # current limit is set to 1A manually\n",
    "MagnetY.initialize(visaAddress='TCPIP0::192.168.0.98::7180::SOCKET') # current limit is set to 1A manually\n",
    "MagnetZ.initialize(visaAddress='TCPIP0::192.168.0.97::7180::SOCKET') # current limit is set to 1A manually\n",
    "Magnet_list=(MagnetX,MagnetY,MagnetZ)\n",
    "FieldtoCurrentRatio_list=(18.49,18.7,24.58)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44a0de54-a8f7-451f-a21e-45140587352e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:36.825199Z",
     "iopub.status.busy": "2024-04-01T18:27:36.825199Z",
     "iopub.status.idle": "2024-04-01T18:27:37.816675Z",
     "shell.execute_reply": "2024-04-01T18:27:37.815673Z",
     "shell.execute_reply.started": "2024-04-01T18:27:36.825199Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MagnetX\t| Current  = -0.0076 A\t| Field = -0.1328 mT\t| Persitent switch heater: 0\n",
      "MagnetY\t| Current  = 0.0050 A\t| Field = 0.0843 mT\t| Persitent switch heater: 0\n",
      "MagnetZ\t| Current  = 18.2262 A\t| Field = 448.0082 mT\t| Persitent switch heater: 0\n"
     ]
    }
   ],
   "source": [
    "Field_list = []\n",
    "for Magnet, f2c in zip(Magnet_list,FieldtoCurrentRatio_list):\n",
    "    field = f2c*Magnet.get_supply_current()\n",
    "    Field_list.append(field)\n",
    "    print(\"%s\\t| Current  = %.4f A\\t| Field = %.4f mT\\t| Persitent switch heater: %i\"%(Magnet.name(),Magnet.get_supply_current(), field, Magnet.get_PSwitch()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "452c519b-03e9-4743-bd69-ea4a69b3658f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:37.818676Z",
     "iopub.status.busy": "2024-04-01T18:27:37.817675Z",
     "iopub.status.idle": "2024-04-01T18:27:38.629958Z",
     "shell.execute_reply": "2024-04-01T18:27:38.628954Z",
     "shell.execute_reply.started": "2024-04-01T18:27:37.818676Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MagnetX: (ramp rate in A/s,current limit in A) = (0.0025000,0.2)\n",
      "MagnetY: (ramp rate in A/s,current limit in A) = (0.0020000,0.2)\n",
      "MagnetZ: (ramp rate in A/s,current limit in A) = (0.00200000,20.0)\n"
     ]
    }
   ],
   "source": [
    "# Calibration angles\n",
    "phi = -0.2/446 #-0.116 * np.pi / 180\n",
    "psi = 0.000001 #-0.155 * np.pi / 180\n",
    "\n",
    "# Ramping parameters\n",
    "ramp_rate_z = 0.002 #A/sec\n",
    "ramp_rate_y = 0.002\n",
    "ramp_rate_x = max(0.0025, np.abs(np.sin(phi)/np.cos(phi) * 18.49/18.7 * ramp_rate_y))\n",
    "\n",
    "ramp_rate_list=(ramp_rate_x,ramp_rate_y,ramp_rate_z)\n",
    "current_lim_list=(0.2,0.2,20)\n",
    "\n",
    "for Magnet,current_lim,ramp_rate in zip(Magnet_list,current_lim_list,ramp_rate_list):\n",
    "    Magnet.set_and_check_current_limit(current_lim)\n",
    "    output=Magnet.set_and_check_magnet_ramp_rate(ramp_rate, current_lim)\n",
    "    print(\"%s: (ramp rate in A/s,current limit in A) = (%s)\"%(Magnet.name(),output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "becc660e-9a7c-409e-8999-959e7a9be74b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate field components without ramping the magnet\n",
    "theta = -0.08 * np.pi / 180\n",
    "field_value_mT=446.85\n",
    "\n",
    "calculate_field_xyz(field_value_mT,theta,phi,psi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221fe056-1859-43ae-8759-c5c8b6c1a4c6",
   "metadata": {},
   "source": [
    "## Go to field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f12311-2256-4dae-928c-b205e84021ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = -0 * np.pi / 180\n",
    "field_value_mT=0\n",
    "\n",
    "if True:\n",
    "    go_to_field_xyz(field_value_mT,theta,phi,psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31ec3aae-8cd9-4bfb-8af0-b343cf0280ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#field_value_x_mT=0.38\n",
    "field_value_x_mT=0\n",
    "\n",
    "if True:\n",
    "    go_to_field_x(field_value_x_mT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff4cf9d-a6ae-4574-9f01-1d9ee8010eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "field_value_y_mT = -0.02 # Expected middle of the spin line\n",
    "\n",
    "if True:\n",
    "    go_to_field_y(field_value_y_mT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86fee314-9a99-48d0-bedb-1a22930d81b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "field_value_z_mT=448.0\n",
    "\n",
    "if True: \n",
    "    go_to_field_z(field_value_z_mT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d31e90a-769a-4390-9d54-37d4cdeb6ed4",
   "metadata": {},
   "source": [
    "## Enter / exit persistent mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57e0d7ad-4a93-47a0-a3ce-6b3f085ffe18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 enter, 1 exit persistant mode\n",
    "MagnetX.set_and_check_PSwitch(0)\n",
    "MagnetY.set_and_check_PSwitch(0)\n",
    "MagnetZ.set_and_check_PSwitch(0)\n",
    "\n",
    "better_sleep(180) # set this timer to automatically wait until persistent switches are cooled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc073b26-b766-40e5-9239-de3b1c74dab7",
   "metadata": {},
   "source": [
    "# Yoko Init and Autocalibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f4cf3a90-2b4f-4710-ab86-451e587d09a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:38.632955Z",
     "iopub.status.busy": "2024-04-01T18:27:38.631956Z",
     "iopub.status.idle": "2024-04-01T18:27:38.971954Z",
     "shell.execute_reply": "2024-04-01T18:27:38.970958Z",
     "shell.execute_reply.started": "2024-04-01T18:27:38.632955Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [],
   "source": [
    "yokoBuffer  = Yokogawa7651(name='yokoBuffer')\n",
    "yokoPurcell = Yokogawa7651(name='yokoPurcell')\n",
    "\n",
    "yokoBuffer.initialize (visaAddress='GPIB0::30')\n",
    "yokoPurcell.initialize(visaAddress='GPIB0::19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4c891db8-fa97-4d1c-8ddf-162d9e2f1f1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:27:38.973957Z",
     "iopub.status.busy": "2024-04-01T18:27:38.973957Z",
     "iopub.status.idle": "2024-04-01T18:27:39.237628Z",
     "shell.execute_reply": "2024-04-01T18:27:39.236630Z",
     "shell.execute_reply.started": "2024-04-01T18:27:38.973957Z"
    },
    "scene__Initialise": true,
    "tags": [
     "ActiveScene"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purcell DC bias: 3.800 V\n",
      "Buffer DC bias: 9.890 V\n"
     ]
    }
   ],
   "source": [
    "#yokoPurcell.setOutputValue(4.7, slewrate=0.2)\n",
    "#yokoBuffer.setOutputValue(9.993, slewrate=0.2)\n",
    "\n",
    "print(\"Purcell DC bias: %.3f V\\nBuffer DC bias: %.3f V\"%(yokoPurcell.outputValue(),yokoBuffer.outputValue()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea237a4-3f94-4478-9df4-96727aba6ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This shit is still carcinogenic: You must restart the notebook after using this or it ruins everything\n",
    "\n",
    "from Config import *\n",
    "optimal_voltage = 9.872\n",
    "voltages = sinhspace(-0.4, 0.4, 41, nonlinearity=3) + optimal_voltage\n",
    "\n",
    "_ = autocalibrate_sb(yokoBuffer, voltages, N_iterations=50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0330f8ba-a6e6-4574-9637-b9025ccd98ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "purcell_voltages = [3.5,4,4.5,5,5.5]\n",
    "buffer_voltages = [9.8827,9.9316,9.9664,10.0085,10.0535]\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "plt.plot(purcell_voltages,buffer_voltages,\"o\")\n",
    "\n",
    "lin_func = lambda x,*y: y[0]*x+y[1]\n",
    "est,std,fine,data_fit = fit_function([1,0],lin_func,purcell_voltages,buffer_voltages)\n",
    "\n",
    "plt.plot(fine,data_fit, label = \"fit\")\n",
    "\n",
    "plt.xlabel(\"Purcell bias voltage (V)\")\n",
    "plt.ylabel(\"Optimal buffer bias voltage (V)\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\yoko_autocalibration_sb\\\\20231128222350_\\\\\"+\"purcell_vs_buffer.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a33fdba-0f1b-44e6-9d49-8148a9842196",
   "metadata": {},
   "source": [
    "# IQ mixer calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7e5f2c-697a-4a0f-ba0e-b1ba42f709b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from quantrolab.instruments.all import *\n",
    "from scipy.signal import find_peaks\n",
    "\n",
    "spectrum_analyzer  = RohdeSchwarzSpectrumAnalyzer(name='FSV_RS')\n",
    "spectrum_analyzer.initialize(visaAddress='TCPIP0::192.168.0.219::inst0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27913e3e-5f04-41a3-bbe3-51e1feca76f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run Config.py\n",
    "from Config import *\n",
    "\n",
    "ramp_time = int(0.5e6//4)\n",
    "amplitude = 0.4\n",
    "element = spin_sticky_element\n",
    "duration=int(50e6//4)\n",
    "\n",
    "def raman_IQ_testing(amp1, amp2):\n",
    "    with program() as testing:\n",
    "        index_stream = declare_stream()\n",
    "\n",
    "        save(0, index_stream)\n",
    "        with while_(True):\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, amp1, amp2, duration, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "        with stream_processing():\n",
    "            index_stream.save('interation')\n",
    "    return testing\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "\n",
    "amp1_array = np.linspace(0.1,1,10)\n",
    "amp2_array = np.linspace(0.1,1,10)\n",
    "\n",
    "f_max = np.zeros([len(amp1_array), len(amp2_array), 2])\n",
    "a_max = np.zeros([len(amp1_array), len(amp2_array), 2])\n",
    "for ii, amp1 in enumerate(amp1_array):\n",
    "    for jj, amp2 in enumerate(amp2_array):\n",
    "        PrintStatic(f'Testing amplitudes ({ii}): {amp1:.2f}, ({jj}) {amp2:.2f}')\n",
    "        testing = raman_IQ_testing(amp1, amp2)\n",
    "        job = qm.execute(testing)\n",
    "        res = job.result_handles\n",
    "        \n",
    "        time.sleep(1)\n",
    "        f, a = spectrum_analyzer.getTrace()\n",
    "        peaks = find_peaks(a, height=max(a)/20, width=10)\n",
    "        f_max[ii, jj] = peaks[0][:2]\n",
    "        a_max[ii, jj] = peaks[1]['peak_heights'][:2]\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d1c4257-f460-4f6b-9c85-298498d2a6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08062fbb-129c-486a-91c6-df246b145f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "for ii, amp1 in enumerate(amp2_array):\n",
    "    plt.plot(amp2_array, a_max[ii,:,0]*1e3, 'o--', label=f'$a_1$ = {amp1:.1f}')\n",
    "plt.xlabel(f'$a_2$ (A.U.)')\n",
    "plt.ylabel('Peak height (mV)')\n",
    "plt.legend()\n",
    "    \n",
    "plt.figure(figsize=(5,5))\n",
    "for jj, amp2 in enumerate(amp2_array):\n",
    "    plt.plot(amp2_array, a_max[:,jj,1]*1e3, 'o--', label=f'$a_2$ = {amp2:.1f}')\n",
    "plt.xlabel(f'$a_1$ (A.U.)')\n",
    "plt.ylabel('Peak height (mV)')\n",
    "plt.legend()\n",
    "\n",
    "np.save(path+'IQ_mixer_calib.npy', [a_max])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e829cc37-1c4e-4f7f-9599-1ecb507cc2da",
   "metadata": {},
   "source": [
    "# Testing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b57888-925e-476a-bb0c-f9b2fb69b7e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%run Config.py\n",
    "from Config import *\n",
    "\n",
    "ramp_time = int(5e6//4)\n",
    "amplitude = 0.01\n",
    "element = spin_element\n",
    "duration=int(40e3//4)\n",
    "chirp_rate = calc_chirp_rate(0,20e3,duration*4)\n",
    "\n",
    "print(chirp_rate)\n",
    "with program() as testing:\n",
    "    \n",
    "    index_stream = declare_stream()\n",
    "    \n",
    "    delta_freq = declare(int)\n",
    "    assign(delta_freq, 0)\n",
    "    save(0, index_stream)\n",
    "        \n",
    "    with while_(True):\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "        align()\n",
    "        update_frequency(element, Photon_IF-0*20e3)\n",
    "        chirped_square_pulse(amplitude, element, duration, chirp_rate = chirp_rate\n",
    "                            )\n",
    "        #chirped_flattop_pulse(amplitude, duration, switch_duration_extra, element=spin_sticky_element, chirp_rate = chirp_rate)\n",
    "    \n",
    "        wait(int(100e3//4))\n",
    "        \n",
    "        \n",
    "    with stream_processing():\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(testing)\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f831817f-2646-4258-bae7-23df67dabfe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_pulse_durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92169b61-7e5a-4f53-b49c-058cfcc939b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%run Config.py\n",
    "from Config import *\n",
    "\n",
    "ramp_time = int(5e6//4)\n",
    "amplitude = 0.4\n",
    "element = spin_sticky_element\n",
    "duration=int(5e6//4)\n",
    "\n",
    "spin_chirp_df = 0.0\n",
    "config['waveforms']['rising_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0]\n",
    "config['waveforms']['rising_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1]\n",
    "config['waveforms']['lowering_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1][0]\n",
    "config['waveforms']['lowering_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1][0]\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,12,13))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "\n",
    "with program() as testing:\n",
    "\n",
    "    index_stream = declare_stream()\n",
    "    duration_set=declare(int)\n",
    "\n",
    "    save(0, index_stream)\n",
    "    with while_(True):\n",
    "        \n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            Raman_pulse_chirped(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set,        ramp_time)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration,   ramp_time)\n",
    "\n",
    "            wait(int(1e9//4))\n",
    "\n",
    "\n",
    "    with stream_processing():\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(testing)\n",
    "res = job.result_handles\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f7b1e3a-0fe2-4c37-86dc-094002aa728c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%run Config.py\n",
    "from Config import *\n",
    "\n",
    "ramp_time = int(5e6//4)\n",
    "amplitude = 0.4\n",
    "element = spin_sticky_element\n",
    "duration=int(5e6//4)\n",
    "\n",
    "with program() as testing:\n",
    "    \n",
    "    index_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    save(0, index_stream)\n",
    "    with while_(True):\n",
    "        align()\n",
    "        play('ON',fsv_trigger)\n",
    "\n",
    "        flattop_pulse3_chirped(amplitude, duration, 0, element = element, ramp_time=ramp_time)\n",
    "\n",
    "        ramp_to_zero(element, duration=4)\n",
    "        reset_phase(element)\n",
    "\n",
    "        #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration,   ramp_time)\n",
    "\n",
    "        wait(int(100e6//4))\n",
    "        \n",
    "        \n",
    "    with stream_processing():\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(testing)\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9540f99-1d5c-4eea-8859-92a9fc049e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0])\n",
    "plt.plot(chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1]-(chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19535baa-9363-4ca6-ae2b-0252f7d0a6ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_and_plot=True\n",
    "if sim_and_plot:\n",
    "    simulation = qm.simulate(testing, SimulationConfig(duration=duration), include_analog_waveforms=True,\n",
    "                             include_digital_waveforms=True,\n",
    "                             simulation_interface=LoopbackInterface([(\"con1\", 1, \"con1\", 1)]))\n",
    "    samples = simulation.get_simulated_samples()\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    fig.show()\n",
    "    fig.canvas.draw()\n",
    "    for i,key in enumerate(list(samples.con1.analog.keys())):\n",
    "            #plt.plot(samples.con1.analog[key]+0.1*i)\n",
    "            pass\n",
    "    samples.con1.plot(analog_ports={'1', '2', '3', '4','6','7','8'})\n",
    "    fig.canvas.draw()\n",
    "    plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "706c9d8f-c027-4ab5-a2a7-786338d7ec8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(ErfRising(adiabatic_spin_Nsigma * adiabatic_spin_sigma, adiabatic_spin_sigma, adiabatic_spin_amp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ebeeb8-53e6-41e1-8257-1334076321c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(chirp_cos_raise(chirp_cos_length, chirp_cos_amp, chirp_cos_df, 1)[0])\n",
    "plt.plot(chirp_cos_raise(chirp_cos_length, -chirp_cos_amp, chirp_cos_df, -1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44eb353c-b7a8-4b98-847e-158f8015a6b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_data_old = time_data\n",
    "res_old = res\n",
    "average_number_old=res.clicks.count_so_far()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e16be3b5-31e3-486e-a3d1-576b5a5163c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "chunk = 200\n",
    "for repeat_save in range(1):\n",
    "    res = job.result_handles\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    while average_number<chunk*0:\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Manu...\"))\n",
    "        average_number=res.clicks.count_so_far()\n",
    "    total_time_freqsweep = time.time() - start_time_freqsweep\n",
    "    print(\"Time elapsed  = %.3f s\\nAverage number = %i\"%(total_time_freqsweep, average_number))\n",
    "    # time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    #print(click_array.shape)\n",
    "    \n",
    "    %matplotlib inline\n",
    "    integration_index=10\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    #print((freq_range-Photon_IF)/1e3)\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'frequency_sweep.pdf')\n",
    "    \n",
    "    ############### Plot Background ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('background counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'background.pdf')\n",
    "    \n",
    "    ############### Plot decay ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.ylabel('count rate (/ms)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_BY'+str(np.round(Field_list[1],3))+'mT.pdf')\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    plt.savefig(directory+filename+'_BZ'+str(np.round(Field_list[2],3))+'mT_'+'.pdf')\n",
    "    \n",
    "    fullpath=directory+filename+'2.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BX_mT':Field_list[0],\n",
    "                'BY_mT':Field_list[1],\n",
    "                'BZ_mT':Field_list[2],\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'freq_range':freq_range,\n",
    "                #'persistent_mode_on': [not MagnetX.get_PSwitch(), not MagnetY.get_PSwitch(), not MagnetZ.get_PSwitch()],\n",
    "                }\n",
    "    save_h5(fullpath,datasets, group=str(Field_list[2]), overwrite=True)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "\n",
    "    npts = np.round(click_array.shape[0],-3)\n",
    "    offset = 0\n",
    "    integration_index=10\n",
    "    integration_index_bg=1\n",
    "    plt.figure()\n",
    "    excess_array = []\n",
    "    t_step = time_hist[1]-time_hist[0]\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "        PrintStatic(str(start))\n",
    "        stop = start+chunk\n",
    "    \n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        # plt.figure()\n",
    "        if start%2000==0: \n",
    "            \n",
    "            plt.plot((freq_range-Photon_IF)/1e3,excess,'-o', label = \"%i-%i\"%(start,stop))\n",
    "    PrintStatic(\"\")\n",
    "    \n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "\n",
    "    excess_array.shape\n",
    "    \n",
    "    ################### 2D Plot ###################\n",
    "    use_time = True\n",
    "    chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "    time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "    plt.figure()\n",
    "    if use_time:\n",
    "        yaxis = time_elapsed\n",
    "        ylabel = \"Time (minutes)\"\n",
    "    else:\n",
    "        yaxis = chunk_index\n",
    "        ylabel = 'Slice centre index'\n",
    "        \n",
    "    plot_2d_sweep(excess_array,x=np.round((freq_range-Photon_IF)/1e3,0),y=yaxis,\n",
    "                  xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "                cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.pdf')\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[1],3))+'frequency_sweep_time_resolved.png')\n",
    "    #plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_frequency_sweep_fixed_field\\\\%s\\\\%sSpectroscopy_frequency_sweep_fixed_field_amp0.005\\\\\"%(time_stamp,time_stamp)+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e99d78e-9d34-49fc-85b1-028f43b5f4b0",
   "metadata": {},
   "source": [
    "# Spectroscopy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391efb31-3acd-44d3-8ea0-96ea992ff888",
   "metadata": {},
   "source": [
    "## Frequency sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73da39bd-1ac8-4a3f-a4b3-87103e8b0c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = centre_freq*1e3#10e3\n",
    "span=0.1e6\n",
    "freq_final = offset+span/2\n",
    "freq_init  = offset-span/4\n",
    "freq_step = 0.002e6\n",
    "print(freq_init, freq_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c55d2715-4019-4ebe-8bbe-11215c474714",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = centre_freq*1e3#10e3\n",
    "span=0.1e6\n",
    "freq_final = offset+Photon_IF+span/2\n",
    "freq_init  = offset+Photon_IF-span/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3159abcf-3d12-4419-8483-4f512681ed4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset-span/2 + 27e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1717,
   "id": "ac11c3dc-8e0b-41b0-bb0a-ff3d3879b08d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T07:57:56.102578Z",
     "iopub.status.busy": "2024-03-29T07:57:56.101583Z",
     "iopub.status.idle": "2024-03-29T07:57:56.388649Z",
     "shell.execute_reply": "2024-03-29T07:57:56.387649Z",
     "shell.execute_reply.started": "2024-03-29T07:57:56.102578Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018"
      ]
     },
     "execution_count": 1717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.0045*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dce0d7e0-f65d-4e54-a4d6-75c79553a462",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:33:50.786245Z",
     "iopub.status.busy": "2024-04-01T18:33:50.784234Z",
     "iopub.status.idle": "2024-04-01T18:33:51.897246Z",
     "shell.execute_reply": "2024-04-01T18:33:51.895235Z",
     "shell.execute_reply.started": "2024-04-01T18:33:50.786245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory created\n",
      "210\n",
      "2000000\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "experiment_name='Spectroscopy_frequency_sweep_fixed_field'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "\n",
    "sim_and_plot=True\n",
    "excecute=True\n",
    "\n",
    "param_type=int\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 60000\n",
    "N = 30\n",
    "stream_I = False\n",
    "\n",
    "amplitude_pulse = 0.0045/2*10\n",
    "gauss_duration  = 160_000//4//10\n",
    "\n",
    "offset = 0*1e3#10e3\n",
    "span=0.3e6\n",
    "freq_final = offset+Photon_IF+span/2\n",
    "freq_init  = offset+Photon_IF-span/2\n",
    "freq_step = 0.02e6\n",
    "\n",
    "n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "\n",
    "freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "\n",
    "cycle_time_estimated=22  #in us\n",
    "Integration_time=5000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=10000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    \n",
    "\n",
    "    if stream_I:\n",
    "        I_stream = declare_stream()\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "  \n",
    "        with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "\n",
    "            update_frequency(spin_element, freq_set)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "            with for_(i, 0, i < N_iterations, i + 1):\n",
    "            \n",
    "                with while_(I>I_threshold_reset):\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "\n",
    "\n",
    "                play(pump_pulse, pump_element) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                assign(click, I>I_threshold)\n",
    "\n",
    "                if stream_I:\n",
    "                    save(I, I_stream)  # I is saved into I_stream\n",
    "\n",
    "                save(click, p_stream)  # I is saved into I_stream\n",
    "\n",
    "    with stream_processing():\n",
    "        if stream_I:\n",
    "            I_stream.buffer(N_iterations).save_all('I')\n",
    "        #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "start_time_freqsweep = time.time()\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8011234-65aa-4cd9-bfe9-6b1e73592cfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:04.636061Z",
     "iopub.status.busy": "2024-04-01T18:35:04.635054Z",
     "iopub.status.idle": "2024-04-01T18:35:06.831120Z",
     "shell.execute_reply": "2024-04-01T18:35:06.830116Z",
     "shell.execute_reply.started": "2024-04-01T18:35:04.636061Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed  = 73.488 s\n",
      "Average number = 1196\n",
      "                                                                              \r"
     ]
    },
    {
     "data": {
      "image/png": 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ytOfHp2wDkKcxl/Uv2/rnYHv27IklS5YYrWPq+jw8PAAYjmIac//+faPHy+saS0o/6lvQqLhWqy2vcEyKiYlBZmamPFPgcfr7n3eEOO80+bCwMAQEBJRtkBZOoVBg2LBh8vP1eWVnZ8v7cT/77LMGZU2bNi1S//o/PJX0D01ERBUBR56JiKo4/UhxQECAyYWv9u3bV2AfkiRh+PDhAP4dbdb/t2nTpmjWrFm+NvrnSCMjIw2eVS1t+utr3ry50XIhBA4cOGC0TP8sdVhYmMlR4eTkZJw5c8Zomf4az5w5g4iIiOKEXS70U3TzzhZ43IkTJ8orHJOys7ONPlMO5L5/hw4dAgC0atVKPt66dWs52d6+fXvZB1mBbd++HVqtFra2thg8eLBBWfv27WFvbw8g99l9U65cuQIAJtcGICKqDJg8ExFVcRqNBgBw48YNoyOQ58+fl1ctLoh+dPnKlSs4ffo0fv75Z4Pjj+vcubO8+vL48ePlhcFMKemCV/rr04+sPW758uW4c+eO0bIePXrAyckJADBnzhyjdRYuXGjyWdnBgwfD2dkZWVlZmDBhgslp4UDugkwJCQkmy8uC/o8a+r2XH3fgwAH8/fff5RqTKV988YXRvYp/+uknOfkfMmSIfNze3h6vvPIKAGDevHkm9wnXK8sF1SxZdHS0vFL72LFj8z3zbGtrK9/XpUuXGv2snzp1CsHBwQCAvn37lnHERETmw+SZiKiK69GjBxQKBeLi4vDqq6/KU2AzMzOxceNG9OjRo8AFofQaNGggj/y98847uHfvHpRKpZzAPE6lUmH58uVQqVQ4cuQInnvuOezfv99g0ag7d+5g+fLlaN26NZYuXVqi69NvQ/Xnn3/i888/l5PEhIQEzJkzB++//z7c3NyMtrW3t5e3Ofrhhx8wefJkOclKSkrCvHnzMGPGjHwJh56zszP++9//AsidPt67d2+cOHHCYAXjq1ev4ptvvkHjxo3xxx9/lOgaS+rll1+GQqFAbGwshg0bJq82npaWhp9++gkDBgyAq6trucZkjJ2dHY4cOYJXXnlFjjE9PR3ff/893nnnHQC5i149vur3nDlzULNmTcTExKBdu3ZYu3atwXZq0dHR2Lx5MwYMGGB0OnNpio+PR0xMjPzSfwZSU1MNjicnJxttn7dOTEyMfDw5OdnguLHkdseOHVi0aBFu374tPwuempqKX3/9Fe3atcOdO3fQrFkzzJo1y+i5Z82aBY1GgwcPHuDFF1+UR6B1Oh327duHQYMGQQiBatWqYcKECU90n4iILFpZbyRNRESW7+OPPxYA5JdGoxFWVlYCgPD19RU///yzXFaQb7/91qCfnj17FnrurVu3CkdHR7mNlZWVcHNzE2q12qCv2bNnG7RbtWqVACC8vb0L7D8zM1N07NhR7keSJOHi4iIUCoUAIHr37i2mTZsmAIjAwMB87bOyssSgQYPk9gqFQri4uAilUikAiNdee02MGDFCABBjxowxGsOyZcuEtbW13IdarRZubm7yPda/1q1bV+j9yisoKMhk3HrBwcEFvnefffZZvvdepVIJAKJ///4F3pvAwEABQAQFBZk8v7e3twAgVq1aZbKO/tzBwcEGx/O+x4sXLxaSJAkAwsXFxeDeNWvWTMTExBjt+8qVK8Lf39/g/XN1dRX29vYG192tWzeDdiEhIXJZSEiIydiLSn8fCnuNHDmywHtU2MvYe7Fw4UK5XKlUGnz+AYjnnntOxMbGFhj/X3/9JZydnQ0+J7a2tvL31apVEydOnHji+0REZMk48kxERJg7dy7WrFmDNm3awNbWFllZWahfvz4++eQTnDt3rtCtmvSGDRsGKysr+XtTU7bz6t+/P27duoWgoCC0adMGDg4OSEhIgFqtRrNmzfDmm29i69atmDRpUomuzcrKCnv27EFQUBD8/f1hZWUFIQTatGmDZcuW4ffffy9wNWWVSoWNGzdixYoV8v3Jzs5Gq1atsGLFCqxZs0aebu3s7Gy0j7fffhvXr1/HRx99hGbNmkGtViMhIQEODg5o1aoV3n//fezdu7fMRz+NmTlzJtauXYu2bdvC3t4eOTk5ePrpp7F8+XJs2bLFYlaaHjt2LHbv3o3nn38eCoUCCoUCDRo0wKxZs/D333+bnD3QsGFDXLx4Ed999x169OgBd3d3JCYmQgiB+vXrY/Dgwfj++++xcePGcr6i8tO9e3e8//77aN68OVxcXJCSkoLq1avjxRdfxC+//IKDBw8WOsOgY8eOuHLlCiZMmICAgABkZWVBkiQ89dRT+OSTT3D58mWT+30TEVUWkhAFPIBFREREBRJCoE6dOggPD8eaNWvw2muvmTskIiIiKgMceSYiInoCa9euRXh4OFQqFbp162bucIiIiKiMMHkmIiIqxLBhw7Bp0yaDhZqioqIwd+5cvPXWWwByp6jXqFHDXCESERFRGeO0bSIiokI4OztDq9UCyF352crKSv4eyH0e9I8//pC3tSIiIqLKh8kzERFRIdasWYM///wT586dw8OHD5GcnAxnZ2c8/fTTGDp0KF577TWDhdKIiIio8mHyTERERERERFQIPvNMREREREREVAgmz0RERERERESFYPJMREREREREVAgmz0RERCasXr0akiTBx8fH3KEYdfDgQUiSBEmSzB0KERFRpcfkmYiIyMIkJCRgxowZmDFjBhISEswdjllkZ2dj//79+OqrrzB06FD4+/tDoVBAkiSMGjWq0Pb6PyoU5dW5c+cSx7lp0yb07dsXNWvWhLW1Nezt7REQEIC33noL58+fL3G/RERkeVTmDoCIiIgMJSQkYObMmQCAUaNGwdnZ2Wg9Ozs7BAQElGNk5Sc8PBzdunUrcfvq1asXWJ6VlYW4uDgAQOvWrYvdf0ZGBgYPHozt27fLxxwcHJCZmYkbN27gxo0b+PHHH/H1119j/Pjxxe6fiIgsD0eeiYiIKqg2bdrg2rVruHbtmrlDKROOjo7o0KEDxo0bh59++glPP/10kdtGRkYW+Prkk0/kum+88UaxY5szZ46cOL/77rsIDw9HUlIS0tLScPr0aXTo0AE6nQ4TJ07EmTNnit0/ERFZHo48ExERkcWpU6cOtFqtwfPcP/74Y6n1v3LlSgBAhw4dSjR6v2bNGgBAYGAglixZIh9XKBRo2bIl/vjjD3h5eSE5ORmbNm1Cy5YtSydwIiIyG448ExGR2dy7dw+TJ0/G008/DY1GA1tbW9SrVw/9+vXDmjVrkJ6eDgDYtWsXJEmCSqXCgwcPCuyzY8eOJp+L1el02LhxI/r3749atWpBrVbDw8MDLVu2xMcff4zLly8X+xqSkpIwd+5ctGvXDq6urlCr1ahduzaGDh2Kv//+u9j9derUCb6+vvL3vr6+Bs/ndurUSS4raMGwxxc7O3z4MPr27Ytq1arB3t4ezZs3lxNIvR07dqB79+7w8PCAnZ0dWrdujV9++aXQmI8ePYrhw4fD29sbNjY20Gg0aNOmDebNm4fk5ORi3wMA8vPNZeHYsWO4evUqAODNN98sUR8REREAgFatWhkt12g08Pf3B4AS3QP9+3rw4EGTdTp16gRJkjBjxox8ZWlpafj666/Rrl07uLi4wMrKCh4eHmjUqBFGjhyJzZs3FzsmIqIqTxAREZnBmjVrhI2NjQAgAAhra2vh5uYmVCqVfOzcuXNCCCF0Op3w9fUVAMTnn39uss+rV6/KbY8ePWpQFh0dLZ577jm5HIBwdnYWDg4O8vf9+vUzaLNq1SoBQHh7exs937lz54SXl5fcXqlUCkdHR/l7SZLEnDlzinVfBgwYINzd3eU+3N3dRfXq1eXXgAED5LrBwcFyvcfljf2HH34QCoVCSJIkNBqNwT2YMmWKEEKIzz77TAAQCoUiX51ly5YZjTUnJ0d88MEHBnUdHByEUqmUvw8ICBChoaHFugemBAYGCgBi5MiRT9TP6NGjBQCh0WhESkpKifpo2LChACA6depktDwhIUH+bK1evbrY/evvX3BwsMk6+vsRFBRkcDwxMVE0a9bM4HPo7Oxs8G/L1GeaiIhM48gzERGVux07dmDkyJFIT09H+/btcfjwYaSlpSEmJgYpKSk4fPgw3nrrLVhbWwPIHYUbM2YMgNzptkIIo/3+8MMPAIAmTZrg2WeflY9nZ2ejf//++Ouvv6BWqzFv3jw8fPgQ8fHxSEpKwv379/Hdd9+hUaNGRb6GiIgI9OzZE+Hh4XjppZdw+vRppKWlITExEVFRUZg+fTqUSiU++eQTbNu2rcj9btmyBadOnZK/P3XqlMGzulu2bClyXwAQHR2NsWPH4r333kNUVBQSEhIQGxuLkSNHAgDmz5+P+fPn44svvsDs2bMRFxeHhIQEPHjwAM8//zwA4KOPPoJWq83Xd1BQEL799ltUq1YNS5YsQWxsrPzcb3BwMJo3b47r16/jpZdegk6nK1bcZSU5ORkbN24EAAwbNgx2dnYl6uedd94BkDv6P3bsWNy/fx8AIITA2bNn0adPHyQnJ6Ndu3YYPnx46QRfRIsWLcKFCxfg6uqKzZs3Iy0tDfHx8cjIyMD9+/exZs0a9OjRo1xjIiKqFMydvRMRUdWSlZUljyJ36NBBZGRkFKndw4cPhbW1tQAgdu3ala88PT1dHrH99ttvDcpWrFghj8Dt2LGjyLEWNPKsH7185ZVXTLZfsGCBACCaNWtW5HMKIURISIg8QhgSEmKyXlFGngGIN998M195dna2/D4AELNnz85XR6vVCnt7ewFArF27Nl+MSqVS2NraivPnzxuNLzExUR6Z37p1a8EXXQSlMfL8ww8/yNd8+vTpEveTk5MjJk+eLBQKhcGou/4z6unpKaZMmSJSU1NL1D+eYOS5V69eAkCxZz0QEVHBOPJMRETlKjg4GCEhIQCAhQsXyqPLhfHw8MDAgQMBAN9//32+8q1btyImJga2trZ47bXXDMr0C0298MILeOGFF54kfABAeno61q9fDwD4+OOPTdYbMWIEAODChQuIiop64vOW1JQpU/IdUyqV6Nq1KwDAxsYGH374Yb46Tk5OaNeuHQDg4sWLBmWrV69GTk4Onn/+eTRr1szoeR0dHdG/f38AwO7du5/gCkrPihUrAADNmjV7okW8FAoFvvzyS/z4449wcHAAkDuqnZmZCSD3M6LVapGSkvLkQReTfmsz/XPZRERUOpg8ExFRuTp27BgAwNPT0+RiS6a8/fbbAIDt27fnS0b1U7Zffvllg32Rs7Oz5WnQffv2LWnYBs6cOSMvZtajRw94enoafTVu3FhuExYWVirnLi5XV1fUq1fPaJl+L+RGjRrB3t6+wDrx8fEGx48ePQoA2LNnj8nr9/T0xKpVqwCY7/rz+ueff3DixAkAJV8oTC8mJgZdu3bFqFGj0K5dOxw5cgQJCQmIiIjAli1b4OHhgWXLluGZZ56Rp3SXlz59+gAAFi9ejGHDhmHbtm2IiYkp1xiIiCojblVFRETlKjIyEgDg7e1d7LbPPfccGjVqhCtXrmDVqlXyiOqtW7cQHBwMAPKz0XqxsbHIysoq8TmNybvid1FHlFNTU0vl3MXl6OhoskylUhW5jv4e6unvQUpKSpFGV811/XnpR51tbGye+DnkkSNH4uDBgwgMDMTu3bvllcE1Gg0GDBiA9u3bo3Hjxrhz5w6mTJmCtWvXPnH8RfXKK6/g5MmT+L//+z9s2LABGzZsAADUr18fPXr0wOjRo7l1FhFRCXDkmYiIytWTbj+kH31esWKFvHCY/usmTZrI04xL63zG5OTkyF+npaVBCFHoK+8WU5WB/h58/PHHRbr+grZcKg+ZmZlYt24dAGDgwIEGsxOK6+rVq9i5cycAYOLEiUY/Y9WqVZOn7W/ZssXkIndl5b///S+uX7+OOXPmoFevXnB2dsatW7ewdOlStGrVyug0fSIiKhiTZyIiKleenp4ASj6Nd8SIEbCzs8Pt27dx4MABZGVlYfXq1QDyjzoDudOWraysnuicj9NfQ2n2WdE86ftY3n777Td56vKTTtm+cuWK/LWpKfEA4OfnByB31P3hw4fFOodSqQQA+fEAY4ytgJ5X/fr1MXXqVOzcuROxsbH4+++/5WfQFy1ahN9//71YMRERVXVMnomIqFzpt5CKjIzE6dOni91eo9Fg2LBhAHIXDtM//2xra2t0Kq5KpUKbNm0A5D4rXRpat24tL3RWWn3mpVD8+7/n8h6xLKr27dsDAPbt21dggmcp9FO269evj8DAwCfqK+/7U9AfD/JO6dcvKlZULi4uAIB79+4ZLU9KSsLVq1eL3J9CoUDbtm2xadMm1KlTBwCwd+/eYsVERFTVMXkmIqJy1blzZ9StWxcAMH78eHl14uLQT93etm0b5s+fDyD/QmF5vfHGGwCAnTt3ytNtn4S9vT1eeeUVAMC8efNw9+7dAuvHxcUVq38nJyf564SEhGLHVx5Gjx4NlUqFmJgYBAUFFVg3MzMTycnJ5RRZfnfv3sW+ffsA5Mb9pFP5W7RoIX+9bNkyo3VSUlKwZs0aAEDTpk1NLshmin4F882bNxst//rrr5GRkWG0zNRxIHdEW/+Hn7x/BCAiosLxpyYREZUrpVKJxYsXQ5IkHDlyBF27dsWRI0eg0+kA5CZaBw8exPDhww2mx+bVqlUrtGzZEpmZmfLqycambOu99tpr6NChA4QQGDhwIL766iuD1YcfPHiAhQsXFrjt1OPmzJmDmjVrIiYmBu3atcPatWuRlJQkl0dHR2Pz5s0YMGCAPFJeVM7OzqhVqxYAYNWqVcjOzi5W+/JQr149TJ8+HQAwf/58jBgxApcvX5bLs7Ozcf78ecyaNQv169fH+fPni30OrVaLmJgY+aVftCwjI8PgeGHTl3/88UfodDqoVCqMGjWqSOeeMWMGJEmCJEkIDQ01KPP29pZXbt++fTtee+013L59G0IIZGVl4dixY+jUqRPu3LkDIPe56OLSf2Z2796NoKAgJCYmAshd5fuTTz7B7NmzTf6x6JlnnsEHH3yAgwcPGizm9uDBA7z//vu4desWAJTKtm1ERFVK+W0pTURE9K+ffvpJqNVqAUAAEGq1Wri5uQmVSiUfO3funMn2K1askOs1adKk0PNFR0eLjh07ym0kSRLOzs7CwcFBPtavXz+DNqtWrRIAhLe3t9E+r1y5Ivz9/eX2CoVCuLq6Cnt7e/kYANGtW7di3Jlcn3/+ucG9qV27tvD29hZDhgyR6wQHB8t1HldY7EIIERQUJACIwMBAk3VGjhwpAIiRI0fmK9PpdGL69OlCkiQ5DltbW+Hm5iaUSqXBPThy5EhxLl8IIURgYKBBH6ZeBcWfk5Mj6tSpIwCIF198scjn1t8bACIkJCRfeXR0tGjZsqVBHHZ2dgafXwBi0qRJxb5uIYTIzs4WnTt3Nvi8uri4CEmShCRJ4quvvpLvT1BQkEFbb2/vfJ/zxz+T48ePL1FcRERVGUeeiYjILEaMGIFr167hww8/RKNGjaBSqZCWlgZvb2/0798fa9euRcOGDU22HzRokDz9tqBRZz13d3ccPHgQ69atQ69eveDh4YGUlBTY2dmhZcuWmDJlCubMmVOsa2jYsCEuXryI7777Dj169IC7uzsSExMhhED9+vUxePBgfP/999i4cWOx+gWATz75BIsWLUKrVq1gZWWF8PBwhIWFyVt9WQJJkjBr1ixcvHgR7777Lho2bAilUgmtVgsXFxc8++yzmDRpEo4dOyY/I13e9u3bJ0+rf9KFwvJyd3fH8ePHsWLFCvTs2RPVq1dHVlYWVCoV6tati+HDh+Pw4cPyYwXFpVQqsWPHDsycORMNGjSAtbU1JElCjx49sHfvXnz00Ucm227YsAEzZ85E165d4evri8zMTGRlZcHb2xtDhgzB/v37sWDBgpJeOhFRlSUJYaErkRARERVg8+bNGDRoEGxtbfHgwYMn2nqIiIiIqDAceSYiogrp//7v/wDkPhvKxJmIiIjKGpNnIiKqcL7//nscOnQICoUCEyZMMHc4REREVAWozB0AERFRURw/fhxDhw6FVquVt29699130bhxY/MGRkRERFUCk2ciIqoQ0tPTERYWBqVSibp162LkyJH45JNPzB0WERERVRFcMIyIiIiIiIioEHzmmYiIiIiIiKgQTJ6JiIiIiIiICsHkmYiIiIiIiKgQTJ6JiOiJ+fj4QJIkrF692tyhEBEREZUJJs9ERJWEEAK//vorBgwYAG9vb9ja2sLBwQH16tVDhw4dMGHCBGzduhWJiYnmDpVKwbZt2zBjxgxs27bN3KFQObl9+zbGjBkDX19f2NjYwMPDAz179sTmzZufqN+LFy/iiy++QM+ePVGrVi1YW1vD0dERTZo0wQcffIAbN24U2D40NBTr1q3D+PHjERgYCCcnJ0iSBEmSEBoaWqQYEhMT8cUXX6BVq1bQaDSws7ODv78/xo0bh/Dw8Ce6PiKi0sLVtomIKoGEhAT0798fhw4dko+pVCo4OTkhMTER2dnZ8vFVq1Zh1KhRpXr+rl274v79+/jyyy8xYMCAUu2bjBs1ahR++uknjBw5kiP+VcDOnTsxePBgpKamAgCcnJyQnJwMnU4HAHj99dexcuVKSJJUrH5//vlnDB8+3OCYRqNBcnIycnJyAADW1tb49ttvMWbMGKN96D+LxoSEhMDHx6fAGK5du4ZevXrJibZarYatra28n7uTkxO2bduGzp07F/3CiIjKAEeeiYgqgREjRuDQoUNQKpWYOHEibty4gYyMDMTGxiItLQ0XLlzAvHnz0KxZszI5//79+3Ht2jUmzkRlICQkBC+//DJSU1PRvn17XL9+HVqtFlqtFp999hmA3D+KffXVV8XuOysrC2q1GsOHD8eOHTug1WqRkJCA1NRU7Nu3D02aNEFmZibeeecd7Nu3z2gfCoUC9erVw8svv4y5c+fiyy+/LPL509LS0Lt3b4SGhsLNzQ2bN29GcnIy4uPjcfPmTfTq1QuJiYno378/R6CJyOw48kxEVMHdvHkT/v7+AIAvv/wSU6ZMKbB+WloabG1tyyM0KkMcea46XnvtNaxbtw6enp64evUqnJ2dDcrHjBmD77//Hk5OTggNDYWLi0uR+75+/TocHR1Rs2ZNo+UJCQlo2LAhIiMj0bVrV6MJdE5ODpRKpfz9wYMH5VHiwkaeFy9ejPfffx9A7qMI/fr1MyhPT09HgwYNEBYWhtGjR2PlypVFvjYiotLGkWciogru/Pnz8teP/+JpjLHEOe+CX0lJSZg6dSoCAgJga2sLd3d39O/fHydOnDDZZ0ELhumffTx48CCSkpIwbdo0NGjQALa2tnBzc0OfPn0K7LukYmNjMWvWLDzzzDNwdXWFjY0NfHx80KNHDyxbtgxardZouy1btqBPnz6oXr06rK2tUb16dfTp0wdbt241ea5Ro0ZBkiR5OvymTZvQqVMnuLq6ws7ODk8//TQWLVokT7E15pdffkGvXr1QvXp1WFlZwdnZGX5+fnjxxRexZMkSpKenA8hNTCRJkqfJ/vTTT/I9znuvS0qn02H//v344IMP0LZtW3h5ecHa2hpubm4IDAzE8uXLkZWVZbRtaGiowbOuYWFheOutt1CnTh3Y2NigXr16mDZtGlJSUuQ2ly9fxvDhw1G7dm3Y2NjAz88Ps2fPNnmOTp06QZIkzJgxA5mZmZg7dy6aNm0Ke3t7uLi4oHv37vjzzz9LfP2WJiUlRX6m+Z133smXOAPA1KlTAeQ+N1zcZ+ADAgJMJs4A4OzsjJdeegkAcOrUKaN18ibOxbVjxw4AgJ+fn9GfXzY2Nvjggw8A5P4bSUtLK/G5iIiemCAiogpt48aNAoAAIPbs2VOiPry9vQUAsWDBAhEQECAACGtra+Hk5CT3rVAoxMqVKwtsv2rVqnxl+vbr168X9evXFwCEjY2NsLOzk8usra3F7t27SxS7Mbt37xYuLi5y/yqVSri5uQkrKyv52NatWw3aZGRkiCFDhhhcr4uLi1AoFPKxYcOGiczMzHznGzlypAAgRo4cKcaOHSu3d3Z2ltsCECNGjDAa7+uvv25Qz8HBweD+ABAhISFCCCGOHj0qqlevLmxsbOR7Wb16dYPX0aNHS3zvQkJC8sWi0WgMjnXs2FGkpqYW2Hbz5s3y9Ts5OQmlUmnQPjMzU/zxxx/ydWo0GiFJklxnyJAhRuMLDAwUAMTUqVNFx44d5ff38XsdFBRU4ntgSXbt2iVf08mTJ03Wa9iwoQAghg4dWuoxTJgwQQAQ9vb2RaofHByc73Nrij7ufv36mayzc+dOub+9e/cWI3IiotLF5JmIqIILCQmRk46nnnpKXL9+vdh96JNfjUYjXFxcxMaNG0VWVpYQQogrV67ICYtKpRJnzpwx2b6g5NnFxUU0atRIHDhwQOTk5AidTidOnjwpJ+ve3t4iJyen2LE/7uzZs3Ji2bhxY7Fz50454c3OzhanT58WEydOFPv27TNoN3HiRAFASJIkpk+fLuLj44UQQsTFxYlPPvlEvo6PP/443zn1ybOLi4uwtrYWCxYsEFqtVgghRExMjHjzzTfl9vv37zdoe/jwYTnZnjdvnoiNjZXLYmJixO7du8XIkSPF/fv3jZ5z5MiRT3rLDNy7d0+8+uqr4vfffzeIJSkpSaxatUrUrFlTABDjx4/P1zZv8uzs7Cy6du0q/vnnHyGEEKmpqeLbb7+Vk+hp06YJjUYjhgwZIkJDQ+VzfPrppwUmSvrPokajEWq1WixfvlykpaUJIYS4e/euGDRokNz+t99+K9V7Yw5ff/21fD3G/mChN3jwYPlnQGlr0aKFACDatm1bpPolSZ779Oljss727dvl/hYsWFCc0ImIShWTZyKiSuCtt96Sf7mUJEk0b95cvPvuu2LlypXi0qVLQqfTFdhen/wCyJdUCpGb+Pj5+QkA4oUXXjDZvqDk2cPDQ0RFReUrv3jxolznyJEjRb9oEzp06CAACD8/P5GQkFCkNuHh4UKlUskjmsboR9+srKzEgwcPDMr0iaypeyCEEC1bthQAxJtvvmlwfN68eQKA6NGjR5FiffycpZ08F+bUqVPyKKQ+adXLmzw3btxYpKen52v/2muvyXW6d+9u9LOpH1F+44038pXpk2cARmdC5OTkiOeee06OobiCgoIMRrCL+yosWSwu/efOxcWlwHoffvihACDc3NxK9fwbNmwo8H4bU5zkuVevXgKA8PX1Nflz6ssvv5T7++ijj4p7CUREpYbPPBMRVQJLly7F9OnTYW9vDyEEzp07h6VLl+KNN97AU089BU9PT0yYMAFRUVEF9tO+fXt07do133FbW1tMmjQJALBr1y6TzwsX5D//+Q+qVauW7/hTTz0FX19fALn7zT6Jmzdv4siRIwCAOXPmQKPRFKnd5s2bkZ2dDRsbG5MLrk2bNg1qtRpZWVnYtGmT0Tq1a9fGyJEjjZa9+OKLAPJfo/4Z1ujoaHlrIEvWqlUrVKtWDSkpKQbP2z9u/PjxUKvV+Y737NlT/nrKlClGt1bS1yno81C7dm28/vrr+Y4rFApMmzYNAPDPP//g0qVLJvswxsHBAdWrVy/x60me/zUmKSkJAGBnZ1dgPX25vn5puHHjBt5++20AQIcOHUp9izsAeOGFFwDkLiy2fv36fOXJycn49ttv5e+5Tz0RmROTZyKiSkClUmHWrFm4f/8+1q5dizfffBPNmjWDtbU1AODhw4dYuHAhmjRpgpMnT5rsp0uXLoWW6XQ6nD17ttgxPvPMMybL9AsWxcXFFbvfvI4dOwYgdwGjXr16Fbnd6dOnAQCtW7eGk5OT0TouLi5o1aqVQf3HtW7d2uQ+u6ausWvXrrCxscG5c+fQsWNHrFy5EiEhIUWOvSxkZmZi+fLl6NGjB2rWrAm1Wm2wINnDhw8BoMCtg9q0aWP0ePXq1eWvW7duXWCd+Ph4k/3rFw4zpmPHjlCpVABMv1emfPTRR4iMjCzxq3bt2sU6n6WKjIxE7969kZCQgJo1a+J///sfFIrS/7Xx9ddfl/94NmbMGCxfvhyxsbHIyMjAsWPH0KNHD0RERMjvZ1nEQERUVPwJRERUiWg0GgwfPhw//PADzp8/D61Wi71796Jv374AgJiYGAwcOFBeuflxtWrVMtl33jJ98lQcjo6OJsv0vxibWmG5qCIjIwEA7u7usLe3L3I7/fUUdP0A4OXlZVD/cSW5xnr16mHFihVwcHDA33//jTfffBN169ZFtWrVMGTIEPz2228Q5bir5MOHD9GqVSu888472Lt3LyIiIqBQKODu7i6PruoTmLyrZj/O1L3Q34ei1Cno81DQe2VjYwM3Nzf5eioy/T1KTU0tsJ6+vKDPYFE9fPgQXbt2xa1bt1C9enXs379f/uyXNnt7e/zxxx/w9vZGSkoK3nnnHbi7u8PGxgbt27fH8ePHMX/+fHmGRnG24SIiKm1MnomIKjEbGxt069YNv//+uzydODw8HLt27TJzZGXD1EikpXv11VcRFhaG5cuXY8iQIahduzaio6OxceNG9O/fH4GBgeU2XXX8+PG4dOkS3Nzc8OOPPyIiIgJpaWmIjo6WR1f1o+jlmdRXVfp7HR8fX+A2Tffv3zeoX1IPHz5Ely5dcOXKFVSrVg0HDhxAgwYNnqjPwjRq1AiXLl3CV199hU6dOsHX1xd+fn54+eWXcejQIYwbNw4JCQkAIO9pT0RkDqrCqxARUWXwn//8R94b+Pr160br6H8BL6zM2LPLlsDT0xNA7gh7SkpKkUef9ddT0DTkvOVlcf2urq4YM2YMxowZAwC4ffs2VqxYgXnz5uHw4cOYMWMGFixYUOrnzSsrKwtbtmwBACxevBhDhw7NVycnJwcxMTFlGkdRFPRZzcjIQGxsLIDiv1dff/01vv766xLHderUqVKdut2kSRP568uXL5uc6n758mUAQOPGjUt8rocPH6Jz584GiXOjRo1K3F9xODo64qOPPsJHH32Ur+zUqVPIzs4GADz77LPlEg8RkTFMnomIqggHBwf5a2MLOQFAcHCwyfb6MoVCgebNm5ducKVE/4t1Tk4O/vzzTwwaNKhI7Vq1aoW1a9fi9OnT0Gq1RhcaS0hIMHg2uqzVq1cPX375Je7du4eff/4Ze/fuNSjXT50uzdHf6OhoeUq/qff4yJEjJqf9l6dDhw5BCGF0tsHhw4flZEv/nHpRJScnF7qwXkFKe9G3Dh06wNbWFmlpadi1a5fRz15YWBiuXr0KAOjRo0eJzhMVFZVvxPlJEvHStG7dOgC56yZw5JmIzInTtomIKriQkBDcuHGj0Hr6UWcAaNGihdE6R44cwcGDB/MdT09PxzfffAMgdyVk/fOHlqZ+/fp47rnnAACffPJJkac6Dxw4ECqVCunp6Zg3b57ROnPmzEFGRgasrKwwcODAUos5IyOjwHJbW1sA+RdK0i9spp/OWhqcnJzkZPTChQv5yrOzs/Hpp5+W2vmexN27dw0+03o6nQ5z5swBkDsd+KmnnipWvzNmzIDI3cqzRC8fH5/SuDyZvb29/HlbtmyZ0ZXu9Z9ZR0dH9O/fv9jnyDtVu3r16ggODraYxPn48eNYtmwZAMirqBMRmQuTZyKiCu6ff/5Bw4YN0bt3b6xZswahoaFyWVZWFs6dO4fXX39dnvLbpk0bdOjQwWhfGo0GAwcOxKZNm+SRu2vXrqF37964du0alEolZs2aVebX9CQWLVoEGxsb3Lx5E+3bt8euXbvkhadycnJw6tQpvP3229i3b5/cplatWhg3bhwAYO7cuQgKCpKT0oSEBEyfPh1fffUVAGDChAmoUaNGqcX73nvv4eWXX8bmzZsNFrdKTk7G8uXLsWbNGgBA7969Ddrpp/MePnwY165dK5VYHBwc0L59ewC513ngwAHodDoAudOCX3jhBZw+fbpYi7GVFY1Gg3feeQc//PCDPBJ+7949DBs2TJ4lMXv2bHOGWGpmzZoFe3t7REREoG/fvrh58yaA3AXbZs2aheXLlwPITS6NLailX5ncWGIfHR0tJ86enp4IDg4u9lTtrKwsxMTEyK+8CX58fLxBmbFF4BYtWoT169cbjPg/fPgQ33zzDbp3746srCz85z//QZ8+fYoVFxFRqSvfbaWJiKi07dq1SwAweFlbWwtXV1chSZLB8RYtWoj79+/n68Pb21sAEAsWLBABAQECgFCr1UKj0chtJUkS33//vdEY9O1XrVqVr0zfPjg42OQ1BAYGCgAiKCiohHfB0O7duw1it7KyEm5ubsLKyko+tnXrVoM2GRkZ4uWXX5bLFQqFcHFxEQqFQj42bNgwkZmZme98I0eOFADEyJEjTca0atUqAUB4e3sbbat/OTg4CGdnZ4NjHTp0EMnJyQbt4uLihIeHh1zH3d1deHt7C29vb/H333+X9NaJ06dPC3t7e7lftVotHB0dBQChUqnEmjVrTL7fISEhcruQkBCj/QcHB8t1TDF1r4T497MydepU0aFDB/n9dXFxMbhn06ZNK/E9sEQ7duwQdnZ28vVpNBqhVCrl719//XWh0+mMttXfM2P3c+bMmXIf9vb2onr16gW+7t69m6+PvO9pYS9jPwf69esnl9vY2AgnJyeDnzvvv/++yMnJedJbSET0xDjyTERUwfXs2RM3b97EokWLMHjwYDRs2BBqtRoJCQmws7OTV63dsGEDTp06VeBqvC4uLjh58iSmTJmCOnXqICMjA66urujbty+OHj2Kt956qxyvrOR69OiBmzdv4tNPP0Xz5s1ha2uLlJQU1KpVCz179sR3332Xb09ra2tr/PLLL9i0aRN69eoFNzc3JCUlwc3NDb169cKWLVuwfv16WFlZlWqs06dPx7fffosBAwagQYMGUKlUSE5ORrVq1dC9e3f8+OOPOHjwYL7RXhcXF/z1118YOnQoatWqBa1Wi7CwMISFhT3RM8ktW7bEyZMn8fLLL8Pd3R06nQ6Ojo54+eWXcezYMbz22mtPesmlwtraGvv378ecOXMQEBCAjIwMaDQadO3aFTt27MDnn39u7hBL1QsvvICLFy/irbfego+PD9LT0+Hi4oLu3btj06ZN+PHHH0u02rx+ZgGQO5IdFRVV4Ku0n+kGcvd3fu211xAQEABra2tkZmaibt26eP3113Hs2DF8++233N+ZiCyCJAT3mSAiqup8fHwQFhaGVatWYdSoUeYOh8ikTp064dChQwgKCsKMGTPMHQ4REVUh/DMeERERERERUSGYPBMREREREREVgskzERERERERUSFU5g6AiIjocePGjcMvv/xSrDaLFi3CkCFDyiiiiuWXX36Rt94qqiFDhmDRokVlFBEREVHFx+SZiIgM9oa2BFqt1mDP16JIS0sro2gqnrS0tGLfv7x781qygwcPmjsEIiKqorjaNhEREREREVEh+MwzERERERERUSGYPBMREREREREVgskzERFVSKGhoZAkCZIk5Xtmu6AySzJjxgxIkoROnTqZOxSz079ffKaZiIgsFZNnIiKqUkJDQzFjxgzMmDHD3KEQFVtSUhJmzJiBp556Cg4ODtBoNGjdujW++eYbZGZmPnH/UVFRmDhxIgICAmBrawtXV1d07NgRK1asQEHL5Ny6dQvffPMN+vbtC29vb6jVatjb28Pf3x9vvPEGzpw5U+B5n7Q9EVF54GrbRERU6VhZWSEgIED+Oq/Q0FDMnDkTAJhAU4USFhaGTp06ybMp7OzskJGRgdOnT+P06dP4+eefsX//fri4uJSo/zNnzqBnz56IjY0FADg4OCApKQlHjhzBkSNHsGnTJvz++++wtrY2aHf06FF06NDB4JijoyMyMjJw8+ZN3Lx5E6tXr8ann36KWbNm5Tvvk7YnIiovHHkmIqJKp1atWrh27RquXbuGWrVqmTscoieWnZ2Nvn37IjQ0FDVq1MDevXuRkpKC1NRUbNiwAY6Ojjh37hyGDx9eov61Wi369OmD2NhYNGjQAKdOnUJSUhJSUlKwePFiWFlZYffu3fjwww/ztc3KyoJSqUT//v3x66+/IiYmBomJiUhNTcXJkyfRoUMH6HQ6fP7551i5cmWptyciKi9MnomIiIgs3E8//YRLly4BADZv3oxu3boBABQKBYYMGYLvvvsOALBz507s37+/2P1//fXXiIyMhK2tLXbu3IlWrVoBAKytrTF27Fh5tsb333+PGzduGLStX78+rl69iq1bt2LQoEFwc3MDACiVSrRu3Rr79+9H06ZNAQBffvllvnM/aXsiovLC5JmIqBKLj4/HypUr8fLLL+Opp56Cq6srbGxs4O3tjVdeeQXHjx832fbxxax+//13dO3aFW5ubnBycsKzzz6Lbdu2GbRZu3Yt2rdvDxcXFzg4OOC5554z+Yv844t63bx5E6NGjYKXlxfUajXq1KmDt99+Gw8ePCj2dZtaMMzHxwedO3eWv9fX0b9GjRoll40aNSrfscetXr0akiTBx8fHZJ0///wT3bt3h7OzMxwcHNCsWTPMnz8fWVlZRb6WDz/8EI0bN4aDgwPs7OzQoEEDjBs3Dnfv3i1SH0Xh4+MDSZKwevVqJCUlYerUqfJzr+7u7ujfvz9OnDhhMsaiLNCW9xzFER8fj88++wwtWrSAk5MTrK2t4enpiaZNm+Ltt98uMFk8evQohg8fDm9vb9jY2ECj0aBNmzaYN28ekpOTixWHOf30008AgM6dO6Ndu3b5yocOHQpfX18AwJo1a4rdv75N3n7yev/99+Hg4ICcnBz8/PPPBmVeXl7w8/Mz2be1tbU8In779m3Ex8eXansionIjiIio0goKChIABAChVCqFi4uLUKvV8jFJksSiRYsKbBsYGCg+++wzAUAoFAqh0Wjk9gDE8uXLhU6nEyNHjhQAhEqlEo6Ojgbn/eOPP/L1HxISItfZsGGD3MbBwUHY2trKZa6uruLMmTMFtg8JCSlSWatWrYSLi4tcVr16dYPXBx98INfVX8/IkSNN3t9Vq1YJAMLb27vQ+w9AODs7C5VKJQCI5557TkydOlW+x8asW7fO4P1Sq9UG98bR0VHs3r3bZHzF4e3tLQCIBQsWiICAAAFAWFtbCycnJ/l8CoVCrFy5Ml/bgt4LY+dYtWpVvjJ9++DgYIPj9+7dE3Xq1DGIwcXFRSiVSvmYsfuXk5MjPvjgA4P77+DgYNAuICBAhIaGFvNOlb+UlBShUCgEADF//nyT9d555x0BQHh6ehar/2vXrsn3ZOPGjSbr9erVSwAQbdu2LVb/Qgjx7bffyueIjo4u9/ZERKWBI89ERJVYzZo1ERQUhNOnTyM1NRVxcXFIS0vDnTt3MG7cOADAhAkTcO7cOZN9nD9/Hl988QW++OILxMXFISEhAeHh4ejZsycAYNKkSZgxYwY2btyI5cuXQ6vVIjExETdu3ECrVq2Qk5ODd999FzqdzuQ5xowZA19fX5w4cUJ+znL37t2oU6cO4uLiMGDAACQlJT3x/Th16hS2bNkifx8ZGWnwWrRo0ROfQ+/333+Xp7oOHjwYd+/eRXx8PBITE7FkyRIcP34cy5YtM9l+7969GDFiBHJycjB58mSEhIQgLS0NKSkpuHbtGgYPHoykpCS579Iyc+ZMPHz4EBs3bkRKSgq0Wi2uXLmCwMBA6HQ6jBkzBmfPni218xVmxowZuHv3Lnx8fLBv3z5kZmYiLi4OGRkZCA0NxbJly9C2bdt87YKCgvDtt9+iWrVqWLJkCWJjY5GUlIS0tDQEBwejefPmuH79Ol566aUCP5uW4OrVq3KMTZo0MVlPXxYZGYm4uLgi93/58uV8fRTU/5UrV4rct55+C7IaNWrI07LLsz0RUakwd/ZORETmM3bsWAFAvPHGG/nK8o6azp49O1+5VqsV9vb2cp1169blq3Pr1i25/PDhwwZleUcr3dzcRFRUVL72V65cEdbW1kZH3Eoy8iyEEMHBwXJZQZ505LlRo0byqGhOTk6+8uXLl5scOc3JyRF+fn4CgPjuu+9Mnv/FF18UAMS4ceMKvJai0I8KAxD79u3LV56amirH9MILLxiUleXIc8OGDQUAsX79+iJfS0hIiFAqlcLW1lacP3/eaJ3ExETh5eUlAIitW7cWuW8hDD9DJXkZu/6C/P7773LbCxcumKy3bds2ud6lS5eK3H/eUV2tVmuy3n//+1+5XlJSUpH7P3bsmDxyPn369CK3K632RESlhSPPRERVWO/evQEAR44cMVnHxsbG6Aq7Tk5O8rOXderUwSuvvJKvTr169VC/fn0AwMWLF02e4+2330a1atXyHW/YsCEGDRoEANiwYYPpC7EwFy9elEfnpk2bBoUi//9u33rrLZMrgf/111+4efMm3N3d8eabb5o8z4gRIwAAu3fvLoWoc7Vv3x5du3bNd9zW1haTJk0CAOzatQtarbbUzlkQZ2dnAEBERESR26xevRo5OTl4/vnn0axZM6N1HB0d0b9/fwDFv3/W1taoXr16iV+2trbFOl/eWRd2dnYm6+UtK85MjbLsPzo6GsOGDYNOp4Ofnx8mT55c5LhKoz0RUWniPs9ERJXcnTt3sHTpUgQHB+P27dtISkrKN001PDzcZPtGjRrB3t7eaFn16tUBAK1atYIkSSbr3Lp1q8BFfrp06VJg2fr163Hx4kVkZWXl27fZEp0+fRoAoFKp0LFjR6N1FAoFOnXqlG/xJSB3kSsgd/ugmjVrmjxPZmYmgNz9f0tLYe8FAOh0Opw9e9Zg8bWy0qdPH/z999+YMmUKrl27hpdeegnPPvssnJycTLbR3789e/bA09PTZD39gmHFvX/PPvssIiMji9WmKkpOTsaLL76IsLAwODo64tdff4WDg0O5tSciKm1MnomIKrGtW7di2LBhyMjIkI85OTnBxsYGkiQhMzMT8fHxSElJMdmHo6OjyTKVSlXkOgWtLl3QXsz6suzsbMTFxckJuyV7+PAhAMDd3R1qtdpkPS8vL6PH9SuMZ2VlISoqqtDzpaWllSBK44ryXgD/XmNZmzRpEi5cuICNGzfihx9+wA8//ABJktC4cWM8//zzePPNNxEQEGDQRn//UlJSCvxs66WmppZJ7KUl77+vgmLNW1bQv8nC+jf1h4ni9J+SkoLevXvj+PHjcHBwwM6dO03OAiiL9kREZYHTtomIKqnY2FiMGjUKGRkZ6NKlCw4ePIjU1FRotVpERUUhMjISv/76q7nDJCNycnIAAM888wyEEEV6VVZWVlb45ZdfcP78eXz22Wfo0qUL7OzscPnyZXz99ddo3LgxvvnmG4M2+vv38ccfF+ne6RejslR5Zx/cv3/fZL28ZQXNWHjS/p2cnAocAdYnvn/99Rfs7e2xY8cOdOjQocjxPGl7IqKywpFnIqJKaufOnUhMTISLiwu2b99u9FlGS5l6ev/+/Xyjh3nLgNwRbFdX13KLST9inp6ebrKOqed+9c9vx8TEIDMzE9bW1kbrmUpU9FONS3M6dlEVNTnL+4y6/l4BJbtfRdGsWTN55DE7OxuHDh3CrFmz8Ndff2HSpEno1q2bXO7p6Ynr16+X2f07duwYXnrppRK3X7RoEYYMGVLk+g0bNoRCoYBOp8Ply5fRq1cvo/X0q2Z7enoW699K3hW2L1++jIYNGxbYf6NGjUz2pU98Dx06BDs7O+zYsQPPPfdckWN50vZERGWJI89ERJXUvXv3AAABAQEmFwHat29feYZkUnBwcKFlTZs2LZXnnfMu3lXQiK2LiwuAf++jMSdOnDB6vFWrVgByk7zDhw8braPT6UyOeLZv3x5A7h839M9Pl5eivBcKhQLNmzeXj+vvFWD6ft24cQMJCQmlEqNKpULXrl2xY8cOqNVqCCEMPsv6+7dv374Ck/mSyszMRFRUVIlfxZ1mb2dnJ1/Trl27jNYRQsgLn/Xo0aNY/fv7+6NOnToF9p+SkiJ/lk31n5KSghdeeAGHDh2Cvb09du7cicDAwCLH8aTtiYjKGpNnIqJKSqPRAMhNWowlEOfPn8f69evLOyyjli9fjpiYmHzHr1+/jk2bNgFAsUbqCpL3ec6Ckjn9KOapU6eMJoRXr1412DM6r6ZNm8qjd1988YXRfYR//PFHkwu1de7cWV6lfPz48fLCYKYUZ0/fwhw5csRoUp+eni5Pj+7Zs6e8CjYA2Nvbo169egCAzZs3G+33iy++KFE8eZ/Xf5xarYZSqQRg+EeR0aNHQ6VSISYmBkFBQQX2n5mZKS8cVlSdOnUq8nR6Y69Ro0YV63wAMHLkSAC5f8Aw9kebX3/9FXfu3AHw7yrsRSVJktxmw4YNCA0NzVdnyZIlSE5OhlKpxKuvvpqvXJ/46qdalzRxLml7IqLywOSZiKiS6tGjBxQKBeLi4vDqq6/KU24zMzOxceNG9OjRo1iLCpWlrKwsdO/eHadOnQIAeSSxZ8+eyMjIQO3atfH222+Xyrn8/f3ladQrVqwwOfrct29fODg4ICsrCy+//DKuX78ux/rbb7+hW7duJlchB/5NFoODg/HKK6/IiXJ6ejqWL1+O9957zyABzUulUmH58uVQqVQ4cuQInnvuOezfv99g0bU7d+5g+fLlaN26NZYuXVrs+2CKRqPBwIEDsWnTJmRnZwMArl27ht69e+PatWtQKpWYNWtWvnbDhg0DkPtHgaVLl8qjq/fu3cObb76JX375pcBtkEzx9vbG1KlTcfz4cYNE+tatW3j11VeRmpoKhUKBnj17ymX16tXD9OnTAQDz58/HiBEj5CnHQO6MgPPnz2PWrFmoX78+zp8/X+y4ytvIkSPx1FNPQQiBgQMHYv/+/QByZzD8+uuveOuttwAAvXr1MrrV2IwZMyBJEiRJMpocf/TRR/D09ERqaip69+6NM2fOAMj9ebFs2TL5fv7nP/+Bv7+/QdvU1FT06dMHf/31FxwcHPDnn38Wa6r1k7YnIio3ZbeFNBERmdvHH38sAMgvjUYjrKysBADh6+srfv75Z7nscUFBQQKACAwMNNn/yJEjBQAxcuRIk3UCAwMFABEUFGRwPCQkRD73hg0bhKOjowAgHBwchJ2dnVzm7OwsTp06la/fvO1DQkKKXCaEEG+88YZcbmdnJ+rUqSO8vb3FxIkTDeqtWLFCSJIk13V0dBTW1tYCgGjbtq1YvHixACC8vb2NXvunn35qcP9dXFyESqUSAETHjh3F1KlTC7zHW7dule8LAGFlZSXc3NyEWq026Hf27Nmmbn+ReXt7CwBiwYIFIiAgQAAQarVaaDQa+TySJInvv//eaPukpCTRqFEjua5CoRDOzs5y3P/73//kc6xatSpfe3274OBgo8f1fbq4uAgbGxuDmBYuXJivP51OJ6ZPn27w/tna2go3NzehVCoN+j1y5MgT37/yEBISInx8fAw+u3nvRfPmzUVcXJzRtvp/z6b+TQghxOnTp4Wbm5vB513/8wKA6NGjh0hPT8/X7qeffpLr2NjYiOrVqxf4Onr0aKm2JyIqLxx5JiKqxObOnYs1a9agTZs2sLW1RVZWFurXr49PPvkE586dK9aKvGXpmWeewenTpzFixAhoNBpkZ2ejVq1aeOutt3Dp0iX5GeLSsmTJEsyYMQNPPfUUAODu3bsICwvLN3X8jTfewI4dO9ClSxc4OTkhOzsb/v7+mDt3rvxcZkFmz56NP/74Q26fkZGBhg0bYu7cudi/f7/JhcT0+vfvj1u3biEoKAht2rSBg4MDEhISoFar0axZM7z55pvYunUrJk2a9GQ3JA8XFxecPHkSU6ZMQZ06dZCRkQFXV1f07dsXR48elUc4H+fg4IAjR45gwoQJ8PX1hUqlgpWVFQYOHIi///4bQ4cOLVE8e/bswdSpU9GxY0fUrl1bHtGuX78+Xn/9dZw6dQoffvhhvnaSJGHWrFm4ePEi3n33XTRs2BBKpRJarRYuLi549tlnMWnSJBw7dkx+ntjS+fj44OLFi/jss8/QpEkTSJIEKysrtGzZEl9//TWOHz9u8Px5cbVs2RL//PMPxo8fDz8/P2RlZcHe3h4dOnTADz/8gD///NPo1mt5H0tIT08v9Jnvxx9DeNL2RETlRRKiEu9vQUREFis0NBS+vr4AgJCQEPj4+Jg3oCrOx8cHYWFhWLVqVYmeySUiIqrsOPJMREREREREVAgmz0RERERERESFYPJMREREREREVAiVuQMgIiKi0tG6dWuje1IX5NSpU6hdu3YZRURERFR5MHkmIiKz8PHxMbnHMpVMdHQ0oqKiitUmJycHAIzu/UtERET/4mrbRERERERERIXgM89EREREREREheC07XKk0+nw4MEDODo6QpIkc4dDRERERERUpQkhkJSUhJo1a0KhKHhsmclzOXrw4AEXZSEiIiIiIrIw9+7dg5eXV4F1mDyXI0dHRwC5b4yTk5OZoyEiIiIiIqraEhMTUbt2bTlXKwiT53Kkn6rt5OTE5JmIiIiIiMhCFOWxWi4YRkRERERERFQIJs9EREREREREhWDyTERERERERFQIi06e09LS8Nlnn8Hf3x82NjaoWbMmRo8ejfv37xern0OHDmHmzJno3bs3PDw8IEkSfHx8TNYPDQ2FJEmFvkaPHv2EV0hEREREREQVgcUuGJaeno4uXbrg+PHjqFGjBvr164fQ0FCsWrUKf/zxB44fP466desWqa9x48bhwoULRT63g4MDRo4cabL8l19+QXp6Ojp27FjkPomIiIiIiKjikoQQwtxBGDNt2jR88cUXaNeuHfbs2QMHBwcAwIIFCzBx4kQEBgbi4MGDRepr8uTJ0Gg0aN26Nby8vNC4cWN4e3sjNDS02HFdvXoVjRo1gq2tLSIjI4u1anZiYiI0Gg20Wi1X2yYiIiIiIjKz4uRoFjnynJmZicWLFwMAlixZIifOADBhwgT89NNPOHToEM6cOYOWLVsW2t/8+fPlryMjI58otnXr1gEA+vXrxwSYiIiIiIioirDIZ56PHj0KrVaLevXqoXnz5vnKBw0aBADYvn17ucYlhMD69esBAK+99lq5npuIiIiIiIjMxyKTZ/3zyS1atDBarj9+8eLFcosJAI4cOYLQ0FBUq1YNPXr0KNdzExERERERkflYZPJ89+5dAICXl5fRcv3xsLCwcosJ+HfK9tChQ6FSWeSMdyIiIiIiIioDFpkBJicnAwDs7OyMltvb2wMAkpKSyi2mjIwM/PrrrwCKPmU7IyMDGRkZ8veJiYllEhsRERERERGVLYscebZEO3bsQHx8PBo0aIBWrVoVqc2XX34JjUYjv2rXrl3GURIREREREVFZsMjkWb+6dmpqqtHylJQUAICjo2O5xaSfsl2chcKmTp0KrVYrv+7du1dW4REREREREVEZsshp23Xq1AEAhIeHGy3XH/f29i6XeBISErBz505IkoRXX321yO3UajXUanUZRkZERERERETlwSJHnps1awYAOHv2rNFy/fGmTZuWSzwbN25ERkYGOnbsWG4JOxEREREREVkOi0ye27dvD41Gg9u3b+P8+fP5yjdt2gQA6Nu3b7nEU5Ip20RERERERFR5WGTybG1tjffeew8AMHbsWPkZZwBYsGABLl68iMDAQLRs2VI+vnjxYjRo0ABTp04t1VjCwsJw5MgR2NjYYPDgwaXaNxEREREREVUMFvnMMwBMmzYN+/btw7Fjx+Dn54eOHTsiLCwMJ06cgIeHB3788UeD+jExMbh+/ToiIiLy9bVixQqsWLECAJCVlQUAiIiIQNu2beU6S5cuRYsWLfK1/fnnnyGEQN++faHRaErzEomIiIiIiKiCsNjk2cbGBsHBwfjyyy+xfv16bNu2Da6urhg1ahQ+//xzeHl5Fbmv8PBwnDhxwuBYZmamwTFTezD//PPPAIDhw4eX4CqIiIiIiIioMpCEEMLcQVQViYmJ0Gg00Gq1cHJyMnc4REREREREVVpxcjSLfOaZiIiIiIiIyJIweSYiIiIiIiIqBJNnIiIiIiIiokIweSYiIiIiIiIqBJNnIiIiIiIiokIweSYiIiIiIiIqBJNnIiIiIiIiokIweSYiIiIiIiIqhMrcARARERFRxZWjEzgZEoeHSemo5miDNr6uUCokc4dFRFTqmDwTERERUYnsuhyBmduvIEKbLh+robFBUN9GeL5JDTNGRkRU+jhtm4iIiIiKbdflCLyz7qxB4gwAkdp0vLPuLHZdjjBTZEREZYPJMxEREREVS45OYOb2KxBGyvTHZm6/ghydsRpERBUTk2ciIiIiKpaTIXH5RpzzEgAitOk4GRJXfkEREZUxJs9EREREVCwPk0wnziWpR0RUETB5JiIiIqJiqeZoU6r1iIgqAibPRERERFQsbXxdUd1JbbJcQu6q2218XcsvKCKiMsbkmYiIiIiKRamQ0Nqn4MQ4qG8j7vdMRJUKk2ciIiIiKhZtahYOXY8GADjbWRmU2VgpsGx4C+7zTESVjsrcARARERFRxfLD4TtIyshGA09HbH+vA06HxePs3Xh8tfs6cnQC7eq6mztEIqJSx5FnIiIiIiqyuJRMrDoaAgD4sJs/rFQKtKvnhnc71UMDT0dk5Qhsv/jAzFESEZU+Js9EREREVGTf/XUbKZk5aFLLCT0bV5ePS5KEQS29AACbzoSbKzwiojLD5JmIiIiIiiQ6KQNrjoUBACZ094ckGS4I1u/pWlAqJJy/l4BbD5PMESIRUZlh8kxERERERbLs4G2kZeXg6drO6BxQLV+5h6ManQM8AACbztwv7/CIiMoUk2ciIiIiKlRUYjrWnTA96qynn7q99Vw4cnSi3OIjIiprTJ6JiIiIqFBLgm8hM1uH1j4u6OhnejXtzg2qwdnOClGJGThyK6YcIyQiKltMnomIiIioQPcT0rDh5D0AwPgCRp0BQK1Sol+zmgC4cBgRVS5MnomIiIioQIsP3EJmjg7t6rrh2XqF7+E8qGVtAMDufyKhTcsq6/CIiMoFk2ciIiIiMulubCp+PZ076jyhh3+R2jSp5YSA6o7IzNbhD+75TESVBJNnIiIiIjLp2wM3ka0T6OjnjtY+rkVqwz2fiagyYvJMREREREaFxKRgy9nc5Hdij4Bite3XvCaUCgnn7ibgdnRyWYRHRFSumDwTERERkVGL9t2ATgBdG1TD07Wdi9W2mqMNAv1z93zezNFnIqoEmDwTERERUT43o5Lw24Xc55XHdy/as86P00/d3nL2Pvd8JqIKj8kzEREREeXz3/03IQTQs3F1NKmlKVEfXRtWg8bWCpGJ6TjKPZ+JqIJj8kxEREREBq5GJGLHxQgAJR91Bh7t+fw093wmosqByTMRERERGVi49wYAoHfTGmjg6fREfemnbu/+JxKJ6dzzmYgqLibPRERERCS7FK7FnitRUEjA+G5+T9zfU7U08K/ugIxsnTyaTURUETF5JiIiIiLZwn25o879nq6F+tUcn7g/SZIwsAX3fCaiio/JMxEREREBAM7ejceBaw+hVEj4oOuTjzrrDWheCwoJOBMWjzvc85mIKigmz0REREQE4N9nnV9qXgu+7val1m81pzx7Pp/l6DMRVUxMnomIiIgIp0LjcPhmDFSlPOqsN6hlbQDc85mIKi4mz0RERESEb/ZcBwAMblUbtV3tSr1//Z7PEdp0HLvNPZ+JqOJh8kxERERUxR27HYPjd+JgrVTg/S71y+QcNlZK9G1WAwCwmQuHEVEFxOSZiIiIqAoTQmDBntxnnYe1qY2azrZldi791O1d3POZiCogJs9EREREVdjhmzE4HRYPtUqBdzuXzaizXjMvDepXc0B6lg47ueczEVUwTJ6JiIiIqighBL55tML28LbeqO5kU6bnkyQJg1pyz2ciqpiYPBMRERFVUQeuPcSFewmwtVLi7cB65XJO/Z7Pp8PiERKTUi7nJCIqDUyeiYiIiKogIQQWPBp1HvGsNzwc1eVy3upONnju0Z7PW7jnMxFVIEyeiYiIiKqg3f9E4Z8HibC3VmLMc+Uz6qw3sEXu1O3NZ8Kh457PRFRBMHkmIiIiqmJ0OoGFj0adR3fwhau9dbmev3uj6nC0UeGBNh1/34kt13MTEZUUk2ciIiKiKmbn5Qhcj0qCo40Kb3aoW+7nt7FS4sVmNQFw4TAiqjiYPBMRERFVITk6gf/uuwkAeLNDXWjsrMwSh37V7T8vRyCJez4TUQXA5JmIiIioCvn9wn3cepgMja0VRnfwMVscT9d2Rj0P+9w9ny9xz2cisnxMnomIiIiqiOwcHRY9GnX+z3N14WhjnlFnQL/nc20AwOYz980WBxFRUTF5JiIiIqoitpy7j9DYVLjaW2PUsz7mDkfe8/lkaBxCueczEVk4Js9EREREVUBmtg7f7s8ddX47sC7s1SozRwR4amzQwY97PhNRxcDkmYiIiKgK2HQmHOHxafBwVOO1tj7mDkemXzhs89n73POZiCwak2ciIiKiSi4jOweLD+SOOr/bqR5srZVmjuhfPR7t+Xw/IQ3HueczEVkwJs9EREREldyGk/fwQJsOTycbDGtTx9zhGLCxUqIv93wmogqAyTMRERFRJZaelYMlwbcAAGO71IeNleWMOuv9u+dzJJIzss0cDRGRcUyeiYiIiCqxdcfD8DApA7WcbTGkVW1zh2NU89rOqOtuj7SsHO75TEQWi8kzERERUSWVmpmN5YduAwDe71If1irL/NVPkiQMfDT6zKnbRGSpLPMnKBERERE9sTV/hyEmORN1XO3k5NRSvdSiFiQJOBkSh7BY7vlMRJaHyTMRERFRJZSckY3vHo06j+vqByulZf/aV0Njiw713QHkbltFRGRpLPunKBERERGVyKojIYhPzUJdd3v0e7qmucMpEv3CYVvOhnPPZyKyOEyeiYiIiCoZbVoWfjh8BwAwrpsfVBY+6qzXs7EnHNUqhMen4URInLnDISIyUDF+khIRERFRka08EoLE9Gz4V3dAn6YVY9QZyN3zuU+zGgC4cBgRWR4mz0RERESVSHxKJn48EgIA+LCbP5QKycwRFc+/ez5HIIV7PhORBWHyTERERFSJ/HD4DpIzstGwhhOeb+xp7nCKrUUdF/i62yM1k3s+E5FlYfJMREREVEnEJmdg9bFQAMCE7v5QVLBRZyB3z+dB3POZiCwQk2ciIiKiSmL5odtIzcxBUy8NujWsZu5wSmxA89w9n0+ExOFeXKq5wyEiAsDkmYiIiKhSeJiYjjV/hwEAxnf3hyRVvFFnvZrOtmhfT7/nM0eficgyMHkmIiIiqgSWHryNjGwdWtRxRid/D3OH88T0U7c3c89nIrIQTJ6JiIiIKrgIbRrWn7gLAJjQPaBCjzrr9WzsCQe1Cvfi0nAylHs+E5H5MXkmIiIiquCWBN9CZo4ObXxd0b6+m7nDKRW21kr0aco9n4nIcjB5JiIiIqrAwuNT8cupewByV9iuDKPOevqp2zsvcc9nIjI/Js9EREREFdj/7b+FrByB9vXd0LZu5Rh11mvp7QIfNzukZuZg1+VIc4dDRFUck2ciIiKiCiosNgWbHq1GPaF7gJmjKX2SJGFgC+75TESWgckzERERUQW1aP9N5OgEOgV4oKW3i7nDKRMvtfSCJAF/34nlns9EZFZMnomIiIgqoFsPk7Ht3H0AwPhu/maOpuzUcrbFs/Vyp6NvOXvfzNEQUVXG5JmIiIioAvp2/03oBNCtYXU0q+1s7nDKVN49n4Xgns9EZB5MnomIiIgqmOuRSdh+8QEAYHx3PzNHU/b0ez7fjUvFqdB4c4dDRFUUk2ciIiKiCua/+25ACKBXE080rqkxdzhlzs5ahd5P6fd8vmfmaIioqmLyTERERFSB/PNAiz8vR0KSgPHdK++zzo8b+Gjq9o6LEUjN5J7PRFT+mDwTERERVSAL994EAPRtWhP+1R3NHE35ae3jgjqudkjhns9EZCZMnomIiIgqiAv3ErDvahQUEjCuW+V/1jkvSZLkhcO45zMRmQOTZyIiIqIKYuG+GwCA/s1roZ6Hg5mjKX8vtagFADh2Oxbh8dzzmYjKF5NnIiIiogrgTFg8Dl6PhlIhYVzXqjXqrOflYifv+byVez4TUTlj8kxERERUASzYex0AMKiFF7zd7M0cjfkMbPFo6jb3fCaicsbkmYiIiMjCHb8Ti6O3YmGllPB+1/rmDsesej3lCXtrJcJiU3E6jHs+E1H5YfJMREREZMGEEFiwN/dZ5yGta8PLxc7MEZmXnbUKL+j3fD7NhcOIqPwweSYiIiKyYEdvxeJkSBysVQqM7Vy1R5319Ktu77gUgbTMHDNHQ0RVBZNnIiIiIguVO+qc+6zzK23qoIbG1swRWYbWPq6o42qH5Ixs7P6Hez4TUflg8kxERERkoQ7eiMbZuwmwsVLg3c71zB2OxVAopH8XDuOez0RUTpg8ExEREVkgIQQWPnrW+bW23qjmaGPmiCyLfs/no7djcD8hzczREFFVwOSZiIiIyALtu/oQF8O1sLNW4u1Ajjo/rrarHdrWdYUQwNazHH0morJn0clzWloaPvvsM/j7+8PGxgY1a9bE6NGjcf/+/WL1c+jQIcycORO9e/eGh4cHJEmCj49PkdomJydj5syZaNq0KRwcHKDRaNCkSROMHTsWycnJJbgqIiIiooLpdP+usD3qWR+4OajNHJFlGtSyNoDcqdvc85mIypokLPQnTXp6Ojp37ozjx4+jRo0a6NixI0JDQ3Hy5El4eHjg+PHjqFu3bpH6evrpp3HhwgWDY97e3ggNDS2wXUhICLp27YqQkBDUrVsXLVq0QEZGBq5fv44bN27g3r178PLyKvI1JSYmQqPRQKvVwsnJqcjtiIiIqGrZeSkC7/58Fg5qFQ5P7gwXe2tzh2SRUjKy0fqLfUjNzMGmt9uhlY+ruUMiogqmODmaqpxiKrbZs2fj+PHjaNeuHfbs2QMHBwcAwIIFCzBx4kSMHj0aBw8eLFJfPXr0wODBg9G6dWt4eXmhcePGhbbJyMhAr169cPfuXSxfvhxjxowxKL98+TJcXfkDmoiIiEpXju7fZ51Hd/Bl4lwAe3Xuns+bzoRj89lwJs9EVKYscuQ5MzMT1apVg1arxdmzZ9G8eXOD8mbNmuHixYs4ffo0WrZsWay+IyMjUaNGjUJHnufPn4+PP/4YkyZNwvz580tyGflw5JmIiIgK89v5+xi34TycbFQ4/HEXaGytzB2SRTt+JxZDvz8OR7UKJz/tBltrpblDIqIKpDg5mkU+83z06FFotVrUq1cvX+IMAIMGDQIAbN++vcxi+OGHHwAA77//fpmdg4iIiCiv7BwdFu27CQB4q2NdJs5F0MbHFV4utkjKyMaeK9zzmYjKjkVO29Y/n9yiRQuj5frjFy9eLJPz37t3D7du3YKXlxdq166No0eP4vfff4dWq4Wvry8GDhyI+vXrl8m5iYiIqOr67fwD3IlJgYudFV7v4GvucCoE/Z7Pi/bfxKYz4ej3dC1zh0RElZRFJs93794FAJOLcemPh4WFlcn5r1y5AgCoWbMmxo4di6VLlxqUT5s2DXPnzsXEiRPL5PxERERU9WTl6LBof+6o85jAenBQW+SvaRZJnzwfuRWDBwlpqOlsa+6QiKgSsshp2/otoOzs7IyW29vbAwCSkpLK5Pzx8fEAgLNnz2L58uWYMWMG7t27h4iICMybNw8A8NFHH2HHjh0F9pORkYHExESDFxEREZExm8+E425cKtwdrDGinbe5w6lQ6rjZ4RnfR3s+nyvelqZEREVlkcmzuel0OgBAdnY2xowZg6CgIHh5ecHT0xOTJ0/G+PHjAQBz5swpsJ8vv/wSGo1GftWuXbvMYyciIqKKJzNbh/87cAsA8HZgPdhZc9S5uAa1zJ2ZuJl7PhNRGbHI5Fm/LVVqaqrR8pSUFACAo6NjmZ4fAF5//fV85fpjJ06cQHp6usl+pk6dCq1WK7/u3btX+sESERFRhffL6Xu4n5CGao5qDG/LUeeSeOGpGrCzVuJOTArO3k0wdzhEVAlZZPJcp04dAEB4eLjRcv1xb++y+Z9L3n59fHzyleuP5eTkIC4uzmQ/arUaTk5OBi8iIiKivNKzcrDk0ajz2M71YWPFrZZKwl6twvNNPAEAm84Y/x2SiOhJWGTy3KxZMwC5zxwboz/etGnTMjl/gwYNYGNjA+Df55/zypsw5x2lJiIiIiqu/528i8jEdNTU2GBoGz7i9ST0U7f/uPAA6Vk5Zo6GiCobi0ye27dvD41Gg9u3b+P8+fP5yjdt2gQA6Nu3b5mcX61Wo2fPngCAgwcP5is/dOgQAKBu3bocTSYiIqISS8vMwZLg2wCA97r4Qa3iqPOTaOvrhlrOuXs+7/6Hez4TUemyyOTZ2toa7733HgBg7Nix8jPOALBgwQJcvHgRgYGBaNmypXx88eLFaNCgAaZOnVoqMUyePBkA8Pnnn+PGjRvy8ZCQEEyfPh0A8Pbbb5fKuYiIiKhqWns8FDHJGfBysZVHTankFAoJA/ULh53lqttEVLosdinHadOmYd++fTh27Bj8/PzQsWNHhIWF4cSJE/Dw8MCPP/5oUD8mJgbXr19HREREvr5WrFiBFStWAACysrIAABEREWjbtq1cZ+nSpWjRooX8/bPPPovPPvsMs2bNQvPmzdG+fXsolUocPXoUSUlJ6NWrFyZMmFAWl05ERERVQEpGNpYfugMA+KCrH6xVFjmmUeEMbFEL3+6/iSM3oxGpTYenxsbcIRFRJWGxP6VtbGwQHByM6dOnw87ODtu2bUNYWBhGjRqFs2fPom7dukXuKzw8HCdOnMCJEyfk56UzMzPlYydOnDC6B/PMmTOxefNmtGzZEsePH8ehQ4dQr149LFy4EL///juUSk6tIiIiopJZfSwUcSmZ8HGzw0vNa5k7nErD280ebXxdoRPAlnNcOIyISo8kuBFeuUlMTIRGo4FWq+Wz0kRERFVQjk7gZEgcwuJS8Pn2K0jJzMHCIc0woDmnbJemjafuYfLmi6jrYY/9EwIhSZK5QyIiC1WcHM1ip20TERERVSa7Lkdg5vYriNCmy8eUCglqzmQrdS80rYGg3//BnegUnLuXgBZ1XMwdEhFVAhY7bZuIiIiosth1OQLvrDtrkDgDuSPRY9efxa7L+ddsoZJzUKvQi3s+E1EpY/JMREREVIZydAIzt19BQc/Jzdx+BTk6PklXmvSrl2/nns9EVEqYPBMRERGVoZMhcflGnPMSACK06TgZEld+QVUBbes+2vM5PRt7r0SZOxwiqgSYPBMRERGVoYdJphPnktSjolEoJLzUIncVc07dJqLSwOSZiIiIqAxVcyzaPsNFrUdFN7BF7tTtw4/2fCYiehJMnomIiIjKUBtfV9TQmE6MJQA1NDZo4+tafkFVET7u9mjt4wKdALaeu2/ucIiogmPyTERERFSGlAoJQ1rVNlqm3304qG8jKBXci7gs6BcO23TmHoTgomxEVHJMnomIiIjKUI5OYPejBavsrA33dPbU2GDZ8BZ4vkkNc4RWJbzwVA3YWClwOzoFF8K15g6HiCowlbkDICIiIqrMNp8Nx9WIRDjZqHBgYifcfJiMh0npqOaYO1WbI85ly9HGCr2a1MDWc/ex6cw9PF3b2dwhEVEFxZFnIiIiojKSkpGNr3dfBwC838UP7o5qtKvnhn5P10K7em5MnMuJfuGw389zz2ciKjkmz0RERERl5Lu/7uBhUgbquNphxLPe5g6nympXzw01NTZITM/Gvqvc85mISobJMxEREVEZiNSm4/u/bgMApvZqALVKWUgLKitKhYSXWugXDuOez0RUMkyeiYiIiMrAV7uvIz1Lh9Y+Lni+iae5w6nyBj5adfuvG9F4mMg9n4mo+Jg8ExEREZWyS+FabD6bO8L5ae9GkCQ+22xuvu72aOXNPZ+JqOSYPBMRERGVIiEEZu+4AgDo/3RNru5sQf7d8zmcez4TUbExeSYiIiIqRXuvROFESBzUKgUmPd/A3OFQHi80rQG1SoGbD5NxkXs+E1ExMXkmIiIiKiWZ2Tp8+ec1AMCbHX1Ry9nWzBFRXk42VvLz51w4jIiKi8kzERERUSlZdzwMITEpcHewxjud6ps7HDJCP3X79wsPkJHNPZ+JqOiYPBMRERGVgoTUTCzafxMAMLFHABzUKjNHRMY8W88dNTQ20KZlYf/Vh+YOh4gqECbPRERERKXg/w7cgjYtCw08HfFyq9rmDodMyN3zuRYATt0mouJh8kxERET0hEJiUrDm71AAwCcvNIRSwa2pLNnAFrlTtw9xz2ciKgYmz0RERERPaO6fV5GVI9ApwAPP+XuYOxwqRF0PB7So44wcncC289zzmYiKhskzERER0RM4ficWu/+JglIh4dMXGpo7HCqiQS1zp9Zzz2ciKiomz0REREQlpNMJfLHjKgBgWJva8KvuaOaIqKh6P9rz+UZUMi7d557PRFQ4Js9EREREJbTt/H1cuq+Fg1qFD7v5mzscKgaNrRV6Ns7d83kzFw4joiJg8kxERERUAmmZOZi/6zoAYGzn+nB3UJs5Iiou/Z7Pv3HPZyIqAibPRERERCXww+E7iExMRy1nW7ze3sfc4VAJtK/vDk8nGySkZuEA93wmokIweSYiIiIqpoeJ6Vh+6DYAYEqvBrCxUpo5IioJpULCAO75TERFxOSZiIiIqJi+2XMDqZk5aF7HGX2a1jB3OPQE9Hs+H7wRjeikDDNHQ0SWjMkzERERUTFceZCIjWfuAQCm9W4ESZLMHBE9ifrVHND80Z7Pv3HPZyIqAJNnIiIioiISQuCLnVcgBNCnaQ209HYxd0hUCvQLh/16mns+E5FpTJ6JiIiIiij4+kMcvRULa5UCHz/fwNzhUCnp07QmrFUKXI9Kwj8PEs0dDhFZKCbPREREREWQlaPDFzuuAgBeb++D2q52Zo6ISovG1go9GlUHwIXDiMg0Js9ERERERfC/k3dxOzoFrvbWGNu5vrnDoVKmn7q97fx97vlMREYxeSYiIiIqhDYtCwv33gAAjO/uDycbKzNHRKWto58HqjupkZCaheBr3POZiPJj8kxERERUiKXBtxCfmoX61RwwrHVtc4dDZUCpkDCgee7o86YzXHWbiPJj8kxERERUgLuxqVh1NBQA8OkLDaFS8tenympQy1oAgAPXorDrcgR+O38ff9+ORY6OK3ATEaAydwBERERElmzermvIzNGho587OgV4mDscKkP1qznCx80OobGpeHvdWfl4DY0Ngvo2wvNNapgxOiIyN/7plIiIiMiE06Fx2HEpAgoJ+LR3Q0iSZO6QqAztuhyB0NjUfMcjtel4Z91Z7LocYYaoiMhSMHkmIiIiMkKnE/j80dZUQ1rXRgNPJzNHRGUpRycwc/sVo2X6Sdszt1/hFG6iKozJMxEREZER2y8+wIV7CbC3VmJ8d39zh0Nl7GRIHCK06SbLBYAIbTpOhsSVX1BEZFGYPBMRERE9Jj0rB/N3XQcAvNOpHqo52pg5IiprD5NMJ84lqUdElQ+TZyIiIqLH/Hg0BPcT0lBTY4M3O9Y1dzhUDor6BxL+IYWo6mLyTERERJRHdFIGlgbfBgBMfr4BbKyUZo6IykMbX1fU0NjA1JJwEnJX3W7j61qeYRGRBWHyTERERJTHwn03kJyRjaZeGrzYrKa5w6FyolRICOrbCADyJdD674P6NoJSwRXXiaoqJs9EREREj1yPTMKGk3cBANN6N4KCiVKV8nyTGlg2vAU8NYZTs6s5qbFseAvu80xUxanMHQARERGRpZiz8yp0AujVxJPTc6uo55vUQPdGnjgZEofxv5xDZGIGpjzfgIkzEXHkmYiIiAgADl5/iEM3omGllDClVwNzh0NmpFRIaFfPDf2bewEADt2INnNERGQJmDwTERFRlZedo8OcnVcBACPb+cDbzd7MEZEl6NqwGgDg4I1oZOfozBwNEZkbk2ciIiKq8n45fQ83opLhbGeF97v4mTscshDNaztDY2uFhNQsnLuXYO5wiMjMmDwTERFRlZaUnoWFe28AAD7s6geNnZWZIyJLoVIq0CnAAwBw4NpDM0dDRObG5JmIiIiqtGUHbyMmORN13e3xaltvc4dDFqZLg9yp2weuMnkmquqYPBMREVGVFR6fihVHQgAAU19oCCslfzUiQ4H+HlBIwPWoJITHp5o7HCIyI/4fgoiIiKqs+buuIzNbh3Z13dDt0eJQRHk521mjlXfutmXBnLpNVKUxeSYiIqIq6dzdePx+4QEkCfi0d0NIkmTukMhCdXn0h5X9TJ6JqjQmz0RERFTlCCEwe0fu1lQDW3ihSS2NmSMiS6Z/7vnY7VikZmabORoiMhcmz0RERFTl7LwUiTNh8bC1UuKjHgHmDocsnF81B3i52CIzW4djt2LNHQ4RmQmTZyIiIqpSMrJzMHdX7qjzmMC68NTYmDkisnSSJKFrA07dJqrqmDwTERFRlfLTsVDci0tDdSc1/vNcXXOHQxVE50fJc/C1hxBCmDkaIjIHJs9ERERUZcQmZ+D/9t8CAHzUIwB21iozR0QVRdu6brC1UiIyMR1XIhLNHQ4RmQGTZyIiIqoyFu2/iaSMbDSu6YSBLbzMHQ5VIDZWSrSv7w4AOHCVU7eJqiImz0RERFQl3HqYhJ9P3AWQuzWVQsGtqah4uj7asurAdSbPRFURk2ciIiKqEr7ceQ05OoHujarj2Xru5g6HKqDOAbnJ8/l7CYhJzjBzNERU3pg8ExERUaV35GYM9l97CJVCwtReDcwdDlVQnhobNK7pBCGAg9ejzR0OEZUzJs9ERERUqeXoBGbvuAIAGN7WG3U9HMwcEVVkXfOsuk1EVQuTZyIiIqrUNp25h2uRSXCyUWFcVz9zh0MVXJeG1QEAf92IRma2zszREFF5YvJMRERElVZKRja+3nMDAPBBVz+42FubOSKq6JrW0sDdwRpJGdk4HRpn7nCIqBwxeSYiIqJK67tDtxGdlAFvNzuMaOdj7nCoElAoJHR6tHDYAU7dJqpSmDwTERFRpfQgIQ3fH74DAJjaqwGsVfy1h0qH/rlnJs9EVQv/L0JERESV0te7ryM9S4c2Pq7o2djT3OFQJdLBzx1WSgl3YlIQEpNi7nCIqJwweSYiIqJK52J4Aracuw8AmNanISRJMnNEVJk42lihja8rAI4+E1UlTJ6JiIioUhFCYPaOqwCAAc1roamXs3kDokqpS4PcVbcPXIsycyREVF6YPBMREVGlsvufKJwMiYNapcCkngHmDocqqS6Pnns+cScOSelZZo6GiMoDk2ciIiKqNDKzdZj7Z+6o83+eq4uazrZmjogqK193e9R1t0e2TuDIzRhzh0NE5YDJMxEREVUaa4+HITQ2FR6OarwdWM/c4VAlpx993s/nnomqBCbPREREVCkkpGbi2/03AQATu/vDXq0yc0RU2emT54PXH0KnE2aOhojKGpNnIiIiqhQW7b8JbVoWGng6YnCr2uYOh6qAVj6ucFSrEJOciYv3teYOh4jKGJNnIiIiqvDuRCdj7d9hAIBpvRtBqeDWVFT2rFUKPOfvAQA4cJWrbhNVdkyeiYiIqMKb++c1ZOsEujSohg5+7uYOh6qQzo+mbh+4zueeiSo7Js9ERERUof19OxZ7rkRBqZDwyQsNzB0OVTGdAjwgScDl+4mI1KabOxwiKkNMnomIiKjC0ukEvth5BQDwSps6qF/N0cwRUVXj7qDG07WdAQDBHH0mqtSYPBMREVGFtfXcfVy+nwhHtQofdvMzdzhURXUJeDR1m1tWEVVqTJ6JiIioQkrNzMZXu68DAN7rUh9uDmozR0RVVZeGucnzkZsxSM/KMXM0RFRWmDwTERFRhfTDXyGITEyHl4stRj7rY+5wqAprVMMJnk42SMvKwfE7seYOh4jKCJNnIiIiqnCiEtOx/NBtAMCUXg1gY6U0c0RUlUmSJK+6Hcyp20SVFpNnIiIiqnC+2XMdaVk5aFHHGb2fqmHucIjQ9VHyvP/aQwghzBwNEZUFJs9ERERUofzzQItfz4QDAKb1aQRJkswcERHwbH03WKsUCI9Pw82HyeYOh4jKAJNnIiIiqjCEEPhix1UIAfRtVhMt6riYOyQiAICdtQrP1nMDwFW3iSorJs9ERERUYey/+hDHbsfCWqXA5J4B5g6HyIB+6vaBq0yeiSojJs9ERERUIWTl6DDnz6sAgDc6+KK2q52ZIyIypF807HRYHBJSM80cDRGVNibPREREVCGsP3EXd6JT4GZvjXc71TN3OET5eLnYIaC6I3QCOHQj2tzhEFEpY/JMREREFk+bmoX/7rsBABjf3R+ONlZmjojIuC4NH03d5nPPRJWOytwBEBERERmToxM4GRKHh0np2PNPJOJTs+BXzQFDW9c2d2hEJnVpUA3LDt7GwevRyM7RQaXkWBVRZcHkmYiIiCzOrssRmLn9CiK06QbHezauzmSELFrz2s5wtrNCQmoWzt1LQGsfV3OHRESlhP/3ISIiIouy63IE3ll3Nl/iDABLgm9j1+UIM0RFVDQqpQKd/D0A5K4OT0SVh0Unz2lpafjss8/g7+8PGxsb1KxZE6NHj8b9+/eL1c+hQ4cwc+ZM9O7dGx4eHpAkCT4+PgW2GTVqFCRJMvlavnz5E1wZERERGZOjE5i5/QpEAXVmbr+CHF1BNYjMS7/q9oFrUWaOhIhKk8VO205PT0eXLl1w/Phx1KhRA/369UNoaChWrVqFP/74A8ePH0fdunWL1Ne4ceNw4cKFEsXRs2dPeHp65jseEMC9JYmIiErbyZA4oyPOegJAhDYdJ0Pi0K6eW/kFRlQMgf4eUCok3IhKxr24VG6rRlRJWGzyPHv2bBw/fhzt2rXDnj174ODgAABYsGABJk6ciNGjR+PgwYNF6qtHjx4YPHgwWrduDS8vLzRu3LjIcUyZMgWdOnUqwRUQERFRcT1MMp04l6QekTk421mjpbcLTobEIfj6Q4xo52PukIioFFjktO3MzEwsXrwYALBkyRI5cQaACRMmoGnTpjh06BDOnDlTpP7mz5+PTz/9FD169ICrKxdtICIislTVHG1KtR6RuXRpwC2riCobi0yejx49Cq1Wi3r16qF58+b5ygcNGgQA2L59e3mHRkRERGWoja8ramhsIJkolwDU0NigjS//GE6Wreuj5PnY7VikZmabORoiKg0WmTzrn09u0aKF0XL98YsXL5Z5LFu2bMH777+Pd999F1999RWuXbtW5uckIiKqqpQKCUF9Gxkt0yfUQX0bQakwlV4TWYb61RxQ29UWmdk6HL0Va+5wiKgUWGTyfPfuXQCAl5eX0XL98bCwsDKP5f/+7/+wePFiLFu2DJMnT0ajRo0wduxYZGfzL4hERERl4fkmNfDNy83yHffU2GDZ8BZ4vkkNM0RFVDySJKFLAKduE1UmFrlgWHJyMgDAzs74yoT29vYAgKSkpDKLoXnz5mjXrh26dOkCLy8vREZG4s8//8S0adOwdOlSWFtbY+HChQX2kZGRgYyMDPn7xMTEMouXiIioMvF0yn2m2d3BGtP7NEI1x9yp2hxxpoqkS8Pq+OnvMBy4FgUhmkCS+PklqsgscuTZEowbNw5jxoyBn58fbG1t4evri3fffReHDx+GtbU1Fi9ejHv37hXYx5dffgmNRiO/ateuXU7RExERVWwXwrUAgGd83dDv6VpoV8+NiTNVOM/4usLOWomoxAz884CDKEQVnUUmz/rVtVNTU42Wp6SkAAAcHR3LLSa9xo0b48UXX0R2djb2799fYN2pU6dCq9XKr8KSbSIiIsp1MTwBANCstsa8gRA9ARsrJdrXdwcABHPqNlGFZ5HJc506dQAA4eHhRsv1x729vcstprz8/PwAABEREQXWU6vVcHJyMngRERFR4S7cSwAANPVyNmscRE9Kv+r2fibPRBWeRSbPzZrlLhJy9uxZo+X6402bNi23mPKKj48H8O+z10RERFR6opMy8ECbDkkCmtTiyDNVbJ0fJc8XwhMQk5xRSG0ismQWmTy3b98eGo0Gt2/fxvnz5/OVb9q0CQDQt2/fco4sdxGwHTt2ADC9lRYRERGVnH7Kdn0PBzioLXJtU6Iiq+5kgya1nCAEcPB6tLnDIaInYJHJs7W1Nd577z0AwNixY+VnnAFgwYIFuHjxIgIDA9GyZUv5+OLFi9GgQQNMnTr1ic9/7do1rF271mClbACIjo7G0KFDce/ePTRr1gzt27d/4nMRERGRIf1iYc1qO5s3EKJS0qVBdQDAgWtRZo6EiJ6Exf45d9q0adi3bx+OHTsGPz8/dOzYEWFhYThx4gQ8PDzw448/GtSPiYnB9evXjT6HvGLFCqxYsQIAkJWVBSD3eeW2bdvKdZYuXSqPJEdGRmLEiBEYN24cWrVqBQ8PDzx48ABnzpxBUlISvLy8sHHjRm43QEREVAb0zzs38+KUbaocujSohm/338RfN2KQma2Dtcoix6+IqBAWmzzb2NggODgYX375JdavX49t27bB1dUVo0aNwueffw4vL68i9xUeHo4TJ04YHMvMzDQ4lncPZn9/f3z44Yc4fvw4Ll26hNjYWKjVavj7+6Nv374YN24cXFxcnvwiiYiIyIAQQp62zcXCqLJoWksDdwdrxCRn4nRoHJ59tAI3EVUskhBCmDuIqiIxMREajQZarZYrbxMRERlxLy4VHecHw0op4fLMnlCrlOYOiahUTPr1An49E443Ovhiep9G5g6HiB4pTo7GOSNERERkMS48GnVuWMOJiTNVKl0erbp9gFtWEVVYTJ6JiIjIYvz7vLOzWeMgKm0d/NxhpZQQEpOCO9HJ5g6HiEqAyTMRERFZDP1K2025WBhVMo42VnjG1w0AR5+JKiomz0RERGQRcnQCl+9zmyqqvDpz6jZRhcbkmYiIiCzC7ehkpGbmwM5aiXoeDuYOh6jUdX2UPJ8MiUNSepaZoyGi4mLyTERERBZB/7zzU7U0UCok8wZDVAZ83O1R18Me2TqBwzdjzB0OERUTk2ciIiKyCPqVtjllmyqzLgG5o8/7r3LqNlFFw+SZiIiILMJFLhZGVUCXhrnJ88HrD6HTCTNHQ0TFweSZiIiIzC4jOwdXIxIBcJsqqtxa+7jCUa1CbEqmPNuCiCoGJs9ERERkdtcikpCVI+Bqbw0vF1tzh0NUZqyUCjzn7wEACOaq20QVCpNnIiIiMjv9CFxTLw0kiYuFUeXW5dGq2/uZPBNVKEyeiYiIyOwu3NM/7+xs3kCIykGnAA9IEvDPg0REatPNHQ4RFRGTZyIiIjK7i/qVtrlYGFUBbg5qPP1oVfng6xx9JqoomDwTERGRWSVnZONWdDIAjjxT1dG1AbesIqpomDwTERGRWV0K10IIoJazLTwc1eYOh6hcdGlQHQBw9FYM0rNyzBwNERUFk2ciIiIyq4t5Fgsjqioa1nBEDY0N0rJycPxOrLnDIaIiKPXkWavV4vTp04iMjCztromIiKgSuhjOxcKo6pEkCZ0fTd0+wFW3iSqEEiXPe/bswejRo3Hu3DmD4//3f/8HT09PPPPMM/Dy8sL48eNLJUgiIiKqvC5wsTCqovI+9yyEMHM0RFSYEiXPK1aswK+//go/Pz/52KVLl/Dhhx8iJycHbdu2hZOTE7799lv89ttvpRYsERERVS6xyRkIj0+DJAFNmDxTFfNsPXeoVQrcT0jDzYfJ5g6HiApRouT57NmzePrpp+Hg4CAfW716tfzfo0eP4syZM7C2tsbSpUtLJVAiIiKqfPRTtuu628PJxsrM0RCVL1trJZ6t5waAq24TVQQlSp6joqLg5eVlcGz//v1wdnbG0KFDAQC+vr4IDAzE1atXnzxKIiIiqpT+nbLtbNY4iMyli/zcc5SZIyGiwpQoeVYqlUhPT5e/j4uLw+XLl9GxY0coFP926eHhgejo6CePkoiIiCqlfxcL45Rtqpr0i4adCYtHQmqmmaMhooKUKHn28fHBsWPHkJWVBQDYsmULhBDo3r27Qb3Y2Fi4ubk9eZRERERU6Qgh5G2qmtV2NmssRObi5WKHBp6O0Ang0A0OOhFZshIlz0OGDEF0dDSee+45TJw4EZMnT4aVlRX69+8v1xFC4MyZM6hbt25pxUpERESVyP2ENMQkZ0KlkNCwhpO5wyEym855Vt0mIstVouT5gw8+QJs2bXDixAksXLgQiYmJ+PLLL1GrVi25zoEDBxAdHY3OnTuXWrBERERUeeinbDeo4QgbK6WZoyEyH/2WVYduRCM7R2fmaIjIFFVJGtnb2+PYsWP466+/EB0djaefftpg2yog97nohQsXok+fPqUSKBEREVUu+sXCmnKxMKrimtdxgbOdFRJSs3D2bgLa+LqaOyQiMqJEyTMAKBQK/D979x7fdH3vcfydpJfQ+522FGi5g0C5TEQRQbyAF2Betummm7LpLrrp3LxtTubUeZmXuTndmU6d023Oy7h4QeWqooCAtCB3aKGUlt7o/Z78zh9pCsilbZr0lyav5+PB44zkl+STY4C+8/1+P5/p06ef9P7p06ef8n4AABDccgtcK8/jCM8IcjarRdOHJWvBpoNatv0Q4RnwUx5t2x40aJDuvPPODq+7++67NXjwYE9eAgAABDCn09DmwrZO2/3ptA3MGNlXkrRiO+eeAX/lUXjOz8/v1AiqsrIy5efne/ISAAAggO0tq1VtU6v6hNo0JDnK7HIA000bmiyb1aKdh2pVUFFvdjkATsCj8NxZdXV1Cg0N9eVLAACAXiinbcv26H4xCrH59McRoFeIjQjVxIHxkqTlrD4Dfskn/1o5nU5t27ZNK1as0IABA3zxEgAAoBfLpVkYcBx3123CM+CfOh2ebTZb+y9J+sc//nHMbUf/Cg0N1ejRo3Xo0CFdffXVPiseAAD0TpvaxlRl948ztxDAj5w30hWeP9tbrvrmVpOrAfBVne623b9/f1ksFknS/v37FRERoaSkpBNeGxYWpvT0dM2ZM0c/+9nPvFMpAAAICM2tTm07WC1Jys6gWRjgNjg5Sv0T+qigokGrd5frglF9zS4JwFE6HZ6PbvxltVr1jW98Qy+88IIvagIAAAFsR3GNmh1OxUWEakBChNnlAH7DYrHovBF99dKn+Vq+/RDhGfAzHs15XrFihVJTU71dCwAACAI5beedx/SLbd/VBsBlxoiUtvBcIsMw+DMC+BGPwvO0adO8XQcAAAgSOQWVkqRxnHcGjnPGoARFhNl0qLpJXx6s1uh+HG0A/IVH4dktLy9PH3/8sYqKitTU1HTCaywWi37zm99052UAAEAAyW1rFkanbeB44SE2nT0kSR9sPaTl20sIz4Af8Sg8Nzc36wc/+IFeffVVSZJhGCe9lvAMAADc6ptbtaukRhLNwoCTOW9kij7YekjLtpfoZ+cNNbscAG08Cs/33nuvXnnlFcXFxemaa67RsGHDFB0d7e3aAABAgNlSWC2nIaXG2JUSYze7HMAvnTvcNbIq90ClSmualBwdbnJFACQPw/O//vUvxcXF6YsvvtDAgQO9XRMAAAhQuW3NwrL7s+oMnExKjF1j+sVqc2GVVu4o0Te+1t/skgBIsnryoJKSEk2dOpXgDAAAumRTW7MwzjsDpzZjhGv1efn2EpMrAeDmUXgmNAMAAE+4m4VlE56BU3KH5493lam51WlyNQAkD8PzvHnztHLlSpWWlnq7HgAAEKAO1zVrf0W9JGkMzcKAUxrTL1ZJUeGqbWrV5/kVZpcDQB6G59tvv10XXXSRzj33XK1YseKU3bYBAAAkKbfQteqclRSp2D6hJlcD+Der1aJzhydLkpZtY+s24A88ahg2ZMgQSdK+fft0/vnnKzQ0VKmpqbJaj8/iFotFe/bs6V6VAACg18tpO+/MiCqgc84bmaLXNxzQih0lunf2KLPLAYKeR+E5Pz//mN83Nzdr//793qgHAAAEKHenbZqFAZ1z9tBkhdosyiur097SWg1KjjK7JCCoebRt2+l0dukXAAAIboZhKMfdLIwxVUCnRIWH6IysREl03Qb8gUfhGQAAoCuKqxtVWtMkm9WiUWmEZ6CzGFkF+A/CMwAA8Dn3eefhfaPVJ8xmbjFAL3LeSFd4XpdXoerGFpOrAYKbR2eeP/rooy5df84553jyMgAAIECwZRvwzMDESA1KjtTe0jp9vLNMl4xNM7skIGh5FJ6nT58ui8XS6esdDocnLwMAAAIEzcIAz503IkV7S/O0fHsJ4RkwkUfh+bvf/e4Jw7PT6VRBQYE2btyo6upqzZ07V3Fxcd2tEQAA9GJOp6HctpXnsYypArpsxoi+eu7jPK3cUSKn05DV2vlFLADe41F4fumll055/+HDh3XDDTdoy5Yt+uyzzzx5CQAAECDyyutU09gqe6hVw/pGm10O0Ot8LTNe0fYQldc1K+dApcYPiDe7JCAo+aRhWHx8vF5++WVVVVXp7rvv9sVLAACAXsK9Zfu09FiF2uhVCnRVqM2qc4YlS6LrNmAmn/0LFhERoUmTJmnRokW+egkAANAL5BSwZRvorvPaRlYt20Z4Bszi069/a2trdfjwYV++BAAA8HPuledsmoUBHps2LFkWi7S1qFrFVY1mlwMEJZ+F58WLF+ujjz7SsGHDfPUSAADAz7U4nPryYLUkKbt/nLnFAL1YYlS4xrf9GWLrNmAOjxqGzZs376T31dbWaufOndq8ebMMw9AvfvELj4sDAAC9247iGjW1OhVjD1FmYoTZ5QC92nkj+2rj/kot335I3z5jgNnlAEHHJ922JWnAgAGaP3++vvvd73ryEgAAIAAcGVEVd8IxlwA679zhKfrD+zu0ene5GlscsofazC4JCCoehecVK1ac9L6wsDClpaUpMzPT05oAAECAcJ93plkY0H0j06KVFmtXUVWjPttbrnOHp5hdEhBUPArP06ZN83YdAAAgAOUctfIMoHssFotmjEjRq2v3a/m2EsIz0MMYtggAAHyiodmhnYdqJEnjaBYGeMWMtpFVy7eXyDAMk6sBgotHK89uhw4d0gsvvKCPP/5YhYWFkqR+/frpnHPO0fXXX6++fft6pUgAAND7fHmwSg6noZTocKXG2s0uBwgIZw1OUniIVYWVDdp5qFbDU6PNLgkIGh6H5zfffFPz5s1TbW3tMd96bd68We+//74efvhh/f3vf9cVV1zhlUIBAEDvwpZtwPv6hNk0ZUiSlm8v0bLthwjPQA/yaNv2+vXrdfXVV6uurk6XXXaZ/ve//+mLL77Qpk2btGDBAl1++eWqra3Vt7/9ba1fv97bNQMAgF7A3Swsm2ZhgFed27Z1ewXznoEe5dHK80MPPSSHw6E33nhDl1122TH3jR07VnPmzNH//vc/XXHFFXr44Yf1xhtveKVYAADQe+QUVEqSsjnvDHjVjBEp+o2kDfsO63Bds+Ijw8wuqcc5nIbW5VWopKZRKdF2TcpKkM3KODz4lkfh+ZNPPtFZZ511XHA+2mWXXaYpU6bo448/9rg4AADQO1XVtyi/vF4SY6oAb+sX10cjUqO1vbhGq3aW6uvj+5ldUo9asqVI9y3eqqKqxvbb0mLtmj97lGaNTjOxMgQ6j7ZtV1VVacCAAR1eN2DAAFVVVXnyEgAAoBfLLayUJA1MjFBcRPCtigG+dnTX7WCyZEuRfvzKxmOCsyQVVzXqx69s1JItRSZVhmDgUXhOTU3VF1980eF1mzZtUmpqqicvAQAAerFcmoUBPnXeSFd4XrmjRK0Op8nV9AyH09B9i7fqRAO63Lfdt3irHE5GeME3PArPM2fO1I4dO/SrX/1KDofjuPsNw9A999yj7du3a9asWd0uEgAA9C6b3Oed2bIN+MS4/vGKjwhVdWOrNuw7bHY5PWJdXsVxK85HMyQVVTVqXV5FzxWFoOLRmeff/OY3euutt/TII4/o3//+t775zW8qMzNTkrRv3z69/vrrys/PV2Jiou655x5v1gsAAHqB9k7bNAsDfMJmtWj68BT974tCLd9RojMGJZpdks+V1Jw8OHtyHdBVHoXnjIwMLV++XN/5zne0ZcsW/eEPf5DF4upu5575PGbMGL366qvKyMjwXrUAAMDvHapu1KHqJlkt0mnpMWaXAwSsGSPawvO2Et190Uizy/G5lGi7V68Dusqj8Cy5wnFubq5Wrlypjz/+WAcPHpQkpaena+rUqZo+fbq3agQAAL2Ie0TVsL7Rigjz+EcNAB04Z1iybFaLdpXUqqCiXv0TIswuyWcMw9C2olM3IrZISo11ja0CfKHb/6JNnz6doAwAANodaRbGeWfAl2L7hOprA+O1Nq9Cy7eX6HtnZZpdkk+U1jTp9jdytHJH6UmvcU94nj97FPOe4TMeNQwDAAA4mRzOOwM9xt11e1mAjqxavv2QZv3xI63cUaqwEKvum3Oanv3OBKXFHrs1Ozk6XM9eM4E5z/Apj8Lz888/r4SEBC1ZsuSk17z33ntKSEjQSy+95GltAACglzEMo33lOZsxVYDPuec9r9lTrrqmVpOr8Z7GFofuXbhF815ar/K6Zo1Ijdbim8/W987K1EVj0vTJnTP07xsmKyU6XJL0xDeyCc7wOY/C83/+8x+Fh4frwgsvPOk1F154ocLCwvSvf/3L4+IAAEDvsq+8XlUNLQoLsWp4arTZ5QABb3BylAYkRKjZ4dTq3WVml+MV24qqNfvPn+jlz/ZJkq6fkqkFN0055u8Um9WiMwcnanQ/1/GQfYfrTakVwcWj8Lx161aNHTtWVuvJH26z2ZSdna2tW7d6XBwAAOhd3Fu2R6XFKNTG6TDA1ywWS/vq8/JevnXb6TT090/yNPfp1dpVUqukqHC9dP3pmj/7NNlDbSd8TGZipCQpv6yuJ0tFkPKoYVhFRYWSkpI6vC4pKUllZYHxDRgAAOhYToFry/Y4zjsDPWbGiBS99Gm+lm8vkWEY7SNke5OS6kb94vUcfbzLlR3OG5GiR68cq8So8FM+LivJ1WE8r4yVZ/ieR+E5KSlJu3bt6vC6Xbt2KT4+3pOXAAAAvVBu28oznbaBnnPGoARFhNlUUtOkLw9Wt29l7i0+3HpId76Zq4q6ZoWHWHXPpaN0zRkDOvUlwED3ynM5K8/wPY/2U5199tnasGGDVq5cedJrVq5cqfXr12vKlCme1gYAAHqRVodTWw66x1TFmVsMEETCQ2yaOtS1K3TZtt6zdbuh2aFf/2+zbnh5vSrqmjUyLUZv//RsXTt5YKdXz7OSXOF5f3m9HE7Dl+UCnoXn2267TRaLRV//+tf12GOPqarqyMDy6upqPfbYY7rssstktVr185//3GvFAgAA/7WrpFaNLU5Fh4doUNsPtAB6Rvu55x29IzxvKazSpX/+WK+u3S9JumFqlhbcdJaG9u1ao8H0uD4Ks1nV7HCqqKrBF6UC7TwKz5MmTdLjjz+umpoa3XnnnUpISFBycrKSk5MVHx+vO++8U9XV1Xr00UdZeQYAIEjkFFRKksZkxMpq7X1nLoHe7NzhrvCcU1Cp0pomk6s5OafT0N8+2qPLnlmtPaV1SokO1z+/P0m/vmSUwkNO3BTsVGxWi/on9JEk5XPuGT7mcRvMW265RStWrNDMmTNlt9tVXl6u8vJy2e12zZo1SytWrNBtt93mzVoBAIAfyznAlm3ALCkx9vZeAyv8dPW5uKpR176wVr9/d7taHIYuGNVXS249R1OHJnfred1bt/M49wwf86hhmNs555yjc845Rw6HQ+Xl5ZJczcRONcIKAAAEJnezsGyahQGmOHd4inIPVGnF9hJ982v9zS7nGEu2FOuut3JVWd8ie6hV9156mq6e1N8rncEZV4We0q3w7Gaz2ZSSkuKNpwIAAL1QY4tDO4prJEljGVMFmOK8kSl6atkufbSzVM2tToWFmL+gVd/cqvvf3qp/ryuQJI3uF6M/fmu8hqREee01MpMIz+gZXgnPAAAguH15sFqtTkNJUWFKj7WbXQ4QlEanxyo5OlylNU1al1ehs9s6cJsl90Clbv3PJu0tq5PFIt14ziD94oLhXg/17pVntm3D18z/OgoAAPR6R7Zsx3llGyaArrNaLTp3uOv88PLt5p17djgNPbtyjy5/5lPtLatTaoxdr37/DN190UifrIZnJkVIkgoq6tXqcHr9+QE3wjMAAOi2XJqFAX5hxoi+kqRl2w/JMHp+7vHBygZ95/k1emTJdrU6Dc06LVXv3TJVZw3x3Sp4emwfhYVY1eIwVFTV6LPXAQjPAACg23LaVp7H9qdZGGCms4cmKcxm1b7yeu3t4TPA724u0kVPfaw1eysUEWbTo1eM1bPXTFB8ZJhPX9dqtWhggmv1OY9zz/AhwjMAAOiW6sYW7S11/cCazcozYKqo8BCdMShBkrSih7Zu1zW16vbXc/STVzeqqqFFYzNi9c7Ppuqbp3unm3ZntDcN49wzfIjwDAAAumVz25bt/gl9lODjFSYAHZsxwjUFZ9k234fnTQWVuuRPH+v1DQdksUg3nTtYb/74rPbZyz2lfdYzK8/wIZ+E57KyMrW2tvriqQEAgJ9p37LNqjPgF9zh+fP8ClU3tvjkNRxOQ08v36Urnv1U+eX1So+16983TNbtM0co1Nbz63PMekZP8OiTvX79ev3ud7/T1q1bj7n9f//7n1JTU9W3b18lJibqqaee8kqRAADAf+UWuFaeszM47wz4g4GJkRqcHKlWp6GPd5Z5/fkLKxt09d/W6LEPdsrhNHTJ2DS9d8s5mjwo0euv1VmZia4zz/nl9abVgMDnUXj+85//rN///vfq27dv+215eXm66qqrVFJSotTUVNXV1em2227TypUrvVUrAADwQ7msPAN+57yRR7pue9PinIOa9cePtC6/QpFhNj32jWw9ffV4xUaEevV1usp95plxVfAlj8LzmjVrNH78eCUmHvl26YUXXlBLS4see+wxFRYWau3atbJaraw+AwAQwEpqGnWwqlFWizSmHyvPgL84d7hr6/bKHaVyOLs/sqqmsUW3/XeTfvrvL1TT2Kpx/eP07i1TdeXEDL+Y7Z4aY1d4iFWtTkOFlQ1ml4MA5VF4PnTokAYMGHDMbR9++KEiIyN18803S5ImTpyoqVOnKicnx+PiGhoadO+992rYsGGy2+1KT0/XvHnzVFhY2KXnWbVqle677z5dcsklSk5OlsViUWZmZpeeo7m5WaNGjZLFYlFISEiXHgsAQKByb9kekhKlyHD+fQT8xdcy4xVtD1FFXXN7XwJPbdx/WJf86RO9tbFQVov0sxlD9PqPztTAxJ5tCnYqVqul/dwzTcPgKx79K+dwOI5pCFZbW6uNGzdqxowZCgs70mUzPT1da9as8aiwxsZGzZgxQ2vWrFFaWprmzp2r/Px8vfjii3r77be1Zs0aDRo0qFPPdcstt3QrxEvS73//e23fvr1bzwEAQKBhyzbgn0JtVk0blqy3c4u0fFuJJgyI7/JztDqc+suKPfrT8l1yOA31i+ujP141TqdnJvig4u7LTIrQjkM1rqZhw82uBoHIo5XnAQMGaMOGDe2/f+edd9Ta2qrzzz//mOuqq6sVG+vZFq4HHnhAa9as0ZlnnqmdO3fqtdde09q1a/X444+rtLRU8+bN6/RzXXjhhXrggQf0/vvv68svv+xyLdu2bdNDDz2kG264ocuPBQAgkOUcoFkY4K/cXbeXezDvuaCiXlf9bY2eXOpqCjYnO13v3jLVb4OzdPSsZ5qGwTc8Cs+zZ8/W/v37dfnll+vPf/6zfvnLX8pqtWru3LnHXPfFF19o4MCBXX7+5uZmPf3005Kkv/zlL4qKimq/77bbbtPYsWO1atWqYwL8qTz66KP69a9/rQsvvFAJCV37A28Yhm688UbFxcXp4Ycf7tJjAQAIZIZhtG8Hze4fZ2otAI43fXiKLBZpa1G1iqo6fw54wReFuvipj7V+32FFhYfoyW9l609Xj1dsH3ObgnUki23b8DGPwvMvf/lLZWZmasGCBbrllltUWFioW2+9VUOHDm2/Zu3atSosLNQ555zT5edfvXq1qqqqNHjwYI0fP/64+6+88kpJ0uLFiz0pv0v+7//+T5988okef/xxxcd3fbsLAACBqqCiQZX1LQqzWTUiNcbscgB8RUJkWPt27c6sPlc3tujW/3yhW1/bpJqmVk0cGK/3bpmqy8Zn+LpUr3Cfwc4vJzzDNzw685yUlKTc3Fy98cYbKi0t1cSJEzVjxoxjrikuLtYtt9yia665psvP7z6fPGHChBPe7749Nze3y8/dFUVFRbrrrrt03nnnefQ+AAAIZO5V55Fp0QoL8ej7eAA+NmNEijbsO6wV20v0nTNOviN0fX6FbvnPJhVWNriagp03VDefO0Qhtt7zZzurbdv2gcMNanE4FdqLakfv4HFbzKioKF133XUnvX/u3LnHbePurP3790uSMjJO/C2X+/Z9+/Z59PyddfPNN6uxsVHPPPOMT18HAIDeiGZhgP+bMSJFf3h/hz7ZXabGFofsobZj7m91OPWn5bv19PJdchpS/4Q++uO3xmniQP8923wyfWPC1SfUpoYWhw4cbmgP04C3eH2mRFVVlXbt2qWMjAylpqZ69By1tbWSpIiIiBPeHxnp+oNQU1PjWZGdsHDhQr311luaP3++hg0b5tFzNDU1qampqf331dXV3ioPAADT5bSNqRpLszDAb41IjVZ6rF0Hqxr14id5So/vo5RouyZlJejA4Xrd+tomfbG/UpJ0+fh+um/uaYq2+/fZ5pOxWCwamBih7cWujtuEZ3ibR+H5gw8+0H/+8x/99Kc/PeZM8p///Gfdcccdam5ulsVi0U9/+lM9+eSTXiu2p9TU1Ojmm2/WsGHDdPfdd3v8PA899JDuu+8+L1YGAIB/cDgNbTnoCs/jaBYG+C2LxaLBKVE6WNWoR97f0X57bJ9QNbY41NTqVLQ9RA98fbTmjutnYqXekZUUqe3FNcorq9O5ZheDgOPRQYDnn39er7/++jENwjZv3qxbb71VDodDkydPVkxMjP70pz9p4cKFXX5+d3ft+voTt5mvq3M1AYiOjvag+o796le/0oEDB/TMM88oPDzc4+e5++67VVVV1f6roKDAi1UCAGCe3SW1qm92KDLMpkHJUR0/AIAplmwp0se7yo67vaqhRU2tTg1OjtR7t0wNiOAsHT2uiqZh8D6PVp43btyocePGHTNC6qWXXmr/v9/+9reVl5enUaNG6Zlnnuny2ecBAwZIkg4cOHDC+923ezIGqzMWL14su92u+++/X/fff/9x9zscDk2fPl2S9Mc//lHjxo074fOEh4d3K3wDAOCv3M3CRveLlc1qMbcYACfkcBq6b/HWU15T3+xQWmyfHqrI9xhXBV/yKDwfOnRIp59++jG3LVu2THFxcbrqqqskSVlZWZo2bZq2bj31H9gTyc7OluQK6Sfivn3s2LFdfu7Oamxs1KpVq056v/u+yspKn9UAAIC/ymW+M+D31uVVqKiq8ZTXFFU1al1ehc4cnNhDVfnWwERXzyRWnuELHm3bttlsamw88gexoqJCW7Zs0dSpU2W1HnnK5ORklZaWdvn5p0yZotjYWO3Zs0ebNm067v433nhDkjR79uyuF98J+fn5MgzjhL8k1/t3/969Ag0AQDBxNwvLptM24LdKak4dnLt6XW/gbhJWeLhBza1Ok6tBoPEoPGdmZurTTz9VS0uLJOmtt96SYRi64IILjrmuvLxciYld/xYrLCxMN998syTppptuaj/jLElPPPGEcnNzNW3aNE2cOLH99qefflojRozoVoMvAADQsaZWh7YXuyZI0Gkb8F8p0XavXtcbJEeHKzLMJqchFRw+cf8kwFMebdv+1re+pV//+tc655xzdNZZZ+nFF19UaGiovv71r7dfYxiGNmzYoOHDh3tU2D333KOlS5fq008/1dChQzV16lTt27dPa9euVXJysl544YVjri8rK9OOHTtUVFR03HM9//zzev755yWpPfAXFRVp8uTJ7dc888wzmjBhgke1AgAQTLYV1ajFYSghMkwZ8YFzVhIINJOyEpQWa1dxVaOME9xvkZQa6xpbFShc46oitbWoWvlldRpMQ0N4kUcrzz/72c80adIkrV27Vk8++aSqq6v10EMPqV+/I136li9frtLSUp17rmdN4u12u1asWKHf/OY3ioiI0IIFC7Rv3z5dd9112rhxowYNGtTp5zpw4IDWrl2rtWvXtp+Xbm5ubr9t7dq1zGAGAKCT3Oedx2bEymKhWRjgr2xWi+bPHiXJFZSP5v79/NmjAq7pn3vrNk3D4G0Ww32Qt4ucTqc++ugjlZaWaty4cceMrZKklStXKicnR7Nnz+5S0A1k1dXVio2NVVVVlWJiYswuBwAAj9z23016a2OhbjlvqH5+wTCzywHQgSVbinTf4q3HNA9Li7Vr/uxRmjU6zcTKfOMP72/XX1bs0TWTB+iBr48xuxz4ua5kNI+2bUuS1Wo9ZbOs6dOn00wLAIAAlHugrVlYf847A73BrNFpumBUqtblVaikplEp0a6t2oG24uyW2TauKr+MM8/wLo/D89F27dqlsrIyJSYmatgwvoEGACBQ1Ta1ak9prSRpLJ22gV7DZrUEzDiqjmSybRs+4tGZZ0lqamrSr371KyUlJWnEiBE6++yz9fDDD7ff/8orr2jChAknHDUFAAB6p80HqmQYUr+4PkqKCje7HAA4jnvl+WBVgxpbHCZXg0DiUXhuaGjQ9OnT9cgjjygsLEwXX3yxvnp0esaMGcrJydF///tfrxQKAADMl3NUszAA8EdJUWGKCg+RYUgHGFcFL/IoPD/66KNau3at5s2bp71792rx4sXHXZOenq5Ro0Zp6dKl3S4SAAD4B3en7ez+cabWAQAnY7FYlJkUIUnK49wzvMij8Pzaa69pwIABevbZZ2W3n3yo+vDhw1VQUOBxcQAAwL/kFLiahbHyDMCfHWkaxrlneI9H4TkvL09f+9rXFBJy6n5jYWFhOnz4sEeFAQAA/1JW26TCygZZLNKYfoRnAP6rfdZzOeEZ3uNReO7Tp0+nQnFeXp7i4+M9eQkAAOBn3Fu2ByVFKtoeam4xAHAKrDzDFzwKz+PGjdP69etVWlp60mvy8vL0xRdf6PTTT/e4OAAA4D/cW7Y57wzA37nPPBOe4U0ehecbbrhBNTU1uvrqq1VWVnbc/ZWVlZo3b55aWlp04403drtIAABgvvZmYcx3BuDnjoyramRcFbzGo/B89dVX66qrrtLy5cs1aNAgzZo1S5K0evVqzZ07V5mZmVq1apWuvfZaXXrppV4tGAAA9DzDMJR7gGZhAHqHhMgwRdtd/Zn2V9BxG97hUXiWpFdffVWPPPKI7Ha7PvjgA0nSrl27tHjxYlksFj344IN68cUXvVYoAAAwz4HDDSqva1aI1aKRaTFmlwMAp2SxWI40DWPrNrzk1O2yT8Fisej222/Xbbfdpo0bNyo/P19Op1MZGRk6/fTTFRYW5s06AQCAidyrziPTYmQPtZlcDQB0LDMxUrkHqjj3DK/xODy72Ww2nX766TQGAwAggLnPO7NlG0Bvkdm28pzPuCp4iUfbtmtra5Wbm3vCZmFuZWVlys3NVV0dH1YAAHq7HJqFAehlsto6brNtG97iUXh+4oknNH78eO3Zs+ek1+zZs0fjx4/XU0895XFxAADAfA6noS2F1ZKksf1ZeQbQOwxsn/VMwzB4h0fhefHixRoyZIjOOOOMk15zxhlnaPDgwVqwYIGntQEAAD+wt7RWtU2tigizaWhKtNnlAECnZLWF5+LqRjU0M64K3edReN67d69GjBjR4XUjR45UXl6eJy8BAAD8RE5bs7DR6bGyWS0mVwMAnRMfGabYPqGSpH0VbN1G93kUnhsaGtSnT58Or+vTp49qa2s9eQkAAOAnaBYGoLdqbxrGuWd4gUfhuX///vr88887vO7zzz9Xenq6Jy8BAAD8hHvleWz/OHMLAYAuykp0Nw3j3DO6z6PwPHPmTOXn5+vJJ5886TVPPfWU8vLyNGvWLI+LAwAA5mpudWrbQVezsGxWngH0Mqw8w5s8mvN8xx136J///Kd++ctfatmyZbrxxhs1ePBgSa4u23/729/03nvvKSYmRnfccYdXCwYAAD1ne3G1mh1OxUWEakBChNnlAECXZLWF5zxmPcMLPArPGRkZWrRoka644gq9++67eu+994653zAMJSUl6fXXX9fAgQO9UigAAOh57Vu2M+JksdAsDEDvcmRcFeEZ3edReJakqVOnaseOHXruuee0bNkyFRQUSHKdhz7//PP1gx/8QPHx8V4rFAAA9LzcgkpJbNkG0Du5x1WV1DSprqlVkeEexx/A8/AsSfHx8brjjjvYmg0AQIDKae+0HWdqHQDgidiIUMVHhOpwfYv2lddrVHqM2SWhF/OoYdiePXs6fe3ChQs9eQkAAGCyuqZW7S5xjZxk5RlAb9XeNIxzz+gmj8Lz+PHj9eKLL57ymoaGBt1www26/PLLPSoMAACYa0thlZyGlBZrV0qM3exyAMAj7q3beZx7Rjd5FJ6dTqd+8IMf6Morr1RFRcVx93/++ecaN26c/v73v2vIkCHdLhIAAPS83PZmYaw6A+i9GFcFb/EoPG/cuFETJkzQW2+9pezsbC1dulSSq8v2Aw88oLPPPlu7du3SDTfcoC+++MKrBQMAgJ6xifPOAAIA27bhLR6F52HDhumzzz7T3XffraKiIs2aNUs333yzpk6dqnvvvVdxcXFauHCh/u///k8REcyEBACgN8ptC8/j+seZWgcAdEdmoiuP5JXVm1wJejuPwrMkhYSE6MEHH9TKlSsVFRWlZ599Vp999pkuuOACbd68WbNnz/ZmnQAAoAdV1DWroKJBkjS6H9u2AfRe7pXnstom1TS2mFwNejOPw7MkVVVV6S9/+Yuqq6tlGIYMw9CWLVuUk5PjrfoAAIAJ3KvOg5IiFdsn1NxiAKAbYuyhSowMkyTtK2f1GZ7zODyvWrVKY8eO1WuvvaZx48YpJydHd999t0pKSjRr1izdeuutampq8matAACgh9AsDEAg4dwzvMGj8HzXXXfp/PPPV2FhoW6//XatXbtWY8aM0YMPPqgVK1ZowIAB+vOf/6zTTz9dmzdv9nbNAADAx3IKKiXRLAxAYMhMpOM2us+j8Pzoo48qLS1NS5cu1SOPPKLQ0CPbuc4++2zl5ubqO9/5jrZs2aIzzjjDa8UCAADfMwxDOW0rz9k0CwMQALKSaBqG7vMoPH/zm99Ubm6upk+ffsL7o6Oj9fLLL+u1115Tnz59ulMfAADoYUVVjSqrbVKI1aLT0mPMLgcAuo1t2/CGEE8e9J///KdT133jG9/QlClTPHkJAABgEnezsGF9o2UPtZlbDAB4Adu24Q0eheev2rVrl8rKypSYmKhhw4Ydc196ero3XgIAAPSQTQXuLds0CwMQGNwrz+V1zapubFGMnSkC6DqPu203NTXpV7/6lZKSkjRixAidffbZevjhh9vvf+WVVzRhwgRt2rTJG3UCAIAe4l55zqZZGIAAERUeoqSocEnSPs49w0MeheeGhgZNnz5djzzyiMLCwnTxxRfLMIxjrpkxY4ZycnL03//+1yuFAgAA33M6DW1uH1MVZ24xAOBF7U3DOPcMD3ncbXvt2rWaN2+e9u7dq8WLFx93TXp6ukaNGqWlS5d2u0gAANAz8srrVNPUKnuoVcP6RpldDgB4Deee0V0ehefXXntNAwYM0LPPPiu73X7S64YPH66CggKPiwMAAD3LPd/5tPRYhdg8Pt0FAH6nveM24Rke8uhfxby8PH3ta19TSMip+42FhYXp8OHDHhUGAAB6Xq57vjNbtgEEmKy28My2bXjKo/Dcp0+fToXivLw8xcfHe/ISAADABDnuZmF02gYQYAYmus48s/IMT3kUnseNG6f169ertLT0pNfk5eXpiy++0Omnn+5xcQAAoOe0OJz68mC1JJqFAQg87jPPh+tbVFXfYnI16I08Cs833HCDampqdPXVV6usrOy4+ysrKzVv3jy1tLToxhtv7HaRAADA93YU16i51akYe4gy21ZoACBQRIaHKCXaNa4qn63b8IBH4fnqq6/WVVddpeXLl2vQoEGaNWuWJGn16tWaO3euMjMztWrVKl177bW69NJLvVowAADwDfeW7bEZcbJYLOYWAwA+0N40jPAMD3jcRvPVV1/VI488Irvdrg8++ECStGvXLi1evFgWi0UPPvigXnzxRa8VCgAAfCu3oK1ZGOedAQSorLat23mce4YHTt0u+xQsFotuv/123Xbbbdq4caPy8/PldDqVkZGh008/XWFhYd6sEwAA+NjRK88AEIgYV4Xu8Dg8u9lsNp1++uk0BgMAoBerb27VrpJaSYypAhC43P0c8srrTa4EvZHH27YBAEDg+PJgtRxOQynR4UqNtZtdDgD4BCvP6A7CMwAAUE5BpSQpu3+cqXUAgC+5x1VVNbTocF2zydWgtyE8AwAA5R5oaxaWQbMwAIGrT5hNqTGu3TV03EZXEZ4BAIByaRYGIEhkJrnOPROe0VWEZwAAglxlfbPy25rnjGXlGUCAy0pyj6uiaRi6hvAMAECQc2/ZzkyMUFwEoyYBBDb3uWeahqGrCM8AAAQ5tmwDCCYD3eGZbdvoIsIzAABBLqdt5Zkt2wCCwZFt23UyDMPkatCbhHTmIpvN5vELWCwWtba2evx4AADgW4ypAhBMBia6GobVNLbqcH2LEiI5roLO6VR47t+/vywWyzG3GYah/fv3t/8+Li5OklRZWdl+24ABA457HAAA8B/FVY0qqWmSzWrRaekxZpcDAD5nD7UpPdaug1WNyiurIzyj0zq1bTs/P195eXntv3bu3Kns7Gylp6fr2WefVWVlpSoqKlRRUaGqqir99a9/Vb9+/ZSdna1du3b5+j0AAAAP5bSddx6aEqWIsE59pw4AvV5mEk3D0HUenXl+8MEHtWzZMn300Uf64Q9/qJiYI99UR0dH68Ybb9SqVau0dOlS/e53v/NasQAAwLvczcKyaRYGIIi0h2eahqELPArPr7zyimbMmKFBgwad9JpBgwbpvPPO06uvvupxcQAAwLdyCtqahfWnWRiA4JGVeKRpGNBZHoXnwsJChYeHd3hdeHi4Dh486MlLAAAAHzMMg5VnAEHJ3TSMlWd0hUfhOS0tTStWrDimOdhXHT58WMuXL1dqaqqntQEAAB/KL69XdWOrwkKsGp4abXY5ANBjstrPPNczrgqd5lF4vvrqq1VRUaELLrhAH3300XH3f/zxx5o5c6YqKyv1ne98p9tFAgAA73OvOp+WHqNQm0c/EgBAr9Q/IUIWi1Tb1Kryumazy0Ev4VFbzd/85jf6+OOP9cknn+jcc89VSkqKBg4cKEnat2+fSkpKZBiGpkyZonvuucerBQMAAO9wn3dmyzaAYOMaV9VHhZUNyi+rU1JUx0dSAY++Zrbb7Vq2bJkeeughZWRk6NChQ1q3bp3WrVunQ4cOKSMjQ7///e+1fPly2e12b9cMAAC8wD2mamwGzcIABB/31m2ahqGzPB7oGBoaqjvvvFN33nmnCgoK2huDpaWlacCAAV4rEAAAeF+rw6kvD7atPPePM7cYADBBZlKEPtlN0zB0nsfh+Wj9+/dX//79vfFUAACgB+w8VKvGFqeiw0PaR7YAQDDJTDzSNAzojG6H55ycHK1bt05lZWU67bTTNGfOHElSU1OTmpqaFBMT0+0iAQCAd7mbhY3JiJXVajG3GAAwQSazntFFHrfW3LFjh8466yxNmDBBP/rRj3TPPfdowYIF7ff/61//Unx8vJYsWeKNOgEAgBcdOe8cZ2odAGCWTPe4qvI6xlWhUzwKzwUFBTrnnHO0Zs0azZ49W48++uhxH7hvfvObCgsL05tvvumVQgEAgPe4O22P60+zMADBaUBChKwWqb7ZodLaJrPLQS/gUXj+3e9+p7KyMj3//PNasGCBfvGLXxx3TWRkpMaNG6e1a9d2u0gAAOA9jS0O7ThUI4mVZwDBKyzEqn7xfSRx7hmd41F4XrJkicaOHat58+ad8rrMzEwVFhZ6VBgAAPCNLw9Wy+E0lBQVrrRYRkoCCF5HmoZx7hkd8yg8l5SUaPjw4R1e19LSovp6vsUBAMCf5BRUSpKyM2JlsdAsDEDwap/1zLgqdIJH4TkxMVH79+/v8LqdO3cqLS3Nk5cAAAA+kkuzMACQxMozusaj8DxlyhR9/vnn2rRp00mvWbVqlbZs2aLp06d7WBoAAPCF3AOuZmHZNAsDEOQykyIkMa4KneNReP7lL38pwzA0d+5cvffee3I4HMfcv3z5cl177bUKCQnRrbfe6o06AQCAF1Q1tGhv2w+JrDwDCHbuled95fWMq0KHPArPZ5xxhv70pz/p4MGDuvTSSxUXFyeLxaI333xT8fHxuuCCC3Tw4EE9/fTTGjt2rLdrBgAAHtrcturcP6GPEiLDTK4GAMzVPyFCNqtFDS0OldQwrgqn5lF4lqSf/OQn+vjjjzV79mxZLBYZhqGamho1NTVp5syZWrVqlW688UZv1goAALoph/POANAu1GZVRtu4KrZuoyMh3Xnw5MmTtWDBAhmGobKyMjmdTiUlJclms3mrPgAA4EXuZmHjCM8AIMm1dXtfeb3yy+o0eVCi2eXAj3m08vzRRx9p586d7b+3WCxKTk5W3759jwnOu3bt0kcffdT9KgEAgFe4m4WNzaBZGABIjKtC53kUnqdPn65HHnmkw+seffRRnXvuuZ68BAAA8LKS6kYVVTXKapFG9yM8A4AkZSa6Om4zrgod8fjMc2e60dGxDgAA/5HTtuo8JCVKkeHdOrkFAAFjYJJ71nO9yZXA33kcnjvj4MGDioqK8uVLAACATnKfd87mvDMAtMtqG1eVX14np5PFP5xcp792fvnll4/5/e7du4+7za21tVU7duzQ0qVLNXny5O5VCAAAvMK98jy2f5y5hQCAH8mI76MQq0VNrU4dqmlUWmwfs0uCn+p0eL7uuutksVgkuRqErV69WqtXrz7p9YZhyG6369577+1+lQAAoFsMwzhq5ZnzzgDgFmKzqn9ChPLK6pRXVkd4xkl1Ojzfe++97fOcf/e732ncuHGaO3fuCa8NCwtTenq6LrzwQqWlpXmtWAAA4Jn9FfWqrG9RmM2qEakxZpcDAH4lM9EVnvPL6nXWYLOrgb/qdHj+7W9/2/6/X3rpJZ1//vmaP3++L2oCAABe5t6yPTItWmEhPm15AgC9TmZSpLSjVPmMq8IpeNRqMz8/38tlAAAAX8otqJQkZXPeGQCO0z7rmXFVOAW+egYAIAjkupuF0WkbAI4z0N1xm/CMU+jWkMdPPvlECxcu1K5du1RTU3PCuc4Wi0XLli3rzssAAIBuaHU4tbnQFZ5pFgYAx3OPq9pXUS+n05DVajG5Ivgjj8KzYRj6/ve/r3/84x/tgdndTMzN/Xt3h24AAGCO3aW1amhxKDLMpkHJUWaXAwB+Jz3OrlCbRc2tThVVN6pfHB23cTyPtm3/9a9/1UsvvaSJEyfqww8/1OWXXy5J2rFjh9577z1dd911slqtuv3227V3716vFgwAALomt8C16jwmI1Y2VlMA4DjucVUSW7dxch6F55deekmRkZF67733dN555yk6OlqSNHToUM2cOVMvvPCC/v3vf+uxxx7Tpk2bvFkvAADoopz2+c5xptYBAP7MvXWbpmE4GY/C87Zt23TWWWcpMTFRktq3ZjscjvZrrrzySk2cOFGPPfaYx8U1NDTo3nvv1bBhw2S325Wenq558+apsLCwS8+zatUq3XfffbrkkkuUnJwsi8WizMzMUz7mpZde0lVXXaWRI0cqISGhfXb1lVdeqdWrV3v8ngAA6Gnu8EyzMAA4ucwkmobh1Dw68+x0OtuDsyRFRLi2OBw+fFhJSUnttw8dOlTvvPOOR4U1NjZqxowZWrNmjdLS0jR37lzl5+frxRdf1Ntvv601a9Zo0KBBnXquW265RTk5OV16/aefflo5OTkaM2aMzj77bNntdu3YsUNvvvmm3nrrLT3zzDP60Y9+5MlbAwCgxzS2OLS9qEaSNJZmYQBwUu3hmVnPOAmPVp779eungwcPtv9+4MCBkqQvvvjimOt27typkBDPGno/8MADWrNmjc4880zt3LlTr732mtauXavHH39cpaWlmjdvXqef68ILL9QDDzyg999/X19++WWnHvOXv/xFFRUV2rhxoxYtWqT//ve/ysnJ0cKFC2W1WvXzn/9cZWVlHr03AAB6yraiarU6DSVGhikjngY4AHAymYmuBUG2beNkPArPEyZM0NatW9u3aV944YUyDEN33HGHtm/frpqaGv3hD3/Qhg0bNH78+C4/f3Nzs55++mlJrhAbFXWkM+htt92msWPHatWqVdqwYUOnnu/RRx/Vr3/9a1144YVKSEjo1GPOOOOM9rPcR5szZ46mT5+uxsZGffrpp516LgAAzHJkvnMsEzAA4BQy2848F1Q0yOE8fgQv4FF4njNnjsrKytq3ZGdnZ+uqq65STk6OTjvtNMXFxemuu+5SSEiIHnzwwS4//+rVq1VVVaXBgwefMHxfeeWVkqTFixd7Un63hYaGSpLCwsJMeX0AADqL884A0DnpcX0UZrOq2eHUwcoGs8uBH/JoT/XVV1+tyy+//Jgt2f/4xz80duxYLViwQIcPH9awYcN0xx13aNKkSV1+fvf55AkTJpzwfvftubm5HlTfPcuWLdPy5csVHx+vyZMn9/jrAwDQFTkFlZKk7P6cdwaAU7FZLRqQGKHdJbXKL69rH10FuHl2IFlSeHj4Mb8PDQ3VXXfdpbvuuqvbRe3fv1+SlJGRccL73bfv27ev26/VkRdffFGrVq1SY2Oj9uzZo/Xr1ys2Nlb//ve/FRcX5/PXBwDAUzWNLdrbdnaPlWcA6FhmYqQrPJfVaerQZLPLgZ/xKDxPmDBBgwcP1uuvv+7teiRJtbW1ko508f6qyEjXeYSamhqfvP7RVq9erX/84x/tv09ISNBzzz2nmTNndvjYpqYmNTU1tf++urraJzUCAHAimwurZBhSv7g+SooK7/gBABDkspLcTcPqTa4E/sijM887duxoP/cb6J5//nkZhqGamhqtX79e559/vq644grdeOONHT72oYceUmxsbPuv/v3790DFAAC4uJuFsWUbADqHcVU4FY/C89ChQ1VeXu7tWtq5u2vX15/4G5+6OteH+UTdsH1Z08SJE/Xaa69pzpw5eu655/Tmm2+e8jF33323qqqq2n8VFBT0ULUAABw578yWbQDoHHfH7XzGVeEEPArP3//+97Vq1Spt377d2/VIkgYMGCBJOnDgwAnvd9/uni/d06655hpJ0sKFC095XXh4uGJiYo75BQBATzl6TBUAoGPulef9FfVqdThNrgb+xqPw/NOf/lTXXXedpk2bpieffFK7d+9Wc3Oz14rKzs6WJG3cuPGE97tvHzt2rNdesyuSkpIkSaWlpaa8PgAAHSmrbVJhZYMsFmlMP8IzAHRGWoxd4SFWtToNHaxsNLsc+BmPwrPNZtNzzz2n0tJS/fKXv9Tw4cPVp08f2Wy2434dPc6qs6ZMmaLY2Fjt2bNHmzZtOu7+N954Q5I0e/ZsT8rvtlWrVkmSBg8ebMrrAwDQkdy2+c6Dk6MUbQ+OPiUA0F1Wq0UDE9uahnHuGV/hUXju37+/BgwYoIEDB2rAgAGn/OVJk6ywsDDdfPPNkqSbbrqp/YyzJD3xxBPKzc3VtGnTNHHixPbbn376aY0YMUJ33323J2/pGNu2bdN///vf41bTDcPQf/7zHz366KOyWCz63ve+1+3XAgDAFzYVsGUbADzBuWecjEejqvLz871cxvHuueceLV26VJ9++qmGDh2qqVOnat++fVq7dq2Sk5P1wgsvHHN9WVmZduzYoaKiouOe6/nnn9fzzz8vSWppaZEkFRUVafLkye3XPPPMM5owYYIk6dChQ/rWt76l2NhYTZw4UampqaqsrNTWrVuVn58vq9WqJ554Qqeffrqv3j4AAN3iXnnOplkYAHRJVtu55zzCM77Co/DcE+x2u1asWKGHHnpI//rXv7RgwQIlJCTouuuu0/3336+MjIxOP9eBAwe0du3aY25rbm4+5rajZzCfdtpp+t3vfqeVK1dq586dWr16taxWqzIyMjRv3jzddNNN7UEbAAB/YxjGUWOq4swtBgB6GcZV4WQshmEYZhcRLKqrqxUbG6uqqio6bwMAfKagol5TH12hUJtFW+6bqfAQm9klAUCv8emeMn37ubXKTIzQytvPNbsc+FhXMppHK88vv/xyp64LCwtTYmKisrOzlZKS4slLAQCALspp27I9IjWG4AwAXeTetl1wuEEtDqdCbR61iUIA8ig8X3fddbJYLJ2+3mKx6Pzzz9ef//xnDR061JOXBAAAncR8ZwDwXN9ou+yhVjW2OFV4uKF9GzfgUXi+9957lZ+fr5dffllRUVG68MILNWDAAElSQUGBPvjgA9XU1Ojaa69VeHi4Pv30U33wwQeaOnWqNmzYoH79+nn1TQAAgCNyCiolcd4ZADxhtVqUmRip7cU1yiuvIzyjnUfh+dprr9WkSZM0b948Pf7444qNPfab7erqat1222363//+p7Vr12rQoEG6/fbb9eSTT+rhhx/Wn//8Z68UDwAAjuVwGtpS2NYsjE7bAOARd3jOL6uThptdDfyFRxv47777bsXHx+tvf/vbccFZkmJiYvS3v/1N8fHx+tWvfiWr1aqHHnpIaWlpWrJkSbeLBgAAJ7a3tFZ1zQ5FhNk0JCXK7HIAoFdq77jNuCocxaPwvGLFCp1xxhmyWk/+cKvVqkmTJmn58uWSXM3DsrOzVVhY6FmlAACgQ5vatmyPTo+Vzdr5/iQAgCOykiIkSXnl9SZXAn/iUXiur69XcXFxh9cdOnRIjY2N7b+PiYlRSIjfjpYGAKDXo1kYAHTfwERWnnE8j8LzmDFj9NFHH+mjjz466TUff/yxVq1apTFjxrTfVlBQoOTkZE9eEgAAdEJu25gqmoUBgOfc46oOHK5Xc6vT5GrgLzwKz3fccYccDodmzpypH/7wh/rwww+1fft2bd++XR9++KF+9KMfaebMmTIMQ3fccYckqaqqShs2bNDkyZO9+gYAAIBLU6tD24pqJNEsDAC6IyU6XBFhNjkNV4AGJA+7bV9++eV68skndeedd+q5557T888/f8z9hmEoLCxMTz75pC677DJJUnl5ue677z6dd9553a8aAIBucDgNrcurUElNo1Ki7ZqUldDrzwc7nIZeX39AzQ6nosJtSo+zm10SAPRaFotFAxMjta2oWvnldRqUTANGSBbDMAxPH5yXl6e///3v+vTTT1VUVCRJSktL05QpU3T99ddr0KBBXis0EFRXVys2NlZVVVWKiYkxuxwACEpLthTpvsVbVVR1pCdHWqxd82eP0qzRaSZW5rlAfE8AYLafvLpB724u1m8uHaXvn51ldjnwka5ktG5178rKytIDDzzQnacAAKDHLNlSpB+/slFf/da4uKpRP35lo569ZkKvC5uB+J4AwB9k0jQMX0HrawBAUHA4Dd23eOtxIVOSDEkWSb9d9KXOGJTYa7ZwO5yG5i/68pTv6b7FW3XBqNRe854AwF+0z3ouJzzDpVvheevWrXruuee0bt06lZWVae7cuXr00UclSZ9++qnWr1+va665RgkJCV4pFgAAT63LqzhmW/NXGZKKq5s0/ncf9lxRPmZIKqpq1Lq8Cp05ONHscgCgV3GvPOex8ow2HofnJ554QnfddZdaW1sluQ7Vl5WVHXPNz3/+c4WHh+uHP/xh96oEAKCbSmpOHpwDXTC/dwDwVGZShCTpYGWDmlodCg+xmVwRzOZReH7nnXf0y1/+UllZWXr88cd19tlnKyUl5ZhrzjrrLCUnJ2vhwoWEZwCA6VKiO9d9+uV5p+uMQb1jlXbt3nJ994XPO7yus+8dAHBEclS4IsNsqmt2qKCiQUNS6Lgd7DwKz0888YQiIyP14YcfnrKj9rhx47Rjxw6PiwMAwFsmZSUoLdZ+0q3bFkmpsXZNGZLca84HTxmSrLRYu4qrGk947tn9niZlcXwKALrKYrEoMylSXx6sVn5ZHeEZsnryoA0bNmjy5MkdjqJKSkpScXGxR4UBAOBNNqtFd84accL73FF5/uxRvSY4S673NH/2KElH3oNbb31PAOBPaBqGo3kUnpubmxUdHd3hdSUlJQoJoaE3AMA/FFY2SNJxYTI11t5rRzrNGp2mZ6+ZoNTYY7dm9+b3BAD+IoumYTiKR8k2KytLOTk5p7ymublZubm5GjZsmEeFAQDgTTWNLfrbR3slSY9eMUbpcREqqWlUSrRrW3NvXp2dNTpNF4xK1bq8ioB5TwDgD1h5xtE8WnmeM2eO8vPz9cQTT5z0mkcffVSlpaW6/PLLPS4OAABveeGTfFU1tGhwcqS+Pj5DZw5O1Nxx/XTm4N4z1/lUbFZLwL0nADBbZqKr43Z+Wb3JlcAfeLTyfMcdd+jVV1/V7bffrrVr1+qyyy6TJB06dEj/+9//9L///U+vvvqqsrKydPPNN3u1YAAAuqqqvkXPf+Jadb71/GEESwBAp7hXng9WNaixxSF7KOOqgplH4Tk+Pl5Lly7VlVdeqddff11vvPGGJGnJkiVasmSJDMPQqFGjtGDBgk6djQYAwJee/2SvahpbNbxvtC4ZwxlgAEDnJEaGKTo8RDVNrSqoqNfQvmSbYOZxN69hw4Zp06ZNWrx4sT744APl5+fL6XQqIyNDF1xwga644grZbHwzAwAwV0Vds174JE+S9PMLhsrKqjMAoJPc46o2F1Ypr6yO8BzkutUK22q1au7cuZo7d6636gEAwKv+76M9qmt26LT0GM08LdXscgAAvYw7PNM0DB41DJs3b55eeOGFDq976aWXNG/ePE9eAgCAbiutadLLn+6TJN12wTBZLKw6AwC6JqutaVgeTcOCnkfh+aWXXtInn3zS4XWrV6/WP/7xD09eAgCAbvvrqj1qaHEou3+cZoxIMbscAEAv1D6uilnPQc+j8NxZzc3NnHsGAJjiUHWjXlnDqjMAoHsGJjLrGS4+C8+GYWjjxo1KTk721UsAAHBSz6zYraZWp742MF7nDE0yuxwAQC+V1bbyXFTVqIZmh8nVwEydbhg2Y8aMY36/ZMmS425za21t1Z49e1RcXKxrr722exUCANBFhZUN+ve6AkmsOgMAuic+IlQx9hBVN7Zqf0W9hqfScTtYdTo8r1y5sv1/WywWFRcXq7i4+KTXh4aG6tJLL9Vjjz3WrQIBAOiqp5fvVrPDqcmDEnTWEFadAQCes1gsykqKVM4B17gqwnPw6nR4zstzzcg0DEODBg3SlVdeqT/84Q8nvDYsLExJSUkKDQ31TpUAAHRSQUW9Xl/vXnUebnI1AIBAkNkWnjn3HNw6HZ4HDhzY/r/nz5+v8ePHH3MbAAD+4E/LdqnVaWjq0CRNykowuxwAQADITKTjNroQno82f/58b9cBAEC35ZXV6a0vCiW5zjoDAOAN7qZheYTnoOZReD7a/v37VVRUpKamppNec84553T3ZQAA6NCflu2Sw2loxogUjR8Qb3Y5AIAAMTAxQhLjqoKdx+H5hRde0P3336/9+/d3eK3DQUt3AIBv7S6p0YJNrlXnn5/PqjMAwHvcK8+HqptU39yqiLBur0GiF/Lov/qLL76oH/zgB5Kk0aNHa9iwYYqOpuscAMA8Ty7dJcOQLhzVV2MyYs0uBwAQQOIiwhQXEarK+hbtK6/XyLQYs0uCCTwKz0888YRCQkL0xhtvaM6cOd6uCQCALtleXK13coskST/nrDMAwAcyEyO1qb5S+WV1hOcgZfXkQbt27dI555xDcAYA+IUnP9wpSbpkTBo/0AAAfKK9aRjnnoOWR+E5ISFBSUlJ3q4FAIAu21JYpfe/PCSLRbr1/KFmlwMACFCMq4JH4Xnu3LlavXq1WlpavF0PAABd4l51npudrqF96b8BAPCNzKS2jttl9SZXArN4FJ5///vfKzIyUtdff70OHz7s7ZoAAOiUL/Yf1rLtJbJapJ+dx6ozAMB33CvPbNsOXh41DPvFL36hUaNG6d///rfeeecdTZw4URkZGbJaj8/iFotFf//737tdKAAAX/VE26rz5RMyNCg5yuRqAACBLLPtzHNpTZNqm1oVFc64qmDj0X/xl156qf1/V1VVafny5Se9lvAMAPCFz/Mr9PGuMoVYLfrZDFadAQC+FdsnVAmRYaqoa9a+8jqdls5YxGDjUXhesWKFt+sAAKBLnvjAter8ja9laEBihMnVAACCQWZihCrqmpVfVk94DkIehedp06Z5uw4AADrt0z1l+mxvucJsVt3MqjMAoIdkJkVq4/5K5XPuOSh51DAMAACzGIbR3mH7qkn91S+uj8kVAQCCRZa7aRjjqoIS4RkA0Kt8vKtMn+cfVliIVT+ZPsTscgAAQcTdNIxZz8GpU9u2Bw0aJIvFoqVLlyorK0uDBg3q9AtYLBbt2bPH4wIBAHAzDEOPt606X3PGQKXG2k2uCAAQTNzjqti2HZw6FZ7z8/MlSS0tLcf8HgCAnrRiR4lyCiplD7Xqx9MHm10OACDIZCa5GlSW1TarprFF0fZQkytCT+pUeHY6naf8PQAAvmYYRvtc5++dmank6HCTKwIABJtoe6iSosJUVtusfeX1Gt2PjtvBhDPPAIBe4f0vD2lLYbUiw2z64TRWnQEA5sikaVjQIjwDAPye02noj0tdq87XT8lSQmSYyRUBAIIVTcOCF+EZAOD33t1SpO3FNYoOD9EPpmaZXQ4AIIhltYXnPJqGBR3CMwDArzmchv64dJck6ftTsxQXwaozAMA87R23WXkOOoRnAIBfW5xzULtLahXbJ1TzzmbVGQBgroGJro7b+eX1JleCnkZ4BgD4rVaHU08tc60633jOIMUwEgQAYDL3meeKumZVNbSYXA16EuEZAOC33vqiUHlldUqIDNN1Z2WaXQ4AAIoKD2kfl7iPc89BhfAMAPBLLQ6n/tS26vyjaYMUGR5ickUAALhkMa4qKBGeAQB+6fX1B3TgcIOSosJ17eRMs8sBAKBdZlLbuecyzj0HE8IzAMDvNLU69PRy16rzT6YPVp8wm8kVAQBwRPusZ7ZtBxXCMwDA77z2eYEOVjUqNcaub58xwOxyAAA4Btu2gxPhGQDgVxpbHHp6+W5J0k0zhsgeyqozAMC/DExk5TkYEZ4BAH7llTX7VFLTpH5xffTNr2WYXQ4AAMdxn3murG9RZX2zydWgpxCeAQB+o765VX9dtUeS9NMZQxQewqozAMD/RISFqG+Ma1xVfjlNw4IF4RkA4Dde/myfymqbNSAhQldMZNUZAOC/Mt1btzn3HDQIzwAAv1Db1Kr/a1t1/tl5QxVq458oAID/ykqiaViw4ScTAIBfeGl1ng7Xt2hQUqS+Pi7d7HIAADglxlUFH8IzAMB0VQ0t+ttHeyVJt5w/VCGsOgMA/FxmoqtpGNu2gwc/nQAATPf3T/JU3diqoSlRunQsq84AAP+XedS2bcMwTK4GPYHwDAAwVWV9s174JE+S9PMLhslmtZhcEQAAHRuY4ArP1Y2tOlzfYnI16AmEZwCAqf720V7VNrVqZFqMZp2WanY5AAB0Sp8wm9Ji7ZI49xwsCM8AANOU1zbppU/zJUk/P3+orKw6AwB6EcZVBRfCMwDANP/30V7VNzs0NiNWF4zqa3Y5AAB0SXvHbcJzUCA8AwBMUVLTqJc/y5fkOutssbDqDADoXbKSXB2388rrTa4EPYHwDAAwxTMr9qixxanxA+I0fViy2eUAANBlA9m2HVQIzwCAHldU1aB/rdsvSfrFBcNZdQYA9EpZR23bZlxV4CM8AwB63F9W7FZzq1OTshI0ZUii2eUAAOCRAQkRslikmqZWldc1m10OfIzwDADoUQcO1+u1zwskSbdx1hkA0IvZQ21Kj+0jSdrHuKqAR3gGAPSop5fvVovD0JQhiZo8iFVnAEDvluluGlZG07BAR3gGAPSYfeV1en3DAUmuVWcAAHo7Zj0HD8IzAKDHPLVslxxOQ9OGJWviwASzywEAoNvcTcPy2LYd8AjPAIAesae0Vgu+KJTEqjMAIHAwrip4EJ4BAD3iqaW75DSk80f2VXb/OLPLAQDAK7LazjwzrirwEZ4BAD63o7hGi3MPSpJ+fsFQk6sBAMB7+idEyGqR6podKq1tMrsc+BDhGQDgc08t2ynDkC4anarT0mPNLgcAAK8JD7EpPc49roqO24GM8AwA8KkvD1bp3c3FslikW8/nrDMAIPC0Nw3j3HNAIzwDAHzqyQ93SZIuHZuu4anRJlcDAID3Ma4qOBCeAQA+k3ugUku3HZLVIt16PmedAQCBKbNt5TmfcVUBjfAMAPCZJz7cKUn6+vh+GpwcZXI1AAD4Rmaiq+N2XhlnngMZ4RkA4BMb9h3Wyh2lslkt+tkMVp0BAIHLvfK8r5xxVYGM8AwA8Ikn21adr5yQ0f5DBQAAgah/vGtcVX2zQ6U1jKsKVIRnAIDXrd1brk92lynUZtHNM4aYXQ4AAD4VFmJVRrx76zbnngMV4RkA4FWGYejxtlXnb36tv/onRJhcEQAAvkfTsMBHeAYAeNWne8q1Lq9CYSFWVp0BAEEji6ZhAc+vw3NDQ4PuvfdeDRs2THa7Xenp6Zo3b54KCwu79DyrVq3Sfffdp0suuUTJycmyWCzKzMw86fUtLS364IMPdPPNN2v06NGKiIhQnz59NHLkSP3yl79UaWlpN98ZAAQmwzD0+Ac7JEnfnjRAabF9TK4IAICe0b7yzLbtgBVidgEn09jYqBkzZmjNmjVKS0vT3LlzlZ+frxdffFFvv/221qxZo0GDBnXquW655Rbl5OR0+rVXrVqlmTNnSpIyMzN10UUXqaWlRZ999pkef/xxvfrqq1q5cqWGDx/u0XsDgEC1cmepNu6vVHiIVT+ZPtjscgAA6DGZiWzbDnR+u/L8wAMPaM2aNTrzzDO1c+dOvfbaa1q7dq0ef/xxlZaWat68eZ1+rgsvvFAPPPCA3n//fX355ZcdXm+1WvXNb35Ta9euVV5ent58800tWrRIu3fv1syZM1VcXKzrr7++O28PAAKOYRjtHba/e+ZApcTYTa4IAICec/SZZ6eTcVWByGL44SCy5uZmpaSkqKqqShs3btT48eOPuT87O1u5ublav369Jk6c2KXnLi4uVlpamgYOHKj8/Pwu13bw4EH169dPkpSfn6+BAwd2+rHV1dWKjY1VVVWVYmJiuvzaAODPPtx6SDe8vF4RYTZ9fMe5SowKN7skAAB6TIvDqRG/WSKH09Cau89TaixfIvcGXclofrnyvHr1alVVVWnw4MHHBWdJuvLKKyVJixcv7unSlJ6eruTkZEmuIA0AkJxOQ0+0rTp/76xMgjMAIOiE2qzqH+/q9cG4qsDkl+HZfT55woQJJ7zffXtubm6P1eRWWVmpw4cPS5JSU1N7/PUBwB8t+bJY24qqFRUeohundq4fBQAAgYZxVYHNL8Pz/v37JUkZGRknvN99+759+3qsJre//OUvam1t1ZgxY5SVldXjrw8A/sbhPHLWed7ZWYqPDDO5IgAAzNHeNIyV54Dkl922a2trJUkREREnvD8y0vWhrKmp6bGaJOmLL77QAw88IEl65JFHOry+qalJTU1N7b+vrq72WW0AYJa3cw9qV0mtYuwh+v7ZfKkIAAheWW0rz2zbDkx+ufLsjw4dOqTLL79cjY2NuvXWW3XRRRd1+JiHHnpIsbGx7b/69+/fA5UCQM9pdTj11NJdkqQbpg5SbJ9QkysCAMA8AxNdi39s2w5Mfhmeo6KiJEn19fUnvL+uzvVhjI6O7pF6ampqdPHFFys/P1/f+MY39Pjjj3fqcXfffbeqqqrafxUUFPi4UgDoWQs3HdTesjrFRYTqeladAQBBzr3yvK+8nnFVAcgvt20PGDBAknTgwIET3u++vStjojzV2NioOXPmaOPGjbrwwgv1yiuvyGrt3HcO4eHhCg+n4yyAwNTicOqpZa5V5x+eM1hR4X75TwoAAD2mX1wfhVgtamp1qri6UelxfcwuCV7klyvP2dnZkqSNGzee8H737WPHjvVpHa2trfrWt76llStX6qyzztJbb72lsDAa4QCAJL254YD2V9QrKSpM3zvL919mAgDg70JsVg1IaNu6zbnngOOX4XnKlCmKjY3Vnj17tGnTpuPuf+ONNyRJs2fP9lkNhmHo+uuv16JFizRu3Di988477Y3KACDYNbc69efluyVJP5o2WBFhrDoDACAdGVeVx7nngOOX4TksLEw333yzJOmmm25qP+MsSU888YRyc3M1bdo0TZw4sf32p59+WiNGjNDdd9/tlRpuvfVWvfLKKxoxYoQ++OADxcXFeeV5ASAQvLa+QIWVDUqJDtc1k1l1BgDAjXFVgctvlwruueceLV26VJ9++qmGDh2qqVOnat++fVq7dq2Sk5P1wgsvHHN9WVmZduzYoaKiouOe6/nnn9fzzz8vSWppaZEkFRUVafLkye3XPPPMM5owYYIkaeHChfrTn/4kSerfv79uv/32E9Z41113acSIEd1/swDQizS2OPSXtlXnm84dInuozeSKAADwH1lJrm3beWUnbn6M3stvw7PdbteKFSv00EMP6V//+pcWLFighIQEXXfddbr//vuVkZHR6ec6cOCA1q5de8xtzc3Nx9x29Azmw4cPt//vDz/88KTPe9111xGeAQSdf6/br+LqRqXF2nXVJEbwAQBwtIHulWe2bQcci2EY9FDvIdXV1YqNjVVVVZViYmLMLgcAuqyh2aGpj65QWW2THrxstL5zBlu2AQA4WkFFvaY+ukJhNqu23T9LNqvF7JJwCl3JaH555hkA4J/+uSZfZbVNyojvo29MZNUZAICvSo/rozCbVc0Op4qqGswuB15EeAYAnJLDaeizPeX67/qC9g7bPztvqMJC+CcEAICvslkt6p/gmu+cz7nngOK3Z54BAOZbsqVI9y3eqqKqxvbbbFaLIsNoEgYAwMlkJUVqT2md8srrdPbQJLPLgZcQngEAJ7RkS5F+/MpGfbUxhsNp6OZ/fSGb1aJZo9NMqQ0AAH/GuKrAxJ47AMBxHE5D9y3eelxwPtp9i7fK4aTnJAAAX5WZRHgORKw8AwAkSXVNrdpxqEbbi2q0akfJMVu1v8qQVFTVqHV5FTpzcGLPFQkAQC/gXnnOY1xVQCE8A0CQcTgN5ZfXaUdxjbYXVWtbcY12FNdof0XXm5qU1Jw8YAMAEKwykyIkucZWtTqcCrGx4TcQEJ4BIICV1zZpe3GNthVVu8JycY12HqpRU6vzhNf3jQnX8NQYxdpDtDi3qMPnT4m2e7tkAAB6vfTYPgoLsaq51amiqkb1T4gwuyR4AeEZAAJAY4tDu0tqtb1tNXnHoRptK6pRWW3TCa/vE2rTsNRojegbrRFp0RqRGqMRqdGKjwyT5FqdXr/vsIqrGk947tkiKTXWrklZCb57UwAA9FJWq0UDEyK0q6RWeWV1hOcAQXgGgF7EMAwdONyg7cU12lF8ZMt1XlndCZt3WSzSwIQIjUiN0fDUaI1sC8oDEiJktVpO+jo2q0XzZ4/Sj1/ZKIt0TIB2P2r+7FGyneI5AAAIZplJkdpVUqv88jqdo2Szy4EXEJ4RkBxOQ+vyKlRS06iUaNfqGD/koyd487NX3djSfi55u3vLdXGNappaT3h9XESoRqS6wvHItGgNT43RsL5Rigjz7K/6WaPT9Ow1E46b85waa9f82aMYUwUAwClktXXczqPjdsAgPCPgLNlSdNwP+2n8sI8e4Olnr8XhVF5Z3ZEt121BubCy4YTXh9osGpIS3RaUozUizbXlOiU6XBaLd78kmjU6TReMSuXLKAAAuohZz4GH8IyAsmRLkX78ysbjzmgWVzXqx69s1LPXTCBAwyc689mbeVqqSmua2rZaV2t7kSsk7y6pVbPjxA280mPtGpHm2nLtXlUelByp0B7s2mmzWhhHBQBAF2Umus4555d3fZoF/BPhGQHD4TR03+KtJ2xuZMh1TvO+xVt1wahUVs3gVR199iTpZ//epIgwqyobTrzlOjLM5grIbavII1JjNLxvtGIjQn1WNwAA8J3Mtm3bjKsKHIRnBIx1eRXHbJf9KkNSUVWj1uVVsIoGr+rosydJzQ6nmhucslpc/5iObOtu7WriFaN+cX1O2cALAAD0LqkxdoWHWNXU6lRhZYMGtm3jRu9FeEbAKKk5dXjp6nVAZ3X2M/XLC4frB1OzZA+1+bgiAABgNqvVoszESO045JqKQXju/dg7gIBQ39yqT/eUd+ralGi7j6tBsOnsZ2riwHiCMwAAQSQzqe3cM03DAgIrz+jVWhxO/efzAv1p2S6V1jSd8lqLXCN2JmUl9ExxCBqTshKUFmtXcVXjCc8989kDACA4uc890zQsMLDyjF7J6TS0OOegLnhilX6zYItKa5rUP6GPrp+SKYtcYeVE5s8eRbMweJ3NatH82aNOGpwlPnsAAASjrERmPQcSVp7R63y8q1SPLNmuLYXVkqSkqDD9dMZQXT1pgMJCrDojK+G4WbuS9Ng3shlTBZ+ZNTpNl45N09u5RcfcnsqMcQAAgpb7nHN+OeE5EBCe0WvkFFTq0fe3a/Vu19nmyDCbbjxnsL4/NUtR4Uc+yrNGp+mCUalal1ehkppGPfb+DhUcblDLSeboAt5gGIa2Frm+0PnJ9MEanhqtlGjXVm1WnAEACE5Zbdu2D7T9LBrKuKpejfAMv7entFaPf7BD724uliSF2az6zuQBuvncIUqMCj/hY2xWS/s4qoOVjXpkyXYt3HRQV00a0GN1I7h8ebBae0vrFB5i1U/OHXLMFzoAACA49Y0JV59QmxpaHDpwuKE9TKN34qc7+K3iqkY9tWyn/rv+gBxOQxaLdNn4fvr5+cPUPyGi088zOztNjyzZrjV55SqualRqLN224X0LNxVKks4f2ZfgDAAAJEkWi0UDEyO0vbhG+WV1hOdejp/w4Heq6lv07Ko9enF1nppaXVutzx+Zol/OHK4RqTFdfr6M+Aidnhmvz/MP6+3cg/rB1EHeLhlBztXAznXWec64dJOrAQAA/iQrKVLbi12zns81uxh0C+EZfqOxxaGXPs3XMyt2q7qxVZL0tYHxuvOiETo9s3sjfuZkp+vz/MNauInwDO9bl1+h4upGRdtDNH14stnlAAAAP3JkXBVNw3o7wjNM1+pw6vUNB/THpTt1qNo1q3lY3yjdMXOEzhuZIoul+82WLh6Tpt8u3qrNhVXaW1qrQclR3X5OwG3hpoOSpItHpyk8xGZyNQAAwJ8wripwEJ5hGsMwtGRLsf7wwQ7tLXX9ZdIvro9+fsEwXTa+n1c7FCdGhWvq0CSt3FGqRTkHdev5w7z23Ahuza1OvbvZtWV7Llu2AQDAVwxMdPXqYeW59yM8wxSf7i7TI0u2K+dAlSQpPiJUN88YqmsmD/DZyt3ccemu8LzpoG45b6hXVrSBj3aWqqqhRSnR4TpjUKLZ5QAAAD/jbhJWeLhBza1OhYUwrqq3IjyjR20prNIjS7br411lkqSIMJt+cHaWbjhnkKLtoT597QtGpcoeull7y+q0pbBaYzJiffp6CA4Lc1xbti8dm848ZwAAcJzk6HBFhtlU1+xQweF6Deb4YK9FeEaPyC+r02Mf7NDbua7traE2i749aYBunjFUydEnntXsbVHhITp/ZF+9nVukhZsKCc/otrqmVn241TV/nC3bAADgRFzjqiK1taha+WV1QRWeHU5D6/IqVFLTqJRouyZlJfTqxQbCM3yqpKZRf1q2S/9ZV6BWpyHJFTJ+ccFwDUjs/Kxmb5mTna63c4u0OPeg7r54ZK/+wwvzLd12SI0tTmUmRmgsX8YAAICTyEpyhedgahq2ZEuR7lu8VUVVje23pcXaNX/2KM0anWZiZZ4jPMMnqhtb9LdVe/X3T/LU0OKQJE0fnqzbZw7XaenmhYxpw5MVYw/Roeomrcur0JmDOaMKz7m7bM8Z148z9AAA4KQyk4KradiSLUX68SsbZXzl9uKqRv34lY169poJvTJAE57hVY0tDv3zs336y8rdqqxvkSSN6x+nuy4aocl+0EwpPMSmi8ek6T+fF2hRTiHhGR6rqGvWRztLJbl2NAAAAJxMZtu4qvyyepMr8T2H09B9i7ceF5wlyZBkkXTf4q26YFRqr9sFSqs3eEWrw6n/fl6gcx9bqQff3abK+hYNTo7UX6+ZqP/95Cy/CM5uc9rOpr67uVhNrQ6Tq0Fv9e7mIrU6DY3uF6MhKcFzdgkAAHRdZlLwzHpel1dxzFbtrzIkFVU1al1eRc8V5SWsPKNbDMPQB1sP6Q/v79DuklpJrrMMPz9/mC6f0E8hNv/7fuaMrET1jQnXoeomfbSzTBeM6mt2SeiFFrm3bLPqDAAAOuBeeT5Y1aDGFofsob4ZzeoPSmpOHpw9uc6fEJ7hsbV7y/XIku3auL9SkhTbJ1Q3nTtY3z0z06//QrBZLZo9Nl3Pf5KnhZsKCc/ossLKBq3Lr5DFIs0mPAMAgA4kRYUpKjxEtU2tOnC4XkNSos0uyWdSou1evc6fEJ7RZVsPVuvR97dr5Q7XeU97qFXzpmTph9MGK7aPb2c1e8ucca7wvHTbIdU1tSoynD8K6Ly322Y7T8pMUFpsH5OrAQAA/s5isSgzKUJbCquVVxbY4XlSVoKSo8JVWtt0wvstklJjXWOrehsSAzptf3m9nvhwhxbmHJRhuFZwrzq9v245b6hSYnrXN0dj+sUqKylSeWV1+nDrIX19fD+zS0Iv4u6yPXccnxsAANA5mYmR2lLomvUcyBpaHAo5SSMw963zZ4/qdc3CJMIz1PHw8tKaJj29fJf+tW6/WhyuvnmXjk3TLy4crqy25ge9jcVi0ZzsdD21bJcWbiokPKPTdh2q0daiaoXaLLpodKrZ5QAAgF7C/XNzXgCPq3I6Df3iv5tUVN2o2D4hCg+xqaTmyAp0KnOe0Zudanj5lCFJeu7jPD3/8V7VN7u6Uk8dmqQ7Zo7QmAzzZjV7y5xxrvD80a4yldc2KTEq3OyS0AssatuyPW1YsuIjw0yuBgAA9BZHxlUFbnj+y4rdev/LQwqzWfXi9ZOUnRF3ykW63obwHMRONbz8R69sbG9qIEljM2J156wRmjIkqecL9ZHByVEa0y9Wmwur9O6WYl07eaDZJcHPGYbRvmWbRmEAAKArMpMiJAVueF627ZCeWLpTknT/10/ThAHxkqQzB/vPyNru8r85QugRHQ0vl6TaplZlJUbome9M0MKbpgRUcHab2zbzedGmQpMrQW+wqaBS+yvq1SfURpd2AADQJUfGVTWqscVhcjXetae0Vrf+Z5MMQ7pm8gB96/QBZpfkE4TnINXR8HK3+78+WhePSZPF0nu3V5zKpWPTZbFIn+cfVmFlg9nlwM+5t2xfeFpfRYSxcQcAAHReQmSYou2unx/2V9SbXI331DS26MaX16umqVWnZ8br3ktPM7sknyE8B6nODiUvr2v2cSXmSo2164y2NvmL24IRcCIOp6HFOUWSjuxYAAAA6CyLxXKkaViAbN12Og39/LUc7SmtU2qMXc98Z6LCQgI3YgbuO8MpBfLw8q5yjxtyn2UFTuSzPeUqq21SfESopg5NNrscAADQCwVa07A/Ld+lpdsOKSzEqr9eO1HJ0YHdgJfwHKQmZSUoLdauk23GtsjVdbs3Di/vqotGpyrUZtG2omrtPFRjdjnwUwvbzsVfPCZNoTb+6gQAAF2X2bbynB8A46o+3HpIf1y6S5L0wNdHa1z/OHML6gH8BBikbFaL5s8eJUnHBejePry8q+IiwjRtWIokaRGrzziBxhaHlmwpliTNocs2AADwUFZbx+3evm17d0mtfv7aJknS984cqG9+rb+5BfUQwnMQmzU6Tc9eM0GpscduzU6NtevZayb02uHlnnCfYV2YUyjDOFEPcgSzlTtKVNPUqrRYu07PDPzdGAAAwDcGtm/b7r0Nw6rbGoTVNrVqUlaC7rl0lNkl9RjaxQa5WaPTdMGo1IAaXu6J80f2VUSYTQUVDfqioLJ9Lh0gHemyPSc7XdYg+7MBAAC8J6stPBdXN6qh2aE+YTaTK+oap9PQz/+zSXvL6pQWa9cz35kQVMfZgued4qRsVovOHJyoueP66czBiUEXnCWpT5hNF7bN7WXrNo5W09iipdtKJElz6LINAAC6IT4yTLF9QiVJ+yp639btPy7dqWXbSxQWYtX/XTtRSVGB3SDsqwjPQBt31+23c4vU6nCaXA38xftfHlJzq1NDUqI0Ki3G7HIAAEAv1940rJede16ypVh/Wr5bkvTQZWM0NiPO3IJMQHgG2pw9NEnxEaEqq23SZ3vLzS4HfsLdZXtudrosluDblQEAALwrK9HdNKz3nHvedahGv/jvJknS9VMydcXEDHMLMgnhGWgTarPqkrGuJmnMfIYkldY0afXuMknSbLpsAwAAL+htK89VDS268Z8bVNfs0ORBCfrVxSPNLsk0hGfgKO6t20u2FKuxxWFyNTDbO7kH5TSk7P5x7f/QAQAAdEdW288Ueb1g1rPDaejW/3yhvLI69Yvro798O7gahH1V8L5z4AQmDohXeqxdtU2tWrG9xOxyYDJ3l+25rDoDAAAvOTKuyv/D85Mf7tSKHaUKb2sQlhhkDcK+ivAMHMVqtWi2e+YzW7eD2v7yem3cXymrRbp0bPDMPAcAAL7lHldVUtOkuqZWk6s5ufc2F+npFa4GYY9cMVaj+8WaXJH5CM/AV8zNdm3dXr6jRNWNLSZXA7MsznV9eXLW4CSlxNhNrgYAAASK2IhQxUe0jasq98+mYTuKa/SL13MkSd8/O0tfH9/P5Ir8A+EZ+IqRadEamhKl5lan3t9SbHY5MIFhGFrwhavLNrOdAQCAt7U3DfPDc89V9S268Z/rVd/s0FmDE3X3RSPMLslvEJ6Br7BYLJrbFpjcZ14RXLYX12hXSa3CbFbNPC3V7HIAAECAcW/dzvOzc88Op6Gf/ucL7SuvV7+4Pnr62xMUEsQNwr6K/08AJzCnbev26t1lKqlpNLka9DT3efdzRyQrtk+oydUAAIBA46/jqh77YIc+2lkqe6hVf/vuRCVEhpldkl8hPAMnMCAxQuP6x8lpSO/kFpldDnqQ02losbvL9jjO9wAAAO/zx23bb+ce1LMr90hyNQg7LZ0GYV9FeAZOYi5dt4PSxv2HVVjZoKjwEM0YkWJ2OQAAIABlJkZIkvLK/KNh2Laiat3+eq4k6cZzBrGAcBKEZ+AkLhmbJqtF2lRQqf1+2gkR3uf+smTmaamyh9pMrgYAAAQi98pzWW2Takye7lJZ36wb/7leDS0OnT0kSXfMHG5qPf6M8AycREq0XVOGJEmSFuUUmlwNekKLw6l3Nru26c+lyzYAAPCRGHuoEtvOE5s5rqrV4dRP//2FCioa1D+hj/589XgahJ0C/58BTmFOtitALdh0UIZhmFwNfO2T3WWqqGtWUlSYzhqcaHY5AAAggPnDuec/vL9DH+8qU59Qm/527dcUT4OwUyI8A6cwc3SqwkKs2l1Sq21FNWaXAx9b1LZl+5IxaXzrCgAAfCoz0dyO24tyDur/PtorSfrDN8ZqZFqMKXX0Jvx0CJxCjD1UM4a7mkYtZOt2QGtoduiDL4slSXNokgEAAHwsK8m8pmFbD1brjjdyJEk/mjZYl47luFpnEJ6BDrjPvi7edFBOJ1u3A9Wy7YdU1+xQRnwfTRgQZ3Y5AAAgwJm1bftwnatBWGOLU+cMS9btNAjrNMIz0IFzR6QoOjxEB6satWH/YbPLgY+4u2zPHZcui8VicjUAACDQmbFtu9Xh1M3/3qgDhxs0ICFCf7pqnGxWfu7pLMIz0AF7qE0zR6dKkhZuYut2IKqqb9HKHSWSxFxDAADQI9wrz+V1zaruoXFVjyzZrtW7yxURZtPfvjtRcRE0COsKwjPQCe6t2+/kFqnF4TS5Gnjbe1uK1OIwNCI1WsP6RptdDgAACAJR4SFKigqXJO3rgXPPCzcV6rmP8yRJj30jWyNSaRDWVYRnoBPOHJSopKhwHa5v0Se7yswuB17m3rI9h9nOAACgB7U3DfPxuecthVW6441cSdJPpg/WxWPSfPp6gYrwDHRCiM2qS8e6/pJh63ZgOVTdqDV55ZKk2XSaBAAAPagnzj2X1zbph//coKZWp6YPT9YvLqRBmKcIz0AnuVclP9h6SA3NDpOrgbcszjkow5C+NjBe/RMizC4HAAAEkfaO2z4Kz60Op27+1xcqrGxQZmKEnrpqPA3CuoHwDHTS+P5x6p/QR/XNDi3ddsjscuAli3KOdNkGAADoSVlt4dlX27Z//+52fba3XJFhNv3tu19TbJ9Qn7xOsCA8A51ksVg0N9vVidl9Rha9297SWuUeqJLNauHsDwAA6HEDE1273nyx8vzWxgN6YbWrQdjj38ymKaoXEJ6BLnCvTq7aWaLK+maTq0F3uVedpw5NUmJbt0sAAICe4j7zfLi+RVX13htXtflAle5+a7Mk6aczhmjWaBYJvIHwDHTB0L7RGpkWoxaHofe2FJtdDrrBMAwtcnfZzmbLNgAA6HmR4SFKiXZ9gZ/vpa3bZbVN+uE/16up1akZI1L08/OHeeV5QXgGuswdtOi63bt9ebBae8vqFB5i1YWnpZpdDgAACFLtTcO8EJ5bHE7d9OpGHaxq1KCkSD35rXGy0iDMawjPQBfNznZte1mbV6HiqkaTq4Gn3F9+nD+qr6LCQ0yuBgAABKustq3beV449/zgO9u0Nq9CUeEh+tt3J9IgzMsIz0AXZcRH6PTMeBmG9HYujcN6I4fTONJlmy3bAADARN4aV/X6+gK99Gm+JOmJb2ZrSAoNwryN8Ax4YM44um73ZuvyKnSoukkx9hBNG55sdjkAACCIZbZ13M4rr/f4OXIKKvXrBVskSbecN5QjaT5CeAY8cMmYNIVYLdpcWKU9pbVml4MuWpTj2rJ98Zg0hYfYTK4GAAAEs+6uPJfWNOmH/9yg5lanzh/ZV7ecN9Sb5eEohGfAAwmRYZo6NEmS2js2o3dobnXq3c2uTul02QYAAGZzj6uqamjR4bqujUJtbnU1CCuubtSg5Eg9+a1sGoT5EOEZ8NCctpnPi3IOyjAMk6tBZ320s1RVDS1KiQ7XGYMSzS4HAAAEuT5hNqXG2CV1veP2A+9s1br8CkWHh+i5735N0XYahPkS4Rnw0AWjUmUPtSqvrE6bC6vMLgedtLCtUdjs7HTZ+GYWAAD4gcwk17nnroTn/35eoJc/2ydJevJb4zQ4OconteEIwjPgoajwEJ0/sq8kGof1FnVNrfpwq2vL9txxbNkGAAD+ISvJPa6qc03Dvth/WPe0NQj7+fnDdP6ovj6rDUcQnoFumNvWdfvt3INyONm67e8+3HpIjS1OZSVFaky/WLPLAQAAkHTk3HNnmoaV1DTqR69sULPDqQtH9dVPZwzxdXloQ3gGumHasGTF9gnVoeomrc0rN7scdGDhJleX7TnZ6bJY2LINAAD8w0B3eO5g23Zzq1M/eWWjDlU3aUhKlJ741jgahPUgwjPQDWEhVl08xjVHj67b/q2irlkf7yqTdKTZGwAAgD84sm277pSNaO9b/KXW7zusaHuI/nbtREWFh/RUiRDhGei22W3jjt7dXKSmVofJ1eBk3t1cpFanodH9YmioAQAA/MrARFfDsJrGVlWcZFzVv9ft16tr98tikZ66apwG8fNMjyM8A910Rlai+saEq7qxVat2lJpdDk7CvTNgbnY/kysBAAA4lj3UpvRY97iq45uGbdh3WPcudDUI+8UFwzRjBA3CzEB4BrrJZrVo9ljX6rN7DBL8S2Flg9blV8hikS7NTjO7HAAAgONkJp24adih6kb9+JUNanEYmnVaqm46lwZhZiE8A17g7rq9bNsh1Ta1mlwNvmpx25caZ2QlKC22j8nVAAAAHK89PB/VNKyp1aEfvbJBJTVNGtY3So99M5umpyYiPANeMLpfjAYlRaqxxdk+Rxj+wz2H2/0lBwAAgL/JSjzSNMztt4u+1Bf7KxVjD9Hfrv0aDcJMRngGvMBisbR3cF5I122/sutQjbYVVSvUZtFFo1PNLgcAAOCE+se7dsdtKqjUZ3vK9c81+fr3ugJZLNKfrh7fvjIN8/h1eG5oaNC9996rYcOGyW63Kz09XfPmzVNhYWGXnmfVqlW67777dMkllyg5OVkWi0WZmZmnfMyOHTv05JNP6uqrr9bgwYNlsVhksViUn5/v+RtCQJvT1nX7411lKq9tMrkauC1q27I9bViy4iLCTK4GAADgeEu2FOmetoZgBw436Orn1ug3C76UJN0+c7imD08xszy08dt1/8bGRs2YMUNr1qxRWlqa5s6dq/z8fL344ot6++23tWbNGg0aNKhTz3XLLbcoJyenS6//7LPP6qmnnvKkdASpQclRGtMvVpsLq/Tu5iJde2am2SUFPcMw2ncCzGHLNgAA8ENLthTpx69s1MmmO7u3c8N8frvy/MADD2jNmjU688wztXPnTr322mtau3atHn/8cZWWlmrevHmdfq4LL7xQDzzwgN5//319+eWXnXrMmDFjdOedd+qNN95Qfn6+hg8f7ulbQRCZy9Ztv7KpoFL7K+oVEWbT+SP5xhYAAPgXh9PQfYu3njQ4WyT97u2tcjhPdgV6kl+uPDc3N+vpp5+WJP3lL39RVNSRAeC33Xab/vGPf2jVqlXasGGDJk6c2OHzPfroo+3/u7i4c82cvv/973exakC6dGy6Hnx3m9bvO6wDh+uVER9hdklBzf0lxoWj+ioizC//ugMAAEFsXV6FiqoaT3q/IamoqlHr8ip05uDEnisMJ+SXK8+rV69WVVWVBg8erPHjxx93/5VXXilJWrx4cU+XBpxSaqxdk7Ncf7EtzikyuZrg1upw6u1c138DumwDAAB/VFJz8uDsyXXwLb8Mz+7zyRMmTDjh/e7bc3Nze6wmoLOObN3uWmM7eNdne8tVVtuk+IhQnT00yexyAAAAjpMSbffqdfAtvwzP+/fvlyRlZGSc8H737fv27euxmoDOumh0mkJtFm0vrtGO4hqzywlai9q2bF88Jk2hNr/8qw4AAAS5SVkJSou1y3KS+y2S0mLtmpSV0JNl4ST88ifK2tpaSVJExInPi0ZGujrO1dT4dzBpampSdXX1Mb8Q+GIjQjVtmKs51aIcVp/N0Nji0JItrv4GbNkGAAD+yma1aP7sUZJ0XIB2/37+7FGyWU8Wr9GT/DI8B4qHHnpIsbGx7b/69+9vdknoIe6t24tyDsow6I7Y01buKFFNU6vSY+362sB4s8sBAAA4qVmj0/TsNROUGnvs1uzUWLuevWaCZo1OM6kyfJVftp91d9eur68/4f11dXWSpOjo6B6ryRN33323brvttvbfV1dXE6CDxPkj+yoizKaCigZ9UVCpCQMIcD3J3WV79rh0WfmmFgAA+LlZo9N0wahUrcurUElNo1KiXVu1WXH2L34ZngcMGCBJOnDgwAnvd98+cODAHqvJE+Hh4QoPDze7DJigT5hNM09L1f++KNSiTQcJzz2ourFFy7aXSJLmZrNlGwAA9A42q4VxVH7OL7dtZ2dnS5I2btx4wvvdt48dO7bHagK6ak7b1u23cw+q1eE0uZrg8f6WYjW3OjUkJUoj0/x7dwoAAAB6D78Mz1OmTFFsbKz27NmjTZs2HXf/G2+8IUmaPXt2D1cGdN7ZQ5IUHxGqstpmfbqn3OxygsaiHNeW7bnZ6bJY2OoEAAAA7/DL8BwWFqabb75ZknTTTTe1n3GWpCeeeEK5ubmaNm2aJk6c2H77008/rREjRujuu+/u8XqBEwm1WXXJWFeDB/cZXPhWaU2TVu8uk3Rk5R8AAADwBr888yxJ99xzj5YuXapPP/1UQ4cO1dSpU7Vv3z6tXbtWycnJeuGFF465vqysTDt27FBRUdFxz/X888/r+eeflyS1tLRIkoqKijR58uT2a5555hlNmDCh/fcbN27UT37yk/bfu2dKX3bZZe3nmH/wgx/oBz/4gZfeMQLR3HH99Mqa/Xr/y2I92DJa9lCb2SUFtHdyD8ppSOP6x2lgYqTZ5QAAACCA+G14ttvtWrFihR566CH961//0oIFC5SQkKDrrrtO999/vzIyMjr9XAcOHNDatWuPua25ufmY2746g7m6uvq4x0g6Zhv5rFmzOl0DgtPEAfHqF9dHhZUNWrG9RBeNYdSALy10b9lm1RkAAABeZjEYQttjqqurFRsbq6qqKsXExJhdDnrIw+9t119X7dGs01L112sndvwAeGR/eb3O+cMKWS3Sml+dp5Roe8cPAgAAQFDrSkbzyzPPQCBxr4Iu31GiqoYWk6sJXItyCiVJU4YkEZwBAADgdYRnwMdGpEZraEqUmludev/LYrPLCUiGYbQ3ZZudzZZtAAAAeB/hGfAxi8XSvvq8iK7bPrG9uEa7SmoVFmLVrNGpZpcDAACAAER4BnrAnOx+kqRP95SppLrR5GoCj3vVecbwFMXYQ02uBgAAAIGI8Az0gAGJERo/IE5OQ3o79/hxavCc02loMV22AQAA4GOEZ6CHzG07i7soh63b3rRh/2EVVjYoOjxE545IMbscAAAABCjCM9BDLhmbLqtF2lRQqX3ldWaXEzAWbnJ12Z45OlX2UJvJ1QAAACBQEZ6BHpIcHa4pQ5Ik0TjMW1ocTr272dXBfA5dtgEAAOBDhGegB7kD3oJNhTIMw+Rqer9Pdpepoq5ZSVFhOmtwotnlAAAAIIARnoEeNHN0qsJCrNpTWqetRdVml9PruVfwLx2brhAbf50BAADAd/hpE+hBMfZQndfW1Iqt293T0OzQ+1+2bdmmyzYAAAB8jPAM9DD3OKXFOQfldLJ121NLtx1SfbND/RP6aHz/OLPLAQAAQIAjPAM9bPrwFEWHh+hgVaPW7ztsdjm9lnvk19zsfrJYLCZXAwAAgEBHeAZ6mD3UppmjUyUdGbOErqmqb9HKHSWS2LINAACAnkF4Bkzg3rr9zuYiNbc6Ta6m93lvS5FaHIZGpEZrWN9os8sBAABAECA8AyY4c1CikqLCVVnfok92l5pdTq+zsK3Z2txx/UyuBAAAAMGC8AyYIMRm1aVj0yQdCYLonOKqRq3JK5ckzc5OM7kaAAAABAvCM2AS99btD7ceUn1zq8nV9B5v5x6UYUinZ8YrIz7C7HIAAAAQJAjPgEnG9Y/TgIQI1Tc7tHRbidnl9BruLttz2LINAACAHkR4BkxisVjaV58X0XW7U/aW1ir3QJVsVosubutYDgAAAPQEwjNgojnZrvC8ckepDtc1m1yN/3OvOk8dmqTEqHCTqwEAAEAwITwDJhraN1oj02LU6jT03pZis8vxa4ZhaFF7l21mOwMAAKBnEZ4Bk7mD4EK2bp/SlsJq7S2rkz3UqgtGsWUbAAAAPYvwDJhsdtvW7XX5FSqqajC5Gv/l/nLh/JF9FRUeYnI1AAAACDaEZ8Bk/eL6aFJmggxDejunyOxy/JLDaWhxrnvLNl22AQAA0PMIz4AfmOPeup3D1u0TWZdXoUPVTYqxh+icYUlmlwMAAIAgRHgG/MDFY9IUYrVoS2G1dpfUml2O31nU9qXCxWPSFB5iM7kaAAAABCPCM+AHEiLDNHWoa0XVPY4JLk2tDr272dWJfA5dtgEAAGASwjPgJ9xneRdtKpRhGCZX4z8+2lmmqoYW9Y0J1xlZiWaXAwAAgCBFeAb8xAWj+soealV+eb02F1aZXY7fcHfZnj02XTarxeRqAAAAEKwIz4CfiAwPaZ9fvHATW7clqa6pVUu3HZJEl20AAACYi/AM+JG5bTOfF+cclMPJ1u0Ptx5SY4tTWUmRGt0vxuxyAAAAEMQIz4AfOWdYsmL7hKqkpklr95abXY7p3Fu252Sny2JhyzYAAADMQ3gG/EhYiFUXj2HrtiSV1zbpo11lkuiyDQAAAPMRngE/Myfbdbb33S1Famp1mFyNed7dUiyH09CYfrEanBxldjkAAAAIcoRnwM9MykpQaoxdNY2tWrmj1OxyTLOobcv2XFadAQAA4AcIz4CfsVktmp2dJklalBOcW7cLKxv0ef5hWSzSpWMJzwAAADAf4RnwQ+6xTEu3HlJtU6vJ1fS8xW1fGpyRlaDUWLvJ1QAAAACEZ8AvnZYeo0FJkWpqdeqDL4vNLqfHuZulMdsZAAAA/oLwDPghi8XS3mE62Lpu7zxUo21F1Qq1WXTR6FSzywEAAAAkEZ4BvzUn2xWeP9ldprLaJpOr6TmL2r4smDYsRXERYSZXAwAAALgQngE/NSg5SmMzYuVwGnp3c5HZ5fQIwzDam6TRZRsAAAD+hPAM+DH36vOiINm6vamgUvsr6hURZtP5I/uaXQ4AAADQjvAM+LHZ2emyWKT1+w6roKLe7HJ8zn2+e+ZpqeoTZjO5GgAAAOAIwjPgx/rG2DU5K1GStDg3sFefWx1OvZ3r2p7uXnEHAAAA/AXhGfBz7rO/gb51+7O95SqrbVJ8Pi7/6gAAI+JJREFURKjOHppkdjkAAADAMQjPgJ+7aHSaQm0WbS+u0fbiarPL8Rn3lu1LxqYp1MZfTQAAAPAv/IQK+LnYiFBNH54iKXBXnxtbHHp/S7Ekae64fiZXAwAAABwvxOwCAHRs7rh0fbj1kP67vkDD+0YrJcauSVkJslktZpfWLQ6noXV5FVq6tVg1Ta1Kj7Vr4oB4s8sCAAAAjkN4BnoBh9OQRVJZbbNueW2TJCkt1q75s0dp1ug0U2vz1JItRbpv8VYVVTW231bV0KIPthb32vcEAACAwMW2bcDPLdlSpFv/s0nGV24vrmrUj1/ZqCVbikypqzuWbCnSj1/ZeExwlqS6ZkevfU8AAAAIbKw8A37M4TR03+KtxwVnSe233bvwSw3rG91rtnA7nIZ+s+DLE74nt/sWb9UFo1J7zXsCAABA4CM8A35sXV7FcauzX1VS06QZj6/qoYp8z5BUVNWodXkVOnNwotnlAAAAAJIIz4BfK6k5dXB2Cw+x9prxTi0Op5panR1e19n3DgAAAPQEwjPgx1Ki7Z267qXrJ/WaVdrP9pTr6ufWdHhdZ987AAAA0BN6x1IVEKQmZSUoLdauk538tcjVdXtSVkJPltUtgfieAAAAEPgIz4Afs1ktmj97lCQdFzbdv58/e1SvaqwViO8JAAAAgY/wDPi5WaPT9Ow1E5Qae+w25tRYu569ZkKvnIkciO8JAAAAgc1iGMapJsbAi6qrqxUbG6uqqirFxMSYXQ56GYfT0Lq8CpXUNCol2rWtubevzgbiewIAAEDv0ZWMRsMwoJewWS29pilYZwXiewIAAEBgYts2AAAAAAAdIDwDAAAAANABwjMAAAAAAB0gPAMAAAAA0AHCMwAAAAAAHSA8AwAAAADQAcIzAAAAAAAdIDwDAAAAANABwjMAAAAAAB0gPAMAAAAA0AHCMwAAAAAAHSA8AwAAAADQAcIzAAAAAAAdIDwDAAAAANABwjMAAAAAAB0gPAMAAAAA0AHCMwAAAAAAHSA8AwAAAADQAcIzAAAAAAAdIDwDAAAAANABwjMAAAAAAB0gPAMAAAAA0AHCMwAAAAAAHSA8AwAAAADQAcIzAAAAAAAdIDwDAAAAANABwjMAAAAAAB0gPAMAAAAA0IEQswsIJoZhSJKqq6tNrgQAAAAA4M5m7qx2KoTnHlRTUyNJ6t+/v8mVAAAAAADcampqFBsbe8prLEZnIja8wul06uDBg4qOjpbFYjG7nGNUV1erf//+KigoUExMjNnlwA/wmcDR+DzgaHwe8FV8JnA0Pg84mr9/HgzDUE1NjdLT02W1nvpUMyvPPchqtSojI8PsMk4pJibGLz/UMA+fCRyNzwOOxucBX8VnAkfj84Cj+fPnoaMVZzcahgEAAAAA0AHCMwAAAAAAHSA8Q5IUHh6u+fPnKzw83OxS4Cf4TOBofB5wND4P+Co+EzganwccLZA+DzQMAwAAAACgA6w8AwAAAADQAcIzAAAAAAAdIDwHgbq6Ov3zn//UT3/6U51xxhkKDw+XxWLRb3/721M+zmKxnPJXY2PjCR/X0NCge++9V8OGDZPdbld6errmzZunwsJCH7w7dJWnnwdJOnDggK6//nqlp6fLbrdr2LBhmj9//kk/CxKfh95s5cqVp/w7YPLkySd9rCefFfg//jwHn+nTp5/y74ElS5ac8HEvvfSSJk2apKioKCUkJOjiiy/Wp59+2sPVwxMbNmzQww8/rMsvv1wZGRnt/6074sl/89WrV+viiy9WQkKCoqKiNGnSJL388sveeivwkq5+Jn7729+e8u+Nu+6666SP9ffPBHOeg8CuXbv03e9+16PHRkZG6sorrzzhfTab7bjbGhsbNWPGDK1Zs0ZpaWmaO3eu8vPz9eKLL+rtt9/WmjVrNGjQII9qgXd4+nnYvXu3zjzzTJWVlWn06NGaOnWq1q9fr9/97ndatmyZli1bdlwjCD4PgWHw4ME6++yzT3j7iXjyWYH/489zcLviiisUFRV13O39+vU77rZbb71VTz31lPr06aMLL7xQjY2N+vDDD/XBBx/ojTfe0Ne//vUeqBieuv/++7Vw4cIuPcaT/+ZvvvmmvvWtb8npdOqcc85RUlKSli1bpu9973vKzc3VY4895qV3hO7y5DMhSVOmTNGQIUOOu33ixIknvL5XfCYMBLzdu3cb3//+942//vWvxoYNG4zf/e53hiRj/vz5p3ycJGPgwIFdeq1f//rXhiTjzDPPNGpqatpvf/zxxw1JxrRp07r+BuBVnn4epkyZYkgyfvazn7Xf1tLSYlx22WUnfTyfh95txYoVhiTje9/7Xpce58lnBf6PP8/Badq0aYYkIy8vr1PXf/jhh4YkIzEx0di5c2f77Z9++qkRFhZmxMXFGYcPH/ZNsfCKhx9+2PjNb35jLFq0yCgqKjLCw8ONU0UGT/6bl5eXGzExMYYk480332y/vbi42BgyZIghyVixYoW33xo81NXPxPz58w1Jxosvvtjp1+gtnwnCcxB66KGHfBKem5qajNjYWEOSsXHjxuPuHzt2rCHJWL9+fRcrhi915vOwdu1aQ5KRkpJiNDY2HnNfcXGxERoaasTHxxstLS3tt/N56P08Cc+efFbg//jzHLy6Gp4vuugiQ5Lx5JNPHnffz372M0OS8dhjj3m3SPhUR0HJk//mjzzyiCHJmDt37nGPeeuttwxJxqWXXtrd0uEjvgjPveUzwZlneM3q1atVVVWlwYMHa/z48cfd797+vXjx4p4uDd30zjvvSJJmz5593Hbbvn37aurUqTp8+LA++eST9tv5PAQnTz4r8H/8eUZnNDQ0aPny5ZJ0wiNffE4Cj6f/zd3/VpzoMZdcconsdruWLl1Kn4wg0ls+E5x5xinV1dXpwQcf1P79+xUREaHx48fr8ssvP+G5p5ycHEnShAkTTvhc7ttzc3N9VzB8ojP/bZcvX67c3FxNnz6904+R+Dz0Brt27dLdd9+t8vJyJSUl6eyzz9asWbNktR7//asnnxX4P/484//bu8+wqK40DuD/AaQrWDAo4IgUu5JYcEVR3ASxrGJ3WRB7i9HE7ibE8lgWo2FdjckaFUsSjeLqilGJDdauqJhojCiiIHaCBdARmXc/+MzEcQZmICCC/9/zzIc55d73zjkMc+49595Vq1YhMzMTZmZm8Pb2RnBwMOrUqaNT5uLFi1CpVHBycoKrq6veNthPKp7itnlh3ymWlpZo0qQJEhMTkZycjGbNmpVC5PQq7N+/H0lJSXjy5AlcXV3RpUuXAtc7l5c+wcEzFerevXv45JNPdNImTpyItWvXolu3bjrpaWlpAGDwy/PF9GvXrpVCpFSaitO27A8Vx5EjR/TumNq0aVNs2bIFXl5eOuls94qJ7Upz587VeT958mREREQgIiJCm2asn9jZ2cHR0RFZWVl49OgRKleuXHoB0ytRnDZ/+PAhHjx4UGg9V1dXJCYm4tq1axw8l2Pr16/XeR8REYE+ffpgzZo1OhfiylOf4LRtKtCgQYOwe/duZGRkIDs7G2fOnEFYWBgyMzPRu3dvnDx5Uqd8dnY2AMDW1tbg9uzs7AAAjx49Kt3AqcQVp23ZH8o/BwcHTJkyBceOHUNmZiYyMzOxb98+tGnTBj///DMCAwO1/+w02O4VE9v1zeXv74/169cjJSUFubm5uHjxIubNmwcLCwt8+umnWLJkibassX4CsK9UNMVpc02dwuqxn5Rvnp6eWLRoEc6fP4/s7Gykp6fj22+/hYuLC7Zs2YKwsDCd8uWpT/DKcznQq1cvXLhwoUh11q1bh9atW/+h/a5du1bnvY+PD9atWwc3NzfMnz8fn3zyCeLi4v7QPqjoyqo/UPnzR/vK22+/rbe+tVOnTjh06BACAgJw8OBBLF++HDNmzCixmIno9TJnzhyd997e3vj73/+Oli1bonPnzpg1axZGjhwJGxubMoqQiF43oaGhOu/t7OwQEhKCgIAANG3aFNu2bcOxY8fQpk2bMoqw+Dh4LgdSU1Nx8eLFItXJzc0tpWiAqVOnIjIyEvHx8Xj69CksLS0BQDv9oqB95+TkAACnaf1BZdEfitO27A9lr7T6irm5OaZNm4aDBw8iLi5OZ/DMdq+Y2K70ssDAQLRs2RKJiYk4fvw4OnbsaLSfAOwrFU1x2vzF6bq5ubmoUqWK0TpUMdSqVQtDhgzBokWLsHv3bu3guTz1CU7bLgeSkpIgzx8rZvKrNG/E4+DggJo1a+Lp06fIzMzUpmtuHHL9+nWD9TTpSqWy1GJ7E5RFfyhO27I/lL3S7Cuatc43b97USWe7V0xsVzLk5e8BY/0kJycH9+/fR9WqVcv8BzCVjOK0eZUqVeDg4FBoPX6nVFyGfj+Upz7BwTMVmVqtxsOHDwH8vv4AAJo3bw4AOH36tMF6mnTe+KH8KU7bsj9UbFlZWQB0vwMAtntFxXYlQ17+Hqhfvz6srKxw9+5dZGRk6JVnP6l4itvmhX2n5OXl4dy5c7C2toa3t3cpRE1lqTi/H16nPsHBMxXZ7t27kZOTAw8PD51pFX5+fnBwcEBKSgqSkpL06sXExAB4/vxXKl80d1aPjY2FSqXSybt9+zYOHjyIqlWrws/PT5vO/lCxbdmyBYD+IyWK01fo9ce/Z3rZ3bt3cfDgQQC/fw/Y2NigU6dOAIDNmzfr1WE/qXiK2+aa/xWa/Bft2LEDT548wbvvvgtra+uSDpnKkIhg69atAAr+/fDa9wmhN86CBQsEgMycObPAMhs2bJATJ07opcfHx0vt2rUFgHz++ed6+R9//LEAkLZt20p2drY2ffHixQJAOnToUBKHQCXIlP4gIuLn5ycAZMKECdq0vLw86d27d4H12R/Kt6ioKElLS9NJU6vV8tVXX4mFhYUoFApJTEzUq1ecvkKvP/49v3kOHz4sW7dulWfPnumkp6amav/Oe/TooZO3Z88eASDVq1eX5ORkbfqRI0fEyspKHB0dJSsr61WETyXEyspKChsyFKfNMzMzpUqVKgJAtmzZok2/ffu2eHp6CgA5cOBASR8KlZDC+sSdO3dk2bJl8vDhQ530R48eyahRowSAODs7S05Ojk5+eekTHDy/IYKDg8XX11d8fX3Fzc1NAIiLi4s2LTg4WKd8eHi4ABBvb2/p1auX9O/fX3x8fASAAJCBAwdKfn6+3n4eP34svr6+AkBq1aol/fv31753cnKSlJSUV3XIVIii9gcRkeTkZKlevboAkKZNm8qAAQOkXr162h/TT5480avD/lC+KZVKMTc3l1atWkn//v2lR48e4u7uLgDEzMxMli5darBecfoKvf749/zmiY6O1v7Q7dq1q4SEhIifn59YW1sLAGncuLHcvn1br96ECRMEgNja2krPnj2lS5cuYmFhIebm5rJ169ZXfyBUJDt27ND+HvD19RWFQiEAdNJ27NihU6c4bR4TEyNmZmaiUCgkICBA+vbtK46OjgJAJk6c+AqOlExVlD6RmpoqAMTe3l4CAgIkJCRE3nvvPe3vAkdHRzl06JDB/ZSHPsHB8xtCqVRqB76GXkqlUqf8zp075W9/+5s0aNBAHB0dxcLCQmrWrCldunSRzZs3F7qv3NxciYiIEA8PD7G0tBRnZ2cZPHiwpKenl+IRUlEUtT9opKWlyeDBg8XZ2VksLS3F09NTIiIi5PHjxwXui/2h/PrXv/4l3bt3F3d3d7GzsxNLS0tRKpUSGhpqcGbKi4rTV+j1x7/nN8svv/wiY8aMkXfeeUecnJzEwsJCHBwcpE2bNrJ48WLJzc0tsG50dLS0aNFCbG1txdHRUYKCguTw4cOvMHoqLs1Jk8Je0dHRBusVtc0PHTokQUFB4ujoKLa2ttKyZUtZs2ZNKR0ZFVdR+sTDhw9l2rRp0qFDB3FxcRErKyuxtbWVxo0by6RJk+T69euF7ut17xMKEZFizPYmIiIiIiIiemPwhmFERERERERERnDwTERERERERGQEB89ERERERERERnDwTERERERERGQEB89ERERERERERnDwTERERERERGQEB89ERERERERERnDwTERERERERGQEB89ERERERERERnDwTEREr4xCoSj01bFjx7IOkUrRhg0b0KJFC9ja2kKhUKBu3bravJSUFPTq1Qs1atSAmZkZFAoF4uPjyyzWorh8+TIsLS0xZcoUnfTBgwcX6ThmzZoFhUKBWbNmFVqubt26UCgUuHr1arHivXnzJmxsbDB27Nhi1ScielNZlHUARET05gkPDzeY3qBBg1ccCb0qJ0+eRGhoKKytrREYGAhHR0fUqFEDAKBWq9G3b18kJSWhTZs28PLygpmZGZydnUs9rlmzZmH27NmIjo7G4MGDi7WNGTNmwNLSElOnTi3Z4EpJrVq1MHLkSCxfvhwffvghvL29yzokIqJygYNnIiJ65dasWVPWIdArFhsbC7VajaVLl2Lo0KE6eVevXkVSUhLat2+P//3vf2UUYfGcPn0aMTExGD9+PJycnMo6HJNNnToVy5YtQ0REBL7//vuyDoeIqFzgtG0iIiIqddevXwcA1KtXr0h5r7svv/wSADBo0KAyjqRoXFxcEBAQgK1bt+L27dtlHQ4RUbnAwTMREb12XlwrGhcXh4CAADg6OkKhUOD+/fvacrt370a3bt3g5OQEKysr1KtXDxMnTkRmZqbB7f72228YN24cateuDWtrazRq1AhLliyBiOitwQV+X4Na0JVyzdpTQy5cuIDBgwfDzc0NVlZWeOuttzBw4ECcP39er+yaNWu0a13T0tIQEhICJycn2NjYoGXLloiNjS3ws7pw4QKGDRuGunXrwsrKCjVr1oSfnx8WLVqEZ8+eAQCaNGkChUKBixcvGtxGeno6zM3N4e7uDhEpcF8vyszMxJQpU+Dl5QVra2tUq1YNQUFB+PHHHw0eW3R0NAAgICBAu8Zdk9ehQwcAwNq1aw2ufz9y5AiCg4OhVCphZWUFZ2dntG7dGtOnT0d2drZebMePH0e/fv1Qq1YtWFpawtXVFcOHD0daWppOubp162L27NkAgCFDhuisvzdlnXJ2djY2btwILy8vtGjRwqTPDQCePn2Kfv36QaFQoHfv3lCpVCbXLUjHjh2N3lPg5TXSISEhyMvL40wQIiITcdo2ERG9tr777jusXLkSLVu2RJcuXZCSkqIdrE6fPh2RkZGwtLREq1atUKtWLZw9exZRUVHYvn07Dh8+jLfeeku7raysLLRr1w4XLlyAs7Mzevbsid9++w2TJ0/G5cuXSzTubdu2YeDAgVCpVPDx8UGbNm2Qnp6OTZs2ITY2Frt27YK/v79evatXr6JVq1aoXLky/vznPyMtLQ1Hjx5FcHAwdu3ahcDAQJ3ymzdvRlhYGFQqFRo2bIhevXrhwYMHOH/+PKZMmYLhw4fD0dERo0aNwvjx47Fy5Up89tlnevtdvXo11Go1hg8fXuDJgBdlZGTA398fV65cQZ06dRAcHIy7d+9i7969iIuLw+eff46PPvoIAODp6Ynw8HAcOnQIKSkp6Ny5s3Ytsybv1q1biIuLg4eHB9q1awfg9/XvsbGxCA4OhoigdevWaNu2Le7fv49Lly4hMjISo0ePhr29vTa25cuX44MPPgAAtGrVCu3bt8fFixexatUqbN++HQkJCWjYsCEAoG/fvti7dy/Onj0LPz8/eHp6ardjynrrhIQEZGdnF+lGd9nZ2ejVqxf27t2LoUOHYsWKFTA3Nze5fkGCgoL0Tv5o9rdlyxYA0NuPJu4ffvgB06ZN+8MxEBFVeEJERPSKABBT/vWEh4dry27cuFEvf9OmTQJAmjRpIpcuXdKmq9Vq+fTTTwWADBgwQKfO6NGjBYAEBQVJTk6ONv348eNib28vAESpVOrUmTlzpgCQ6Ohog3EqlUq940lNTRU7Ozuxt7eXPXv26OTt2rVLKlWqJG5ubqJSqbTp0dHR2uOdNGmS5Ofna/OioqIEgLRv315nW8nJyWJtbS0WFhby7bff6uSp1WqJi4uTJ0+eiIjI/fv3xdbWVpycnHT2KyKSn58vderUEXNzc8nIyDB4nC/r3r27AJCQkBCd7R08eFBsbW3F3Nxczpw5o1NH06YHDhzQ296BAwcEgISHh+vl+fv7CwCJiYnRyztx4oQ8fPhQ+/7o0aNibm4uLi4ukpiYqFN25cqVAkB8fX110o21cWGmTZsmAGTFihUG818+5nv37knr1q0FgEyZMkWvvCaWmTNnFrpfTb9LTU0ttJxarZbg4GABIGPHjjVYpkaNGmJlZSWPHz8udFtERCTCadtERPTKmTqttFu3bhgwYIBe/Xnz5gF4/uijF68WaqY++/j4ICYmBvfu3QMA5OTkYO3atTAzM8OyZctga2urrdO6dWu8//77JXZs//znP5GTk4MFCxbg3Xff1ckLCgrCmDFjkJ6ejh9++EGvrru7O+bPnw8zs9//PY8bNw5Vq1bFsWPH8PTpU216VFQUnjx5guHDhyMkJERnOwqFAoGBgbCysgIAODg4YODAgbh79y7++9//6pT98ccfkZaWhm7duqF27dpGj+/KlSvYsWMH7O3tsXTpUlhaWmrz2rVrh9GjRyM/Px9ffPGF0W2Z4u7duwCg91kC0F6l1/jHP/6B/Px8fPXVV3rTqIcNG4YePXrg+PHjOHPmTInE9tNPPwEA6tevb7Ss5mr9iRMnsGDBAixcuLDAsrNnzy50+vW1a9dMii8iIgLbtm1DQEAAlixZYrBM/fr1oVKpcOHCBZO2SUT0JuPgmYiIXrnw8HCDrxen3wJAjx499OreuXMHZ8+ehZeXF5o0aaKXr1Ao4Ofnh/z8fJw6dQoAcOrUKTx+/BgtW7aEh4eHXp2//vWvJXRk0K757d27t8H89u3bAwBOnDihl9exY0edwSgAWFhYwN3dHXl5eTpruffu3QsAGDVqlElxjR49GgDw9ddf66Rr3o8cOdKk7Rw6dAjA8xMB1apV08sPCwsDABw8eNCk7RmjGQSHhYXh5MmTUKvVBsup1Wrs27cPtra26Ny5s8EyhX32xXHnzh0AQNWqVQstd+nSJfj5+eHXX3/FihUrMH369ELLN2/evMC/kfDwcNjZ2RmNbePGjZg3bx48PDwQExMDCwvDK/U0bag5SUFERAXjmmciInrlTL1BUZ06dfTSNFenL126ZHR9rubK840bNwAASqXSYDlDa0WLSxOfi4uLSbG9yNXV1WBZzdXVF28slZ6eDgAGTwYY0qpVK7zzzjvYu3cvUlNT4e7ujtu3byM2Nhaurq4ICgoyaTuaz7Kgz0yTnpGRYdL2jJk/fz5+/vlnxMbGIjY2FlWrVkW7du3Qo0cP7XOjgeefp+bmYS+fgHiZoc++OB48eAAAOle/DRk7diyePXuGyMhIjBgxwuh2g4ODMWvWrALz4+PjkZOTU2D+qVOnMHToUFSuXBnbt283eJJDo0qVKgCgcyM+IiIyjINnIiJ6bWkGRi/SXHl0dnYu8AqjRkGD5ZJi6CqoJi08PLzQur6+vnppL07XLg2jR4/GyJEjsWrVKsydOxdr165FXl4ehg4dWiI3rQJg0g3HisLNzQ2JiYnYv38/duzYgYSEBO1AeuHChTh69CiqV6+u/dzt7e3Rp0+fQrfZuHHjEonNwcEBAPDo0aNCyw0YMADfffcdoqKi0KNHD+3N0ErDrVu30LNnT6hUKmzatAmNGjUqtLzmBICjo2OpxUREVFFw8ExEROWK5upsjRo1TL6CXatWLQAocK1oQemaK5iGHoeUn5+PW7duGYwvJSUFixcvRvXq1U2Krzjc3Nxw6dIlpKSkwMfHx6Q6ISEhmDx5MqKjozFr1iysXLkSZmZmGDZsmMn71ayLLugzM/XKe1FYWFggMDBQe7fxa9euYejQodi/fz8iIyOxcOFC1KhRA9bW1jAzM0N0dHSJD+INqVmzJoDnj0ArzPDhw+Hn54exY8eiU6dOiI+Ph7e3d4nHo1KpEBwcjIyMDERGRqJ79+5G62RlZQEAnJycSjweIqKKhmueiYioXHF1dUWDBg3wyy+/IDk52aQ6LVq0gI2NDU6dOoUrV67o5W/cuNFgPc2g29B+Dhw4gLy8PL309957DwCwdetWk2IrLs0NtFasWGFyHTs7O4SGhuLGjRuYOnUqLl26hM6dOxucHl8QzaOkdu/ebXCq7zfffAPg9/XFpUGpVGofrXTu3DkAzwfYHTt2xMOHD7Fv3z6Tt6U5QaJ5JnZRNG/eHAAKfH72i8aMGYNly5bh5s2bCAgIKPHHowHAiBEjcPz4cYSGhmLq1Kkm1fn1119hZWWlfXwXEREVjINnIiIqdyIiIqBWq9GnTx8kJSXp5WdmZurcGMve3h5hYWHIz8/HBx98gMePH2vzEhMTsWzZMoP70TyL+ZtvvtG5E3hqairGjx9vsM6kSZNgY2ODyZMn4z//+Y9evkqlQkxMDK5fv27KoRboww8/hLW1Nb7++mt8//33Onkigj179uiskdbQ3DgsKioKAExag/uievXqoVu3bnj06BEmTJigcwLh6NGj+PLLL2Fubl5idzCPiooyeIV/586dAJ5fgdf4+OOPYWZmhiFDhiA+Pl6vTnZ2NlavXq3T/por6aYMgF+mOUFw8uRJk8q///77WLJkCW7cuIGAgACkpKQUeZ8FWbhwIdavXw9fX1+9m8IVJCUlBZmZmWjdurXBJRJERKSL07aJiKjcCQkJwfnz5zF//ny0aNECPj4+8PDwgIggJSUFP/30E+zt7XUGhgsWLEBCQgJ27twJDw8P+Pv7IysrC/v378eoUaMMPlrJw8MDgwYNwrp16+Dj4wN/f3/k5ubi2LFj6Nq1K3Jzc/WmL3t6emLDhg0ICQlBnz594OnpiYYNG8LOzg4ZGRk4ffo0cnJycObMmQJvEGYKb29vREdHY9CgQRg4cCDmzJmDZs2a4cGDBzh37hzS09ORlZWlfVyVRtOmTdG2bVscOXIEzs7O+Mtf/lLkff/73/9G+/btsW7dOiQkJOBPf/oT7t69i/j4eOTn52Px4sUmTyU3Zvbs2Zg8eTKaN28OLy8viAjOnj2L5ORkVKtWDZMnT9aWbdeuHb744guMGzcOAQEBaNKkCby9vVGpUiVcvXoVSUlJUKlU6N27N2xsbAAAgYGBsLa2RlRUFM6dO4fatWtDoVBgypQpRh9B5e/vD3t7e4MD9YKMHz8earUaH330EQICApCQkAB3d/difTYvmjFjBoDnd/7WnCB52aJFi1CjRg3te03c3bp1+8P7JyJ6I5Txc6aJiOgNAkBM+dcTHh4uAOTAgQOFlktISJB+/fpJ7dq1pVKlSlK9enVp1qyZjBs3ThISEvTK37t3T8aMGSPOzs5iZWUlDRo0kEWLFolarRYAolQq9eqoVCqZPn26uLm5iaWlpXh4eMjcuXPl2bNnolQqCzyey5cvy9ixY8XLy0usra2lcuXKUr9+fRk4cKBs2rRJVCqVtmx0dLQAkJkzZxrcVocOHQSApKam6uWdPXtWQkNDxcXFRSpVqiQ1a9YUPz8/Wbx4seTl5Rnc3ieffCIAZMaMGQbzTXHv3j2ZNGmSeHh4iKWlpTg6OkpgYKDExcUZLF9Ymx44cEAASHh4uF7eunXrJCQkROrXry+VK1eWypUrS6NGjWTixIly/fp1g/s6c+aMhIeHi1Kp1MbWuHFjGTp0qOzYsUPUarVO+bi4OPHz8xN7e3ttHzXW9zRGjBghAOTEiRNFOuZFixZp+5ymXWfOnFloP9DQ9LsX+4Mm7sJeL/efTp06SaVKleTWrVsmHSsR0ZtOISLyqgbqREREryuFQgGlUqkzPbsiEhE0bNgQycnJuHz5MurVq1fWIZVrSUlJePvttzFu3DgsXbq0rMMx2fXr16FUKtG3b1+9af9ERGQY1zwTERG9QWJiYnDx4kV07dqVA+cS4OPjg379+mH16tW4c+dOWYdjss8++wxmZmaYM2dOWYdCRFRucPBMRET0Bhg+fDj69u2LsLAwmJubY/bs2WUdUoWxYMEC5OXlYeHChWUdiklu3ryJFStWYMSIEUbXdRMR0e84bZuIiAgVf9q2QqGAhYUFvLy8MGfOHPTt27esQyIiIipXOHgmIiIiIiIiMoLTtomIiIiIiIiM4OCZiIiIiIiIyAgOnomIiIiIiIiM4OCZiIiIiIiIyAgOnomIiIiIiIiM4OCZiIiIiIiIyAgOnomIiIiIiIiM4OCZiIiIiIiIyAgOnomIiIiIiIiM+D8+9kdiKMAGbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+vvj4+NC7d28eeughMjMznfk2na+q6OFFRT/gQ6l0dxdCCCFqUCwWi6W1B2FLaWkpF154IZs2bSIqKooxY8aQkpLCli1bCA8PZ9OmTSQmJjZ6nSeffJJ//vOfKIrCoEGD6NGjB5mZmaxfv56ysjJGjx7N8uXL8fX1tZ6zYsUKJkyYAEBCQgKDBw+moqKCjRs3kpWVRWRkJKtXr6Znz55Nek/5+fkEBQVhNBoJDAxs2hekKcwmeH0I5CbzdMUtZPa+hbdvHOK6+wkhhBCtrCk/Y9125mfu3Lls2rSJESNGcOjQIb788ks2b97Myy+/TGZmJjNmzLDrOn5+fjzyyCOkpKSwY8cOvvjiC1auXMmePXvo0qUL69atY+7cubXO0el0TJ8+nc2bN5OcnMw333zDkiVLOHLkCJMmTeLUqVPcdtttrnjbzqHTw4hZANyu/5mTWQWtPCAhhBDCfbjlzE95eTkREREYjUZ27NjBOeecU+v1gQMHsnv3brZt28aQIY7PaHz++edcf/31JCQkkJycbNc56enpxMTEAJCSkkJ8fLzd92uxmR+A8mJML/dGX5bH/ZYHefXZp1AUxbX3FEIIIVpJm5/5Wb9+PUajkaSkpDqBD8DVV18NwNKlS5t1n4EDBwJqQGOv6OhowsPDm3xei/PyxXLu7QDcZFlCTlF5Kw9ICCGEcA9uGfzs2rULgMGDB9t8XXt+9+7dzbrPsWPHAIiMjLT7nLy8PHJzc5t8XmvwGH435XgwRHeYrP1rW3s4QgghhFtwy+DnxAm1OnFsbKzN17Xnjx8/3qz7zJ8/H4Bp06bZfc6bb75JZWUl/fv3p2vXrs26v8sFdGadzzj1wx0LWnkwQgghhHtwy+CnsLAQoNYOrJr8/PwAKChwPJF3wYIFrFixguDgYB577DG7zvnjjz+sydEvvPBCo8eXlZWRn59f69HSdkTfAEBUxgrIOdbi9xdCCCHcjVsGP662du1a7rvvPhRF4cMPPyQ6OrrRc06fPs2VV15JaWkp999/PxdffHGj58ybN4+goCDrIy4uzhnDbxJDTF9+Mw1EwQKb3m7x+wshhBDuxi2DH39/fwCKi21XJi4qUov2BQQENPnae/fuZdq0aZSXlzN//nyuuOKKRs8pKCjgkksuISUlhWuuuYaXX37Zrns9/vjjGI1G6+PkyZNNHm9zdQn14z3TFPWTPxZCcU6Lj0EIIYRwJ24Z/HTp0gWA1NRUm69rzzdlmzlAcnIyEydOJDc3l2effZZ777230XNKS0u57LLL2LFjBxMnTmThwoXodPZ92by9vQkMDKz1aGnxIb5sMPflIF2hohi2fdjiYxBCCCHciVsGP9oW9B07dth8XXt+wIABdl8zIyODCRMmkJGRwX333cczzzzT6DmVlZVce+21rF69mpEjR/Ltt9/i5eVl9z3dQXyoL6DwdnnVMt2Wd6GyrNXGYzJb2Hg0m8U709h4NBuT2e3KTAkhhGjnPFp7ALaMGjWKoKAgjh49ys6dOxk0aFCt1xctWgTA1KlT7bpebm4ukyZN4ujRo9x22228+uqrjZ5jsVi47bbbWLJkCYMGDeLHH3+0Jlq3JcG+XgQaPPihdDj/9vsWz8JTsOdrOOfGFh/Lsr0ZzF66jwxjqfW5qCADz0ztw+R+US0+HiGEEB2TW878eHl5cc89anPOWbNmWXN8AF555RV2797NBRdcUKu68xtvvEGvXr14/PHHa12ruLiYKVOmsGfPHqZPn857771nV6Xj+++/n4ULF9KrVy9++eUXgoODnfPmWkF8qB+VeJCcdJP6xIY3oIULey/bm8HMhTtqBT4Ap4ylzFy4g2V7M1p0PEIIITout5z5AbUh6YoVK9iwYQPdu3dnzJgxHD9+nM2bNxMeHs6HH9bOXcnKyuLgwYNkZNT+IfrEE0+wceNG9Ho9Hh4e3H777Tbv9/HHH1s/Xrx4Ma+99hoAcXFxPPzwwzbPeeyxx+jVq1cz3mXL6BLiy540I5uCp9LD623I3A9HVkL3i1rk/iazhdlL92Er3LIACjB76T4m9IlEr5MWHEIIIVzLbYMfg8HAb7/9xrx58/jss8/4/vvvCQkJ4dZbb2XOnDn1FkA8m1aN2WQy8dlnn9V7XM3gRzsH4Ndff633nFtvvbVtBD+har2kw/l6GHwLbHoTNr7eYsHPluScOjM+NVmADGMpW5JzGJEU2iJjEkII0XG5ZWPT9qpFG5vW8MWWEzz27R7O7xHOf6+MhPmDwGKCv66DyP4uv//inWnc98XORo+b/5dBTBsU4/LxCCGEaH/afGNT4VzazM+J7CII7gJ9L1df2PBGi9w/IsDg1OOEEEKI5pDgpwOID1V3qaXmllBpMsMINZmcvYvAmOby+w/tGkJUkIH6snkU1F1fQ7uGuHwsQgghhAQ/HUBkoAEvvY5Ks0XNvYkZDPGjwFwJW95x+f31OoVnpvax+ZoWED0ztY8kOwshhGgREvx0AHqdQmyIDwAncqpahoysqm697WModX3D1cn9onj7xsF1ApzIIANv3zhY6vwIIYRoMRL8dBDxIWrez/HsquCn+yQI6wFlRrXqcwuY0Cey1j+4O8d0Zd2j4yTwEUII0aIk+OkgtLyf4zlVBSN1Ojj/EfXjDa+3yOzPqfxSKmq0s/Dx8pClLiGEEC1Ogp8OokuItuOruPrJfleqsz+lebDZ9bk/KVlFtT4/ZSxx+T2FEEKIs0nw00HEh5617AWg08MFj6ofb3wdSo0uHUNKdu3gp6HCh0IIIYSrSPDTQVhnfnKKqVXXsu8VENZTDXxcPPujBV49OwcAal8vIYQQoqVJ8NNBxFUFP4VlleQUlVe/oNPDWG325w0oyXPZGLRlr+GJaj0fCX6EEEK0Bgl+OgiDp57IQLWC8vGc4tov9rkcwntVzf4scNkYtJmfYYlq/66CskoKSitcdj8hhBDCFgl+OpDqNhdnBT+1cn/ecsnsj9lsse406xsdSIBB7al7Ol9mf4QQQrQsCX46kDq1fmrqczmE91br/mx62+n3Pl1QSmmFGQ+dQkywD1FB6iyUJD0LIYRoaRL8dCDajq8TZy97gVr3R8v92fQWlOQ69d4pWeo9Yzv54KHXERmkVpyW4EcIIURLk+CnA+lSVejwRE6R7QN6T4OIvlCWry5/OdHxqm3uCWHqGKKq8o8k6VkIIURLk+CnA2lw2Qtqz/5sXgDFOU67d0rVPROqArBIWfYSQgjRSiT46UC0Za8zBWWUlJtsH9RrKnTup87+bHLe7I8286ONQcv5kSrPQgghWpoEPx1IsK8XgVW7rGzm/YA6+6Pt/NrkvNmf5KoaPzLzI4QQorVJ8NPBWBucZteT9wPQ61Lo3B/KC9TCh81ksVisS23VMz9qwvMp2eouhBCihUnw08HUbHNRL50Oxj6mfrz5nWbP/mQWlFFSYUKnQGwn9f7azE9ecUX9S3BCCCGEC0jw08F0sdXg1JZeUyCyP5QXwobXm3VPLdk5tpMvXh7qP7lAgwe+XnpAZn+EEEK0LAl+Ohjrjq+GZn4AFAXGPq5+vOVdKMp2+J4pZyU7q5dXauT9SNKzEEKIliPBTwdT3eKigZwfTc9LIHJA1ezPaw7f01rjpyrfSFO940tmfoQQQrQcCX46GC3hOTW3BJPZ0vDBtWZ/3oOiLIfuqVV3rjnzAxAZKFWehRBCtDwJfjqYyEADXnodlWYL6Xl2LDf1vBiiBkFFkcOzPyky8yOEEMKNSPDTweh1CrEh6oxLgzu+NGfP/hRmNul+Nbe5a60tNFLrRwghRGuQ4KcDarTNxdl6TILoc6CiGDbMb9K9sovKKSyrRFEgriro0lhnfvIl4VkIIUTLkeCnA7IWOqyvwenZas3+vN+k2R8t2Tk6yAdvD32t1yJl2UsIIUQrkOCnA7IWOrR35geg+0SIHgyVJbD+P3aflpylLXn51nlNq/KcVVhOWaUUOhRCCNEyJPjpgOLtLXRYU83Zn60fQMFpu06rbmjqV+e1Tr6e1qKHZ/LL7B+LEEII0QwS/HRANVtcWCyNbHevqfsEiDlXnf2xc+eXVt05IbTuzI+iKNa8H0l6FkII0VIk+OmA4qqCn8KySnKKyu0/0YHZn/oKHGoiA6XKsxBCiJYlwU8HZPDUW4OORttcnK3beIg9z67cH4vFQnJWVfATZjv4kVo/QgghWpoEPx1UdZuLJgY/ilLd8X3rB5B3st5D84orKCitVO8XUnfZCyAySKo8CyGEaFkS/HRQ8TXyfposaTzEjwZTGax8rt7DkquWvKKCDBg89TaPkZkfIYQQLU2Cnw5KKzi47nAWG49mN97nqyZFgUlz1Y/3fAWp220edtxGN/ezWROe8yX4EUII0TIk+OmAlu3N4MN1KQBsScnhuvc2MfqFVSzbm2H/RaLPgYHXqR8v/wfY2DWmNTStL9kZqmv9nJKEZyGEEC1Egp8OZtneDGYu3EFeSUWt508ZS5m5cEfTAqBxT4GHD5zcBPuX1HnZutOrnmRnqK7yfKagjAqT2f57CyGEEA6S4KcDMZktzF66D1sLXNpzs5fus38JLCgGRt6rfvzr01BZu1BhQzV+NKF+XnjqFSwWyCxwTaFDk9nCxqPZLN6Z1vQlPiGEEO2OR2sPQLScLck5De6qsqDuutqSnMOIpFD7LjrqPtjxCeSmwJZ3q4MhGq7urNHpFDoHGkjNLSHDWEp0sE+9xzpi2d4MZi/dV+t9RwUZeGZqHyb3i3LqvYQQQrQNMvPTgZwpsC+p2N7jAPD2V5e/ANb8G4qyATAWV5BbrC6tNZTwDK7b8aUt8Z0d8Dm0xCeEEKLdkOCnA4kIMDj1OKtB10PnflBmhDUvAJBSNesTEeCNr1fDE4zVtX6cl/Ts9CU+IYQQ7YYEPx3I0K4hRAUZUOp5XUGdhRnaNaRpF9bpYWLV1vdtH0DWYWvw09BOL40rZn6assQnhBCiY5HgpwPR6xSemdoHoN4A6JmpfdDr6nu1AUkXQo/JYK6EX5+2doxPCGt4yQtq9PdyYq0flyzxCSGEaBck+OlgJveL4u0bB1u3mGs6+Xry9o2Dm5cEPGEOKHo4+BMeJ9YCDSc7a1wx8+OyJT4hhBBtngQ/HdDkflGse3Qcn985nNHdwgCYNiim+bufwnvAuTPUe6S9jg6zXctekS4Ifly2xCeEEKLNk+Cng9LrFEYkhXL1kFgA/jiZ55wLj30MvANJrDzGlfq1je70guoqz6fzS52WgFxzia8+Di/xCSGEaNMk+OnghsR3AuDPNCMl5abmX9AvjNKRfwfgIY+viA9oPJgJD/BGr1OoNFvILnReoUNtiS/Ez6vW80E+TljiE0II0WZJ8NPBxXbyoXOgN5VmC7tT85xyzaMJN3DCHE6kkkvAjgWNHq/XKUQEeAM0uEPLEZP7RfG3sUm1npvYp7MEPkII0YFJ8NPBKYpinf3ZdjzXKddMNpp4obKq6en6+ZDfeDFBLe/H2cEPwLEsddt9z84BAOxJMzr9HkIIIdoOCX4EQ+LVpN8dTgp+jmcX86N5GCk+faGiGFbNbfSc6h1fzu/ufuR0IQBXDYkB4NDpAorLK51+HyGEEG2DBD/COvOz/UQuZickHCdnFQEKW3o8pD6x83+QsbvBcyIDq6o8O7HWj+ZIphr8jEwKIzLQgNkCe9PynX4fIYQQbYMEP4K+0YEYPHXkFVdYl4iaQ2toakgcDv2uAizwyxNgqT+w0mZ+MvKcG/xkF5aRU1SOokBSuD8D44IA2OWs3W1CCCHaHAl+BJ56HQNigwHYfrz57R5StOrOob4w/hnQe0Py73BoWb3nuKLWD8CRM+qsT0ywDz5eegbGBQOw00nJ3UIIIdoeCX4EAOdqS1/NzPspKqsks0Ddrh4f6ged4mH4TPXFX54EU4XN86wzP/nOzfk5XBX8dI/wB2BgVZDnrJ1tQggh2h4JfgSA03Z8aT29Qvy8CPLxVJ8c8wD4hkH2Edj2kc3ztJmf08Yyp+QdabSZn25VwU//WHXZ62ROiVNrCgkhhGg7JPgRAAzuogY/xzKLyCkqd/g6Wr5PrcrOhiC48B/qx6vnQUlenfMiAgwoCpSbzOQUO37/sx3N1GZ+1G3ugQZPksLVlhu7U2XLuxBCdEQS/AgAOvl5WYOC5mx5T64Kfur09Bp8C4T3gpIcWPtSnfO8PHSE+auFDp2Z93O4apt7UtXMD1Cd9yNJz0II0SFJ8COszq2q99Ocpa/jWeqyV52eXnoPmFhV72fzO5BzrM65UU4udFhQWsGpqq3z3WoEP4Oqgh/J+xFCiI5Jgh9hpeX9NGfmJ6W+mR+AbhdB0jgwlcOvT9d5OTLQuYUOtXyfiADv6vwjsO5s25VqxNLA9nshhBDtkwQ/wmpIghr87ErNo7zS7NA1tITnhDAbwY+iqLM/ih72L4VDy2u97OyZHy346d7Zv9bzvaMC8NQr5BSVk5rr/IrSQggh3JsEP8IqMcyPTr6elFWa+TO96cnAJeUm6zJTwtnLXprOfWHE39SPf3wIyquLKkYGqVWenZXzY93pFV47+PH20NMnKhCQvB8hhOiIJPgRVjWbnDpS7+d4jhrIBPl4EuzrVf+BYx+HoC5gPKHu/qriqpmfmvk+Gi3pWSo9CyFExyPBj6hlcDOCn5SsGpWdG+LlB1NeVj/e+Ja175e1yrOT+nsdtgY/AXVeG2Atdijb3YUQoqOR4EfUUnPHV1OTgatr/NjI9zlbj4nQ53KwmOCH+8FsqjHzU9LsROTSChMnc9VgzNbMz6CqHl970oxUmhzLbxJCCNE2SfAjahkQG4SnXiGzoKzJycC1enrZY/Lz4B0Iadth6wd0rtrtVVphxlhiuw2GvY5mFmKxQLCvJ2H+dZfgEsP88ff2oKTCZJ0hEkII0TFI8CNqMXjq6Rutzopsa2KTU23mx+ZOL1sCo2B81Zb3lc9hKDlNiJ8aqDQ376dmsrOiKHVe1+kUBsRKh3chhOiIJPgRdTia9Kxtc7dr2Utz7u0Qcy6UF8DPj9ao9eOc4Ofsbe41WZOeJe9HCCE6FAl+RB1ah/dtKfYHP6UVJtKrihPavewFoNPB1PlVtX+WMNnzD8B5Mz9J4Q0EPzLzI4QQHZIEP6IObebn4OkCCkrty705mVOMxQIB3h7WpSu7RfaDkfcAcFPuG/hS2uwqz9UzP3V3emm0mZ+DpwsoKTc1635CCCHaDgl+RB0RgQbiQnywWOCPE3l2naMlO8eH+drMsWnUBY9CcBc6VZzm7x6LmjXzU2Eyk5yl5h/Z2umliQw0EBHgjclscaiooxBCiLZJgh9h05AuTcv7adI2d1u8/GDKKwDM0P+Md9Zex66DmntUabbg66Unumr7vC2KokjejxBCdEAS/AibhiSo9X7sDX60hqZdHQ1+ALpPIDN+CnrFwk2Zr4DZsaWoI2cKAHXWp7FZKMn7EUKIjsetg5+SkhKefvppevTogcFgIDo6mhkzZpCWlmb3NfLy8vjss8+47rrr6Nq1K15eXgQEBDBs2DDmz59PRUX9OS0mk4lXX32V/v374+PjQ3h4ONOnT2f//v3OeHtuTZv5+eNELiZz4wUHterO8U1Jdrah6MI55Ft86Wk+Alvec+ga9fX0sqV65ifPoXsJIYRoe9w2+CktLWXcuHHMmTOHwsJCpk2bRlxcHB999BHnnHMOx44ds+s6L730EjfccANffvklnTp14sorr2To0KHs2rWL+++/n3HjxlFcXFznPLPZzDXXXMMDDzxAamoqU6ZMoW/fvixatIhzzz2XLVu2OPstu5WekQEEeHtQVG7iwKn8Ro9PaWqNn3qER8XzQuVfALCseg6M9ge6Gmtbiwa2uWsGxAQD6lJZblF5k+8lhBCi7XHb4Gfu3Lls2rSJESNGcOjQIb788ks2b97Myy+/TGZmJjNmzLDrOn5+fjzyyCOkpKSwY8cOvvjiC1auXMmePXvo0qUL69atY+7cuXXO+/DDD/nuu+/o3r07Bw4cYNGiRaxevZqvv/6a4uJibrjhBiorK539tt2GXqcwqEswADsaWfoqqzSRnqfuzmruzI+ftwc/eE5ku7k7SnkR/PxIk6/RlJmfIF9PEqsCNpn9EUKIjsGh4Ke8vJzVq1cze/ZsrrjiCkaPHk2/fv0YPXo0V1xxBbNnz2b16tWUlzv2m3R5eTlvvPEGAG+++Sb+/tU/xB544AEGDBjAmjVr2L59e6PXevzxx3nhhRfo0qVLree7d+/O888/D8Dnn39e57xXXlGTb1988UU6d+5sff6qq67isssu48iRIyxevLjpb64N0ba8b2sk+EnNLcFsAV8vPeH+3s2+b2SQH/+ouB2z4gEHfoADP9p9rtls4Whm49vca9KWvqTJqRBCdAweTTn4wIEDLFiwgIULF5KbW3/jy8WLF6MoCsHBwdx8883cdddd9O7d2+77rF+/HqPRSFJSEuecc06d16+++mp2797N0qVLGTJkSFPeQi0DBw4EID09vdbzycnJ7N+/Hx8fH6ZMmWLz/kuWLGHp0qVcddVVDt/f3WlNThtLeq6508uhbe5niQwysOZ0Fw4m3kLvox/ATw9D1/PBu/FgJi2vhNIKM156HXGdfOy634DYIL77I02SnoUQooOwK/hJTU3lySefZOHChZjNZrp06cKUKVMYOnQovXr1IiQkhMDAQIxGI7m5uezfv58tW7awZs0a5s+fz+uvv85NN93EnDlziI2NbfR+u3btAmDw4ME2X9ee3717t73v0yYtbygyMtLm/fv164enp6fL7u/uBnUJRqeoMzun80utjUfPpiU7dw1r3pKXRuvuviriNnpnr4C84/Dbv2DyvEbPPVy10ysx3A8PvX0TmzWTni0Wi1MCOACT2cKW5BzOFJQSEWBgaNcQ9DrnXFsIIYTj7Ap+evToAcCdd97JjTfeyKhRoxo8fvz48daP161bx6effsqnn37K119/TWFh4x20T5w4AVBvoKQ9f/z4cXuGX6/58+cDMG3atFa5v7vz9/agV2Qg+zLy2X48l0v6R9k8LqW5NX7OElkV/KQWWeDSV2DhVbB5AQyYDtF1ZwJrsra1aKC44dn6RAXioVPIKiwnLa+E2E7ND+KW7c1g9tJ9tYo1RgUZeGZqHyb3s/11FEII0TLs+tX47rvv5tixY7z99tuNBj5nGz16NO+88w5Hjx7lzjvvtOscLUDy9bX9Q8jPT/0hW1BQ0KSx1LRgwQJWrFhBcHAwjz32mEvuX1ZWRn5+fq1HWzPEjj5fWnXnJvX0aoA285NhLIVuF0G/q8FihqX3ganhJPOmJDtrDJ56ekcFAs7J+1m2N4OZC3fUqVJ9yljKzIU7WLY3o9n3EEII4Ti7gp9XX321ztJQU0VFRfHqq6826xrOsnbtWu677z4UReHDDz8kOjraJfeZN28eQUFB1kdcXJxL7uNK5yZUVXo+UX/w0+zqzmeJDFJzdayd3SfPA0MQZOyCLe82eO5hO7q52zLAScUOTWYLs5fuw1Y2nPbc7KX77KqdJIQQwjXccqu7trvLVv0dgKIi9YdtQIB9u3lq2rt3L9OmTaO8vJz58+dzxRVXuOz+jz/+OEaj0fo4efJkk8fb2gZXFTv8M81os/lnhclMaq7Wzd05wU+tmR8A/wiY8Jz68aq5YEy1eZ7FYqme+WnCshdU5/3sbGbwsyU5p8G+ZBbU97UlOadZ9xFCCOE4pwc/y5Yt46WXXuLLL79ssHpyQ7Rt6amptn/Iac/Hx8c36brJyclMnDiR3Nxcnn32We69916X3t/b25vAwMBaj7YmtpMPnQO9qTRb2G2jDk5abgkmswWDp47Ogc3f5g7VOT/GkgqKy6uWuc65GeKGQ0URLL0fbOw0PFNQRkFpJToFujax2OKgquBnT5qxWbMyZwrsa8hq73FCCCGcz6Hg56233iIxMZH169fXen769OlMmTKFRx99lOuvv54xY8ZQWtr0b/LaFvQdO3bYfF17fsCAAXZfMyMjgwkTJpCRkcF9993HM8880+j99+7dazOAc+T+bZWiKA3W+7FWdnbSNneAQIMn/t5qLr516Uung6nzQe8NR36FbR/UOU+b9YkP9cPbQ9+keyaF++Pnpae43GStE+SIiID6G6k6cpwQQgjncyj4+e677yguLmbEiBHW55YtW8aiRYuIiYnhscceY+jQoWzdupX33mt6f6ZRo0YRFBTE0aNH2blzZ53XFy1aBMDUqVPtul5ubi6TJk3i6NGj3HbbbY3mHnXt2pXevXtTUlLCjz/WLbDX1Pu3dUOq6v3YqvSckqXl+zgn2Vmjzf6cqrmEFNELJsxWP17+JGQdrnXO4dPVDU2bSq9T6F+V99Ocpa+hXUOsy3a2KKjLekO7hjh8DyGEEM3jUPBz8OBB+vXrh05XffoXX3yBoigsWrSIf/7zn/z222+EhYWxcOHCJl/fy8uLe+65B4BZs2ZZc2xArby8e/duLrjggloFDt944w169erF448/XutaxcXFTJkyhT179jB9+nTee+89u2YoHnjgAQAeeeQRzpw5Y33+22+/ZcmSJXTr1q3OFvn2Spv52X4iF/NZS0LVO72ck++jqZP3oxl6N3S9ACpL4Ns7wVQ9M3ck07F8H83A2GCgeUnPep3CM1P72HxN+1f3zNQ+Uu9HCCFaUZMqPGsyMzM5//zzaz23Zs0a4uLiGDp0KAAGg4GRI0fWWRqz15NPPsmKFSvYsGED3bt3Z8yYMRw/fpzNmzcTHh7Ohx9+WOv4rKwsDh48SEZG7W3ETzzxBBs3bkSv1+Ph4cHtt99u834ff/xxrc9nzJjBTz/9xHfffUevXr0YP348WVlZrFmzBh8fHxYuXIiHh0Nfvjanb3QgBk8decUVHMsqqhVcOHunlyayqqDiqfyzgh+dDi5/G94eAel/wO//hgv/AcDh01U7vRwNfpzU4X14YijeHjrKKs21no+UOj9CCOEWHPrpHRQURFZWlvXz5ORkjh8/zs0331zrOD8/v1qzNk1hMBj47bffmDdvHp999hnff/89ISEh3HrrrXZXigZ1yQvAZDLx2Wef1Xvc2cGPTqfj66+/Zv78+Xz44Yf88MMP+Pn5cdVVVzF79mz69LH923175KnXMSA2mC3JOWw/nnNW8OPcGj+a6pmfkrovBsXApa/Cohnw+0vQbQLEnWfN1XF45qcq+DmQUUBphQmDZ9PyhjQfrU+hrNJMr8gALuodwRu/HaVX5wB+vG+MzPgIIYQbcGjZq1u3bvz+++/WSsjvvvsuiqIwefLkWselpqY2qz6Qj48Pzz33HEeOHKGsrIyMjAw++ugjm4HPs88+i8ViqRPEfPzxx1gslkYftuj1eh544AH27t1LSUkJWVlZfP311x0q8NGcqy191cj7qTSZOZlbFfw0cXdVY+rU+jlbv6ug/3SwmODbO8nNzSGrUG2km9SEAoc1RQcZCPNXd7bty3CsIGVBaQUfrU8G4P/Gd+fCXhEAFJZXSuAjhBBuwqHgZ+bMmZSWljJgwACGDBnCiy++SHh4OJdeeqn1mJKSErZt29YhA4X2yNaOr/S8UipMFrw8dNZlKmepN+enpkv+DYGxkJtMxc9qrldMsA9+3o4tRyqKwsBmFjv878bj5JdW0i3Cn8l9I4mqEcRJYUMhhHAPDgU/N9xwAw8++CBlZWX88ccfxMTE8Pnnn1uLAwJ89dVXFBcX1+rzJdourdjhscwicorUGRZrT68QX3ROntWwudvrbD7BcMXbgELEoS+4SLe9ST29bLHm/TgQ/BSXV/LBOnXWZ9aFSeh0ChEB3ugUqDRbyCosa9bYhBBCOIfDRQ7//e9/k5eXx+nTpzlx4gQXXnhhrdfHjRvHH3/8wR133NHsQYrW18nPi6RwdWlL2/LuqmRnqJ75yS4qp7SibmVpq67nw4hZADzv+R4DO5U3677VSc9N7/H12eYT5BSVEx/qy9QBassUD72OzlWzYul5NvKXhBBCtLhmVXj29vYmPDzc5mtxcXEMHDiw1myQaNvOrar3o/X5cnZD05qCfDwxeKr/PM/kNzJjMv5pTnh2JUzJ59qMf9us/mwvbdkrOasIY7H9FcpLK0y8+/sxAP42NgkPffV/rehgdemrwSU8IYQQLcYte3sJ92St95Ny1syPk5OdQc2/0fJlbO74qsnDmye4lzKLB7FnVsOOTxy+b7CvlzWY252WZ/d5X287yZmCMmKCfbjinNoJ+doslsz8CCGEe3C4UE1mZiZvvfUWa9asISMjg7Iy27+dK4rC0aNHHR6gcB9Dqjq870rNo7zSbJ356eqCZS9Qa/0kZxXVrfVzlsKyStYWRPKSfjpPeH4Gy/4BCWMgNMmh+w6IDSYlu5hdJ/MY0932zGZN5ZVmFqxRZ33+ekEiXh61f6fQZn7S82TmRwgh3IFDwc+ePXsYN24cOTk59W4TF+1PYpgfnXw9yS2uYE+akRNVwY+zW1to7NrxBRyt6un1veEKnog7CSlr4du7YMZy0Df9n/jAuGCW7Epn50n78n6++yOVtLwSwgO8uebcuDqvN1izSAghRItzaNnrvvvuIzs7mxtvvJFdu3ZRUFCA2Wyu9yHah5pNTpfuSqfcZMZTr1hnNpzNrh1fVDc0TeocoFZ/9g6EtG2w7hWH7jsormq7e2peo8F9pcnMW6vVmc27z0+0WRjROvMjOT9CCOEWHAp+Nm/ezIABA/jkk0/o378/fn6uWfYQ7mdwVfCzZFc6AHEhvi4r3mfvjMnhMzUqOwfHwSUvqS+sfh7Stjf5vn2jg9DrFDILyhpdcvthdwbHs4sJ8fPi+mFdbB4THaQte8nMjxBCuAOHgh9/f38pXthBaTu+tFo/QQZPlxXva7TKcxVt5qd7RID6xIDp0PeKqurPd0F501qsGDz19OysXquhej9ms4U3fjsCwO2ju+LrZXuJLSpYDeKyCssor5SZUCGEaG0OBT/jxo1j165dzh6LaANOnTUL88fJPEa/sIplezPqOcNx9ub8HDlTANTo6aUoMOUVCIiG7CPwy1NNvrdW76ehvJ9lf57iyJlCAg0e3Dwivt7jQv288PLQYbHA6UZmkoQQQrieQ8HP3LlzyczM5Mknn8RkaqAAnWhXlu3N4L4vdtZ5/pSxlJkLdzg9ANJyfjILy6gw2Z4xKa0wcSJHTbyu1c3dNwQuf1P9eNsHcOiXJt1by/vZXU+Hd4vFwuur1Fmf20Z1JcDgWe+1FEUhWra7CyGE23Bot1dSUhIbNmxg2rRpfPnll4wdO5aYmBh0urqxlKIoPPVU03/zFu7FZLYwe+k+bC1wWQAFmL10HxP6RDotByjE1wsvvY5yk9laQ+dsKdlFmC0QYPAgPMC79otJ42DYX2HzAlg8C/62EfzC7Lq3NvOzO9WI2Wyp075j5f4z7M/Ix89Lz22jEhq9XlSQDynZxaTLji8hhGh1DgU/FRUV/POf/+TAgQNYLJYG6/hI8NM+bEnOaXD5yYK6PLUlOYcRSaFOuadOp9A5yJuTOSWcMpbYDH4On9byffxRFBtB10XPwrHVkHkAlt4H1y5Ul8Ua0S3cHx9PPYVllRzLKqSblk9E1axPVa7PTSMSCPb1avR6Wt6P1PoRQojW51Dw8+STT/LJJ5/QuXNnrr/+ehITE6WNRTt3psC+H9r2HmevqEAfTuaU1Bt4Ham508sWTx+48l14bzwc+EGt/jzk1kbv66HX0T8miC0pOew8aawV/Kw9nMWuk3kYPHXcMaarXe8j2t5q1UIIIVzOoeDnf//7H+Hh4ezatYuIiAhnj0m4oYgAg1OPs1djtX7q7PSyJWogjHsCVjwLPz2ifh59TqP3HhinBj+7TuZx9ZDqlhVvVOX6XD80njB/7/pOr8Xa30tmfoQQotU5lPCcm5vLmDFjJPDpQIZ2DSEqyEB9C0YK6u6soV1DnHrfxnZ8NTrzoxl5H/S4GExl8OXNUJzT6L2r837yrM9tPpbNlpQcvPQ67jo/sfE3UEVb9kqThGchhGh1DgU/ffv2paCgwNljEW5Mr1N4Zqpa2+nsAEj7/JmpfZxe8LChmZ9Kk5ljWXYGPzodXLEAOnUF4wn45g4wN7xTcWBsMAD7MvIpq1SP1XZ4TT8v1jo2e1Qve8nMjxBCtDaHgp8HH3yQ3377jT/++MPZ4xFubHK/KN6+cXCdH/qRQQbevnEwk/tFOf2eDVV5PpFTTIXJgsFTZzMZug6fYLj2U/DwgaMrYc0LDR4e28mHED8vKkwW9mcUsONELuuOZOGhU7j7/KY1TY2umvkxllRQXF7ZpHOFEEI4l0M5PyNGjOCee+5h7Nix/P3vf2fChAn1bnUH6NLFdtl/0fZM7hfFhD6RbEnO4UxBKREB6lKXq1pcNFTlWWtrkRTuX2crev0X7A9T58N3d6nBT8wQ6DHJ5qGKojAgJpDVh7JYuPE4xzLV+11xTgxxIU1r5hpg8CTA24OCskrS80obn6kSQgjhMg4FPwkJCSiKgsViYc6cOcyZM6feYxVFobJSftNtT/Q6xWnb2RujzfycLijDZLbUCrKqk52bGEgMvBZSt8DW9+HbO+GuNRBSd9fWsr0ZbDueB8CiHanW5/vFBDXxXaiigg0UnC4kPa9Egh8hhGhFDgU/559/vu2aKkI4WZi/N3qdgslsIauwjM6B1Utudic72zJpHmTsgtSt8NVNcPuv6rb4Ksv2ZjBz4Q6bRR2fXfInnQO9m7zMFxXkw6HThbLdXQghWplDwc/q1audPAwhbNPrFDoHeJNuLCXDWFpP8NPANvf6eHjBNZ/AO+fDqT3wwwNw+VugKA1Ws9Y4Us1a2+4uhQ6FEKJ12ZXwvGbNGsxm6UYtWkf1jq/qGROz2dK8mR+AoBi4+kNQdLDrM9j+EdC0atZNIf29hBDCPdgV/Fx44YWEh4dz00038fXXX5Ofn+/qcQlhFRVUd8Yk3VhCSYUJT71CfGjTko9rSbwAxj+tfvzzo5C63WXVrKOCZbu7EEK4A7uCnzlz5tCjRw8+++wz/vKXvxAeHs7EiRN5/fXXSUlJcfEQRUdnnfnJrw4atFmfhFA/PPUOVWyoNup+6HUpmMrhq5uJ9iy267SmVrO2zvxIzo8QQrQqu35qPPHEE2zcuJGMjAzeffddJk+ezIYNG7jvvvtISkqif//+PPnkk2zatMnV4xUdkK0qz81e8qpJUdR8n5AkyE/l3G0PERPo6fRq1jVbXFgsDWUUCSGEcKUm/cocERHB7bffzuLFi8nOzmbJkiXccccd5Obm8q9//YtRo0YRGRnJHXfcwZIlSygpkd9wRfPZyvlxeJt7fQxBasd3T1+U5NX8N3EF4Nxq1tr7KKkwkVdc0cwBCyGEcJTD6wXe3t5ceumlvPPOO6SmprJ161aeeOIJoqOj+fDDD7niiisIDQ1l6tSpvPvuu2RmZjpz3KIDsTXzYy1w6Mx6OZ37wGWvq9c98A6LxuU5tZq1wVNPqJ8XIEtfQgjRmhza6m7LkCFDGDJkCM899xypqaksWbKEJUuWsGLFCn766SdOnTrF008/7azbiQ5Eq/J8Or8Us9mCotjZzd0R/a9Wa/9sXsCQ7Y+x7s7VbDEGO62adVSwgeyicjLySukb7VixRCGEEM3jtOCnptjYWP72t7/xt7/9jaKiIpYvX05AgJN/SIkOIyLAG0WBCpOF7KJyLFgwllSgKJAY7uf8G06YA+k74eQm9F/dxIg7VoCXcypaRwf5sDctXwodCiFEK2rmNpnG+fn5ceWVVzJhwgRX30q0U556HeH+3oDa40ub9ekS4ovBU+/8G3p4wTUfg18EnPkTfrgfnJSgrCU9p0mhQyGEaDV2BT9vv/02FRXNS9CsqKjgrbfeatY1RMdVs7u7dadXuAv7YwVGwTUfgaKH3V+qfcCcoKEu9UIIIVqGXcHPrFmz6N69O6+99lqTE5fPnDnDq6++SlJSEvfee69DgxSiZq0fa/DT2cXNQRNGw4TZ6sfLHofjG5p9yaga292FEEK0DruCn9WrVxMREcH9999PTEwMF198Mc8//zwrV64kNTWVoqIiAIqKijh58iQrVqxg3rx5TJo0idjYWB588EGio6OlJ5hwmFblOcNYyuHTLTDzoxlxD/S5HMwV8MX1kHW4WZeLCZZCh0II0drsSng+//zz2bJlC9999x1vvfUWv/zyC8uXL2+ws7vFYkFRFC666CJmzZrFZZdd5rRBi46nutZPKUcynVjgsDGKApe/DcZUSNsG/7sabl8B/uEOXU4L4k4ZSzGZLc3aOSaEEMIxTdrtdcUVV3DFFVeQkpLCTz/9xNq1a9m9ezdnzpzBaDQSFBREREQEAwcOZMyYMVx88cUkJCS4aOiiI9FyZQ6cKiCzoAxooeAHwMsXrvsCPrgIclPg87/ArT+Ap0+TLxUR4I1OgUqzhazCslpd6oUQQrQMh7a6JyQkWLeyC9ESIquChP0Z+dbPAwyeLTcA/3C4YRG8f5E6A/TtnXDNJ6Br2m4zD72OzoEGMoylpOeVSPAjhBCtwOVb3YVwBm25SNPd1cnOtoR1h+s+B70X7F8Kvzzl0GWipbu7EEK0Kgl+RJsQEehd6/Oklkh2tiV+pJoDBLDpTdj8TpMvoS3hpedJ0rMQQrQGCX5Em2Dw1BPiW73MpdeBydxKndH7Xw3jq1q1LHsMDvzUpNO1mZ902e4uhBCtQoIf0SYs25tBfmml9fMP1qUw+oVVLNub0ToDGv0ADL4FLGZYNAPSttt9arQUOhRCiFYlwY9we8v2ZjBz4Q4qz5rpOWUsZebCHa0TACkKTHkZksZDZQl89hfIPW7XqVqhw3TJ+RFCiFYhwY9wayazhdlL92FrgUt7bvbSfa2zBKb3hOmfQOf+UHQG/ncNlOQ2elp0kLbsJTM/QgjRGiT4EW5tS3JOg7uiLKi7prYk57TcoGryDoAbvoKAaMg6CF/eBJVlDZ4SVVXlOauwjPJKc0uMUgghRA0S/Ai3dqbAvqUhe49zicBouOFr8AqAlLWw5N4Gu8CH+nnh5aHDYoHT+bL0JYQQLa3Zwc+uXbt47733mDdvHkuWLLE+X1ZWRn5+fnMvLzq4iAD7igDae5zLRPZTl8C0LvC//aveQxVFsSY9p8nSlxBCtDiHg5+DBw8ycuRIBg8ezF//+leefPJJvv/+e+vrn332GZ06dWLZsmXOGKfooIZ2DSEqyEB9HbAU1Lo5Q7uGtOSwbOs2Hqb+R/349xdhx6f1HlrdqFWCHyGEaGkOBT8nT57k/PPPZ9OmTUydOpUXX3wRy1nT/NOnT8fLy4tvvvnGKQMVHZNep/DM1D4AdQIg7fNnpvZxnwahg2+GMQ+pH/9wPxxdZfMwLe9Hav0IIUTLcyj4ee6558jKyuL999/n+++/58EHH6xzjJ+fH4MGDWLz5s3NHqTo2Cb3i+LtGwdbO7trIoMMvH3jYCb3i2qlkdVj3JPQ/xowV8JXt8DpP+scEhMsMz9CCNFaHGpsumzZMgYMGMCMGTMaPC4hIYFffvnFoYEJUdPkflFM6BPJluQczhSUEhGgLnW5zYxPTYoC096E/Aw4vk7dAj9jGQR3sR4SFSRVnoUQorU4NPNz5swZevbs2ehxFRUVFBcXO3ILIerQ6xRGJIUybVAMI5JC3TPw0Xh4w18WQlgPyE+DT6ZCfrr15eplL5n5EUKIluZQ8BMaGsqJEycaPe7QoUNERbnZkoQQLcWnE9y8GDolQG6KGgAVnAaqCx1KZ3chhGh5DgU/o0aNYuvWrezcubPeY9asWcPevXsZO3asg0MToh0IjIZblkJQHGQfgf9Og6IsoqtmfowlFRSVVTZyESGEEM7kUPDz0EMPYbFYmDZtGj///DMmk6nW66tWreKmm27Cw8OD+++/3xnjFKLtCu4CtyyBgCjI3A+fXk6AuYAAbzXlTpKehRCiZTkU/AwbNozXXnuN9PR0Lr30UoKDg1EUhW+++YZOnToxYcIE0tPTeeONNxgwYICzxyxE2xOSqM4A+UXAqT2w8CqSgtRfGiTpWQghWpbDRQ7/9re/sXbtWqZOnYqiKFgsFgoKCigrK2PSpEmsWbOGu+66y5ljFaJtC+uu5gD5hED6Dv5dOgdfSmXmRwghWphiObs6oQMsFgtZWVmYzWbCwsLQ6/XOGFu7k5+fT1BQEEajkcDAwNYejmgtGbvhk0uh1MhGUx+2jXqHeyfLDKkQQjRHU37GOjTz8/vvv3Po0CHr54qiEB4eTufOnWsFPocPH+b333935BZCtF9RA+Cm7yjX+zFCv49Je/4OFbL0JYQQLcWh4Gfs2LG88MILjR734osvcuGFFzpyCyHat5ghrB++gCKLNz2KtsFXN0NleWuPSgghOgSHc37sWS1zwoqaEO2Wd9eR3F7xMKV4weHlsOg2MFW09rCEEKLdczj4sUd6ejr+/v6uvIUQbVZ0sA+bzH2YZXoYi94bDvwA390NZlPjJwshhHCY3b29/vvf/9b6/MiRI3We01RWVnLw4EFWrFjB8OHDmzdCIdoprVHryoq+FF3zIf7f3wp7vwG9F0x7C3Qu/d1ECCE6LLt3e+l0OhTF/l5KFosFg8HAkiVLuOiiixweYHsiu73E2YbM+ZXsonJ+/L/R9DX+rnaBt5hg8C0wdb7aJFUIIUSjmvIz1u6Zn6efftpaz+e5555j0KBBTJs2zeaxXl5eREdHM3HiROntJUQDooN9yC4qJyOvlL59psJV78E3d8COT9TmqBe/KAGQEEI4md3Bz7PPPmv9+OOPP+aiiy7imWeeccWYhOgwooIM7EkzVhc67HeVuuvr+5mw5V11CWziXAmAhBDCiewOfmpKSUlx8jCE6Jiig9Xu7mk1W1wMug5MZbD0Ptj4BpQVwKWvgk6KhwohhDNIRqUQrSiqKum5TouLIbfC1NdA0alLYItug8qylh+gEEK0Qw7N/GjWrVvH4sWLOXz4MAUFBTbr+iiKwsqVK5tzGyHaLW3mJ8NWc9Mht4BPsJoDtG8xlObDtQvBW8pHCCFEczgU/FgsFm6//XY++eQTa8CjJUNrtM+bskNMiI4mOlid+UnLq6e5aZ9p4B0IX9wAx36D/14GNywC35AWHKUQQrQvDi17LViwgI8//pghQ4bw66+/cuWVVwJw8OBBfv75Z2699VZ0Oh0PP/wwx44dc+qAhWhPooLUmZ/T+aWYzPVUnUi6EG5ZqnaDT9sOH10M+ektOEohhGhfHAp+Pv74Y/z8/Pj5558ZP348AQEBAHTv3p1Jkybx4Ycf8vnnn/PSSy+xc+dOZ45XiHYlIsAbnQKVZgtZhQ3k9MQOgRnLICAaMg/AB5Mg60jLDVQIIdoRh4Kf/fv3M3LkSEJDQwGsS1smU3VZ/quvvpohQ4bw0ksvOWGYQrRPHnodkYHq0ld6fUtfmvCecPtyCO0GxhPw4STI2NUCoxRCiPbFoeDHbDZbAx8AX19fAHJzc2sd1717d/bs2dOM4QnR/kVVJT2n20p6PltwF7htGUQNhOIs+PhSSFnv4hEKIUT74lDwExMTQ3p6dc5BfHw8AH/88Uet4w4dOoSHh+MbykpKSnj66afp0aMHBoOB6OhoZsyYQVpaWpOus2bNGmbPns2UKVMIDw9HURQSEhIaPe/QoUPcdtttxMfH4+XlRUBAAOeddx6vvvoq5eXlDr4rIWqrd7t7ffzD4ZYfIH40lOXDwivh4M8uHGHjTGYLG49ms3hnGhuPZtefvySEEG7Aochk8ODBrFy5EpPJhF6vZ+LEiTz66KM88sgjfP7558TExLBgwQK2b9/O+PHjHRpYaWkp48aNY9OmTURFRTFt2jRSUlL46KOP+OGHH9i0aROJiYl2Xeu+++5j166mLQ9s2LCBCRMmUFxcTO/evbn88ssxGo2sXbuWBx54gMWLF7NixYpmBXdCQPV2d7tmfjSGQLjxG7X+z8Gf1N1g095UCyS2sGV7M5i9dB8ZxurxRwUZeGZqHyb3k/Y2Qgj349DMz2WXXUZWVhY//vgjAAMHDuQvf/kLu3btom/fvgQHB/PYY4/h4eHBP//5T4cGNnfuXDZt2sSIESM4dOgQX375JZs3b+bll18mMzOTGTNm2H2tiRMnMnfuXJYvX86ff/5p1zn33HMPxcXFzJs3j3379vHVV1+xfPlyjhw5QmJiImvWrOHTTz916L0JUVN0U2d+NJ4GmP4pDLxebYb6/V9h45suGGH9lu3NYObCHbUCH4BTxlJmLtzBsr0ZLToeIYSwh91d3c9WVlaGh4cHer1acr+iooKXX36Z77//ntzcXHr06MEjjzzCmDFjmnzt8vJyIiIiMBqN7Nixg3POOafW6wMHDmT37t1s27aNIUOGNOnap06dIioqivj4+HrbdBQWFhIQEICvry8FBQXodLVjxFdffZUHHniAmTNn8tZbb9l9b+nqLmxZ/ucp7v50OwNjg1h8z+imX8Bshl+fUlthAIx5CMY96fJ+YCazhdEvrKoT+GgUIDLIwLpHx6HXSb0vIYRrNeVnrMPtLby9va2BD4CnpyePPfYYmzZt4uDBgyxdutShwAdg/fr1GI1GkpKS6gQ+oO4kA1i6dKljg2+Ep6dnnYDHlppJ30I4Krqq1k96PUFEo3Q6tfnp+KfVz9e+xKnP/8biP064NP9mS3JOvYEPgAXIMJayJTnHJfcXQghHORT8DB48mGuuucbZY7HS8nMGDx5c7/0Bdu/e7ZL7e3t7c/7551NcXMyLL75Y67X09HTefPNNPD09uemmm1xyf9GxRFVVec4qLKO80uzYRRQFxjzI3sGzMaMQeegzPL+9ndveW8PoF1a5ZPnpTIF9wZq9xwkhREtxKPg5ePAgnp6ezh6L1YkTJwCIjY21+br2/PHjx102hgULFhAXF8fjjz9Onz59mD59OpMnT6Zbt25YLBZ+/PFHevTo4bL7i44j1M8LLw8dFota6dlRy/ZmMHVDd+4pv5dyi55L9Fv42ms2GNNckn8TEWBw6nFCCNFSHAp+unfvTnZ2trPHYlVYWAhU1w86m5+fHwAFBQUuG0PPnj1Zt24dgwcPZv/+/Xz99dcsX76c0tJSLrzwQvr27dvoNcrKysjPz6/1EOJsiqJYk57r7fHVCJPZwuyl+7AAP5mHc335E2RZAumvS2Gx91MMUo4we+k+py6BDe0aQlSQgfqyeRTUXV9Du0ofMiGEe3Eo+Ln99ttZs2YNBw4ccPZ43MaqVasYOHAgFRUVrFq1ivz8fJKTk3niiSf46KOPGDVqFJmZmQ1eY968eQQFBVkfcXFxLTR60dZoPb6avOOrytn5N9ssvbi8fA77zXFEKHl84TWHYQUrnJp/o9cpPDO1T4PHPDO1jyQ7CyHcjkPBz7333sutt97KBRdcwKuvvsqRI0ecWvTP398fgOLiYpuvFxUVAVh7ijlbTk4O11xzDRUVFfz8889ceOGFBAQEkJCQwJw5c5g1axYpKSmNtu54/PHHMRqN1sfJkyddMl7R9ml5P02q9VODrbyaVEs4V5XP5hfTELyVCv7j9Rahm/6l7g5zksn9oph1YVKd53299Lx942Cp8yOEcEsOBT96vZ733nuPzMxMHnroIXr27ImPjw96vb7Ow5EigF26dAEgNTXV5uva81plaWf78ccfycnJYfjw4cTExNR5XUv2/v333xu8jre3N4GBgbUeQtgSE9y8mZ/68mqKMXB3xd95o3IaAD0Ovw9fXA9lzlsyPpal/jIyqW9n7hjdFYDIQG8JfIQQbsuh8sRxcXHWZqauMHDgQAB27Nhh83Xt+QEDBrjk/lpwFRQUZPN17fmze5kJ4Sht2cvRmR8t/8bW1nMLOl6uvJZMQ1eetbyNcuhn+GAiXPc5dEpozrDJLizj132nAbj/oh5EB/nwwfpkjmUVc6agVJKdhRBuyaHgp77igM4yatQogoKCOHr0KDt37mTQoEG1Xl+0aBEAU6dOdcn9IyMjAbVXmdbCo6atW7cC2NUfTAh7VC97OTbzo+Xf/HWh7V8YAEZc/leU4MvUmZ8z++C9cWqF6IRRDt0T4Ls/0qgwWRgQG0TvKHVms09UIH+m57P5WA5TB0Y7fG0hhHAVh4scupKXlxf33HMPALNmzbLm+AC88sor7N69mwsuuKBWdec33niDXr168fjjjzf7/pMnT8bb25vk5GSeeuopzDVyJA4ePMjTT6vF5LRii0I0V/Wyl+Nb3cf37oy/d93fZzx0Cm/dUJV/EzsE7voNogZBcTb8dxps/8Sh+1ksFr7YquaxXXtedTL/8ES1+OemY67bESqEEM3hlsEPwJNPPsmwYcPYsGED3bt359prr2X48OE8+OCDhIeH8+GHH9Y6Pisri4MHD5KRUbeWyfvvv8/w4cMZPnw4U6ZMASAjI8P63PDhw2stsUVFRfHSSy+hKArz5s2je/fuXH311Vx44YUMHDiQ9PR0LrnkEm699VaXfg1Ex6F1djeWVFBUVunQNVYfzKSwrJJQP08W3j6U56/sj5deR6XZQrCvV/WBgdFw28/Q90owV8DS/4OfHwNT0+6740QeR84UYvDU1ZrhkeBHCOHu3Db4MRgM/Pbbbzz11FP4+vry/fffc/z4cW699VZ27Nhhd0d3UHN4Nm/ezObNm61BTnl5ufW5zZs316nBc88997Bq1Souv/xyiouLWbx4sbXP2JtvvsmSJUuko7twmgCDJwFVszaOJj1/u0PNVbv8nFhGdw/nL0O7cM25akHQjzck1z7Yyxeu/hAufEL9fPPb8Nk1UJJn9/2+qpr1uaR/FIGG6qKnQxNCUBQ4mlnEmWYUbRRCCFdxuLGpaDppbCoaMvHVNRw6Xch/Zwzl/B7hTTo3r7icof9cSbnJzE//N4Y+0eq/r8OnC5jw6u/oFFjz8IXEhdgoHLpvMXz3V6gohtDucN0XENatwfsVllUy9J8rKC438dXdI+oUMpzy2lr+TM/ntevO4TLJ+xFCtIAWaWwqhHCu6GZsd1+6K51yk5neUYHWwAege+cAxnQPw2yBTzfV0w6mzzSYsQwCYyH7MLw/Dg6vaPB+P+5Op7jcRGKYH+cldKrz+ghZ+hJCuDEJfoRwE9p29zQHtrsv2pEGwFWD69alum1UAgBfbDlBcXk9eT1RA+HOVRB7HpQa4X9XwYpnwVRh83At0Xn6ebbLXkjejxDCnUnwI4Sb0Pp7ZTRxu/uRM4XsOpmHXqcwbVDd4GdsjwgSQn3JL63k26ogyaaAznDLD3DeHern616Fj6dAXu3K5IdOF/DHCfV+V9oItgDO66rm/RyTvB8hhBtyKPg5ceIEOTmN9wjKzc21dmgXQjQsysHt7lqi8wU9wgkP8K7zuk6ncMvIBAA+3pBCg2l+ngaY8jJc8wl4B8LJzbBgNBz4yXrIl1WzPuN7RdRbxDDIx5O+VctvG2X2RwjhZhwKfrp27crDDz/c6HGPPPJIk3ZlCdGRRWuFDpuQ82MyW/juD23JK7be464eEou/twdHzhSy7khW4xfueznc/TtED4bSPPjiOlj2D8rKSqz3q1nbx5bhXbWlL+c1UxVCCGdwKPixWCwN//Z41rFCiMZFW1tclNj9/2bTsWwyjKUEGjwY3zui3uMCDJ5cPUQNjj5an2LfgEK6wozlMHxW1c3epOTti/ArPknnQG8uaGRHmpb3s1lmfoQQbsalOT9ZWVn4+Pi48hZCtBuRVTk/pRVm8optJxqf7Zvt6pLXpQOjMXjqGzz2lpEJKAqsOnCG5KyiBo+18vCCyf+Cv3wOhmCC8/byo9c/+EfCQTz0DX/7OK9rCDpFbXx6WvJ+hBBuxO4qfWd3MD916lS9Xc0rKys5ePAgy5cvp2/fvs0boRAdhMFTT6ifF9lF5aQbS+jk59Xg8YVllfy89xTQ8JKXpmuYHxf2jGDVgTN8siGFZy9rwv/NXpeQcd2vpH1wA+fqDjHt0D/gh6Mw6V9qnpANat5PEHvSjGw6lm0zGVsIIVqD3cHP2LFja21pXb58OcuXL6/3eIvFgqIoPPjgg80boRAdSHSwD9lF5WTkldI3OqjBY3/ek0FJhYmuYX4M7hJs1/VvG5XAqgNnWLQ9lQcn9iCgRmXmxnx5GN4of5KXwn7k8sIvYdsHakL0NR9DWHeb5wxPDKkKfnIk+BFCuA27g5+bb77ZGvx88sknJCUlMWqU7W7QXl5eREdHM3XqVAYPHuyckQrRAUQFGdiTZrQr6Vnbtn7lOTE2a+3YMrpbGN0i/DlyppBF21O5bVRXu84zmS18vS2VSjxQLnoG/KfDt3fD6b3wzgVw6asw8No65w1PDOW9tclS70cI4VbsDn4+/vhj68effPIJo0ePrtNcVAjRPFqV5/RGCh2m5hZbt5BfUU+tHVsUReHWkQk8+f1ePtmQwi0jEtDpGg+c1h/JIi2vhECDB5P6RoJnDMxcD9/cASlr4bu7IPl3uORF8PKznqfl/SRnFXHKWGrNaxJCiNbkUMKz2WyWwEcIF9C6uzfW4uK7qlmfEYmhxHay0a+rAVcOjiHQ4EFKdjGrD52x6xytts8V58RUJ1YHRMLNi2HsP0DRwc6F8O6FkLHbel6gwZN+Mery3eZkmf0RQrgHqfAshBux9vdqYObHYrHwrVbbZ0jjic5n8/Xy4C9DuwD2bXvPKSrnl31qYvX0s2v76PQw9lG4eQn4R0LWQXjvQvhtHlSWA9LqQgjhfuxe9jpbWVkZn3/+Ob///jsZGRmUlZXZPE5RFFauXOnwAIXoSLRCh2kNtLjYcSKP5KwifDz1TO4X6dB9bhoez/trj7H2cBaHTxfQvXNAvcd+uyOVCpOF/jFB9Sdhdx2jLoP98HfYvwTWPA8HfoDL32J4YiTv/n5Mih0KIdyGQ8FPWloa48eP5/Dhw40WY7M3EVMIUd3c9HR+KSazBb2NfJxvqtpZXNwvEn9vx35/iQvxZUKfziz/8zQfb0jhn1f0t3mcxWLhq23VTUwb5BcG0/8Lf34HPz2kJkO/N45Rw+/DWxkoeT9CCLfh0LLXww8/zKFDhxgxYgSLFi1i9+7dJCcn23wcO3bM2WMWot2KCPBGp0Cl2UJWYd3Z1NIKEz/sSgccW/Kq6daR6k6vb3ekYaynqOLOk3kcOl2IwVPHZQOjG7+ookC/K+Fvm6HPNDBX4r3hZZb7Pk1fRXZ9CSHcg0O/Ni5fvpwuXbqwYsUKDAb5LU4IZ/HQ64gMNJBuLCU9r4TOgbX/f63cf4b80kqigwyMqMqlcdTwxBB6RQZw4FQBX247wV3nJ9U5Rkt0vqRfFEE+9tcEwj+8ehboxwdJKE5hsddTrNlwCPr/GzzqNmBtaSazhS3JOZwpKCUiwMDQriE2Z9qEEO2PQzM/ZWVlDBs2TAIfIVwgqoHt7tqS1+XnxNi1Rb0hiqJw26gEAD7ZcByTufYSdlFZJUurZpkaXfKqT98rYNYWzsRdjIdiZnzmJ/DuWEj/oxkjb75lezMY/cIqrntvE/d9sZPr3tvE6BdWsWxvRquOSwjRMhwKfvr3709Wlh2doYUQTVbfdvfMgjLWHMoEmr/kpZk2KIZOvp6k5ZXw677TtV77cXcGReUmEkJ9GdY1xPGb+IXhc8OnzKr4P7IsgXBmH7w3HlbOgUrbGyVcadneDGYu3EGGsXZwecpYysyFOyQAEqIDcCj4efTRR/n999/ZsmWLs8cjRIcXU8/Mz+KdaZjMFgbFBZMU7u+Uexk89VxXte394w3JtV77skaic3M3LgQYPEmNmsTEshdJjZ4MFhOsfUmtDp22o1nXbgqT2cLspfuwtU1De2720n11ZsGEEO2LQ8HP4MGDeeCBBxg/fjzPPPMM69atIyUlhRMnTth8CCHsV9/Mzzc7HK/t05CbRsSj1ylsOpbD/ox8AI6cKWD78Vz0OoWr7Wiaao/hiaHkEMjrIU/ANZ+Abxhk7of3L4IVs1tkFmhLck6dGZ+aLECGsZQtybItX4j2zKGE54SEBBRFwWKxMHfuXObOnVvvsYqiUFlZ6fAAhehoqnN+qoOffen57M/Ix0uvY+qAKOfeL8iHyf0i+XF3Bh+sO8ZVg+N49/ejAIztEU5EoHNy+4YnhvLO78fYlJwNV18OCWPg54dh7zew7hU4+BNMeQUSbPcMdIYzBQ23DWnqcUKItsmh4Of888+X+j1CuEh0Va2f9BozFFqi8/jeEQT7ejn9njNGJfDj7gwWbU9j0fY06/M7TuSybG8Gk/s1P+A6N6ETep3C8exi0vNKiA4Ohas/hD6Xw48PQOYB+PgS6D8dJs5R22c4WUSAfYGcvccJIdomh4Kf1atXO3kYQgiNVuU5s6CMskoTOkVh8c6qJS8nLUGd7Uy+7SWnvOIKZi7cwds3Dm52ABRQ1edr18k8Nidnc8U5Ve+lz2WQMBpWzobtn8Cer9RZoLGPwbC/gr4JW+wb4e2hQwGbOT8aBSgqs133yBlki70QrU96ewnhZkL8vPD2UP9rnjaWsfZwJlmF5YT6eXFBz3Cn389ktvDcD/tsvubsJODhiequsY1Hzyp26BsCU+fDnSshZgiUF8IvT8KC0Wq3eCf4M93IbR9vtb6n+sINC3DHf7fz0vKDTk98li32QrgHCX6EcDOKoliTntONJXxTtQx12aBoPPXO/y/bkknA1U1O67lWzBC4fQVc9jr4hqpLYZ9Mha9vBWOa7XPscPBUATd9sAVjSQWDuwTz6rWD6rTZiAoy8Pp153DLiHgA3vjtCDd/uJlsG5W2HdERt9ibzBY2Hs1m8c40Nh7Nll10LiBfY8c4tOz13HPP2X2soig89dRTjtxGiA4rKsiHlOxiDmTkW+vvuGzJqwWTgM+NV/N+TuQUk5ZXYt3WX4tOB4Nvhl6Xwm//gm0fqJWiD/0CFzwMw2eBh/15T0czC7nh/c3kFJUzIDaIj2cMJdDgyWUDo20uP00dGM3g+E489s0e1h/J5tLX1/HmDYMZ3KWTw++7sS32Curs2oQ+ke1mCWzZ3gxmL91XK9iLCjLwzNQ+TskhE/I1bg7F0lhnUht0Op11t5fNi1YlQ1ssFhRFwWQyNW+U7UR+fj5BQUEYjUYCAwNbezjCjT3w5U6+/SONUD8vsovK6dnZn2X3u2ajwcaj2Vz33qZGj/v8zuGMSGpeSw2AaW+uZ9fJPF6ZPpAr7QnoMnbDTw/DyaoxhnaDi1+EbuMbPfV4dhHT39nI6fwy+kQF8tmdw+xOGD90uoC/LtzOscwiPPUKT1zSm1tGqjtdm5q309Jf49amzXKd/RNC+wo5I4eso5OvcV1N+Rnr0MzPRx99ZPN5s9nMyZMn+fXXX1m/fj2zZs3i3HPPdeQWQnRYy/Zm8Mu+UwBkF5UD6s6v5X+ecsk3s6FdQ4gKMnDKWGpzZkIBIoPUH/DOMCIxlF0n89h0LNu+4CdqAMxYBru+gF+fhuwjsPBK6D0VJv0LgrvYPC01t5jr39vM6fwyenT2Z+Ed9gc+AD06B7DkntE8umg3P+7J4Nml+9h+Io/xvSJ4YdkBu37bPp1fysaj2Xyxxb56Z+1hi31HnOVqafI1bj6HZn7s8eKLL/Lcc8+xceNG+vfv74pbtDky8yMaU99vc6B+Q3PVb3PafaH2TihX/Ba5+uAZbv1oK3EhPqx9ZFzTTi41wurnYfM7apVoDx8Y8yCMvBc8q3N4MowlTH9nIydzSkgM9+PLu0YQHuBYM1WLxcKH61OY99N+KuvJp9C+Tv++egB+3h5sOJrNhqNZHM0satK92sPMT0eb5WoN8jW2rSk/Y12W8PzII48QGxvLP/7xD1fdQoh2paHf5jSuar0wuV8Ub984uE4ScGSQwekB17kJ6hLRyZwSUnOLm3ayIQgmz4O/roP40VBZAr/NhbeGwZ5FYDZzJr+U69/bzMmcEuJDffnsjuEOBz6gLuPfPror/7tjGPX9Em2pejy0aDcz/7eDTzcd52hmEYoC/WOCuHNMVzr5eta7w0xBnT1y1uxaa5JCkq4nX+Pmc2jZy179+/dnxYoVrryFEO1GU3ZdueK3ucn9opjQJ9LlNWj8vT3oHxPEzpN5bD6WQ+wQ36ZfpHMfuPUHzHu+ofLnf+CVmwLf3E7F2v/wSuFVJOd0JybYl8/uHF4noHOU2aI+GhPXyYfxvTszMimUYV1DCfJV6xQNie/EzIU76tQZ0r66z0zt0+aXKDILyvjZzl1r7a2QZEvWb5Jinc3n0uDn6NGj0tpCCDu5w29zep3SItPkwxND2VmV9+Nor7Jlf55i9o+dMBrnMUP/M3d7/EDAmT08zx6u9ulH7NQXibS1m8xB9n7dH5rUk2mDYuo8r82unb07J7Id7M7JLSrnnd+P8cmGFEoqGt7g4uwcMnfQ0ruuzkvohI+XnpJy21/r9vg1djaXLHvl5uby4IMPsnPnToYOHeqKWwjR7nSk3+a0AGtTcnYjR9pWs2ZOMQbeMF3B+WWv8n7lxZRZPDjXspfIry+BL2+CzENOGbMz/n4m94ti3aPjuPv8RAB6RwWw7tFxbh34NFRHJr+0gld+PcSYF39jwZqjlFSYGBQXzH3ju6NQt5Bke5rl0rR0/SaLxcLcH/c3GPhA+/oau4JDMz+JiYn1vlZYWEh2djYWiwUfHx/mzZvn8OCE6EhaetdVa9Lq/Wh5P7Gd7F/6qi83KpdA5lbexIeVF/MP3++YYlmDsn8JHPgRzrkBxj4OgdEOj9lZfz96ncIVg2N45/djnMwpqTcPyB3UN6Px6ORepOWV8O7vxzCWqK1A+kQF8uDEHozrFYGiKPSOCmiXs1w1tfSuK4vFwryfD/DxhhQAbh4Rz6/7Trfrr7GrODTzk5KSUu/DaDQSFxfHTTfdxNatW2XmRwg76XUKz0ztA7T/35j9vD0YEBsENFDtuR6N5UalE8Y9xXey69KfoOcl6q6wHf+F185Rt8oXO1ap2pl/P93C/TF46igsqyQ5u2k7wlpKfTMaGcZS7v9yJ/9efhBjSQXdI/x5+4bB/HDvaMb37mytRaXNcl01WF0CvLBnuNvPcjVVS1ZHB3jl10O8+/sxAP51RX+em9aPdY+O44IeYQBce15cu/sau4pDwY/ZbK73UVZWRkpKCp988gl9+vRx9niFaNdactdVa6tuddG0pS97c2+O67vAdZ/DjOXQZQRUlsL6+fDaIFj7CpQ3cacZzvv78dDr6ButBn+7U/OaPA5Xs2fnoV6n8Mo1A1l2//lc3D8KnY2gT69TrDNhZgvtInCvqSXz9F5feZjXVx0B4Nmpfbh+mFrfSq9T6B2l/lvy8/Jod19jV3FpwrMQoulaatdVaxueGMrbq482OfgJ97dv27o196bLcLjtZzj8C6yYDWf+VDvIb3kXLngUzrmxSZ3jnfX3MyA2iO3Hc9mdaqzucO8mGpvRADVAigr2afR9RwWpSeenGrleW2RvHliwr/3/vmx5Z81RXv5VzV37xyW9uHVU11qvh/mrxTuzi5zTh64jcFrwk5ubC0CnTo73vxFCqFpq11VrOje+Ex46hdTcEk7mFBMX0njej7G4gvfWHmvwGJu5N4oCPSZBt4tgz9ew6p9gPAE/3K/OBo3+Owy8zu6eYc74+9GW/fakGpt1HVdw5oxGzSa97c15CZ3w89JTVE/ysebZJX/ywlUeDuXrfbQ+mXk/HwDgoYk9uOv8pDrHhGrBT2F5k6/fUTVrt9dPP/3EpEmT8Pf3JywsjLCwMPz9/Zk8eTI//fSTs8YohGiHaub9bLYjJ2JvmpEpr6/lt4OZeFTNNjQ590anh4F/gXu3weQXwDcMcpNh6f+py2Gb3nZoOcwR/WOCAdibbqTSZG6Re9rLmTsPo6rKDRSUVlJY1r5Kn7y26ki9gY/2ry/Q4EFyVjHT39nIk9/voaC0wu7r/2/zcWYv3QfA/43rxj3juts8LtRPnQ3V2uGIxjkc/Pz9739n6tSp/PrrrxQXFxMYGEhQUBDFxcX88ssvTJ06lQceeMCZYxVCtDNa3s/Go/UvfVksFr7YcoIr395Aam4JXUJ8+X7WKBY0J/fGwxuG/xXu2wUT/wn+kZCfBsseg//0h7Uvq600XCgxzA9/bw9KK8wcySx06b2aStvZ5oyK1P7eHgR4q4sMp9rR7M+bvx3htZWHAZh+bqx1hksTGWRgwY2DWfvIOP5yXhwACzedYMIrv7Ni3+lGr//VtpM88d1eAO4+P5G/T+hR77HVMz+y7GUvh5a9vvzyS+bPn09ERARPPvkkN910E0FB6m9w+fn5fPrpp8ydO5f58+czfPhwpk+f7tRBCyHah+GJobzVQN5PSbmJpxbvZdH2VAAu6h3By9cMIsjXk34xQc3PvfH2h5H3wNA7Yef/YN1/IO84rHwO1s1Xnx/+N/Bz/hKkTqfQLyaQTcdy2J1qpFek+/T703a2af3eanJk52FkkIGCM4VkGEvpFhHgxJG2jvd+P8a/lx8E4PGLe3H3BUkNVnh+/qoBXDYwmse/28Px7GLu+O82Lh0QxbOX9SXM37vOuaeMJTz6zW4AbhuVwGMX97LuorNFm/nJKSrHbLbYTD4XtTnU2PSCCy5g69at7Ny5kx49bEejhw4dYtCgQQwdOpTVq1c3d5ztgjQ2FaK2orJKBjy7HJMFnrm0D72iAq0/NFKyipj5vx3sz8hHp8CDE3sy84Ik135jN1XC3m9g3SuQqeZZ4OkLQ25Vm6c2o06QLf/6aT/v/n6MG4d3Ye7l7tcA+qP1ydZlF40jlYtv/nALvx/K5MWrBjC9ahakrfp4fTLPVn1NHpzQg3vH216KsqWk3MR/Vh7i/bXJmMwWgn09mTYwmuX7TttMCL9hWBfmXt6vwcAHoLzSTI8nfwZg59MTCPa1L3etvWnKz1iHZn527drFuHHj6g18AHr06MG4ceNYt26dI7cQQnQAaw9notMpmEwWZv+g/kCJCjIwbVA0/9t8goLSSkL9vHj9unMY2S3M9QPSe8DAa6H/NXDwR/j9JcjYCZvegi3vwaDrYfT9EFJ/odemcOek55r6RQdy5/mJDu9siwpUl4Qa20Hm7j7bfMIa+NxzYbcmBT4APl56Hr+4N1MHRPPIot3sy8jnk43H6z1+VFJYo4EPgJeHjkCDB/mllWQVlnfY4KcpHMr5KS8vx8/Pr9Hj/Pz8KC+XBCwhRF1aEb0KU+3J5wxjKQvWHKOgtJIh8Z348f/GtEzgU5NOB72nwl2r4cZvIH4UmCtgxyfw+hD45g44va/RyzRmQFXS8/6MAsor3SvpGWD9kSwApgyIZtqgGEYkhTpUciEqWA1+TuW33ZyfRdtTeeL7PQDcdX4iD06s/5f/xvSLCeLbv4205kLZogBzftxXq51IQ8KqSkBI3o99HAp+kpKSWLNmDUVF9VcmLS4uZs2aNSQl1d2WJ4To2Owpoufnped/dwxzWld2hyiKuj3+tp/gtmXqxxazul3+7RHw2bVwdBU0PXsAgLgQH4J9PSk3mTl4qsDJg2+eCpPZWn17TPfmBZ/W7e55bXPmZ/HONB5ZtAuLBW4dmcDjjeTg2OOPE3kUNLD7ranVoa1Jz7Ljyy4OBT/Tp0/nzJkzXH755Rw+fLjO60ePHuXKK68kMzOTa6+9ttmDFEK0L/YU0SsqN/HHibyWGZA94keos0B3rYHelwEKHFoGn14Bbw6Dre9DWdN2bSmKQv+YqkrPaXnOH3Mz7DqZR2FZJZ18PekT1bwcxbZS6NBWE9ef92TwwFe7MFvguqFdeGZqn2YHPuD86tDW7e4y82MXh3J+HnroIRYvXszKlSvp06cPgwcPJiEhAYDjx4+zfft2TCYT5557Lg8++KAzxyuEaAdasi2A00UPgms/hawjapXonf+DrIPw44Ow4jkYfBOcdweEdG30UqDm/aw9nMXuk0ZuGObaoTfF2sPqktfIbmHNTjJvC4UObTVx7eTribGkArMFrh4Syz/tSD62lzNrKYHM/DSVQzM/Pj4+rF69mlmzZuHl5cXWrVv5+uuv+frrr9myZQteXl7MmjWLVatW4ePj4+wxCyHaOGd/428VYd3gkhfhgf1qwcSQRCgzwsY31Caqn18HR39rdElsQGwwALvT3CvpeV1Vvs8YJ+RbaUuX7lrosL4mrrnFauBzbnwnXrhqgFN3GjqzlhJAqJ9UeW4Kh9tb+Pv78/rrr/PCCy+wfft20tPTAYiOjmbIkCH4+jZeql4I0TFp3/hPGUtt5v3YbFHhrgyBasHEoXfBkRWweQEcXQkHf1If4b3U1wb+BbzqbhTRdnwdOl1AaYUJg6e+pd9BHQWlFew8mQfAKCcEPwEGTwK8PSgoq+SUsZRuEf7Nvqaz2JN/lpbn/BmrmrWUFKh1f0dqKYVqCc/S38suzWpvAeDr68uYMWO49tprufbaaxkzZowEPkKIBmnf+MGBFhXuSqeDHhPhpm/hnm1qwOPlr9YL+vEBeKU3LH8CclNqnRYZaLAWuvszPb91xn6WTcdyMJktJIT62tVzzR7a7E+Gmy192ZN/1pTE46aY3C+Kt5tTqbwGbdkrS2Z+7OLQzE9hYSHHjh0jOjqasDDbvxVkZWWRnp5OUlKSXdvihRAdi/aN/+w8i0gHiui5nbDucMm/YdyTsPMz2PyO2kNs4xuw8U3oeQmcOwOSLkTR6RkYG8TKA2fYk5rHkPjWbw6tbXF3xqyPJirYh8NVVZ7dSWvnn03uF9X8SuVIwnNTORT8vPLKK8yePZsNGzbUG/wcPXqUkSNHMmfOHP7xj380a5BCiPbJWd/43ZYhCIbPhKF3w5Ff1SDo6Eq1gOLBHyEwBgZdz6iwkazEffJ+1h7OBJq/xb0ma6FDN9vu7g75Z3qdwoik5rVQCZOE5yZxKPhZunQp3bp1Y9iw+rcmDBs2jKSkJL7//nsJfoQQ9XLGN363p9NBj0nqI/OQui1+95dqM9Xf/80MoJdnH1YnT4aKnuDZehtFMowlHM0sQqfAiETnBT/a0o67FTpsL/lnWs5PXnEFFSYznvpmZ7W0aw59dY4dO0avXr0aPa53794kJyc7cgshhGifwnuou8QePAhXfwhJ47CgMFK/j3+UvILlpR7wwwOQtsPh4onNsa5qi3v/2GCCfD2ddt3oYPcsdFgz/+xsbSn/LNjHE22IucUy+9MYh4KfkpISu7aw+/j4UFjYtKJfQgjRIXgaoN9VcNN3KPfv5j39X0i1hKGU5cO2D+C9C2HBaNi0AIqdn2xbH2duca8p0o0LHWr5Z94etX8kOpJ43Fp0OoUQ2e5uN4eWveLi4ti6dWujx23dupXoaOd2QRZCiHYnuAvbEu7kX39eyhvDC5hSuRL2L4XTe2HZo/DrU9BrCpxzIyReCDrXbIe3WCwuSXYGiHbT3V6aCX0i8dIrlFXCAxN6cF5CSJvLPwv18yarsFyCHzs4NPMzadIkUlJSePXVV+s9Zv78+SQnJzN58mSHByeEEB3FgNhgLOhYVtIbrv4AHjwAl7wEUQPBVA5/fgcLr4L/DIBfn4aM3U5fFjtwqoCswnJ8PPUMjg926rW1nJ/80kqK3LDQ4Z/pRgrKTAR4e/C3sUkON3FtTdVVnmXHV2McCn4eeeQRAgMDeeihh7j00ktZsmQJf/75J3/++SdLlizh0ksv5YEHHiAwMJBHHnnE2WMWQoh2Ryt2uCc1T33CNwSG3gl3/w53r1V3jBmCIT8V1s+Hd8bAG+fBb/PUJGon0PJ9hiWG4O3h3NklrdAh4Hbb3QE2HM0GYFhiKB5tNFlYS3qWWj+Nc2jZKzY2liVLlnDVVVfx008/8fPPP9d63WKxEBYWxtdff018fLxTBiqEEO2Z1uA0JbsYY3FF7WTjqAEQ9SJMeE5tprr3Gzi0HLIPw5rn1UdkfzWHqO+V0Mmx77tavs9oJy95aSKDDBScKSTDWOJWVZ6hOvgZ2YZ3Hla3uJCZn8Y43N5izJgxHDx4kPfee4+VK1dy8uRJQM0Huuiii7jjjjvo1Kn1i3UJIURbEOzrRXyoL8ezi9mTZmS0rRo7ngboe7n6KM2Hgz+rgdDRlXBqj/pY8SzEngf9rlaPC4i06/5llSY2J6sBgM17O0FkkMEtCx2WV5rZWlXBeWS3thv8WGv9yMxPoxwOfgA6derEI488IktbQgjhBP1jgjieXcyu1LzGAxBDIAy8Vn0U58D+JWoglLwWUreqj2WPQcJodUaozzR1Ka0eO47nUVphJszfm56dA5z8zlTRbrrja+fJPEoqTIT6edEjwjXvvSVU9/eS4KcxbXNhUwgh2qHqvJ8mVnr2DYEht8ItS9VE6ckvQOxQwAIpa+GH++Gl7rDwatj+MRScrnOJdUfUqs6ju4WiKK5J9HXX/l4bjqrLfSOSQp3aub2lWbe6S8Jzo5o18yOEEMJ5BsQGA7CnOW0uAiLVLvPD/wq5x9VdYnsXqUtiR35VH9wPseeqPcZ6TYGwHtZk59Hdw5v9PuqjFTp0t2WvDUe0fB/XLPe1FFn2sp8EP0II4Sb6xQShKJCWV0JWYRlhVcsYDusUD6PvVx+Zh2D/YjjwE6TvqF4aWzkbU6dEpmT2xlMZwujEsU54J7ZphQ7dqb9XcXklf5zMBdp2sjNIc9OmkGUvIYRwE/7eHiSFq7ugmrz01ZjwHnD+w3DXb/DAfpjyCnS7CPRe6HOPcZfHjyzyfo7I9wbA4llqkFRe7NQhuGOhw20puVSYLMQE+xAf6tvaw2kWrc5PUbmJknJTK4/GvUnwI4QQbmRA1Zb33c4OfmoKjIbzbocbv4GHj/J5/Gy+N42kRB8AxVnwx0L44jp4MRE+v179vCir2bd1x0KH62vk+7gq16ml+Ht74FXVokPyfhomy15CCOFG+scG8e0faezWih26miGQBVkDOV7RnQ+uHch43yPqrM/Bn8B4Eg7+qD5QIHoQJI2DpPEQNxT0TWt8GmDwxN/bg8KySjKMpW5R62djO6jvo1EUhTA/L9KNpWQXlhPbqW3PZLmSBD9CCOFGtKTn3WlGLBaLy2cjTuYUczy7GL1OYVj3SPCOhcSxcPELapL0wZ/gwI9wajek/6E+1r4MXgHQ9XzoVhUMhXS1635RVbV+TrlB8GMsrmBvVXJ5W0921oT6e5NuLCVHtrs3SIIfIYRwI32iAtHrFDILyjidX2ZdKnIVrarzOXHB+HvX+JGgKFWVpQfA2Meg4BQcXQVHVsKx36A4u8asEBCSqAZB3cZDwhjwth3YaIUO090g72dTcjZmCySG+7n869xStO3uWZL03CAJfoQQwo34eOnpHuHPgVMF7ErNIzLIvgrNjqre4t7IzEdAJAy6Xn2YzZCxU60sfWQVpG6BnGPqY+t7oPOELsPVJbJu46Fzf9CpuSjuVOiwPS15aaqbm8rMT0Mk+BFCCDczMDaYA6cK2JNqZFJf1wU/JrPFmvDbpH5eOh3EDFYf5z+sttpIWavOCh1dCbkp6ucpa2HlbPANhfiRkDCGfh6xfIXZLXZ8acUNR7WTJS/AWh5Btrs3TIIfIYRwM/1jg/hy20l2N6fYoR32peeTV1yBv7cHA+OCHb+QIVAtlthrivp59tHqJbLk39Ulsv1LYf9SbgIu9fbn2OGBsGmq2n4joq91ZqilnCko5dDpQgCGJ7ajmR8/KXRoDwl+hBDCzWhtLnan5rk06XltVUuL4YmheOqdGHyEJqmPoXdCZbmaJH18HaSsw3R8E50qCxlSsh6WrVePNwRD/ChIGKUGQ537gU7vvPHYoC159YkKpFNVwNAeaP29smTZq0FuXeenpKSEp59+mh49emAwGIiOjmbGjBmkpaU16Tpr1qxh9uzZTJkyhfDwcBRFISEhwa5zCwsLmT17NgMGDMDf35+goCD69evHrFmzKCwsdOBdCSFEw3pGBuCl15FXXEFqruuWh9Yf0Za8XDjz4eEFXYbBmAfhpu84OuNPriibzatcrxZZ9PKH0jw1cXr5P+Cd8+GFrvDZX2DD65C6XQ2gnEwLfka14S7utlhzfmTZq0FuO/NTWlrKuHHj2LRpE1FRUUybNo2UlBQ++ugjfvjhBzZt2kRiYqJd17rvvvvYtWtXk8eQnJzM+PHjSU5OJjExkYsvvpiysjIOHjzIW2+9xeOPP46/f+vXqRBCtC/eHnp6RQWwO9XIrtQ84kKcX6+ltMLE1hS1rYMr+3mdLSokgD8s3fmjtDt3XTMJPw8gY5eaH3R8PRzfCGVGOPSz+gDwMEDUIIg7T23YGjdUTcBuBi3Xqb1scdeEVbW4kK3uDXPb4Gfu3Lls2rSJESNG8Msvv1iDjFdeeYUHH3yQGTNmsHr1aruuNXHiRK655hrOO+88YmNj6du3b6PnlJWVcfHFF3PixAkWLFjA3XffXev1vXv3EhIS0uT3JYQQ9ugfE8TuVCN7Uo1cOiDa6dffkpxDeaWZyEADSeF+Tr9+fWwWOowdoj5G3w+mSrWmUMo6NRg6uQVKcuDkJvWhCeqiNmeNG6oGRJH91VkmO5zMKeZkTgkeOoXzurav7+MhNZqbtkSdqLbKLYOf8vJy3njjDQDefPPNWrMrDzzwAJ988glr1qxh+/btDBkypNHrvfjii9aPT506ZdcY5s+fz8GDB3n44YfrBD4A/fr1s+s6QgjhiIGxwfxv8wmXtbmwLnl1D2vxH5ANFjrUe1TvJBv1f2CxqAnUqVvUQCh1K5zZB8YT6uPPb9XzmjA7pC15DTy7tlE7oCU8l5vMFJRVEmhoWhXujsIt/9bXr1+P0WgkKSmJc845p87rV199Nbt372bp0qV2BT+OeO+99wC49957XXJ9IYRoSP+qpOe9aUbMZgs6nXMDlLVV9X3GNFbfxwWaVOhQUSCsm/oYdL36XFkBpG2Hk1vVoCh1K5Tk1p0dCoxRA6LoQdV/+kdYt7i3p/o+GoOn3jqzll1YLsFPPdwy+NHycwYPHmzzde353bt3u+T+J0+e5MiRI8TGxhIXF8f69etZsmQJRqORrl27ctVVV9GtWzeX3FsIIQC6R/hj8NRRUFZJcnaRtdu7M2QXlrEvIx9onZyXqKpqyg4XOvQOUFtwJI5VP7dYIPtI1czQFjUoOrMP8tPUh1aFGrAERHNFYQxd9PFc7D0ZCoIhoHOz3o+7CfX3qgp+yuga1nJLmm2JWwY/J06cACA2Ntbm69rzx48fd8n99+3bB0B0dDSzZs3irbfeqvX6k08+yfPPP8+DDz7okvsLIYSHXkff6CC2H89lT6rRqcHP+qpln16RAYQHeDvtuvaKqqrynOGsKs+KAmHd1cc5N6jPlRWovcnSd6rVqNP/gKzDKAXpXEA6F3huhd8WwW9AQFTdGaJmJlS3plA/L45nF5MltX7q5ZbBj7aF3NfX9g4HPz81ki0oKHDJ/XNz1R0QO3bsYNu2bTz77LPcfvvteHh48N///pcnnniChx56iF69ejFlypR6r1NWVkZZWfV2w/z8fJeMVwjRPvWPUYOfXal5XH5OjNOuu+6wWt+nSVWdnUib+XFplWfvALWqdPzI6ufKCli28le2rF/FuKB0RvumQtYhKMhQH9ruMgDfMOjcV6051LmP+nF4L/D0cd2YnUSr9ZNd5H7b3U1mC1uSczhTUEpEgIGhXUPQO3lJ1x5uGfy0NrPZDEBlZSUzZ87kmWeesb72yCOPkJWVxb///W/+9a9/NRj8zJs3j9mzZ7t8vEKI9kkrdrjHiUnPFovF/n5eLhIV3Er9vbwD+C67C8tNFxN6Xk9GX9gNygrVGaKMndWzRFmHoDgLkteoD42ig9BuENGnKijqqz6Cu6izT24izN89qzwv25vB7KX7as34RQUZeGZqHyb3i2rRsbhl8KPt7iouLrb5elFREQABAQEuvT/AbbfdVuf12267jX//+99s3ryZ0tJSDAbb3YAff/xxHnjgAevn+fn5xMXFOX/AQoh2aUBsMAB/pudTaTLj4YQqzMlZRaQbS/HS6xjaStu8tZmf9LyW7e9lMlvYdCwHgBFasrO3P8SPUB+a8mLIPACn/1QfZ/6EU3vVLfdZh9THvu+rj/cKqJ4diugD4T0hrCf4R7RKUKR1dndlrZ+mzuAs25vBzIU7sJz1/CljKTMX7uDtGwe3aADklsFPly5dAEhNTbX5uvZ8fHy8S+5f87q2KkFrz5lMJnJycoiOtl2Dw9vbG2/vll9PF0K0D4lhftadO0cyC+kVGdjsa2pb3AfHB+Pr1To/ArTgJ7+0kqKySvxaaLv5/ox8jCVqL7MBMUH1H+jlW73dXmOxQOFpOL23Kijap/6ZeQDKC+DkZvVRkyEYwnqowZAWEIX3UGsUubCXWWhVocMsF1V5buoMjslsYfbSfXUCHwALoACzl+5jQp/IFlsCc8vgZ+DAgYCac2OL9vyAAQNccv9evXphMBgoLS0lNzeX8PDa1U9zcnKsH0uFZyGEq+h0Cv1iAtl0LIfdqUanBD/VW9xbrqrz2WoWOjyVX+rUZO6GaIHfsK4hTZ9FUxQ1CTogUm3LoTFVQNZhdXfZ6b1qUJR1EHKPq207Uqt2oNXk4VO1fV8LiqoCpJAkuws1NiTUhctejc3g/OvK/nSL8LcWkjyZW8yfacYGk9stqMnvW5JzqmfkXMwtg59Ro0YRFBTE0aNH2blzJ4MGDar1+qJFiwCYOnWqS+7v7e3NpEmTWLx4MatXr6ZHjx61Xl+zRl0DTkxMJDCw+d+MhBCiPgNig9l0LIc9qUamn9u8ZfNKk7lGT6vWbesQGWTgyJlCMvJaLvjZUPXenfoDVu9ZteTVB/pfXf18RYm6/T7zoLpMpv2ZfQQqS9Q8o1N7al9L0UFQnNoUNiRRDYZCk9Q/g7vYHRiFuSjhubEZHIDHv91j41X7nClouRwwtwx+vLy8uOeee/jnP//JrFmz+OWXX6w7vF555RV2797NBRdcUKvA4RtvvMEbb7zBFVdcwbx585o9hkceeYTFixczZ84cxo4daw2AkpOTeeqppwD461//2uz7CCFEQ/rHVHd4b67daUYKyioJ8vG0Xre1RGnBjyt3fNVQXmlma4o6a98igZ+nj9pyI7J/7edNlZB3vCoYOgiZh6r/LC9QX8s7DkdX1T5P0UNwnBoIhSRWB0WhVYGRvrqYoatmfrYk59hVniA8wIvuEQHEdfIlLsSH0goTb/x2tNHzIgJs58+6glsGP6DW0lmxYgUbNmyge/fujBkzhuPHj7N582bCw8P58MMPax2flZXFwYMHycjIqHOt999/n/fffx+AiooKADIyMhg+fLj1mLfeeqtWUcWRI0fy9NNP89xzz3HOOecwatQo9Ho969evp6CggIsvvrhWMrMQQrjCwKqk5/0ZBZRXmvHyaHquiJac+unGFABGJLbO9uKaqre7t8xv+7tS8yguNxHi50XPzq7ZLGMXvYcasIQmAZdUP2+xQOEZyDmqtvOw/pmsflxRDLkp6uPoytrXVPQQFAud4iE4njjfWC7T5ZNaEo4pvz/6gM5OSby2d2bmySl9mDaoujSDyWzhmx1pnDKW2pw1UlBnAlsyAd9tgx+DwcBvv/3GvHnz+Oyzz/j+++8JCQnh1ltvZc6cOfUWQLQlNTWVzZtrJ6KVl5fXes5WDZ7Zs2czcOBA/vOf/7Bp0yYqKyvp2bMnt9xyC/fccw96vd7xNyiEEHaIC/EhyMcTY0kFh04X0K+JMza2klM3HM1m2d6MFt9eXJPTCx02YsORqiWvxFCntwpxCkVRK00HdK5dmwjUwKjglBoE5RyrERwdUz+vLKmeMQL8gNe0FbJXnlVzjIK7WIMj65/acz6d7BqivTMzZx+n1yk8M7UPMxfuQIFaAZD2N/HM1D4tGpArFovFViAmXCA/P5+goCCMRqPkCgkh7HbTB5tZeziLf17RjxuG2b/Ltb7kVFB/6LT09uKavthygse+3cPYnuF8fNtQl9/v2nc2sjk5p8lfQ7dnsagFGnNT1CTrvOOQe5ztu/4g0nKGaCUHxea/gBq8A9U+aEGxEFT1Z2Bs1eexEBgNHt6YzBZGv7Cq0RmcdY+OsxnIuLrOT1N+xrrtzI8QQgjVgNgg1h7OUosdDrPvnIaSUzUtvb24ppYsdFhSbuKPE3lA6/QycylFUYOTwOhaM0aPJq/hyJlCPp9xDiNCS6xBUZ0/i7OgLB8y8yFzf/338e+MPjCG78LC+LFIT7ollHRLKKcsIZy2dCKTYCrxaHAGZ3K/KCb0iZQKz0IIIRrXPyYYgF1NqPTcWHJqa2wvrqklc362Hc+h3GQmKshAQqjttkntTaifF0eArBJq5BjZUF4ExjQwnlSbwBpT635eWarWOCo8TSRwez2RQ5l3KN7rYmBXlFoSIDC6qjxAtPVzvU9Iq/x7O5sEP0II4ea0NheHThdQWmHC4Nl4vqG9yaktub24psiq4MdYUkFxeaVLCy5qW9xHJoWhuFEbCleybndvrNChl59aeDG8h+3XLRYozqkVDOWeSmbdtj+I1WXT07cIQ1kmOnMF3mXZcCobTu2u/346T7WRbEAkDJgOQ+908B02jwQ/Qgjh5qKCDIT5e5NVWMa+jHwGd2k4QbW80mz31viW3F5cU2CNQocZRtfW+qkOflp/xqGlWLe7N7fFhaKAX6j6iB4EwK6DZ7h341Z6RQaw7P7zwWxWW38UZEB+RnWj2LM/L8oEcwUYT6iPxAua+S4dJ8GPEEK4OUVRGBAbxKoDZ9h9Mq/e4KfSZOb7nenMX3mIkzkN189pje3FZ2uJQofGkgr2VAWCI7t1oODH2uLC+VWeT+aq/7ZiO1V1uNfpwC9MfZxd16imynJ1+azglBoM1bcU1wIk+BFCiDagb3Qgqw6c4cc9GfSMDKyVKGo2W/hhTwb/WXGIY5lq4+cwf2/G9Qrn621qL0R32F58tpYodLglOQezRe2Tpm2v7wiqCx06v79Xaq7adDy2UxPzpzy81EKNwa3f4FuCHyGEcHPL9mawcJNaw2VrSi7XvbeJqCADT1/aB51O4dVfD3HgVAEAnXw9+esFSdw8IgEfLz3jekXU2V4c6cTtxc3REknPG46q/bzcIcm2JYW6sLN7atWsYlxI200el+BHCCHcWH21ejKMpcz8X3Xz5wCDB3eNSeS20V3xr9El3Z22F58tsgUKHWrFDdvdFvdGhFr7e7li2Uub+Wm7M2kS/AghhJuyp1aPAswcm8Td5ycR5Otp8xi9TnHLmY/oqpmfUy5a9sosKOPgaXVGbHhi6+U2tQZt2SvLJcteVTM/TV32ciNNbxIjhBCiRdjTSNICjOkeXm/g484iXbzstemYOuvTOyrQOhPSUYRVJTwXlFZSVmly2nWLyiqtS2mxIW135keCHyGEcFPuXqunuaKDXbvspeX7dKQt7ppAHw88qpY2nZn3o836BPl4EmhoewG3RoIfIYRwU442kmwrzi506Gwdsb6PRlGUGju+nBf8nMxR833i2vCsD0jwI4QQbmto1xCiggzUl5qsoO6Yas1aPc2hFToE58/+pOYWczy7GL1OabNfn+aqrvXjvLwfa7JzcNvN9wEJfoQQwm3pdQrPTO0DUCcAcpdaPc0VaU16dl7wYzJb+HSjWhqga5ivS1tnuDNt5scVy14y8yOEEMJlJveL4u0bB1uDBE1kkIG3bxzc6rV6mkur9ZOe55wdX8v2ZjD6hVW88/sxAI6cKWL0C6tYtjfDKddvS7RaP65Z9mrbMz8dMxwWQog2xJ1r9TRXlBNnfuqriXTKWMrMhTvaRbDYFNoOt6wi5y17pZ7d2qKNkuBHCCHaAHet1dNcWqHD9GYGPw3VRLKgLhPOXrqPCX0i20XQaA+XJDxX5fy05Ro/IMteQgghWpGzCh02VhPJgppUvSU5p1n3aUu0Wj/O6u9lLK6goFTdlRfTxmd+JPgRQgjRapxV6LC910RyhHXmx0kJz9qsT5i/V5tPIpfgRwghRKtxVqHD9l4TyRHW/l5OWvZyuJu7G5LgRwghRKtxVqFDrSZSfdp6TSRHWHd7FZVhsTTUIc4+J3PaR7IzSPAjhBCiFQV4e+DnpQeaN/tTsybS2dpLTaSm0pa9SivMFJc3v7+XNvPT1re5gwQ/QgghWpGiKERVLX01d7v75H5RdIvwr/N8e6mJ1FS+Xh74eKqBpTOWvk62k23uIFvdhRBCtLKoIANHzhQ2u9BhUVklx7OLAPjPtQNRFKVd1URyRKi/F6m5JWQVldEltHkzNqntZJs7SPAjhBCilTmr0OHm5GwqTBbiQny4/JxYZwytzQv19yY1t6TZMz8Wi8Wa8yPLXkIIIUQzaYUOM/KbF/ysO6x2cR/dLazZY2ovwqwtLppX6ye7qJySChOKAtHBbX/HnAQ/QgghWpVW6DCjmcte645kAjC6W3izx9ReOKvWj9bWonOAAW8PfbPH1dok+BFCCNGqnFHo8HR+KYdOF6IoMLIdtgFxVEhVleesZs78aA1N20OyM0jwI4QQopVFBTW/0OH6I1kA9IsOolPVUo9QqzED5DRz5udkO9rmDhL8CCGEaGVRwc0vdLjusBr8jO4u+T41Oau5qbbsFSczP0IIIUTzNbfQocViYV3VzI8kO9cW6vRlL5n5EUIIIZqtuYUOD58p5ExBGd4eOobEd3L28No0ZyU8p2kFDkNk5kcIIYRwiqhmJD2vrVryGto1BINn29+J5ExhVc1Nc4rKMZsd6+9lNltqLHvJzI8QQgjhFJGBjm93Xy9LXvXq5KvO/JjMFowlFQ5d40xBGeUmM3qd0mDz2LZEgh8hhBCtTlv2amqhw/JKM5uOVRU3lGTnOrw8dAQa1GYO2UWO5f1obS2iggx46NtH2NA+3oUQQog2LcrBQoc7T+ZRXG4i1M+L3pGBrhham6ctfTm640vb5t5eavyABD9CCCHcgKM5P+sOq1WdR3YLQ9dBm5c2prlJz6k57SvfByT4EUII4Qa0QoenmrjstbYq32eM5PvUS9vu7mh/r/ZW4BAk+BFCCOEGtEKHecUVlJSb7Donv7SCXSfzABgl+T710mZ+shxd9qqa+ZFlLyGEEMKJahc6tC/vZ+PRbMwWSAzzIya4/fxgdrZQLefH0YTnPJn5EUIIIZxOUZQmNzjVtriPkiWvBoU1o8VFpclMep769yEzP0IIIYSTRQc3rcGp9POyT4if48FPhrEUk9mCl15H54D2UeMHJPgRQgjhJppS6DAtr4RjWUXoFBiRFOrqobVp1oRnB5a9tMrOMZ182tVuOgl+hBBCuIWmFDpcXzXrMzAumECDp0vH1daFNWOre3us8QMS/AghhHATWq0fe5qbyhZ3+2kJz3nFFVSYzE06V5v5aS/d3DUS/AghhHALWvCT3siyl9lsYYMkO9st2McTbcUqt4mzP6k5MvMjhBBCuIy9hQ73n8onu6gcXy8953Tp1BJDa9N0OoWQqryfptb6aY8FDkGCHyGEEG5C2+reWKFDbZfX8MRQvDzkx5g9qvN+mpb0rC17xcnMjxBCCOF8gQb7Ch2ukyWvJnNku3tZpck6Cyc5P0IIIYQL2FPosLTCxJbkHADGSH0fu1VXebY/+MnIK8ViAR9PvXXmqL2Q4EcIIYTbaKzQ4Y7juZRVmokI8KZ7hH9LDq1NC7XO/Ni/7FVzm7uitJ8aPyDBjxBCCDeiFTo8Vc+yl7bFfXS3sHb3A9mVHGlx0R4bmmok+BFCCOE2tEKH6fXM/EhLC8c40tw0tZ3u9AIJfoQQQriRhgod5haVszfdCEiyc1Npy15N2ep+MldmfoQQQgiXi2yg0OGGo9lYLNCjsz+dA9tPk82W4MjMz8mqAodx7WynF0jwI4QQwo1EN1DoULa4Oy7Uga3u1ho/suwlhBBCuE5DhQ7XHckEZIu7I0KrEp6Ly00NFpDUlJSbyKraGSbLXkIIIYQL1Vfo8Hh2ESdzSvDQKQzrGtpaw2uz/L09rNWw7Vn6SstTl7wCvD0I8vF06dhagwQ/Qggh3EbNQoc1k561Ja/BXTrh5+3RKmNryxRFIawJS1/aNveYdljjByT4EUII4Wa0Bqc1t7vLFvfma0rSc3ttaKqR4EcIIYRbqd7urs4+mMwWNhzNBiTZuTm0vB97trtXNzSV4EcIIYRwOS340WZ+9qYZMZZUEGDwYGBsUGsOrU37//buPCjK+w4D+LOcy7lyKgiCIKjUgoAaMRo8qqESJ4m1dUysgyamk8LE1HTSxHigaDA1sW002AYSk2rpODEeQzQkY6KYQoGo0VW8GiooFsMpLMeyuLz9A99FwqKA7L7Lvs9nhhl5f++++2V2lMff6eVyt+enT8NeXUdbWCOGHyIisijiLs/inB9xvk9ciBfsbPlra6C8XPt+vheHvYiIiMzoxye7c77P4DDs9dOHk90rrHh3Z4Dhh4iILIy40WFlQytadXqcLq8H0HmYKQ1c14Tn+4cfjbYdt1vaAbDnh4iIyCzu3egw72o1dPoOjBzmhNHeLhJXNrT1ddhL7PXxcLaHq5VuK8DwQ0REFsVdaQfnuxsd7j9dAQB4dIyXVe43Y07efZzw3DXZ2Tp7fQCGHyIisjAKhcKw4uv4lSoAwPQwHylLsgqGnp/mNgiC0Ot9NwxnelnnfB+A4YeIiCzQiLuntus7On9JPzLaU8pyrILn3QnP7XoBjdo7vd5XUc+eHyIiIrPKvVCJM9dvd7v21Hv5yL1QKU1BVkJpb2uYw3O/eT/i0RaBVrrSC2D4ISIiC5J7oRIv7j2D1vbuJ4/fatDixb1nGIAeUtfQV+/zfgw9P1a60gtg+CEiIguh7xCwMecijM1GEa9tzLloGAqj/vN6wOGmgiDcc7QFe36IiIhMqvhanWFjQ2MEdG58WHytznxFWZkHHW56u6UdTW2d84E454eIiMjEqjS9B5+B3Ec9ebvev+dH7PXxcXOE0t7WbHWZm0WHn9bWVqxfvx7h4eFQKpXw9/fHihUrcPPmzX49Jy8vDxs3bkRiYiJ8fHygUCgQHBzcr2fodDpERERAoVDAzs46N30iIpKSr5tyUO+jnroONzXe83Oj3roPNBVZ7G9xrVaL2bNno7CwEH5+fnjyySdRVlaG3bt347PPPkNhYSFCQkL69KxVq1bh3LlzD1XPm2++icuXLz/UM4iIqHdTRnvCT6XErQat0Xk/CnTu/jyFy94HTJzwXNPLhGdxg8NAKx7yAiy452fz5s0oLCxEXFwcrl69in379qGoqAjvvPMOqqursWLFij4/a968edi8eTO++OILlJSU9LuWS5cuIT09HStXruz3a4mIqG9sbRTYsCACQGfQuZf4/YYFEbC14U7PA+Xpcv8jLqz9QFORRYYfnU6HnTt3AgDee+89uLq6GtpWr16NyMhI5OXl4fTp03163h//+Ee88cYbmDdvHjw9+/c/BkEQ8MILL2DYsGHYunVrv15LRET9kzDBD7uWxhjO9xKNUCmxa2kMEib4SVSZdfB2vf8RF+Kwl7UeaCqyyGGv/Px8NDQ0IDQ0FNHR0T3aFy1aBLVajZycHMTGxpq0lr/97W/417/+hT179sDDw8Ok70VERJ0BaG7ECBRfq0OVRgtft86hLvb4PDxx2Kuul2GvrmXuDD9mJ87PiYmJMdouXler1Sato7KyEq+99hrmzJmDpUuXmvS9iIioi62NAnGhXlKXYXXECc91LTroO4RugbJzjx95THi2yGGv69evAwACAgKMtovXy8vLTVpHSkoKtFotMjIyTPo+RERE5uDhbA+FAhAEoL6le+9PdVMbtO0dUCgA/2HWHX4ssuenqakJAODsbLzbzcXFBQCg0WhMVsPhw4dx4MABbNiwAeHh4QN6RltbG9rauiaVNTY2DlZ5RERE/WZnawMPZwfUNetQ26QzzAECuoa8Rrgr4WBnkX0jg8a6f7oB0mg0SElJQXh4OF5//fUBPyc9PR0qlcrwFRgYOIhVEhER9Z9XLyu+5LLMHbDQ8COu7mppaTHa3tzcDABwc3MzyfuvWbMGFRUVyMjIgKOj44Nf0IvXX38dDQ0Nhq8bN24MYpVERET9Jy53//FeP4Zl7p7WPeQFWOiw16hRowAAFRUVRtvF60FBQSZ5/5ycHCiVSqSlpSEtLa1Hu16vx8yZMwEAf/7znzFx4kSjz3F0dHyo8ERERDTYupa7d+/56ZrsbP09PxYZfqKiogAAZ86cMdouXo+MjDRZDVqtFnl5eb22i223b982WQ1ERESDrbfl7jfqrP80d5FFhp9HH30UKpUKpaWlOHv2bI+elf379wMAFixYYJL3Lysr67VNoVDA1tYWd+7cMcl7ExERmZK43L2m6cfDXvLp+bHIOT8ODg5ISUkBACQnJxvm+ADA9u3boVarER8f322Dw507d2LcuHEPNUGZiIjI2nm59pzwrO8QcPP23Z4fzvmRztq1a3Hs2DEUFBQgLCwMM2bMQHl5OYqKiuDj44MPP/yw2/01NTW4cuUKKisrezwrKysLWVlZAID29nYAnRsYTp061XBPRkZGr5sqEhERWQtvMfzcM+z1Q6MW7XoBdjYKjHBX9vZSq2Gx4UepVOL48eNIT09HdnY2Dh06BE9PTyQlJSEtLa3XDRCNqaioQFFRUbdrOp2u2zXuwUNERHLgZWTCs7jSy2+YEna2FjkoNKgUgiAIUhchF42NjVCpVGhoaIC7u7vU5RARkQyVVjdhzjt5cHO0w/mNjwMAPj1dgVc+OYdpoV7IXjn1AU+wTP35HWv98Y6IiIgMvO9OeNa03YG2XQ9APgeaihh+iIiIZMTdyQ52dw80FZe735DJgaYihh8iIiIZUSgUPfb6MRxt4cmeHyIiIrJCXXv9dE56NhxtwZ4fIiIiskZde/3o0K7vQGWDuMcPe36IiIjIChnO92puQ+VtLToEwMHOBj6u8jiPkuGHiIhIZsST3WubdF3HWgxzgs3didDWjuGHiIhIZsRhr5omXddKL5kMeQEMP0RERLIj7vVT29wmu8nOAMMPERGR7Ny71N2wzF0mGxwCFny2FxEREZlG1/leOtjfPctLDqe5ixh+iIiIZMbLRZzz04Z2fQcAIEBGPT8c9iIiIpIZcdir7U4HqjSdGx0Gcs4PERERWStnBzs4O9gavneytzUsf5cDhh8iIiIZujfsBHo6QaGQxx4/AMMPERGRLHnds5uznFZ6AQw/REREsuR9T8+PnPb4ARh+iIiIZMnDxd7wZ70gQN8hSFiNeTH8EBERyUzuhUp8fuGW4fu9hdcx/a2vkXuhUsKqzIfhh4iISEZyL1Tixb1n0Nym73b9VoMWL+49I4sAxPBDREQkE/oOARtzLsLYAJd4bWPORasfAmP4ISIikonia3WobND22i4AqGzQovhanfmKkgDDDxERkUxUaXoPPgO5b6hi+CEiIpIJXzfloN43VDH8EBERycSU0Z7wUynR217OCgB+KiWmjPY0Z1lmx/BDREQkE7Y2CmxYEAEAPQKQ+P2GBRGwtbHuoy4YfoiIiGQkYYIfdi2NwQhV96GtESoldi2NQcIEP4kqMx87qQsgIiIi80qY4Ie5ESNQfK0OVRotfN06h7qsvcdHxPBDREQkQ7Y2CsSFekldhiQ47EVERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLJiJ3UBciIIAgCgsbFR4kqIiIisi/i7Vfxdez8MP2ak0WgAAIGBgRJXQkREZJ00Gg1UKtV971EIfYlINCg6Ojrwv//9D25ublAoFP16bWNjIwIDA3Hjxg24u7ubqEKSAj9b68XP1rrx87UsgiBAo9HA398fNjb3n9XDnh8zsrGxQUBAwEM9w93dnX/JrBQ/W+vFz9a68fO1HA/q8RFxwjMRERHJCsMPERERyQrDzxDh6OiIDRs2wNHRUepSaJDxs7Ve/GytGz/foYsTnomIiEhW2PNDREREssLwQ0RERLLC8GPhWltbsX79eoSHh0OpVMLf3x8rVqzAzZs3pS6NHsLp06exdetWLFy4EAEBAVAoFP3e+4ksT0tLCw4dOoTnnnsOY8eOhVKphIuLC6KiorBp0yY0NTVJXSI9pO3bt2PhwoUICwuDSqWCo6MjgoKCsGzZMpw/f17q8qiPOOfHgmm1WsyaNQuFhYXw8/PDjBkzUFZWhuLiYvj4+KCwsBAhISFSl0kD8NRTT+Hw4cM9rvOv49CWlZWFlStXAgDGjx+PCRMmoLGxEQUFBdBoNBg3bhzy8vLg6+srcaU0UN7e3mhubkZkZCRGjhwJACgpKcHVq1dhb2+PAwcO4IknnpC4SnoQhh8LtnbtWmzZsgVxcXH48ssv4erqCqDzfx6vvPIK4uPjceLECWmLpAF566230NzcjMmTJ2Py5MkIDg5GW1sbw88Q9/HHH6OgoAAvv/wyxo8fb7heWVmJxMREfPfdd1iyZAmys7MlrJIeRn5+PmJjY6FUKrtdz8jIQHJyMoYPH46KigrY2XEPYUvG8GOhdDodfH190dDQgDNnziA6Orpbe1RUFNRqNU6dOoXY2FiJqqTBolQqGX6s3L///W9MmzYNjo6OaGxshIODg9Ql0SAbM2YMSktLce7cOURGRkpdDt0H5/xYqPz8fDQ0NCA0NLRH8AGARYsWAQBycnLMXRoRDUBUVBQAoK2tDbW1tRJXQ6Zgb28PAAy2QwDDj4U6d+4cACAmJsZou3hdrVabrSYiGrj//ve/ADp/QXp6ekpcDQ22PXv24MqVKwgLC0NYWJjU5dADcFDSQl2/fh0Aej0IVbxeXl5utpqIaOD+8pe/AAASEhK4I7AV2LZtG0pKStDc3IxLly6hpKQE/v7++Oc//wlbW1upy6MHYPixUOKSWGdnZ6PtLi4uAACNRmO2mohoYI4ePYoPPvgA9vb2SEtLk7ocGgRffPEFvvrqK8P3QUFB+Pvf/845mEMEh72IiEzo8uXLWLp0KQRBwLZt2wxzf2hoO3bsGARBQH19PU6ePImwsDDEx8djy5YtUpdGfcDwY6HEZe0tLS1G25ubmwEAbm5uZquJiPrn5s2bSEhIQH19PVavXo1Vq1ZJXRINsmHDhmHGjBk4evQoYmNjsW7dOnz77bdSl0UPwPBjoUaNGgUAqKioMNouXg8KCjJbTUTUd3V1dZg3bx7Ky8uxfPlyvP3221KXRCZkb2+PxYsXQxAErsIdAhh+LJTYNX7mzBmj7eJ17iVBZHmamprw85//HBcvXsTChQuRmZnJ40tkwNvbGwBQXV0tcSX0IAw/FurRRx+FSqVCaWkpzp4926N9//79AIAFCxaYuTIiup+2tjY8+eSTKC4uxuOPP87VPzKSl5cHAAgNDZW4EnoQhh8L5eDggJSUFABAcnKyYY4P0Hm8hVqtRnx8PFcWEFkQvV6PJUuW4Ouvv8aMGTNw4MABbnhnRfLz85Gbm4uOjo5u19vb27Fjxw7s2bMHTk5OWLx4sUQVUl9xqbsFW7t2LY4dO4aCggKEhYVhxowZKC8vR1FREXx8fPDhhx9KXSIN0JEjR7otedbpdACAqVOnGq6tW7cOiYmJZq+NBm7nzp04ePAggM4hkN/+9rdG73v77bcNQyQ0dPznP//B8uXL4e3tjdjYWHh5eaGmpgbnz59HZWUllEolPvroIwQGBkpdKj0Aw48FUyqVOH78ONLT05GdnY1Dhw7B09MTSUlJSEtL63UDRLJ81dXVKCoq6nH93mucNzD01NfXG/4shiBjUlNTGX6GoPj4eKxZswZ5eXlQq9WoqamBg4MDgoODsWjRIrz00ksYM2aM1GVSH/BgUyIiIpIVzvkhIiIiWWH4ISIiIllh+CEiIiJZYfghIiIiWWH4ISIiIllh+CEiIiJZYfghIiIiWWH4ISIiIllh+CEiIiJZYfghIskEBwdDoVBIXcaAzZ49GwEBAWhra5O6FABAZWUlnJycej1TjIg6MfwQkUmUlZVBoVBg5syZUpdiEkeOHMHx48exZs0aODo6Sl0OAMDPzw8vvPACMjMzcfXqVanLIbJYDD9EJJmvvvoKly5dkrqMAVmzZg18fHzw/PPPS11KN6+++io6Ojqwbt06qUshslgMP0QkmdDQUIwbN07qMvotPz8farUaixcvhoODg9TldDNy5EjMmjULBw8exA8//CB1OUQWieGHiAZdamoqRo8eDQDIy8uDQqEwfCUlJRnuMzbn597hsubmZqxevRqBgYFwcnJCTEwMcnJyDPd+8skneOSRR+Di4oLhw4fjpZdeQmtrq9GaWlpakJ6ejujoaLi6usLV1RVTp07Fxx9/3O+fLysrCwCwZMmSHm0nTpww/JxVVVV47rnnMGLECLi4uGD69OkoKCgw3PvXv/4VkZGRcHJyQmBgIFJTU9HR0dHjmeXl5XjxxRcRHh4OZ2dneHp64ic/+Ql+85vf4MqVKz3uf+aZZ9De3o6PPvqo3z8bkRzYSV0AEVmfiRMn4he/+AU+/fRTDB8+HAkJCYa26dOn9+kZOp0Oc+bMwbVr1/DYY4+hpqYGJ0+exNNPP43c3FycP38er776KuLj4/H444/j5MmT2LFjB2pra/GPf/yj27Oqqqowd+5cqNVqjBgxAvHx8RAEAQUFBUhKSsKpU6ewY8eOPv98R44cgZOTE6ZMmdLrPfX19YiLi4Ner8fMmTNRVlaG/Px8zJ07F8XFxXj//feRmZmJWbNmISgoCHl5edi4cSPa29uxZcsWw3Nu3LiBmJgY1NXVISwsDPPnz4der0d5eTkyMzMRFxeHsWPHdntvcZ7VkSNH8Ic//KHPPxeRbAhERCZw7do1AYAQHx/f6z1BQUHCj/8ZEl8HQJg9e7bQ1NRkaNu9e7cAQBgzZozg4eEhfPvtt4a2mzdvCr6+vgIAobS0tNsz58+fLwAQVq1aJWi1WsP1W7duCZMmTRIACJ9//nmffq5Lly4JAIRp06YZbT9+/Lih/qVLlwo6nc7QtmHDBgGAEBERIfj7+wvff/+9oa2kpERwcHAQnJ2dBY1GY7i+fv16AYCQkpLS473Ky8u7PeNe3t7egqOjo9Da2tqnn4tITjjsRUQWycbGBrt27YKLi4vh2rJly+Dt7Y3vv/8eycnJmDRpkqHN398fzz77LADg5MmThutnz57F0aNHMXnyZGzfvr3byqzhw4fj/fffBwDs2rWrT3Wp1WoA6NHb8mPu7u549913YW9vb7j2u9/9DgqFAhcvXsSmTZsQGhpqaIuIiEBiYiJaWlpw6tQpw/Xq6moAwM9+9rMe7zFq1Khuz7jX2LFj0dbWNmQnlBOZEsMPEVmk4OBghIeHd7tmY2ODoKAgAMC8efN6vCYkJARA5343oi+//BIA8NRTT8HGpuc/eeIcoOLi4j7VVVVVBQDw8PC4732TJk3qcY9KpYKnp2e/6o+NjQXQubrss88+g1ar7VOd4vuI4YmIujD8EJFFGjlypNHrrq6uvbaLbfduOlhWVgYAeOONN7pNvL73q6mpCTU1NX2qq6GhAQDg5uZmlvqTkpLwq1/9ChcvXsSCBQvg4eGBxx57DG+++SZu3brV6/u7u7sDAG7fvn3fOonkiBOeicgiGeul6U+7SFw9NX369F6HiPpDpVIBADQazX3vG6z6bW1tsW/fPrz22ms4fPgwvv76axQVFeGbb77B1q1bkZubi2nTpvV4nRjShg0b1qf3IZIThh8ismoBAQEAOoe9XnnllYd+nq+vLwCgrq7uoZ/VH9HR0YiOjkZqaioaGxuRmpqKP/3pT3j55ZeNDtnV19cDAHx8fMxaJ9FQwGEvIjIJcfO/O3fuSFrH3LlzAQAHDx4clOdFRUUBgNH9dczF3d0d6enpUCgUuHDhgtF7Ll++DEdHR4wfP97M1RFZPoYfIjIJb29v2Nvbo7S0FHq9XrI6HnnkEcydOxf5+flITk5GY2Njj3vOnTuH3NzcPj1v7Nix8PX1xdmzZ80S7Pbs2WM04Hz++ecQBAGBgYE92kpLS1FbW4spU6ZAqVSavEaioYbhh4hMwsHBAQkJCbh16xaioqKwbNkyPP/889i9e7fZa9m7dy+io6ORkZGBoKAgzJo1C88++yyeeOIJjBo1ChMnTuxz+AGA+fPno7W1FUVFRSasutOnn36Kn/70pxgzZgyefvppPPPMM4iLi8PChQthY2ODzZs393jNiRMnAACJiYkmr49oKGL4ISKTycrKwq9//WvU1tYiOzsbH3zwAfLy8sxeh6+vLwoKCvDuu+8iIiIC3333Hfbv3w+1Wo2QkBBs27YNv//97/v8vJUrVwIAsrOzTVWywerVq5GcnAw3Nzd88803OHjwIKqqqrB48WIUFRXhl7/8ZY/XZGdnw97evttRIkTURSEIgiB1EUREQ010dDQqKipQUVHRbeNEqVVUVCAoKAiLFi3Cvn37pC6HyCKx54eIaAC2bNmCmpoaZGZmSl1KN9u2bYONjQ02bdokdSlEFos9P0REAzR79mxcvXoVpaWlFtH7U1lZiZCQECxfvhwZGRlSl0NksRh+iIiISFY47EVERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLLC8ENERESywvBDREREssLwQ0RERLLC8ENERESy8n8m5IjSrrOhsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "chunk = 100\n",
    "for repeat_save in range(1):\n",
    "    better_sleep(0)\n",
    "    res = job.result_handles\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    while average_number<chunk*3:\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Manu...\"))\n",
    "        average_number=res.clicks.count_so_far()\n",
    "    total_time_freqsweep = time.time() - start_time_freqsweep\n",
    "    print(\"Time elapsed  = %.3f s\\nAverage number = %i\"%(total_time_freqsweep, average_number))\n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    #print(click_array.shape)\n",
    "    \n",
    "    %matplotlib inline\n",
    "    integration_index=5\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    #print((freq_range-Photon_IF)/1e3)\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'frequency_sweep.pdf')\n",
    "    \n",
    "    ############### Plot Background ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('background counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'background.pdf')\n",
    "    \n",
    "    ############### Plot decay ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.ylabel('count rate (/ms)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_BY'+str(np.round(Field_list[1],3))+'mT.pdf')\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    plt.savefig(directory+filename+'_BZ'+str(np.round(Field_list[2],3))+'mT_'+'.pdf')\n",
    "    \n",
    "    fullpath=directory+filename+'2.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BX_mT':Field_list[0],\n",
    "                'BY_mT':Field_list[1],\n",
    "                'BZ_mT':Field_list[2],\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'freq_range':freq_range,\n",
    "                #'persistent_mode_on': [not MagnetX.get_PSwitch(), not MagnetY.get_PSwitch(), not MagnetZ.get_PSwitch()],\n",
    "                }\n",
    "    save_h5(fullpath,datasets, group=str(Field_list[2]), overwrite=True)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "\n",
    "    npts = np.round(click_array.shape[0],-3)\n",
    "    offset = 0\n",
    "    integration_index=10\n",
    "    integration_index_bg=1\n",
    "    plt.figure()\n",
    "    excess_array = []\n",
    "    t_step = time_hist[1]-time_hist[0]\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "        PrintStatic(str(start))\n",
    "        stop = start+chunk\n",
    "    \n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        # plt.figure()\n",
    "        if start%10000==0: \n",
    "            \n",
    "            plt.plot((freq_range-Photon_IF)/1e3,excess,'-o',label = \"%.1f\"%((freq_range[np.argmax(excess)]-Photon_IF)/1e3))# label = \"%i-%i\"%(start,stop))\n",
    "    PrintStatic(\"\")\n",
    "    \n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "\n",
    "    excess_array.shape\n",
    "    \n",
    "    ################### 2D Plot ###################\n",
    "    use_time = True\n",
    "    chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "    time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "    plt.figure()\n",
    "    if use_time:\n",
    "        yaxis = time_elapsed\n",
    "        ylabel = \"Time (minutes)\"\n",
    "    else:\n",
    "        yaxis = chunk_index\n",
    "        ylabel = 'Slice centre index'\n",
    "        \n",
    "    plot_2d_sweep(excess_array,x=np.round((freq_range-Photon_IF)/1e3,0),y=yaxis,\n",
    "                  xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "                cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.pdf')\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[1],3))+'frequency_sweep_time_resolved.png')\n",
    "    #plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_frequency_sweep_fixed_field\\\\%s\\\\%sSpectroscopy_frequency_sweep_fixed_field_amp0.005\\\\\"%(time_stamp,time_stamp)+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917a4564-9277-4c83-943c-ce1041dba073",
   "metadata": {},
   "source": [
    "## Frequency tracking with sb pumping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dac11f7a-dd35-478f-909a-2cce3aab04b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "experiment_name='Spectroscopy_frequency_sweep_fixed_field'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "\n",
    "sim_and_plot=True\n",
    "excecute=True\n",
    "\n",
    "param_type=int\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 60000\n",
    "N = 30\n",
    "stream_I = False\n",
    "\n",
    "amplitude_pulse = 0.0045/2\n",
    "gauss_duration  = 160_000//4 \n",
    "\n",
    "offset = centre_freq*1e3#10e3\n",
    "span=0.2e6\n",
    "freq_final = offset+Photon_IF+span/2\n",
    "freq_init  = offset+Photon_IF-span/2\n",
    "freq_step = 0.002e6\n",
    "\n",
    "n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "\n",
    "freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "\n",
    "cycle_time_estimated=22  #in us\n",
    "Integration_time=5000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=10000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    \n",
    "\n",
    "    if stream_I:\n",
    "        I_stream = declare_stream()\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        \n",
    "        sideband_pump_red(centre_freq, 0, enable_fsv_trigger=False)\n",
    "        wait(int(5e6/4))\n",
    "        with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "\n",
    "            update_frequency(spin_element, freq_set)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "            with for_(i, 0, i < N_iterations, i + 1):\n",
    "            \n",
    "                with while_(I>I_threshold_reset):\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "\n",
    "\n",
    "                play(pump_pulse, pump_element) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                assign(click, I>I_threshold)\n",
    "\n",
    "                if stream_I:\n",
    "                    save(I, I_stream)  # I is saved into I_stream\n",
    "\n",
    "                save(click, p_stream)  # I is saved into I_stream\n",
    "\n",
    "    with stream_processing():\n",
    "        if stream_I:\n",
    "            I_stream.buffer(N_iterations).save_all('I')\n",
    "        #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "start_time_freqsweep = time.time()\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a151f708-560d-460f-9f3e-4164414b8458",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "chunk = 200\n",
    "for repeat_save in range(3):\n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    while average_number<chunk*3:\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Manu...\"))\n",
    "        average_number=res.clicks.count_so_far()\n",
    "    total_time_freqsweep = time.time() - start_time_freqsweep\n",
    "    print(\"Time elapsed  = %.3f s\\nAverage number = %i\"%(total_time_freqsweep, average_number))\n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    #print(click_array.shape)\n",
    "    \n",
    "    %matplotlib inline\n",
    "    integration_index=5\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    #print((freq_range-Photon_IF)/1e3)\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'frequency_sweep.pdf')\n",
    "    \n",
    "    ############### Plot Background ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('background counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'background.pdf')\n",
    "    \n",
    "    ############### Plot decay ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.ylabel('count rate (/ms)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_BY'+str(np.round(Field_list[1],3))+'mT.pdf')\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    plt.savefig(directory+filename+'_BZ'+str(np.round(Field_list[2],3))+'mT_'+'.pdf')\n",
    "    \n",
    "    fullpath=directory+filename+'2.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BX_mT':Field_list[0],\n",
    "                'BY_mT':Field_list[1],\n",
    "                'BZ_mT':Field_list[2],\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'freq_range':freq_range,\n",
    "                #'persistent_mode_on': [not MagnetX.get_PSwitch(), not MagnetY.get_PSwitch(), not MagnetZ.get_PSwitch()],\n",
    "                }\n",
    "    save_h5(fullpath,datasets, group=str(Field_list[2]), overwrite=True)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "\n",
    "    npts = np.round(click_array.shape[0],-3)\n",
    "    offset = 0\n",
    "    integration_index=10\n",
    "    integration_index_bg=1\n",
    "    plt.figure()\n",
    "    excess_array = []\n",
    "    t_step = time_hist[1]-time_hist[0]\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "        PrintStatic(str(start))\n",
    "        stop = start+chunk\n",
    "    \n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        # plt.figure()\n",
    "        if start%10000==0: \n",
    "            \n",
    "            plt.plot((freq_range-Photon_IF)/1e3,excess,'-o',label = \"%.1f\"%((freq_range[np.argmax(excess)]-Photon_IF)/1e3))# label = \"%i-%i\"%(start,stop))\n",
    "    PrintStatic(\"\")\n",
    "    \n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "\n",
    "    excess_array.shape\n",
    "    \n",
    "    ################### 2D Plot ###################\n",
    "    use_time = True\n",
    "    chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "    time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "    plt.figure()\n",
    "    if use_time:\n",
    "        yaxis = time_elapsed\n",
    "        ylabel = \"Time (minutes)\"\n",
    "    else:\n",
    "        yaxis = chunk_index\n",
    "        ylabel = 'Slice centre index'\n",
    "        \n",
    "    plot_2d_sweep(excess_array,x=np.round((freq_range-Photon_IF)/1e3,0),y=yaxis,\n",
    "                  xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "                cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.pdf')\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[1],3))+'frequency_sweep_time_resolved.png')\n",
    "    #plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_frequency_sweep_fixed_field\\\\%s\\\\%sSpectroscopy_frequency_sweep_fixed_field_amp0.005\\\\\"%(time_stamp,time_stamp)+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0de52d41-a51c-4741-bfe8-cd038747392f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "experiment_name='Spectroscopy_frequency_sweep_fixed_field'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "\n",
    "sim_and_plot=True\n",
    "excecute=True\n",
    "\n",
    "param_type=int\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 60000\n",
    "N = 30\n",
    "stream_I = False\n",
    "\n",
    "amplitude_pulse = 0.0045/2\n",
    "gauss_duration  = 160_000//4 \n",
    "\n",
    "offset = centre_freq*1e3#10e3\n",
    "span=0.2e6\n",
    "freq_final = offset+Photon_IF+span/2\n",
    "freq_init  = offset+Photon_IF-span/2\n",
    "freq_step = 0.002e6\n",
    "\n",
    "n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "\n",
    "freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "\n",
    "cycle_time_estimated=22  #in us\n",
    "Integration_time=5000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=10000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    \n",
    "\n",
    "    if stream_I:\n",
    "        I_stream = declare_stream()\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        \n",
    "        sideband_pump_blue(centre_freq, 0, enable_fsv_trigger=False)\n",
    "        align()\n",
    "        wait(int(20e6/4))\n",
    "\n",
    "        with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "            align()\n",
    "            update_frequency(spin_element, freq_set)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "            with for_(i, 0, i < N_iterations, i + 1):\n",
    "            \n",
    "                with while_(I>I_threshold_reset):\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    play(qubit_pulse, qubit_element)\n",
    "                    align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                wait(int(waiting_time/4), readout_element)\n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "\n",
    "\n",
    "                play(pump_pulse, pump_element) \n",
    "                align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                assign(click, I>I_threshold)\n",
    "\n",
    "                if stream_I:\n",
    "                    save(I, I_stream)  # I is saved into I_stream\n",
    "\n",
    "                save(click, p_stream)  # I is saved into I_stream\n",
    "\n",
    "    with stream_processing():\n",
    "        if stream_I:\n",
    "            I_stream.buffer(N_iterations).save_all('I')\n",
    "        #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "start_time_freqsweep = time.time()\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8003b52e-8612-4276-aec7-d5647da72093",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "chunk = 200\n",
    "for repeat_save in range(1):\n",
    "    better_sleep(0)\n",
    "    res = job.result_handles\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    while average_number<chunk*3:\n",
    "        time.sleep(1)\n",
    "        PrintStatic((\"Annoying Manu...\"))\n",
    "        average_number=res.clicks.count_so_far()\n",
    "    total_time_freqsweep = time.time() - start_time_freqsweep\n",
    "    print(\"Time elapsed  = %.3f s\\nAverage number = %i\"%(total_time_freqsweep, average_number))\n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    \n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    #print(click_array.shape)\n",
    "    \n",
    "    %matplotlib inline\n",
    "    integration_index=5\n",
    "    integration_index_bg=5\n",
    "    excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "    background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "    #print((freq_range-Photon_IF)/1e3)\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,excess,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'frequency_sweep.pdf')\n",
    "    \n",
    "    ############### Plot Background ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot((freq_range-Photon_IF)/1e3,background,'-o')\n",
    "    plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('background counts')\n",
    "    #plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[2],3))+'mT_'+'background.pdf')\n",
    "    \n",
    "    ############### Plot decay ###############\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.ylabel('count rate (/ms)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(directory+filename+'_BY'+str(np.round(Field_list[1],3))+'mT.pdf')\n",
    "    \n",
    "    def exp_decay(t,T,alpha,beta):\n",
    "        return alpha*np.exp(-t/T)+beta\n",
    "    \n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array.mean(1).mean(0),[1,0.1,5])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        plt.legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    plt.savefig(directory+filename+'_BZ'+str(np.round(Field_list[2],3))+'mT_'+'.pdf')\n",
    "    \n",
    "    fullpath=directory+filename+'2.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BX_mT':Field_list[0],\n",
    "                'BY_mT':Field_list[1],\n",
    "                'BZ_mT':Field_list[2],\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'freq_range':freq_range,\n",
    "                #'persistent_mode_on': [not MagnetX.get_PSwitch(), not MagnetY.get_PSwitch(), not MagnetZ.get_PSwitch()],\n",
    "                }\n",
    "    save_h5(fullpath,datasets, group=str(Field_list[2]), overwrite=True)\n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "\n",
    "    npts = np.round(click_array.shape[0],-3)\n",
    "    offset = 0\n",
    "    integration_index=10\n",
    "    integration_index_bg=1\n",
    "    plt.figure()\n",
    "    excess_array = []\n",
    "    t_step = time_hist[1]-time_hist[0]\n",
    "    for start in offset+np.linspace(0,npts-chunk,npts//chunk,dtype = int):\n",
    "        PrintStatic(str(start))\n",
    "        stop = start+chunk\n",
    "    \n",
    "        excess=(click_array[start:stop].mean(0)[:,:integration_index].sum(-1))*t_step\n",
    "        \n",
    "        excess_array.append(excess)\n",
    "        # background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "        \n",
    "        # plt.figure()\n",
    "        if start%10000==0: \n",
    "            \n",
    "            plt.plot((freq_range-Photon_IF)/1e3,excess,'-o',label = \"%.1f\"%((freq_range[np.argmax(excess)]-Photon_IF)/1e3))# label = \"%i-%i\"%(start,stop))\n",
    "    PrintStatic(\"\")\n",
    "    \n",
    "    plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n amplitude_pulse = %.3f'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, amplitude_pulse))\n",
    "    plt.xlabel('Frequency offset (kHz)')\n",
    "    plt.ylabel('integrated excess counts')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    excess_array = np.array(excess_array)\n",
    "\n",
    "    excess_array.shape\n",
    "    \n",
    "    ################### 2D Plot ###################\n",
    "    use_time = True\n",
    "    chunk_index = int(chunk/2)+np.linspace(0,npts-chunk,npts//chunk,dtype = int) # Use this for y axis if you want to plot chunk centre index\n",
    "    time_elapsed = np.linspace(0, total_time_freqsweep/60, len(chunk_index)).round(1)\n",
    "    plt.figure()\n",
    "    if use_time:\n",
    "        yaxis = time_elapsed\n",
    "        ylabel = \"Time (minutes)\"\n",
    "    else:\n",
    "        yaxis = chunk_index\n",
    "        ylabel = 'Slice centre index'\n",
    "        \n",
    "    plot_2d_sweep(excess_array,x=np.round((freq_range-Photon_IF)/1e3,0),y=yaxis,\n",
    "                  xlabel = 'Frequency offset (kHz)',ylabel = ylabel,clabel = 'integrated excess counts',xtick = 'auto',ytick = 'auto',\n",
    "                cmap = \"Blues\",horizontal_ticks = False, fontsize = None)\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.pdf')\n",
    "    plt.savefig(directory+filename+'_'+str(np.round(Field_list[1],3))+'frequency_sweep_time_resolved.png')\n",
    "    #plt.savefig(\"Z:\\\\SMPD3-8\\\\SpinRun3\\\\Spectroscopy_frequency_sweep_fixed_field\\\\%s\\\\%sSpectroscopy_frequency_sweep_fixed_field_amp0.005\\\\\"%(time_stamp,time_stamp)+filename+'_'+str(np.round(BZ,3))+'mT_'+'frequency_sweep_time_resolved.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216b7162-0bbe-4e79-86af-032e1b97f7b0",
   "metadata": {},
   "source": [
    "# Re-Zero SMPD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1ee87f-4de2-4199-af46-53708da9ff35",
   "metadata": {},
   "outputs": [],
   "source": [
    "yokoBuffer  = Yokogawa7651(name='yokoBuffer')\n",
    "yokoPurcell = Yokogawa7651(name='yokoPurcell')\n",
    "\n",
    "yokoBuffer.initialize (visaAddress='GPIB0::30')\n",
    "yokoPurcell.initialize(visaAddress='GPIB0::19')\n",
    "\n",
    "centralBuffer = 9.9\n",
    "centralPurcell = 3.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba4b34d-c967-4d5e-a7c8-f71ea44fa931",
   "metadata": {},
   "outputs": [],
   "source": [
    "yokoBuffer.setOutputValue(centralBuffer, slewrate=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1950713a-ba52-4823-8363-05b5c76e8604",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "voltBuffer_array = centralBuffer + np.linspace(-0.02, 0.02, 5)\n",
    "voltPurcell_array = centralPurcell + np.linspace(-0.02, 0.02, 5)\n",
    "\n",
    "counts_total = []\n",
    "for voltBuffer in voltBuffer_array:\n",
    "    yokoBuffer.setOutputValue(voltBuffer, slewrate=0.2)\n",
    "    counts_partial = []\n",
    "    for voltPurcell in voltPurcell_array:\n",
    "        \n",
    "        yokoPurcell.setOutputValue(voltPurcell, slewrate=0.2)\n",
    "        \n",
    "        ####################### Define the readout frequencies #######################\n",
    "        readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "        readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "        freq_electron = readout_freqs[-1]\n",
    "\n",
    "        ####################### Ramsey parameters #######################\n",
    "\n",
    "        amplitude_pulse_ramsey = 0.079 # Pi pulse amplitude\n",
    "        gaussian_pulse_length_ramsey = 5000//4\n",
    "\n",
    "        waiting_time= 1000\n",
    "        waiting_time_spin= 80000\n",
    "\n",
    "        cycle_time_estimated=17 #in us\n",
    "        Integration_time=1200  #in us\n",
    "        N_iterations = int(Integration_time/cycle_time_estimated)  #30000\n",
    "\n",
    "        ####################### Save params #######################\n",
    "\n",
    "        experiment_name='prepared_ramsey'\n",
    "        time_stamp=get_timestamp()\n",
    "        filename=time_stamp+'%s'%(experiment_name)\n",
    "        directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "        shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "        shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "        ####################### Measurement time estimate #######################\n",
    "\n",
    "        N_repetition = 1e9\n",
    "\n",
    "        ####################### Run program #######################\n",
    "\n",
    "        with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "            n = declare(int)           # Index for repeated preparation pulses\n",
    "            m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "            j = declare(int)           # Index for Ramsey sweep\n",
    "            k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "            t = declare(int) \n",
    "            meas_angle = declare(fixed)\n",
    "\n",
    "            rabi_stream  = declare_stream()\n",
    "\n",
    "            update_frequency(spin_element, Photon_IF + centre_freq*1e3)\n",
    "\n",
    "            assign(k, 0)\n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "                with if_(k == 1000):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep/np.sqrt(2), detuned_sideband_amplitude_a_prep/np.sqrt(2), raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep/np.sqrt(2), detuned_sideband_amplitude_b_prep/np.sqrt(2), raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                    wait(int(40e6//4))\n",
    "                    assign(k, 0)\n",
    "\n",
    "                assign(k, k+1)\n",
    "                align()\n",
    "\n",
    "                # Ramsey sequence  \n",
    "\n",
    "                align()\n",
    "                reset_frame(spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse_ramsey), spin_element, duration=gaussian_pulse_length_ramsey) \n",
    "                wait(int(waiting_time_spin/4), readout_element)\n",
    "                align()\n",
    "                measure_SMPD(rabi_stream,N_iterations,waiting_time, accumulate=True)\n",
    "\n",
    "                wait(int(2e6/4))\n",
    "                measure_SMPD(rabi_stream,N_iterations,waiting_time, accumulate=True)\n",
    "\n",
    "            with stream_processing():\n",
    "                rabi_stream.buffer(2).save_all('clicks')\n",
    "\n",
    "        qmm = QuantumMachinesManager()\n",
    "        qm = qmm.open_qm(config)\n",
    "        job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "        \n",
    "        better_sleep(60)\n",
    "        res = job.result_handles\n",
    "\n",
    "        average_number=res.clicks.count_so_far()\n",
    "        print(average_number)\n",
    "\n",
    "        click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])\n",
    "\n",
    "        counts_partial.append(click_array.mean(0))\n",
    "\n",
    "        filename = 'fluorescence'\n",
    "        fullpath=directory+filename+'.hdf5'\n",
    "        datasets= {\n",
    "                    'click_array': click_array,\n",
    "                    'time_axis': time_axis,\n",
    "                    'yokoBuffer': voltBuffer,\n",
    "                    'yokoPurcell': voltPurcell,\n",
    "\n",
    "                    #'calibration':calib_dic,\n",
    "                    'amplitude_pulse':amplitude_pulse,\n",
    "                    }\n",
    "        save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "        shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "        shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    counts_total.append(counts_partial)\n",
    "\n",
    "yokoBuffer.setOutputValue(centralBuffer, slewrate=0.2)\n",
    "yokoPurcell.setOutputValue(centralPurcell, slewrate=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9efca4f4-1aca-410d-95c3-700895bbb7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_total = np.array(counts_total)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plot_2d_sweep(counts_total[:,:,0]-counts_total[:,:,1], \n",
    "              x = np.round(voltBuffer_array, 2),\n",
    "              y = np.round(voltPurcell_array, 2),\n",
    "              xlabel = \"Buffer Voltage (V)\",\n",
    "              ylabel = \"Purcell Voltage (V)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              title=r'$\\pi$ pulse - no pulse',\n",
    "             )\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plot_2d_sweep(((counts_total[:,:,0]-counts_total[:,:,1])/np.sqrt(counts_total[:,:,1]))**2, \n",
    "              x = np.round(voltBuffer_array, 2),\n",
    "              y = np.round(voltPurcell_array, 2),\n",
    "              xlabel = \"Buffer Voltage (V)\",\n",
    "              ylabel = \"Purcell Voltage (V)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              title=r'SNR**2',\n",
    "             )\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plot_2d_sweep(counts_total[:,:,0], \n",
    "              x = np.round(voltBuffer_array, 2),\n",
    "              y = np.round(voltPurcell_array, 2),\n",
    "              xlabel = \"Buffer Voltage (V)\",\n",
    "              ylabel = \"Purcell Voltage (V)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              title=r'$\\pi$ pulse',\n",
    "              vmax=0.5,\n",
    "              vmin=0\n",
    "             )\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plot_2d_sweep(counts_total[:,:,1], \n",
    "              x = np.round(voltBuffer_array, 2),\n",
    "              y = np.round(voltPurcell_array, 2),\n",
    "              xlabel = \"Buffer Voltage (V)\",\n",
    "              ylabel = \"Purcell Voltage (V)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "              title=r'no pulse',\n",
    "              vmax=0.5,\n",
    "              vmin=0\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21c67d0-556c-4b6c-bbf9-a36e73389135",
   "metadata": {},
   "source": [
    "# Re-Zero centre frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0e03c99-fbf3-4ab6-aea8-0de561d42204",
   "metadata": {},
   "outputs": [],
   "source": [
    "centre_freq = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "181469c2-1e43-4487-a393-feab236f8871",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:31:00.511527Z",
     "iopub.status.busy": "2024-04-01T18:31:00.510527Z",
     "iopub.status.idle": "2024-04-01T18:32:20.443300Z",
     "shell.execute_reply": "2024-04-01T18:32:20.442299Z",
     "shell.execute_reply.started": "2024-04-01T18:31:00.511527Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current centre frequency = 38.5 kHz\n",
      "Current centre frequency = 38.5 kHz\n",
      "New centre frequency offset: 27.8 kHz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "############################# re-zero frequency #############################\n",
    "from Config import *\n",
    "import time as time\n",
    "spacing = spacing1+spacing2\n",
    "tolerance = 5\n",
    "#centre_freq = 37.5\n",
    "repeat_measurement=0\n",
    "N=30\n",
    "directory = \"\"\n",
    "filename = \"\"\n",
    "print(\"Current centre frequency = %.1f kHz\"%centre_freq)\n",
    "\n",
    "res,freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 4)\n",
    "excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,0,repeat_measurement, freq_range,Photon_IF,max_shift=40,plot = True, fit_double_lorentz = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690c4498-4368-4262-8892-6c4d05498c6f",
   "metadata": {},
   "source": [
    "# Fluorescence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdba35e-34d1-4e9f-a199-7d1e4762073a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "\n",
    "\n",
    "experiment_name='spin_T1'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "gauss_duration = 5_000//4\n",
    "amplitude_pulse = 0.079\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "BX_field = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY_field = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ_field = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=8000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=1000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        save(j, index_stream)\n",
    "        play('ON',fsv_trigger)\n",
    "\n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(amplitude_pulse/2), spin_element, duration = 2*gauss_duration) \n",
    "        align()\n",
    "        wait(int(waiting_time_spin), readout_element)\n",
    "        align()\n",
    "        \n",
    "        measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.boolean_to_int().buffer(N_iterations).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1)\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad5240f0-9245-407c-bf25-00c1ce0485d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_and_plot=True\n",
    "if sim_and_plot:\n",
    "    simulation = qm.simulate(Spin_T1, SimulationConfig(duration=5000), include_analog_waveforms=True,\n",
    "                             include_digital_waveforms=True,\n",
    "                             simulation_interface=LoopbackInterface([(\"con1\", 1, \"con1\", 1)]))\n",
    "    samples = simulation.get_simulated_samples()\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    fig.show()\n",
    "    fig.canvas.draw()\n",
    "    for i,key in enumerate(list(samples.con1.analog.keys())):\n",
    "            #plt.plot(samples.con1.analog[key]+0.1*i)\n",
    "            pass\n",
    "    samples.con1.plot(analog_ports={'1', '2', '3', '4','6','7','8'}, digital_ports={'1', '3'})\n",
    "    fig.canvas.draw()\n",
    "    plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83adb4e-145f-4841-a2dd-db8d7434156d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist=time_axis*1e-6\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()]).mean(0)/cycle_time\n",
    "\n",
    "click_array = click_array.reshape(N, int(click_array.shape[0]/N)).mean(-1)\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "print('shape of click array:',click_array.shape)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array,'o')\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array, [0.1,0.1,0.05])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.title(f\"efficiency: {popt[0]*popt[1]:.4f}\")\n",
    "    plt.legend()\n",
    "except Exception as e:\n",
    "    print('fit failed: ',e)\n",
    "plt.xlim(0, None)\n",
    "#plt.ylim(0, None)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Count rate (1/ms)')\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "filename = 'fluorescence'\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'T1_decay.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'amplitude_pulse':amplitude_pulse,\n",
    "            'Bz_field': BZ_field,\n",
    "            'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020e651c-11f9-4a0d-bce0-7333bb11171a",
   "metadata": {},
   "source": [
    "## Arrival time g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1823,
   "id": "a3d09371-a280-46ec-9f98-e55812d1451f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T09:04:39.536368Z",
     "iopub.status.busy": "2024-03-29T09:04:39.535370Z",
     "iopub.status.idle": "2024-03-29T09:04:41.058209Z",
     "shell.execute_reply": "2024-03-29T09:04:41.057211Z",
     "shell.execute_reply.started": "2024-03-29T09:04:39.536368Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory created\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "\n",
    "experiment_name='spin_T1_arrival_time'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "gauss_duration = 5_000//4\n",
    "amplitude_pulse = 0.079\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=8000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated)  #30000\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    \n",
    "    click=declare(bool)\n",
    "    \n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    \n",
    "    p2_stream = declare_stream()\n",
    "    index2_stream = declare_stream()\n",
    "    \n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        play('ON',fsv_trigger)\n",
    "\n",
    "        # Save the beginning of the sequence and send a pulse\n",
    "        save(j, index_stream)\n",
    "        \n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "        \n",
    "        align()\n",
    "        wait(waiting_time_spin_prep)\n",
    "        \n",
    "        align()\n",
    "        measure_SMPD(p_stream,N_iterations,waiting_time_SMPD_prep, timestamp=True)\n",
    "        \n",
    "        #-----------------------------------------------------------------------------------#\n",
    "        \n",
    "        # Save the beginning but not send a pulse after\n",
    "        save(j, index2_stream)\n",
    "        \n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(0), spin_element, duration = gauss_duration) \n",
    "        \n",
    "        align()\n",
    "        wait(waiting_time_spin_prep)\n",
    "        \n",
    "        align()\n",
    "        measure_SMPD(p2_stream,N_iterations,waiting_time_SMPD_prep, timestamp=True)\n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        p_stream.timestamps().save_all('pulse_timestamp')\n",
    "        index_stream.timestamps().save_all('pulse_start')\n",
    "        \n",
    "        p2_stream.timestamps().save_all('no_pulse_timestamp')\n",
    "        index2_stream.timestamps().save_all('no_pulse_start')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1)\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1828,
   "id": "dd95e3d9-82d3-4b89-a0c7-512a799931ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T09:05:26.913952Z",
     "iopub.status.busy": "2024-03-29T09:05:26.912951Z",
     "iopub.status.idle": "2024-03-29T09:05:27.615091Z",
     "shell.execute_reply": "2024-03-29T09:05:27.614088Z",
     "shell.execute_reply.started": "2024-03-29T09:05:26.913952Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3909\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\spin_T1_arrival_time/20240329100440_\\\\fluorescence.hdf5'"
      ]
     },
     "execution_count": 1828,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kAQ4HUF0NZGcDRiOgdE2UFBjKrXjhhRewbt06dO/eHb///e/b7O/xeODxePwf19bWxrI8Is2z2+2wWCwBz/oVRRFWq1W5bR/tdsBiAZo+f1gUAasV4FaUFGMcvm6Bw+GAxWKBIAh49dVX0a9fvzb/zeLFi5GZmel/0+v1caiUSJvsdjvMZnNAIAOAy+WC2WyG3W5XoijAbA4MZG9R3nYlaqKkwtnXIfznP//BlVdeiePHj+Ovf/0rfvOb34T170JdKev1es6+JmpGkiQYDIagQPYRBAGiKKKioiJ+Q9mSBBgMwYH8v6K8V8wVFRzKpohw9vV5qKiowDXXXIPjx4/jD3/4Q9iBDACpqanIyMgIeCOiYA6Ho8VABrwbADmdTjgcjngW1XIge4sCnE5vP6IYYSg3UV1djauvvhrV1dWwWCx49NFHlS6JKCFVV1dHtV9UhPta8ayJkg5D+QfHjx/HpEmTsH//ftx22214+umnlS6JKGFlZ2dHtV9UhPta8ayJkk5ShfKyZcswdOhQzJ8/P6D99OnTmDJlCsrLy3HjjTfi5ZdfbteDLYgoPEaj0f8QmVAEQYBer4fRaIxnUd57xi397AsCoNd7+xHFiKaXRK1ZswaLFi3yf3z27FkAwOjRo/1tCxcuxJQpUwAAR44cwd69e4OGxB5++GF8/vnn0Ol06NixI26//faQr/f6669H+TMgSk46nQ5WqxVmsxmCIARMzvQFdXFxcXzXK+t03mVPZrM3gJtOGPUFdXExJ3lRTGk6lGtqarBly5ag9qZtNTU1bZ7n+PHjALwzQktKSlrsx1Amih6TyQSbzRZynXJxcbEy65RNJsBmC71OubiY65Qp5hJqSZTa8IEURG3jjl6U6CLJAk1fKROR9ul0OuTn5ytdRiCdDlBbTZQUkmqiFxERkZoxlImIiFSCoUxERKQSDGUiIiKVYCgTERGpBGdfExFpgCqXjlHUMZSJiFTObreH3GTFarUqs8kKxQyHr4mIVMxut8NsNgc96tLlcsFsNsNutytUGcUCd/SKIe7oldw43EjnS5IkGAyGFp89LQgCRFFERUUFv7d8VLgbWyRZwCtlohiw2+0wGAwoKCjAjBkzUFBQAIPBwKsaiojD4WgxkAFAlmU4nU44HI44VqVidjtgMAAFBcCMGd73BoO3XSMYykRRxuFGipbmT7Q7334JzW73PuGr+R8xLpe3XSM/dwxloiiSJAkWiwWh7gr52oqKiiBJUrxLIw3Kzs6Oar+EJUneJ3uFuhvraysq8vZTOYYyURRxuJGiyWg0QhRF/zOmmxMEAXq9HkajMc6VqYzDEXyF3JQsA06nt5/KMZSJoojDjRRNOp0OVqsVAIKC2fdxcXExJ3mF+/OkgZ87hjJRFHG4kaLNZDLBZrMhJycnoF0URdhsNq5TBryzrKPZT0FcEhVDXBKVfHxLWFwuV8j7ylzCQu2lxiV2qqlJkryzrF2u0PeVBQEQRaCiQpHlUZFkAXf0Iooi33Cj2WyGIAgBwczhRjofOp0O+fn5Spfhp6pdxnQ6wGr1zrIWhMBg9g37Fxcrvl45HBy+JooyDjdSorPb7TDfcEPwsr+qKphvuEGZZX8mE2CzAc1+7iCK3naN/Nxx+DqGOHyd3FQztEcURZIkwfCjH6Hq6NGQxwUAYq9eqPj+e+WGsjW8oxeHr4liRG3DjUTR4CgrazGQAUAG4Dx6FI6yMuRPnBi/wnx0OkDDP3ccviYiorBVl5VFtR8FYigTEVHYwl1UpP7FR+rEUCYiorAZ8/MhwnvvOBQBgP6HfhQ5hjIREYVNl58Pa69eAIKD2fdxca9e0DGU24WhTERE4dPpYHrpJdgANFt8BBGADYDppZcUn/GsVQxlIiKKjMkE08qVqMzJQSmAEgClACpEEaaVKzWzJliNuE45hrhOmYgSmgrXBKsR1ykTEVHsaXxNsBpx+JqIiEglGMpEREQqwVAmIiJSCYYyERGRSjCUiYiIVIKhTEREpBIMZSIiIpVgKBMREakEQ5mIiEglGMpEREQqwW02idSO+wsTJQ2GMpGa2e2AxQJUVf2vTRQBq5VP4iFKQJoevt6+fTueeOIJmEwmiKIIQRAgCM0fux2+48ePw2KxYMCAAUhNTcWAAQNQVFSEEydORK9oonDZ7YDZHBjIAOByedvtdmXqIm2TJKCsDFixwvtekpSuiJrQ9KMbr7/+eqxatSqovT2f0pEjRzBmzBjs27cPubm5+MlPfoL//ve/+O9//4vBgwfj888/R8+ePSM6Jx/dSO0mSYDBEBzIPoLgvWKuqOBQNoWPIy+KiCQLIrpSLi8vx913341JkyZhzpw52L17d1CfnTt3Ijc3N7KK22nMmDFYuHAhPvjgA1RXVyM1NbXd5yoqKsK+fftgMpmwd+9evPvuu/jPf/6D3/zmN/j6668xd+7cKFZO1AaHo+VABgBZBpxObz+icHDkRRPCvlLeuHEjJk6ciIEDB+Liiy/Gtm3b4HQ6sXDhQixcuNDfb8uWLRg7diwkBYZE0tLS4PF4Ir5Srq6uhiiK6NixIw4ePIgf/ehH/mMejwd6vR7Hjh3DoUOH0KdPn7DPyytlarcVK4AZM9ruV1ICFBbGvh7SNo68KComV8oPPfQQTCYTysvL8c477+Cbb77BkiVLsHjxYhQWFsLj8Zx34Ur5+OOP0djYCKPRGBDIAJCamorrrrsOkiTho48+UqhCSjrZ2dHtp2a8xxl7HHnRjLBD+csvv8SsWbP8E6k6dOgAi8WC0tJSlJWVYcKECThy5EjMCo2lL7/8EgAwatSokMd97bt27YpbTZTkjEbvlUtLExcFAdDrvf20zG73XsEVFHhHBgoKvB8rPJQqSRLKysqwYsUKlJWVKTLyF1XV1dHtRzET9pKoLl264OTJk0HtV1xxBT7//HNMmTIFo0ePxoIFC6JaYDwcPHgQACCKYsjjvvYDBw7EraamWvuFIAgCOnToEFZfANA1GZpK5L6NjY2t3saIpG+HDh38f4zGte/TT6PDjTcCggChyTHZ92+eesrb0Ozroli9LfSVZRmNjY3Bnd57z/v5yTKa/ukh/3CPs/H//g+YNi3gnzT9fm/xvOfZd+XKlSgqKkJVkytLURTx9NNPY9q0aXGpIZK+QBi/I8IcUZH69EHTwWs1/Cyr4XdEPIUdyqNGjcKqVatw/fXXBx0zGAzYtGkTpk2bhl//+tfRrC8ufH9sdOnSJeTx9PR0AEBdXV2r5/F4PAHD+LW1tVGpz9HKkFLPnj0xbNgw/8cbN25s8Ye5e/fuGDFihP/jzZs349y5cyH7duvWDZdeeqn/423btuHMmTOQJAnl5eU4evQoevXqhUsuuQTdunXD5Zdf7u+7fft2nD59OuR509LSMHr0aP/HO3fubPHr2qlTJ4wbN87/cXl5eYvL0zp06IArr7zS//F//vMfHDt2LGRfAMjPz/f/91dffYWampoW+xqNRv8P6Ndff43vvvuuxb5jx45FSkoKAGDfvn04dOhQi31Hjx6NtLQ0AEBFRQWcTmdgh9690fsPf8DAZcuQ1qQ+qW9f7Ln7bhzp3TvkcOOoUaP8962qqqrw7bfftljDiBEj0L17dwDeuRXffPNNi30vueQS9OrVCwBw+PBh7Nmzp8W+F154oX/+RU1NTfCkUEnC6NmzkdoskAFAkGXIAM7Nno3NPXoE3OMcNGgQcnJyAAButxs7d+5ssYbc3Fz0798fgPdnd8eOHS32NRgMMBgMsNvtmD59etAv66qqKkyfPh2PPfYYfvnLXyIvLw+A92d+8+bNLZ63X79+GDx4MADg3Llz2LRpU4t9+/bti6FDhwLwBkZrP/dZWVm46KKL/B+3+TvCN/LicnmHqpuRAXiysrCne3eMaNLent8RoXTp0kXTvyPiKexQvuWWW2C1WnH8+HH06NEj6HhmZiY++eQT3HPPPfjnP/8Z1SK1YvHixXjssceULiNmNmzYgGXLlgUEWFZWFn77298G/MBR9By58kocGTcOV5w9i84nTgDZ2ajS63GkeYBrTPfy8oA/NJoTAKTV1KB7eTlONPlDMpYkSYLFYmn16mnZsmX4xS9+EZd6okqn8y57MpshA4EjEz+833fvvZzkpQKaXqfcXHtnX5tMJrz33nuwWq247777go77RghMJhNWrlzZ4nlCXSnr9frznn2thuFrm82GG2+8Mehr6xuitNlsMP2wzlFrQ1NqGOJN5L6hhmKFd95Bh5tvbvHf+zT+7W+Qm4RgLIeDN2zYgIKCgjZr+uc//4kJEybEpIaYDV/7+trtkC0WCE2G5mVRROPTT/tvFahtmFkNvyPOVySzr7nNJuAf4qpqYXair33AgAGtnic1NfW81kq3JJJvjlj0lSQJ999/f8hvYFmWIQgCioqKMHXqVOh0OsXrBRDwC4t9le0rCELw/7sfhqDbfJ2cnBav3kKeN5IamqkOc5LT999/H7Ma2tMXiOBnw2SCMHVqwF7qQpPbM+0+rwb7RvI9HE9RC+U333wzrH4pKSno1asXhg8fHtGa31gaPnw4ALR4z8nX3vTebTJxOBwt/sECeIPZ6XTC4XAodh+GNKaNe5z+dbNxnF2eHeZkqHD7qZZOB/DnVLWiFsozZ86MaN9pQRBw1VVX4ZlnnsGgQYOiVUa7XHvttejQoQMcDgcOHz4c8MeCx+PBhx9+CJ1Oh8mTJytYpXLCvYIItx9R03ucEITAYPb9Hikujus9TqPRCFEU4XK5Qo4KCYIAURRh1PoyNFK1qF2/P/LII/jVr34FWZaRnp6OadOmwWKxwGKxwGQyoWvXrpBlGTfffDNuv/12DB06FP/4xz9gNBrhcrmiVUarli1bhqFDh2L+/PkB7dnZ2SgsLMTZs2cxe/ZsNDQ0+I/NmzcPNTU1uPnmm1VzZR9vSXMFQfFlMgE2W/BQtih62+O8F7NOp4PVagWAoAsM38fFxcWKLZWhJCFHyb59++SePXvKt99+u3zixImg4263W7799tvlnj17yt98840sSZI8d+5cWRAE+d57723Xa65evVq+4oor/G+CIMgAAtpWr17t7//oo4/KAORbb7016Fw1NTVyXl6eDEDOy8uTb7rpJvniiy+WAciDBg2Sjx49GnF9brdbBiC73e52fX5q0dDQIIui6P/6Nn8TBEHW6/VyQ0OD0qWSFjU0yHJpqSyXlHjfK/x9tHLlSlkUxYDvcb1eL69cuVLRuhKeyr4PoimSLIhaKE+fPl3Oy8uTJUlqsY8kSXJeXp48ffp0WZZl2ePxyP369ZMHDhzYrtd87bXXQoZE07fXXnvN37+1UJZlWT569Kj8m9/8Rtbr9XJKSoqs1+vl++67Tz5+/Hi76kuUUJZl7y8qQRCCgtnXxl9YlEgaGhrk0tJSuaSkRC4tLeUfnLG2cqUsi6Ise29keN9E0dueACLJgqgticrKysI111yDt99+u9V+M2bMwD/+8Q//lpyTJ09GWVlZiwvJtSzRHkhht9thsVgCJn3p9XoUFxf7l0MREUXE9/Sq5lHku4WgwK2MaFNkSdTp06db3enI5/vvvw/Y9SUjIwMdO3JllhaYTCZMnToVDocD1dXVyM7ODtjtiogoIpLkfb5zqGtDWfYGc1ERMHVq0mxsErU0vOSSS7BhwwZs2LAhYCuzphwOB9avX4/LLrvM3+Z0OpGVlRWtMijGdDodlz0RUXRE8vSqJPm9E7XZ1/PmzYMkSZg0aRLuuusufPrpp9izZw/27NmDTz/9FHfffTcmTZoEWZYxb948AN69a7dv3x6wzykRESUJPr0qSNSulE0mE55++mn87ne/w8svv4xXXnkl4Lgsy0hJSfE/aQUAjh49isceewwTJ06MVhkUa5IUsBsQjMakGVYioihLpueGhynqe19XVFRg+fLl2LRpk38ziezsbIwbNw633XYbcnNzo/lyqpZoE71gt3vv/zQdbhJF7yYQGp+IQUQKkCTv87Pb2tmtokLTf/xHkgUJ9UAKtUmoUE6CGZJEpADf7xYg9M5uCfC7JZIsUOeO3KQubc2QBLwzJNt4QgsRURCV7eymNK5ForZxhqSiJEniMjRKbCaTd9kT56swlCkMnCGpmFAbtoiiCKvVyg1bKLHw6VUAGMoUjmSaIami2eV2ux1msznoiUUulwtmsxk2m43BnExU9L1JscOJXjGUMBO9kmSGpJpml0uSBIPB0OJzrH2PEayoqOBQdjJQ0fcmRY4TvSi6fM++Bf43I9JHoWffRp1vBmjzEHS5vO12e1zLcTgcLQYy4F3373Q64XA44lgVKUJl35sUWwxlCk8iz5BU4ezy6jDvz4fbjzRKhd+bFFsMZQqfyQRUVgKlpUBJifd9RYW2AxmIbHZ5nGSHeX8+3H6kUSr83qTY4kQvikwizpBU4exyo9EIURThcrmCJnoB/7unbDQa41YTKUCF35sUW7xSJlLh7HKdTgfrD/fxhWb38X0fFxcXc5JXolPh9ybFFkOZyGj03htvPonNRxAAvd7bL45MJhNsNhtymt3HF0WRy6FCkCQJZWVlWLFiBcrKyiAlwn1WlX5vUuxwSVQMJcySqGSg4v13uaNX2xJ6kxUVf29SePhACpVgKGtMqLWger13uRd/6alWS5us+Ib5E2JUgd+bmsZQVgmGsgZx1yRNSapNVvi9qVmRZAFnXxM1lYizyxNYJJus5Gv9/yu/N5MCJ3oRkWZxkxVKNAxlItIsbrJCiYahTESa5dtkpflabh9BEKDX67nJCmkGQ5mINIubrFCiYSgTkaZxkxVKJFwSFUNcEkUUP9xkhdSKS6KIKOnodDrtL3uipMfhayIiIpVgKBMREakEQ5mIiEglGMpEREQqwVAmIiJSCYYyERGRSjCUiYiIVIKhTEREpBLcPCRZ8AHpRESqx1BOBnY7YLEATR8GL4qA1QpwX2AiItXg8HWis9sBszkwkAHA5fK22+3K1EVEREEYyolMkrxXyKGeOeJrKyry9iMiIsUxlBOZwxF8hdyULANOp7cfEREpjqGcyKqro9uPiIhiSvOhXF9fj0ceeQSDBw9GWloa+vXrh1mzZsHlckV8rk8//RRTpkxBVlYWOnXqhF69euGaa67Be++9F4PK4yA7O7r9iIgopgRZDnXDURvOnDmDgoICbN682f9Q88rKSmzduhVZWVnYvHkzcnNzwzpXcXEx7r//fgiCgDFjxkCv18PpdOLzzz+HLMt46KGH8Pjjj0dUXyQPto4JSQIMBu+krlD/mwXBOwu7okL7y6O45IuIVCqiLJA17OGHH5YByGPGjJHr6ur87UuXLpUByOPHjw/rPIcPH5ZTU1PlTp06yWVlZQHH1q9fL6empsqCIMj79++PqD632y0DkN1ud0T/LqpWrpRlQfC+eaPZ++ZrW7lSudqiZeVKWRbFwM9PFBPjcyMizYskCzQ7fH327FksW7YMAPDss8+ia9eu/mNz587FsGHDsH79emzfvr3Nc23ZsgUejwcTJkzA+PHjA45deeWVmDRpEmRZxhdffBHdTyIeTCbAZgNycgLbRdHbrvV1ylzyRUQJRLOhvHHjRrjdbuTl5WHkyJFBx81mMwDgww8/bPNcqampYb1mr169IitSLUwmoLISKC0FSkq87ysqtB/IXPJFRAlGs6H85ZdfAgBGjRoV8rivfdeuXW2e6/LLL0f37t3x2WefYf369QHHNmzYgE8++QSDBg2C0Wg8z6oVpNMB+flAYaH3fSLcb+WSLyJKMJrdZvPgwYMAAFEUQx73tR84cKDNc2VmZmL58uWYMWMGCgoKMHbsWIiiiKqqKmzatAnjxo3Dm2++iZSUlFbP4/F44PF4/B/X1taG++lQe3DJFxElGM2G8smTJwEAXbp0CXk8PT0dAFBXVxfW+UwmE9auXYsbb7wRGzdu9LdnZGTgmmuuQU7ze7IhLF68GI899lhYr0dRwCVfRJRgNDt8HW1Lly7FVVddhSuvvBK7du3CyZMnsWvXLkyYMAGPPPIITGHcf50/fz7cbrf/zel0xqHyJGY0eiesCULo44IA6PXefpT4JAkoKwNWrPC+51wC0iDNhrJvtvXp06dDHj916hQAoFu3bm2eq6ysDA888ABGjBiBv//977jkkkuQnp6OSy65BDabDSNGjMCaNWuwdu3aVs+TmpqKjIyMgDeKIZ3O+6QrIDiYfR8XFyfG/XNqnd3uXZNfUADMmOF9bzBw9j1pjmZDuX///gCAqhYm+vjaBwwY0Oa53nrrLQDAtGnT0KFD4JdEp9P5r5I3bNjQ7nopRhJ9yRe1jcviKIFo9p7y8OHDAQA7duwIedzXPmzYsDbP5QvwzMzMkMd97cePH4+4TooDkwmYOpU7eiWjtpbFCYJ3WdzUqfx+IE3QbCiPGzcOmZmZ2L9/P3bu3IkRI0YEHLfZbACA6667rs1z9e3bFwBa3Bxk27ZtAACDwdD+gim2fEu+KLlEsiyO3x+kAZodvk5JScG9994LAJgzZ47/HjIAPPXUU9i1axfGjx+PSy+91N++bNkyDB06FPPnzw841/XXXw8AePvtt7F69eqAY6tWrUJJSQk6dOiAadOmxeizIaJ24bI4SjCavVIGgAULFmDdunXYtGmTf3OPAwcOYMuWLcjKysKrr74a0P/IkSPYu3cvqpv9gF5//fWYPn06/v73v+O6667DT37yE1xwwQWoqKjwXz0//vjjGDJkSNw+NyIKA5fFUYLR7JUyAKSlpaG0tBQLFy5Ely5d8P777+PAgQOYOXMmduzYEfYTogRBwLvvvovly5fjyiuvxL59+/Dee++hsrISkydPxtq1a/HQQw/F+LMhoohxWRwlGE0/ulHtFH90I1Ey8M2+BgInfPmCmrPwSWGRZIGmr5SJiLgsjhKJpu8pE/lIkgSHw4Hq6mpkZ2fDaDRCxyUwyYPL4ihBMJRJ8+x2OywWS8BGMqIowmq1hrU9KiUILoujBMDha9I0u90Os9kctLOby+WC2WyGnbs5EZGGMJRJsyRJgsViQai5ir62oqIiSHwwARFpBEOZNMvhcLS49zngDWan0wmHwxH2OSVJQllZGVasWIGysjIGOhHFFe8pk2Y13wTmfPvx3jQRKY1XyqRZ2WHu0hROP96bJlIWR6m8uHlIDHHzkNiSJAkGgwEulyvkfWVBECCKIioqKlpdHuU7T0tD4eGeh4jaJ9FHqbh5CCUFnU4Hq9UKwBucTfk+Li4ubjNIY3FvWrUkCSgrA1as8L5P0qsRUg+OUgViKJOmmUwm2Gw25DTbzUkURdhstrD+yo72vWnVstsBgwEoKABmzPC+Nxi87UQK4AqKYJzoRZpnMpkwderUdu/oFc1706rl2x+6+S8/l8vbHul2lJLE3bPovEUySpWfJBvDMJQpIeh0unb/0BqNRoii2Oa9aaNWnzQkSYDFEhzIgLdNEICiIu82leEEq93uPV/TX6aiCFit3GeaIpI0o1QR4PA1Jb1o3ZtWLYcjMECbk2XA6fT2a4vvirv5+XxX3BwKpwgkxShVhBjKRIjOvWnVCvcqo61+bV1xA94r7iS6/0fnxzdK1fyPYR9BEKDX67U7StUODGWiH5hMJlRWVqK0tBQlJSUoLS1FRUWFtgMZ8N7zjUa/aF5xEyEJRqnagaFM1ITv3nRhYSHy8/MT45eB0ei959vC1QgEAdDrvf1aE60rbqImEnqUqh040Yso0el03klYZrM3gJsOP/uCuri47Ule0briJmrmfFdQJBLu6BVD3NGLVCXUrGm93hvI4VyNSJJ3XbPLFfq+siB4r8grKrg8iqiJSLKAV8pEycJk8i57au/64mhdcRNRixjKRMlEpwPOZxMGk8m70UiodcrhXnETUYsYykQUmfO94iaiFjGUiShy53vFTUQhcUkUERGRSjCUiYiIVIKhTEREpBIMZSIiIpVgKBMREakEZ1+TciSJy2qIiJpgKJMyQm35KIreHaO4AQURJSkOX1P82e3erRqbPwbQ5fK22+3K1EVEpDCGMsWXJHmvkEM90MDXVlTk7UdElGQYyhRfDkfwFXJTsgw4nd5+RERJhqFM8VVdHd1+REQJhKFM8ZWdHd1+REQJhKFM8WU0emdZ+56/25wgAHq9tx8RUZJhKFN86XTeZU9AcDD7Pi4u5nplIkpKDGWKP5MJsNmAnJzAdlH0tnOdMhElKW4eQsowmYCpU7mjFxFREwxlUo5OB+TnK10FEZFqcPiaiIhIJTQfyvX19XjkkUcwePBgpKWloV+/fpg1axZcLle7zldZWYm7774bF1xwAVJTU9G7d2+MGTMGS5YsiXLlREQUbZIkoaysDCtWrEBZWRkkje0OKMhyqP0OteHMmTMoKCjA5s2bkZ2dDaPRiMrKSmzduhVZWVnYvHkzcnNzwz7f2rVrYTabUV9fj1GjRmHQoEE4evQoysvLkZ6ejn379kVUX21tLTIzM+F2u5GRkRHpp0dERBGw2+2wWCyoarJroCiKsFqtMCk4gTSiLJA17OGHH5YByGPGjJHr6ur87UuXLpUByOPHjw/7XF999ZWclpYmZ2VlyRs3bgw4JkmSvG3btojrc7vdMgDZ7XZH/G+JiCh8K1eulAVBkAEEvAmCIAuCIK9cuVKx2iLJAs1eKZ89exZ9+vSB2+3Gjh07MHLkyIDjw4cPx65du/DFF1/g0ksvbfN8kydPxtq1a7FmzRpMnjw5KjXySpmIKPYkSYLBYAi4Qm5KEASIooiKigroFFjhEUkWaPae8saNG+F2u5GXlxcUyABgNpsBAB9++GGb53I6nfjkk0+Qm5sbtUAmIqL4cDgcLQYyAMiyDKfTCYcGHnSj2SVRX375JQBg1KhRIY/72nft2tXmucrKytDY2IixY8eioaEBdrsdGzduhCRJuPjii3HTTTehR48e0SueiIiipjrMB9iE209Jmg3lgwcPAvDexA/F137gwIE2z7V7924AQNeuXWE0GrF58+aA4w8//DBsNhsKCgrOp2QiIoqB7DAfYBNuPyVpdvj65MmTAIAuXbqEPJ6eng4AqKura/Ncx48fBwC88sor2LNnD0pKSnDs2DHs3bsXN998M44dO4Zp06a1uczK4/GgtrY24I2IiGLLaDRCFEUILTzoRhAE6PV6GDXwoBvNhnI0NTY2AgAaGhrw4osvorCwED169MDgwYPx1ltv4bLLLoPb7cZzzz3X6nkWL16MzMxM/5ter49H+URESU2n08H6w4Numgez7+Pi4mJFJnlFSrOh3LVrVwDA6dOnQx4/deoUAKBbt25hn6tr166YPn160PHbbrsNALB+/fpWzzN//ny43W7/m9PpbPO1iYjo/JlMJthsNuQ0e9CNKIqw2WyKrlOOhGbvKffv3x8AWpxx52sfMGBAm+fy9enfv3/I4Q+DwQAAOHz4cKvnSU1NRWpqapuvR0RE0WcymTB16lQ4HA5UV1f7N5XSwhWyj2ZDefjw4QCAHTt2hDzuax82bFib5/ItqfLdW27u2LFjAP53RU1EROqk0+mQr+EH3Wh2+HrcuHHIzMzE/v37sXPnzqDjNpsNAHDddde1ea6xY8eiV69e+O6777B3796g475h61DroYmIiKJFs6GckpKCe++9FwAwZ84c/z1kAHjqqaewa9cujB8/PmA3r2XLlmHo0KGYP39+wLk6duyIuXPnQpZlzJkzJ2DW9Lp16/D6669DEATcddddMf6siIgomWl2+BoAFixYgHXr1mHTpk0YNGgQjEYjDhw4gC1btiArKwuvvvpqQP8jR45g7969IReQP/jggygtLcW6deswePBgjB49GkeOHMHmzZshSRIef/xxXH755fH61IiIKAlp9koZANLS0lBaWoqFCxeiS5cueP/993HgwAHMnDkTO3bsiOgJUZ06dcJHH32EP//5z+jduzc++eQTlJeXY/z48fjwww/x0EMPxfAzISIi0vijG9WOD6QgIqKkeCAFERFRomEoExERqQRDmYiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEpreZpOISM0kSdL0YwQp/hjKREQxYLfbYbFYAp75LooirFYrTCaTgpWRmnH4mogoyux2O8xmc0AgA4DL5YLZbIbdbleoMlI7hjIRURRJkgSLxYJQjxXwtRUVFUGSpHiXRhrAUCYiiiKHwxF0hdyULMtwOp1wOBxxrIq0gqFMRBRFoZ7Xfj79KLkwlImIoig7Ozuq/Si5MJSJiKLIaDRCFEUIghDyuCAI0Ov1MBqNca6MtIChTEQURTqdDlarFQCCgtn3cXFxMdcrU0gMZSKiKDOZTLDZbMjJyQloF0URNpuN65SpRYIcat4+RUVtbS0yMzPhdruRkZGhdDlEFGfc0YuAyLKAO3oREcWITqdDfn6+0mWQhnD4moiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEgxlIiIilWAoExERqQRDmYiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEgxlIiIilWAoExERqQRDmYiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEpoP5fr6ejzyyCMYPHgw0tLS0K9fP8yaNQsul+u8zvvNN9+gc+fOEAQBV111VZSqJSIiapmmQ/nMmTOYMGECFi1ahJMnT2Lq1KnQ6/V47bXXMHLkSHz77bftPvedd94Jj8cTxWqJiIhap+lQ/tOf/oTNmzdjzJgx+Prrr/Huu+9iy5YtWLp0KWpqajBr1qx2nXf58uUoKyvDHXfcEeWKiYiIWqbZUD579iyWLVsGAHj22WfRtWtX/7G5c+di2LBhWL9+PbZv3x7Reb///ns8+OCDuPrqq1FYWBjVmomIiFqj2VDeuHEj3G438vLyMHLkyKDjZrMZAPDhhx9GdF6LxYL6+no899xzUamTiIgoXB2VLqC9vvzySwDAqFGjQh73te/atSvsc3700Ud499138cc//hEDBw5EVVXV+RdKpCKSJMHhcKC6uhrZ2dkwGo3Q6XRKl0VEP9BsKB88eBAAIIpiyOO+9gMHDoR1vlOnTmH27NkYMmQIfve737WrJo/HEzA5rLa2tl3nIYoFu90Oi8US8MemKIqwWq0wmUwKVkZEPpodvj558iQAoEuXLiGPp6enAwDq6urCOt+CBQtw4MABvPDCC0hJSWlXTYsXL0ZmZqb/Ta/Xt+s8RNFmt9thNpuDRn9cLhfMZjPsdrtClRFRU5oN5Wj64osv8Ne//hW33HIL8vPz232e+fPnw+12+9+cTmf0iiRqJ0mSYLFYIMty0DFfW1FRESRJindpRNSMZkPZN9v69OnTIY+fOnUKANCtW7dWz9PQ0IA77rgD3bt3x5NPPnleNaWmpiIjIyPgjUhpDoej1fkRsizD6XTC4XDEsSoiCkWz95T79+8PAC3+svG1DxgwoNXzVFVVYefOnejbty+mT58ecOzEiRMAgO3bt/uvoMvKytpfNJECqquro9qPiGJHs6E8fPhwAMCOHTtCHve1Dxs2LKzzfffdd/juu+9CHjtx4gTWr1/fjiqJlJednR3VfkQUO5odvh43bhwyMzOxf/9+7Ny5M+i4zWYDAFx33XWtnsdgMECW5ZBvpaWlAICJEyf624i0xmg0QhRFCIIQ8rggCNDr9TAajXGujIia02wop6Sk4N577wUAzJkzx38PGQCeeuop7Nq1C+PHj8ell17qb1+2bBmGDh2K+fPnx71eIqXodDpYrVYACApm38fFxcVcr0ykApoNZcC7jOmKK67Apk2bMGjQINx0000YPXo0fvvb3yIrKwuvvvpqQP8jR45g7969vHdGScdkMsFmsyEnJyegXRRF2Gw2rlMmUglNh3JaWhpKS0uxcOFCdOnSBe+//z4OHDiAmTNnYseOHcjNzVW6RCLVMJlMqKysRGlpKUpKSlBaWoqKigoGMpGKCDJvlMZMbW0tMjMz4Xa7uTyKiChJRZIFmr5SJiIiSiQMZSIiIpVgKBMREakEQ5mIiEglGMpEREQqwVAmIiJSCYYyERGRSjCUiYiIVIKhTEREpBIMZSIiIpXQ7POUtcC3g2ltba3ClRARkVJ8GRDOrtYM5Riqq6sDAOj1eoUrISIipdXV1SEzM7PVPnwgRQw1Njbi0KFD6NatW4sPmG9LbW0t9Ho9nE4nH2oRI/waxxa/vrHHr3Fsne/XV5Zl1NXVoV+/fujQofW7xrxSjqEOHTpAFMWonCsjI4M/bDHGr3Fs8esbe/wax9b5fH3bukL24UQvIiIilWAoExERqQRDWeVSU1Px6KOPIjU1VelSEha/xrHFr2/s8WscW/H8+nKiFxERkUrwSpmIiEglGMpEREQqwVAmIiJSCYayStXX1+ORRx7B4MGDkZaWhn79+mHWrFlwuVxKl6Z5p0+fxvvvv4/bb78dQ4YMQVpaGtLT0zF8+HD88Y9/xMmTJ5UuMeEcPXoUffr0gSAIGDhwoNLlJJSamho88MADGDJkCDp37oyePXti1KhRePDBB5UuTfO2bduGG2+8Ef369UOnTp3QvXt3GI1GvPbaa2FtmdkenOilQmfOnEFBQQE2b96M7OxsGI1GVFZWYuvWrcjKysLmzZuRm5urdJma9corr+COO+4AAPz4xz/GxRdfjNraWmzatAl1dXUYOnQo1q9fjz59+ihcaeKYOXMm3nzzTciyjLy8POzbt0/pkhLC9u3bMWnSJBw9ehQXXXSR/3t59+7dqKqqQkNDg9IlatbKlStx0003QZIkjBo1CgMHDkRNTQ0cDgcaGhowY8YMvP3229F/YZlU5+GHH5YByGPGjJHr6ur87UuXLpUByOPHj1euuATw+uuvy3feeae8e/fugPZDhw7JI0eOlAHIhYWFClWXeNatWycDkO+8804ZgJyXl6d0SQnh8OHDcu/eveUuXbrIq1atCjq+ZcsWBapKDOfOnZP79OkjA5DffvvtgGO7d++We/bsKQOQP/vss6i/NkNZZTwej5yZmSkDkHfs2BF0fNiwYTIA+YsvvlCgusS3adMmGYCcmpoqezwepcvRvNOnT8t5eXnyhRdeKH/99dcM5Si65557ZADys88+q3QpCae8vFwGIA8ZMiTk8fvuu08GIP/5z3+O+mvznrLKbNy4EW63G3l5eRg5cmTQcbPZDAD48MMP411aUhg+fDgAwOPx4OjRowpXo32PPfYYvv32W7zwwgvo1KmT0uUkjPr6evztb39Deno6brvtNqXLSTjhbhLSq1evqL82Q1llvvzySwDAqFGjQh73te/atStuNSWTb7/9FgDQqVMn9OzZU+FqtG3Xrl1YunQpbrvtNhiNRqXLSShffPEF6urqMHLkSHTu3Blr167F3LlzMXv2bBQXF+PQoUNKl6hpubm5yMvLw969e1FSUhJw7KuvvsLf/vY39OjRA9OmTYv6a/MpUSpz8OBBAGjx6VK+9gMHDsStpmRitVoBANdeey23LDwPjY2N+PWvf43u3bvjL3/5i9LlJJzdu3cDAPr06YPrr78eq1atCjj+0EMPYfny5SgsLFSiPM3T6XR444038POf/xy//OUvsXTpUgwaNAiHDx+Gw+HAhRdeiNdffz0mf7jzSlllfMtxunTpEvJ4eno6AO/Dsim6PvroIyxfvhydOnXCokWLlC5H05555hls27YNS5YsickQX7I7fvw4AOCDDz7Axx9/jGeffRaHDx9GZWUlHnjgAdTX1+PWW2/Fzp07lS1Uw8aNG4f169cjNzcXO3bswLvvvovS0lJ06NABV199dcxWwDCUiQDs2bMHN998M2RZxpIlS/z3lilyBw8exIIFCzB+/HjMnDlT6XISUmNjIwCgoaEBf/zjHzF79mxkZWVhwIABWLJkCaZPn45z585hyZIlCleqXStWrMDll18OvV6PLVu24OTJk/j6668xc+ZMLF26FBMmTIDH44n66zKUVaZr164AvBtchHLq1CkAQLdu3eJWU6JzuVy49tprcfz4ccydOxcWi0XpkjRtzpw5OHv2LF544QWlS0lYvt8TAEJO9PK1rV+/Pm41JZJvvvkGt956K3r37o3Vq1fj8ssvR3p6OgYNGoQXX3wRP//5z7Fjxw68+uqrUX9t3lNWmf79+wMAqqqqQh73tQ8YMCBuNSWyY8eO4ZprrsGBAwdw22234cknn1S6JM1bvXo1unfvjrvvvjug/cyZMwC8fwTl5+cDAN555x307ds33iVqnu/nv0uXLsjKygo6bjAYAACHDx+OZ1kJ45133sG5c+dw7bXXBvwB5HPjjTdi9erV2LBhA+65556ovjZDWWV8w6Y7duwIedzXPmzYsLjVlKhOnjyJn/3sZ9i9ezdMJhNefvllCIKgdFkJ4cSJEy1epZ05c8Z/zBfUFBnfcsn6+np4PJ6gSYnHjh0DgJCBQm3zXfxkZmaGPO5r993bjyYOX6vMuHHjkJmZif3794ecpGGz2QAA1113XZwrSywejwdTp07F1q1bMWnSJKxYsQI6nU7pshKC7N2UKOitoqICAJCXl+dv813RUWT69++P4cOHQ5blkH/8+NpC7XVAbfON3nzxxRchj2/btg0AYvP9G/XtSOi8+bbZHDt2rHzy5El/O7fZjI6GhgZ52rRpMgDZaDTKp06dUrqkpFBRUcEdvaLo7bfflgHIl1xyiXzo0CF/+7///W//NpD/93//p2CF2rV9+3YZgAxAfu655wKOff7553J6eroMQP7000+j/tp8IIUKnTlzBvn5+diyZYv/gRQHDhzAli1b+ECKKLBarSgqKgIATJs2DRkZGSH7Pfnkk+jdu3ccK0tslZWVuOCCC/hAiiiaOXMm3njjDXTv3h1jx45FfX09Nm3aBI/HgzvuuAMvvfSS0iVq1oMPPuifY3LRRRfhwgsvxKFDh/D555+jsbERd955J1588cXov3DUY56i4vTp0/LChQvlvLw8OSUlRe7bt688c+ZM2el0Kl2a5j366KP+v4Jbe6uoqFC61ITCK+Xoa2xslF966SX50ksvlbt06SKnp6fLY8aMkV9//XWlS0sIdrtdvuaaa+RevXrJHTt2lHv06CEXFBTIJSUlMXtNXikTERGpBCd6ERERqQRDmYiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEgxlIiIilWAoE6mMIAgRvfk2xc/Pz4cgCKisrFS0/vaaNWsW0tPTVfO4wfr6emRnZ2Py5MlKl0JJhI9uJFKZW2+9NajtX//6F/bv34/hw4djxIgRAccSYX/u8vJyvPHGG/jtb3+LPn36KF0OAKBz586YN28e5s6di88++wwTJkxQuiRKAtxmk0gDfA8eePTRR/GHP/whZJ+DBw/i9OnTyMvLQ6dOneJb4HmaOnUq1q5di6qqKtWEMuC9Wu7Xrx8GDx6MLVu2KF0OJQEOXxMliP79+2Po0KGaC2Sn04nVq1dj0qRJqgpkwHu1fMMNN2Dr1q3497//rXQ5lAQYykQJoqV7yr77zg0NDVi0aBEGDhyIzp0748c//jFee+01f7/PPvsMBQUFyMjIQI8ePXDLLbfg6NGjIV+roaEBzz//PMaMGYOMjAx07twZI0aMQHFxMRoaGiKq+9VXX0VjYyMKCwuDjlVWVkIQBOTn5+PUqVOYO3cu9Ho9OnfujFGjRuHDDz/09/373/+OK664Aunp6fjRj36E++67D/X19UHnrKmpwe9//3tceOGF6Nq1KzIzMzF48GDccsst2Lp1a1D/GTNmAAAfg0hxwXvKREnixhtv9AdvXl4e1q9fj1mzZgEAunXrhsLCQowePRqTJk3C559/jrfeegsVFRXYsGEDBEHwn6e+vh5TpkxBaWkpevbsidGjRyMtLQ1btmzB/fffj9LSUrz33nvo0CG8v/lXr14NwPtHRUvOnj2LiRMnoqKiAldeeSWOHDmCDRs2YNq0afj4449RXl6OefPmYfz48Zg0aRI2bNiAZ555BkePHsXbb7/tP09dXR2uuOIKVFRUQK/X4+qrr0bHjh1x8OBBvPPOO8jNzcXll18e8Npjx45Fp06dsGbNmnC/1ETtF7OHQhJR1Nx6660yAPnRRx9tsc/48eNDPgcaPzwf+uKLL5YPHz7sb//ss89kAHJ2drbcq1cvefXq1f5jbrdbvuiii2QA8meffRZwvtmzZ8sA5Jtuukk+ceKEv722tlaePHmyDEB+/vnnw/q86urqZJ1OJ/fr1y/kcd8zmAHIEyZMkE+ePOk/9tprr8kA5IEDB8o9evSQt23b5j/mcrnkPn36yADk/fv3+9tfffVVGYD8//7f/5MlSQp4rcOHD8vl5eUh67j00ktlAPK3334b1udF1F4cviZKEsXFxcjKyvJ/XFBQgJEjR6K6uho/+9nPMGXKFP+xjIwM3HnnnQCA9evX+9sPHz6Ml19+GXq9Hq+99hoyMzP9x7p164bly5cjJSUFzz//fFg17d69G5IkYciQIa3269ChA55//nmkp6f722655Rb07t0b+/btw5w5c/CTn/zEf6xfv3745S9/CQDYsGGDv72mpgYAMGHChKAr+aysLFx88cUhX3/o0KEAgJ07d4b1eRG1F0OZKAl06tQp5PBwbm4uAOCaa65p8Vh1dbW/raysDOfOncO1116Lzp07B/2bvn37YtCgQSgvLw95P7c535rkHj16tNrPYDBg8ODBAW0dOnTAgAEDIqr/0ksvBQAsWbIE77zzDurq6tqsEQB69uwJ4H+hThQrDGWiJNC3b1/odLqg9q5duwIAcnJyWjzm8Xj8bb5JZC+//HKLm5n897//hSzLOHbsWJt1ud1uAN6r7NaEqq899U+cOBH3338/Dh06hMLCQvTs2RNXXHEFFixYgG+//bbF18/IyAAAnDhxotU6ic4XJ3oRJYG2Jl2FOymrsbERADBixAgMHz681b6pqaltns83/N3WFWu06geAp556CnfddRdWrVqFdevWYePGjdi6dSv+8pe/YMWKFbjhhhuC/o3vj4fu3buH/TpE7cFQJqKwiaIIAPjpT3+KZ5555rzP51uXHM5VdTQNGTIE8+bNw7x583DmzBksW7YMDz74IO65556QoXz8+HEACLgnTxQLHL4morAVFBRAp9Nh9erVOHfu3Hmf76KLLkLHjh2xd+/eKFTXPmlpaXjggQeQnZ2NmpqakHtvf/XVVwAQtMUpUbQxlIkobDk5OZg1axYqKytRWFiI77//PqjPvn37sHLlyrDOl56e7p8B7nK5ol1ukPfffx+bN28Oat++fTu+//57dO3aNWiI+syZMygvL4der8cFF1wQ8xopuXH4mogiYrVaUVlZiZUrV+Ljjz/GiBEj0L9/f5w6dQq7d+/Gvn37MHXq1JDDwKFMmTIF27ZtQ1lZmX8ZU6yUlZXBarUiJycHI0eOREZGBg4dOgSHw4HGxkY89thjSElJCfg3GzduxLlz5wKWjBHFCkOZiCLSuXNnrF27Fm+//TbeeOMN7Ny5E1u3bkVWVhYGDBiAX/3qV/jFL34R9vlmzZqFRYsWoaSkJOahPHPmTHTs2BEbNmzA1q1b4Xa70bdvX0yePBkWiwUTJ04M+jclJSUAgDvuuCOmtREBfEoUEanAtGnTsHr1ajidTvTt21fpcvz4lCiKN95TJiLFLVq0CI2NjXjyySeVLiXACy+8gBMnTmDx4sVKl0JJglfKRKQKs2bNwrvvvouKigpVPMKxvr4eubm5GDlyJD766COly6EkwVAmIiJSCQ5fExERqQRDmYiISCUYykRERCrBUCYiIlIJhjIREZFKMJSJiIhUgqFMRESkEgxlIiIilWAoExERqQRDmYiISCX+P9th6ZzaMATLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0)\n",
    "\n",
    "def calculate_arrivals_optimized(pulse_times, arrival_times, delay=0):\n",
    "    # Find the indices in \"arrival_times\" where elements of \"pulse_times\" should be inserted to maintain order.\n",
    "    inds = np.searchsorted(arrival_times, pulse_times)\n",
    "    \n",
    "    # Split the \"arrival_times\" at the positions found, which corresponds to intervals defined by \"pulse_times\"\n",
    "    return [arrival_times[inds[i]:inds[i+1]] - pulse_times[i] - delay for i in range(len(inds)-1)]\n",
    "\n",
    "def flatten(container):\n",
    "    # Function to flatten a rasted/ragged array\n",
    "    for i in container:\n",
    "        if isinstance(i, (list,tuple,np.ndarray)):\n",
    "            for j in flatten(i):\n",
    "                yield j\n",
    "        else:\n",
    "            yield i\n",
    "        \n",
    "def calculate_g2(arrivals, bins, delta_t):\n",
    "    # Calculate g2 from a rasted list of arrivals and the time bins to discretize the continous data.\n",
    "    arrival_bins = np.zeros([len(arrivals),bins])\n",
    "    \n",
    "    # For every iteration create an integer mask of the bin that the event would be placed in. \n",
    "    # eg: for events arriving at 1, 17.3 and 20.9 ms with a bin size of 1 ms the mask is [1, 17 20]\n",
    "    # Set the elements of arrival_bins for that iteration to 1, to signify an arrival\n",
    "    for i, times in enumerate(arrivals):\n",
    "        mask = (times//delta_t-1).astype(int)\n",
    "        arrival_bins[i, mask] = 1\n",
    "    # The g2 is calculated as g2_k = <n_0^j * n_k^j>_j / <n_0^j>_j * <n_k^j>_j\n",
    "    # https://iramis.cea.fr/spec/Pres/Quantro/static/wp-content/uploads/2010/10/thesis-book_ZW.pdf page 98\n",
    "    return (arrival_bins[:,0]*arrival_bins.T).mean(1) / (arrival_bins[:,0].mean() * arrival_bins.mean(0))\n",
    "\n",
    "average_number=res.pulse_timestamp.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "arrival_times = np.array([x[0] for x in res.pulse_timestamp.fetch_all()])\n",
    "pulse_times = np.array([x[0] for x in res.pulse_start.fetch_all()])\n",
    "\n",
    "no_arrival_times = np.array([x[0] for x in res.no_pulse_timestamp.fetch_all()])\n",
    "no_pulse_times = np.array([x[0] for x in res.no_pulse_start.fetch_all()])\n",
    "\n",
    "\n",
    "# Calculate the arrival time after sending a pulse and not sending a pulse.\n",
    "# The result is a list of arrays, each array corresponds to a different iteration of the experiment. \n",
    "# The size of the array is the number of detections and the values the time after the sequence started\n",
    "# eg. if 2 photons arrive at 1 and 3.5 ms the array for that iteration would be [1000000, 3500000], if no photon arrives then []\n",
    "ragged_arrivals = calculate_arrivals_optimized(pulse_times, arrival_times)\n",
    "ragged_no_arrivals = calculate_arrivals_optimized(no_pulse_times, no_arrival_times)\n",
    "\n",
    "# Flatten the arrays and plot the histograms\n",
    "arrivals = np.sort(list(flatten(ragged_arrivals)))\n",
    "no_arrivals = np.sort(list(flatten(ragged_no_arrivals)))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.hist(arrivals*1e-6, color='r', alpha=0.7, bins=20)\n",
    "plt.hist(no_arrivals*1e-6, color='k', alpha=0.7, bins=20)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "\n",
    "# Calculate the discretization of the points and g2\n",
    "t0, t1 = min(arrivals), max(arrivals)\n",
    "bins = 20\n",
    "bin_size = (t1-t0)/bins\n",
    "time_bins = np.arange(bins)*bin_size*1e-6\n",
    "\n",
    "g2 = calculate_g2(ragged_arrivals, bins, bin_size)\n",
    "no_g2 = calculate_g2(ragged_no_arrivals, bins, bin_size)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(time_bins[1:-1], g2[1:-1], 'ro')\n",
    "plt.plot(time_bins[1:-1], no_g2[1:-1], 'ko')\n",
    "plt.hlines(1, 0, time_bins[-1], linestyle='--', color='gray', alpha=0.5)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel(f'g$^2$')\n",
    "\n",
    "filename = 'fluorescence'\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'arrival_times': arrival_times,\n",
    "            'pulse_times': pulse_times,\n",
    "    \n",
    "            'no_arrival_times': no_arrival_times,\n",
    "            'no_pulse_times': no_pulse_times,\n",
    "    \n",
    "            'N_iterations':N_iterations,\n",
    "            'Integration_time': Integration_time,\n",
    "    \n",
    "            'gauss_duration':gauss_duration,\n",
    "            'amplitude_pulse': amplitude_pulse,\n",
    "    \n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327d1c0e-18f4-47e6-b70b-ac7fa57a44d7",
   "metadata": {},
   "source": [
    "## g2 with long pulses and state prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2006,
   "id": "f12609d4-5cce-4a6d-95d9-edcd9f6b0641",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T09:45:47.257354Z",
     "iopub.status.busy": "2024-03-30T09:45:47.256354Z",
     "iopub.status.idle": "2024-03-30T09:45:51.909069Z",
     "shell.execute_reply": "2024-03-30T09:45:51.908067Z",
     "shell.execute_reply.started": "2024-03-30T09:45:47.257354Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory created\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "\n",
    "experiment_name='spin_T1_arrival_time'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=8000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated)  #30000\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "\n",
    "gauss_duration = 5_000//4\n",
    "amplitude_pulse = 0.079\n",
    "\n",
    "heat_wait_time = int(10e6//4)\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    \n",
    "    click=declare(bool)\n",
    "    \n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    \n",
    "    p2_stream = declare_stream()\n",
    "    index2_stream = declare_stream()\n",
    "    #timing_stream = declare_stream()\n",
    "    #delta_freq_stream = declare_stream()\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    # delta_freq = declare(int) \n",
    "    # assign(delta_freq,0)\n",
    "    # delta_freq_stream = declare_stream()\n",
    "    # Y=declare(fixed)\n",
    "    # angle=declare(fixed)\n",
    "    # delta_freq_acc = declare(int) \n",
    "    # assign(Y,0)\n",
    "    # assign(delta_freq_acc,0)\n",
    "    # \n",
    "    #click_acc=declare(int)\n",
    "    \n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        \n",
    "        ################### Preparation ###################\n",
    "        chirped_pumping(centre_freq, 0,pump_steps = 1, enable_fsv_trigger=False)\n",
    "    \n",
    "        #save(0, timing_stream)\n",
    "        \n",
    "        #amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        #gaussian_pulse_length = 5000//4\n",
    "        #delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        #delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        #save(delta_freq, delta_freq_stream)\n",
    "\n",
    "        \n",
    "        #align()\n",
    "\n",
    "        #chirped_pumping(centre_freq, delta_freq, pump_steps = 10, enable_fsv_trigger=False)\n",
    "        #align()\n",
    "        align()\n",
    "        wait(heat_wait_time)\n",
    "        # update_frequency(spin_element, Photon_IF+centre_freq*1e3+delta_freq)\n",
    "        # align()\n",
    "        #save(0, timing_stream)\n",
    "        ################### Main Sequence ###################\n",
    "        \n",
    "        align()\n",
    "        save(j, index_stream)\n",
    "        play('ON',fsv_trigger)\n",
    "\n",
    "        # Save the beginning of the sequence and send a pulse\n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(amplitude_pulse*4), spin_element, duration = gauss_duration//4) \n",
    "        \n",
    "        align()\n",
    "        wait(waiting_time_spin_prep)\n",
    "        \n",
    "        align()\n",
    "        measure_SMPD(p_stream,N_iterations,waiting_time_SMPD_prep, timestamp=True)\n",
    "        \n",
    "        #-----------------------------------------------------------------------------------#\n",
    "        \n",
    "        # Save the beginning but not send a pulse after\n",
    "        align()\n",
    "        save(j, index2_stream)\n",
    "        \n",
    "        align()\n",
    "        play(spin_gauss_pulse*amp(0), spin_element, duration = gauss_duration//4) \n",
    "        \n",
    "        \n",
    "        align()\n",
    "        wait(waiting_time_spin_prep)\n",
    "        \n",
    "        align()\n",
    "        measure_SMPD(p2_stream,N_iterations,waiting_time_SMPD_prep, timestamp=True)\n",
    "        #save(0, timing_stream)\n",
    "        \n",
    "    with stream_processing():\n",
    "        p_stream.timestamps().save_all('pulse_timestamp')\n",
    "        index_stream.timestamps().save_all('pulse_start')\n",
    "        \n",
    "        p2_stream.timestamps().save_all('no_pulse_timestamp')\n",
    "        index2_stream.timestamps().save_all('no_pulse_start')\n",
    "        #delta_freq_stream.save_all('delta_freq')\n",
    "        #timing_stream.timestamps().buffer(3).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1)\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2008,
   "id": "f9e6bfe8-a7fc-4c35-8c5d-09e4ff612c67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T17:02:52.274046Z",
     "iopub.status.busy": "2024-03-30T17:02:52.273043Z",
     "iopub.status.idle": "2024-03-30T17:03:21.299403Z",
     "shell.execute_reply": "2024-03-30T17:03:21.298401Z",
     "shell.execute_reply.started": "2024-03-30T17:02:52.274046Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1856479\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "    def calculate_arrivals_optimized(pulse_times, arrival_times, delay=0):\n",
    "        # Find the indices in \"arrival_times\" where elements of \"pulse_times\" should be inserted to maintain order.\n",
    "        inds = np.searchsorted(arrival_times, pulse_times)\n",
    "\n",
    "        # Split the \"arrival_times\" at the positions found, which corresponds to intervals defined by \"pulse_times\"\n",
    "        return [arrival_times[inds[i]:inds[i+1]] - pulse_times[i] - delay for i in range(len(inds)-1)]\n",
    "\n",
    "    def flatten(container):\n",
    "        # Function to flatten a rasted/ragged array\n",
    "        for i in container:\n",
    "            if isinstance(i, (list,tuple,np.ndarray)):\n",
    "                for j in flatten(i):\n",
    "                    yield j\n",
    "            else:\n",
    "                yield i\n",
    "\n",
    "    def calculate_g2(arrivals, bins, delta_t):\n",
    "        # Calculate g2 from a rasted list of arrivals and the time bins to discretize the continous data.\n",
    "        arrival_bins = np.zeros([len(arrivals),bins])\n",
    "\n",
    "        # For every iteration create an integer mask of the bin that the event would be placed in. \n",
    "        # eg: for events arriving at 1, 17.3 and 20.9 ms with a bin size of 1 ms the mask is [1, 17 20]\n",
    "        # Set the elements of arrival_bins for that iteration to 1, to signify an arrival\n",
    "        for i, times in enumerate(arrivals):\n",
    "            mask = (times//delta_t-1).astype(int)\n",
    "            arrival_bins[i, mask] = 1\n",
    "\n",
    "        # The g2 is calculated as g2_k = <n_0^j * n_k^j>_j / <n_0^j>_j * <n_k^j>_j\n",
    "        # https://iramis.cea.fr/spec/Pres/Quantro/static/wp-content/uploads/2010/10/thesis-book_ZW.pdf page 98\n",
    "        return (arrival_bins[:,0]*arrival_bins.T).mean(1) / (arrival_bins[:,0].mean() * arrival_bins.mean(0))\n",
    "\n",
    "    average_number=res.pulse_timestamp.count_so_far()\n",
    "    print(average_number)\n",
    "\n",
    "    arrival_times = np.array([x[0] for x in res.pulse_timestamp.fetch_all()])\n",
    "    pulse_times = np.array([x[0] for x in res.pulse_start.fetch_all()])\n",
    "\n",
    "    no_arrival_times = np.array([x[0] for x in res.no_pulse_timestamp.fetch_all()])\n",
    "    no_pulse_times = np.array([x[0] for x in res.no_pulse_start.fetch_all()])\n",
    "\n",
    "\n",
    "    # Calculate the arrival time after sending a pulse and not sending a pulse.\n",
    "    # The result is a list of arrays, each array corresponds to a different iteration of the experiment. \n",
    "    # The size of the array is the number of detections and the values the time after the sequence started\n",
    "    # eg. if 2 photons arrive at 1 and 3.5 ms the array for that iteration would be [1000000, 3500000], if no photon arrives then []\n",
    "    ragged_arrivals = calculate_arrivals_optimized(pulse_times, arrival_times, delay=heat_wait_time*4)\n",
    "ragged_no_arrivals = calculate_arrivals_optimized(no_pulse_times, no_arrival_times)\n",
    "\n",
    "# Flatten the arrays and plot the histograms\n",
    "arrivals = np.sort(list(flatten(ragged_arrivals)))\n",
    "no_arrivals = np.sort(list(flatten(ragged_no_arrivals)))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.hist(arrivals*1e-6, color='r', alpha=0.7, bins=20)\n",
    "plt.hist(no_arrivals*1e-6, color='k', alpha=0.7, bins=20)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "\n",
    "# Calculate the discretization of the points and g2\n",
    "t0, t1 = min(arrivals), max(arrivals)\n",
    "bins = 20\n",
    "bin_size = (t1-t0)/bins\n",
    "time_bins = np.arange(bins)*bin_size*1e-6\n",
    "\n",
    "g2 = calculate_g2(ragged_arrivals, bins, bin_size)\n",
    "no_g2 = calculate_g2(ragged_no_arrivals, bins, bin_size)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(time_bins[1:-1], g2[1:-1], 'ro')\n",
    "plt.plot(time_bins[1:-1], no_g2[1:-1], 'ko')\n",
    "plt.hlines(1, 0, time_bins[-1], linestyle='--', color='gray', alpha=0.5)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel(f'g$^2$')\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'g2.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'arrival_times': arrival_times,\n",
    "            'pulse_times': pulse_times,\n",
    "\n",
    "            'no_arrival_times': no_arrival_times,\n",
    "            'no_pulse_times': no_pulse_times,\n",
    "\n",
    "            'N_iterations':N_iterations,\n",
    "            'Integration_time': Integration_time,\n",
    "\n",
    "            'gauss_duration':gauss_duration,\n",
    "            'amplitude_pulse': amplitude_pulse,\n",
    "\n",
    "            }\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1756,
   "id": "80041ca3-a7e2-4ac6-838a-8c9467592b61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T08:23:32.293039Z",
     "iopub.status.busy": "2024-03-29T08:23:32.293039Z",
     "iopub.status.idle": "2024-03-29T08:23:32.590119Z",
     "shell.execute_reply": "2024-03-29T08:23:32.589119Z",
     "shell.execute_reply.started": "2024-03-29T08:23:32.293039Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 142)"
      ]
     },
     "execution_count": 1756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_timing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1766,
   "id": "05521843-3ee9-46f2-a120-12c14e0a586a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T08:25:20.991968Z",
     "iopub.status.busy": "2024-03-29T08:25:20.991968Z",
     "iopub.status.idle": "2024-03-29T08:25:21.394974Z",
     "shell.execute_reply": "2024-03-29T08:25:21.392974Z",
     "shell.execute_reply.started": "2024-03-29T08:25:20.991968Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.0000000e-06 3.7182844e+01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time (ms)')"
      ]
     },
     "execution_count": 1766,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "\n",
    "delta_timing=((timing[:,1:]-timing[:,:-1])*1e-6).T\n",
    "label=['Preparation','Pulse sequence']\n",
    "bar_colors = [colors[0],colors[1],colors[0],colors[2],colors[3]]\n",
    "\n",
    "print(delta_timing.mean(1))\n",
    "sizes = delta_timing.mean(1)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize = (7,6))\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    return f\"{pct:.1f}%\\n({absolute:d} ms)\"\n",
    "\n",
    "bar = ax.bar(label,sizes,color = bar_colors, width = 0.8)\n",
    "ax.bar_label(bar, fmt='%i')\n",
    "ax.set_xticklabels(label, rotation = 90)\n",
    "ax.set_ylabel(\"Time (ms)\")\n",
    "#ax.set_yscale(\"log\")\n",
    "#ax.pie(sizes, labels=label, autopct=lambda pct: func(pct, sizes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5087ad-f532-4631-a27e-43b34aa52303",
   "metadata": {},
   "source": [
    "## Raman heating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c558001-eb8e-4016-9164-3a5a309cee6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(800e3)\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,3,21))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "raman_pulse_duration = raman_pulse_durations[-1]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "\n",
    "experiment_name='raman_heating'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "BX_field = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY_field = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ_field = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=8000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=1000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        save(j, index_stream)\n",
    "        ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "        align()\n",
    "\n",
    "        Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "        align()\n",
    "        wait(int(waiting_time_spin), readout_element)\n",
    "        align() \n",
    "        measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "        align()\n",
    "        wait(int(1000e6//4))\n",
    "        align()\n",
    "        \n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.boolean_to_int().buffer(N_iterations).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75f33ad0-45fd-48d6-8858-eb1b266545bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist=time_axis*1e-6\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()]).mean(0)/cycle_time\n",
    "\n",
    "click_array = click_array.reshape(N, int(click_array.shape[0]/N)).mean(-1)\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "print('shape of click array:',click_array.shape)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array,'o')\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array, [0.1,0.1,0.05])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.title(f\"efficiency: {popt[0]*popt[1]:.4f}\")\n",
    "    plt.legend()\n",
    "except Exception as e:\n",
    "    print('fit failed: ',e)\n",
    "plt.xlim(0, None)\n",
    "#plt.ylim(0, None)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Count rate (1/ms)')\n",
    "# plt.yscale(\"log\")\n",
    "filename = 'fluorescence'\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'decay_fit.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'raman_pulse_duration':raman_pulse_duration,\n",
    "            'detuned_electron_amplitude_a':detuned_electron_amplitude_a,\n",
    "            'detuned_sideband_amplitude_a':detuned_sideband_amplitude_a,\n",
    "            # 'Bz_field': BZ_field,\n",
    "            # 'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48a5a6d-1e63-4d41-bdd6-e49b73542784",
   "metadata": {},
   "source": [
    "### As a function of amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8998f40-c493-4ece-a318-5e158c29b964",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "gauss_duration = 5_000//4\n",
    "amplitude_pulse = 0.079\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(800e3)\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,3,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "raman_pulse_duration = raman_pulse_durations[-1]\n",
    "\n",
    "experiment_name='raman_heating'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "factors = np.linspace(0.2,2,10)\n",
    "factors = [float(x) for x in factors]\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=20000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_repetition = 1e9\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "    factor = declare(fixed)\n",
    "    \n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        with for_each_(factor, factors):\n",
    "            align()\n",
    "            save(j, index_stream)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, factor*detuned_electron_amplitude_a, factor*detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "            #play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "\n",
    "            align()\n",
    "            wait(int(waiting_time_spin), readout_element)\n",
    "            align() \n",
    "            measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "            align()\n",
    "            wait(int(20e6//4))\n",
    "            align()\n",
    "        \n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(len(factors)).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9d834f-2056-49e4-8027-364b3c5d8e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "filename = 'raman_heating'\n",
    "\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist=time_axis*1e-6\n",
    "\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "a = click_array.mean(0)\n",
    "plt.figure(figsize = (6,5))\n",
    "plot_2d_sweep(a, \n",
    "              x = time_hist.astype(int),\n",
    "              y = np.round(factors,2),\n",
    "              xlabel = \"Time (ms)\",\n",
    "              ylabel = \"amplitude factor\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "             )\n",
    "\n",
    "plt.tight_layout()\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'2D_plot.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "est_list=[]\n",
    "for i in range(a.shape[0]):\n",
    "    data_decay = a[i]\n",
    "    plt.plot(time_hist,data_decay,'o')\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, data_decay, [0.1,0.1,0.05])\n",
    "        label=\"f = %.2f\"%(factors[i])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        est_list.append(popt)\n",
    "        plt.legend()\n",
    "        plt.xlabel('Time (ms)')\n",
    "        plt.ylabel('Count rate (1/ms)')\n",
    "    except Exception as e:\n",
    "        print('fit failed: ',e)\n",
    "est_list = np.array(est_list)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "x=np.round(factors,2)\n",
    "plt.plot(x, est_list[:, 0])\n",
    "plt.xlabel(\"Cooldown time (ms)\")\n",
    "plt.ylabel(\"Estimated decay rate (1/ms)\")\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'fit_decay.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    \n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'raman_pulse_duration':raman_pulse_duration,\n",
    "            'detuned_electron_amplitude_a':detuned_electron_amplitude_a,\n",
    "            'detuned_sideband_amplitude_a':detuned_sideband_amplitude_a,\n",
    "            # 'Bz_field': BZ_field,\n",
    "            # 'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ce183d-9039-4d36-a7a4-0788c3c7ac0e",
   "metadata": {},
   "source": [
    "### As a function of duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "739b13eb-4c08-4f87-8926-5e3d4aa98260",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "gauss_duration = 5_000//4\n",
    "amplitude_pulse = 0.079\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(800e3)\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,10,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "\n",
    "experiment_name='raman_heating'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=20000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_repetition = 1e9\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "    raman_pulse_duration = declare(int)\n",
    "    \n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        with for_each_(raman_pulse_duration, raman_pulse_durations):\n",
    "            align()\n",
    "            save(j, index_stream)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "            #play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration = gauss_duration) \n",
    "\n",
    "            align()\n",
    "            wait(int(waiting_time_spin), readout_element)\n",
    "            align() \n",
    "            measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "            align()\n",
    "            wait(int(20e6//4))\n",
    "            align()\n",
    "        \n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(len(raman_pulse_durations)).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f14cc04-a1da-47e9-9be8-8779b82be79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "filename = 'raman_heating'\n",
    "\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist=time_axis*1e-6\n",
    "\n",
    "x=np.round(np.array(raman_pulse_durations)*4e-6,2)\n",
    "\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "a = click_array.mean(0)\n",
    "plt.figure(figsize = (6,5))\n",
    "plot_2d_sweep(a, \n",
    "              x = time_hist.astype(int),\n",
    "              y = x,\n",
    "              xlabel = \"Time (ms)\",\n",
    "              ylabel = \"pulse duration (ms)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "             )\n",
    "\n",
    "plt.tight_layout()\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'2D_plot.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "est_list=[]\n",
    "for i in range(a.shape[0]):\n",
    "    data_decay = a[i]\n",
    "    plt.plot(time_hist,data_decay,'o')\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, data_decay, [0.1,0.1,0.05])\n",
    "        label=\"d = %.2f ms\"%(x[i])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        est_list.append(popt)\n",
    "        plt.legend(fontsize=8)\n",
    "        plt.xlabel('Time (ms)')\n",
    "        plt.ylabel('Count rate (1/ms)')\n",
    "    except Exception as e:\n",
    "        print('fit failed: ',e)\n",
    "est_list = np.array(est_list)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(x, est_list[:, 0])\n",
    "plt.xlabel(\"pulse duration (ms)\")\n",
    "plt.ylabel(\"Estimated decay rate (1/ms)\")\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'fit_decay.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(x, est_list[:, 1])\n",
    "plt.xlabel(\"pulse duration (ms)\")\n",
    "plt.ylabel(\"heating amplitude rate (1/ms)\")\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'detuned_electron_amplitude_a':detuned_electron_amplitude_a,\n",
    "            'detuned_sideband_amplitude_a':detuned_sideband_amplitude_a,\n",
    "            # 'Bz_field': BZ_field,\n",
    "            # 'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f73299-6321-412c-ac87-b60292e57d03",
   "metadata": {},
   "source": [
    "### As a function of wait time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f186baa3-ec52-4cf7-aab3-be724baae4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.387e3)\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,3,21))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "raman_pulse_duration = raman_pulse_durations[-1]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "\n",
    "experiment_name='raman_heating'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "cooldown_times = np.round(np.geomspace(1e6,100e6,11))\n",
    "cooldown_times = [int(x//4) for x in cooldown_times]\n",
    "\n",
    "\n",
    "\n",
    "BX_field = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY_field = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ_field = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=8000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=1000 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "k_loops = 100\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "    cooldown_time = declare(int)\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):  \n",
    "        with for_each_(cooldown_time, cooldown_times):\n",
    "            wait(cooldown_times[-1])\n",
    "            align()\n",
    "            with for_(k, 0, k<k_loops, k+1):\n",
    "                save(j, index_stream)\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                align()\n",
    "                wait(int(waiting_time_spin), readout_element)\n",
    "                align() \n",
    "                measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "                align()\n",
    "                wait(cooldown_time)\n",
    "                align()\n",
    "        \n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(k_loops).buffer(len(cooldown_times)).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f536b6-c85d-4014-bc12-51af6603d8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "filename = 'raman_heating'\n",
    "\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist=time_axis*1e-6\n",
    "\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "a = click_array.mean(0).mean(1)\n",
    "plt.figure(figsize = (6,5))\n",
    "plot_2d_sweep(a, \n",
    "              x = np.linspace(0,len(a[0])-1,len(a[0]), dtype = int),\n",
    "              y = np.round(np.multiply(cooldown_times,4e-6),0),\n",
    "              xlabel = \"Bin\",\n",
    "              ylabel = \"Cooldown time (ms)\",\n",
    "              clabel = \"Count rate (ms$^{-1}$)\",\n",
    "              cmap = \"Blues\",\n",
    "              horizontal_ticks=True,\n",
    "             )\n",
    "\n",
    "plt.tight_layout()\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'2D_plot.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "est_list=[]\n",
    "for i in range(a.shape[0]):\n",
    "    data_decay = a[i]\n",
    "    plt.plot(time_hist,data_decay,'o')\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist, data_decay, [0.1,0.1,0.05])\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        est_list.append(popt)\n",
    "        plt.legend()\n",
    "    except Exception as e:\n",
    "        print('fit failed: ',e)\n",
    "est_list = np.array(est_list)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "x=np.round(np.multiply(cooldown_times,4e-6),0)\n",
    "plt.plot(x, est_list[:, 0])\n",
    "plt.xlabel(\"Cooldown time (ms)\")\n",
    "plt.ylabel(\"Estimated decay time (1/ms)\")\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'fit_decay.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    \n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'raman_pulse_duration':raman_pulse_duration,\n",
    "            'detuned_electron_amplitude_a':detuned_electron_amplitude_a,\n",
    "            'detuned_sideband_amplitude_a':detuned_sideband_amplitude_a,\n",
    "            # 'Bz_field': BZ_field,\n",
    "            # 'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e062091d-2251-4e13-84e0-ea1f9af883b7",
   "metadata": {},
   "source": [
    "## Correlation measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adb24922-1a12-4a3e-bdea-c62188ee222c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "\n",
    "\n",
    "experiment_name='spin_T1'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "amplitude_pulse_fluo = 0.0045/2\n",
    "gauss_duration_fluo  = 160_000//4 \n",
    "\n",
    "amplitude_pulse_fluo = 0.073\n",
    "gauss_duration_fluo  = 5000//4\n",
    "\n",
    "waiting_time = 1000//4\n",
    "waiting_time_spin= 120000//4\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs_RB = [int(readout_freqs[-1]), int(readout_freqs[0])]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####### Parameters to sweep\n",
    "\n",
    "BX_field = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY_field = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ_field = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "N = 30\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=1200 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_repetition = 1e9\n",
    "\n",
    "with program() as Spin_T1:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "\n",
    "    i = declare(int)\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    red_stream = declare_stream()\n",
    "    blue_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "    click=declare(int)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        save(j, index_stream)\n",
    "         ################# Preperation into down-down with conditional and tracking #################\n",
    "\n",
    "        click_acc = nuclear_spin_RO(\n",
    "                prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "        )\n",
    "\n",
    "        raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "\n",
    "\n",
    "        align()\n",
    "\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "\n",
    "        align()\n",
    "        update_frequency(spin_element, readout_freqs_RB[0])\n",
    "        play(spin_gauss_pulse*amp(amplitude_pulse_fluo), spin_element, duration = gauss_duration_fluo) \n",
    "        align()\n",
    "        wait(int(waiting_time_spin), readout_element)\n",
    "        align()\n",
    "        \n",
    "        measure_SMPD(red_stream,N_iterations,waiting_time, accumulate=False)\n",
    "        \n",
    "        align()\n",
    "        update_frequency(spin_element, readout_freqs_RB[1])\n",
    "        play(spin_gauss_pulse*amp(amplitude_pulse_fluo//5), spin_element, duration = gauss_duration_fluo) \n",
    "        align()\n",
    "        wait(int(waiting_time_spin), readout_element)\n",
    "        align()\n",
    "        \n",
    "        measure_SMPD(blue_stream,N_iterations,waiting_time, accumulate=False)\n",
    "\n",
    "        \n",
    "    with stream_processing():\n",
    "        red_stream.boolean_to_int().buffer(N_iterations).save_all('clicks_red')\n",
    "        red_stream.timestamps().buffer(N_iterations).save('timestamp_red')\n",
    "        blue_stream.boolean_to_int().buffer(N_iterations).save_all('clicks_blue')\n",
    "        blue_stream.timestamps().buffer(N_iterations).save('timestamp_blue')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_T1, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679a59e0-085a-456d-a181-46d55f93bf0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "N=15\n",
    "res = job.result_handles\n",
    "\n",
    "#res.wait_for_all_values()\n",
    "average_number=res.clicks_red.count_so_far()\n",
    "print(average_number)\n",
    "\n",
    "time_data_red=res.timestamp_red.fetch_all()\n",
    "time_data_blue=res.timestamp_blue.fetch_all()\n",
    "time_axis_red=time_data_red-time_data_red[0]\n",
    "time_axis_blue=time_data_blue-time_data_blue[0]\n",
    "cycle_time_red=(time_axis_red[1:]-time_axis_red[:-1]).mean()*1e-6\n",
    "cycle_time_blue=(time_axis_blue[1:]-time_axis_blue[:-1]).mean()*1e-6\n",
    "\n",
    "time_hist_red=time_axis_red*1e-6\n",
    "click_array_red=np.array([sublist[0] for sublist in res.clicks_red.fetch_all()]).mean(0)/cycle_time\n",
    "time_hist_blue=time_axis_blue*1e-6\n",
    "click_array_blue=np.array([sublist[0] for sublist in res.clicks_blue.fetch_all()]).mean(0)/cycle_time\n",
    "\n",
    "click_array_red = click_array_red.reshape(N, int(click_array_red.shape[0]/N)).mean(-1)\n",
    "time_hist_red=time_axis_red.reshape(N, int(time_axis_red.shape[0]/N)).mean(-1)*1e-6\n",
    "click_array_blue = click_array_blue.reshape(N, int(click_array_blue.shape[0]/N)).mean(-1)\n",
    "time_hist_blue=time_axis_blue.reshape(N, int(time_axis_blue.shape[0]/N)).mean(-1)*1e-6\n",
    "\n",
    "print('shape of red click array:',click_array_red.shape)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist_red,click_array_red,'o')\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist_red, click_array_red, [0.1,0.1,0.05])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist_red,exp_decay(time_hist_red,*popt),label=label)\n",
    "    plt.title(f\"efficiency: {popt[0]*popt[1]:.4f}\")\n",
    "    plt.legend()\n",
    "except Exception as e:\n",
    "    print('fit failed: ',e)\n",
    "plt.xlim(0, None)\n",
    "#plt.ylim(0, None)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Count rate (1/ms)')\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist_blue,click_array_blue,'o')\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist_blue, click_array_blue, [0.1,0.1,0.05])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist_blue,exp_decay(time_hist_blue,*popt),label=label)\n",
    "    plt.title(f\"efficiency: {popt[0]*popt[1]:.4f}\")\n",
    "    plt.legend()\n",
    "except Exception as e:\n",
    "    print('fit failed: ',e)\n",
    "plt.xlim(0, None)\n",
    "#plt.ylim(0, None)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Count rate (1/ms)')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist_red,click_array_red,'o',color= colors[3],label = \"Count rate after red pulse\")\n",
    "plt.plot(time_hist_blue+time_hist_red[-1]+(gauss_duration_fluo+waiting_time_spin)*4e-6,click_array_blue,'o',color = colors[0], label = \"Count rate after blue pulse\")\n",
    "plt.vlines([time_hist_red[-1],time_hist_red[-1]+gauss_duration_fluo*4e-6,time_hist_red[-1]+(gauss_duration_fluo+waiting_time_spin)*4e-6],0,max(click_array_red))\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Count rate (1/ms)')\n",
    "plt.legend()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'decay_fit.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "\n",
    "filename = 'fluorescence'\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array_red': click_array_red,\n",
    "            'time_axis_red': time_axis_red,\n",
    "            'click_array_blue': click_array_blue,\n",
    "            'time_axis_blue': time_axis_blue,\n",
    "            #'theta': theta,\n",
    "    \n",
    "            #'calibration':calib_dic,\n",
    "            'amplitude_pulse':amplitude_pulse,\n",
    "            'Bz_field': BZ_field,\n",
    "            'By_field': BY_field\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.tight_layout()\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a01a96-238a-4a23-8a67-2562a8327819",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Rabi time calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89cefe4-e85c-42ce-a676-1a472827245e",
   "metadata": {},
   "source": [
    "## Rabi amplitude with Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aa55a37-f127-40cc-a60e-8cddb54cbaee",
   "metadata": {},
   "outputs": [],
   "source": [
    "centre_freq = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "id": "caefe7b3-19a4-463f-94af-9b7a08919d6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:17:23.146639Z",
     "iopub.status.busy": "2024-03-26T16:17:23.145639Z",
     "iopub.status.idle": "2024-03-26T16:17:24.520125Z",
     "shell.execute_reply": "2024-03-26T16:17:24.519126Z",
     "shell.execute_reply.started": "2024-03-26T16:17:23.146639Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory created\n",
      "250\n",
      "480000\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "experiment_name='Spin_detection_calibration_Rabi_amplitudeGauss'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "sim_and_plot=True\n",
    "excecute=True\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "param_type=int\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 100000\n",
    "N = 50\n",
    "stream_I = False\n",
    "\n",
    "gauss_duration = 20000//4\n",
    "\n",
    "\n",
    "amp_param_init=0.\n",
    "amp_param_fin=0.05\n",
    "n_step_amp = 11\n",
    "rabi_amps = np.linspace(amp_param_init,amp_param_fin,n_step_amp)\n",
    "amp_param_step=np.diff(rabi_amps)[0]\n",
    "\n",
    "# rabi_amps=np.arange(amp_param_init,amp_param_fin+  amp_param_step//2,amp_param_step)\n",
    "\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=5000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=2400 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "filename=time_stamp+'%s_duration%.3f'%(experiment_name, gauss_duration)\n",
    "\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "############################ Frequency tracking ############################\n",
    "spacing = 60\n",
    "tolerance = 5\n",
    "#res,freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 10)\n",
    "#excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "#centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,tolerance,repeat_measurement, freq_range,Photon_IF,plot = True)\n",
    "\n",
    "############################# Main sequence ############################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "    j = declare(int)\n",
    "    amplitude_param = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        with for_(amplitude_param, amp_param_init, amplitude_param < amp_param_fin+amp_param_step/2, amplitude_param + amp_param_step):\n",
    "            \n",
    "            align()\n",
    "            play(spin_gauss_pulse*amp(amplitude_param), spin_element, duration = gauss_duration) \n",
    "            align()\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align()\n",
    "            save(j, index_stream)\n",
    "\n",
    "            measure_SMPD(p_stream, N_iterations,waiting_time, accumulate=False)\n",
    "            \n",
    "                \n",
    "        wait(int(10e6//4),spin_element)\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_step_amp).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "        index_stream.timestamps().buffer(N_iterations).save('timestamp_index')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "id": "cc968943-3591-4505-af81-1c07900f2cca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:13:09.786537Z",
     "iopub.status.busy": "2024-03-26T16:13:09.785538Z",
     "iopub.status.idle": "2024-03-26T16:13:15.320801Z",
     "shell.execute_reply": "2024-03-26T16:13:15.319805Z",
     "shell.execute_reply.started": "2024-03-26T16:13:09.786537Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if __name__ == \"__main__\":\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_and_plot=True\n",
    "if sim_and_plot:\n",
    "    simulation = qm.simulate(Spin_detection_linewidth_angle, SimulationConfig(duration=10000), include_analog_waveforms=True,\n",
    "                             include_digital_waveforms=True,\n",
    "                             simulation_interface=LoopbackInterface([(\"con1\", 1, \"con1\", 1)]))\n",
    "    samples = simulation.get_simulated_samples()\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    fig.show()\n",
    "    fig.canvas.draw()\n",
    "    for i,key in enumerate(list(samples.con1.analog.keys())):\n",
    "            #plt.plot(samples.con1.analog[key]+0.1*i)\n",
    "            pass\n",
    "    samples.con1.plot(analog_ports={ '3', '4'}, digital_ports={'1', '3'})\n",
    "    fig.canvas.draw()\n",
    "    plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "id": "5bed996d-46eb-45de-a7ab-eed91978b6a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:19:34.915291Z",
     "iopub.status.busy": "2024-03-26T16:19:34.914291Z",
     "iopub.status.idle": "2024-03-26T16:19:36.417032Z",
     "shell.execute_reply": "2024-03-26T16:19:36.416032Z",
     "shell.execute_reply.started": "2024-03-26T16:19:34.915291Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2168\n",
      "(2168, 11, 50)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Spin_detection_calibration_Rabi_amplitudeGauss\\\\20240326171723_Spin_detection_calibration_Rabi_amplitudeGauss_duration5000.000_config.py'"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "plot_guess = 0\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "print(click_array.shape)\n",
    "\n",
    "%matplotlib inline\n",
    "integration_index=10\n",
    "integration_index_bg=1\n",
    "excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "\n",
    "def rabi_fit(t,f,a):\n",
    "    return a*(1-np.cos(2*np.pi*f*(t)))\n",
    "\n",
    "def rabi_fit_heating(t,f,a,b):\n",
    "    return a*(1-np.cos(2*np.pi*f*(t)))+b*t**2\n",
    "\n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "try:\n",
    "    guess=[0.5/rabi_amps[np.argmax(excess)],max(excess-excess[0])/3,(excess[-1]-excess[0])/rabi_amps[-1]/2]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating,rabi_amps,excess-excess[0],guess)\n",
    "    label_rabi=\"$\\pi$ pulse amp = %.3f\" %(1/popt[0]/2)\n",
    "    fine_rabi_amps = np.linspace(rabi_amps[0],rabi_amps[-1],100)\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=\"Guess\")\n",
    "    plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*popt),'-',label=label_rabi)\n",
    "except:\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=label_rabi)\n",
    "    print('fit failed')\n",
    "\n",
    "plt.plot(rabi_amps,excess-excess[0],'o')\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n duration_pulse = %.3f us'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, 1e-3*gauss_duration*4))\n",
    "plt.xlabel('Rabi amp (A.U.)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'Rabi.pdf')\n",
    "\n",
    "############### Plot Background ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(rabi_amps,background,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "plt.xlabel('Rabi amp (A.U.)')\n",
    "plt.ylabel('background counts')\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'background.pdf')\n",
    "\n",
    "############### Plot decay ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('count rate (/ms)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT.pdf')\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "try:\n",
    "    guess = [0.2,0.1,0.1]\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist[2:], click_array.mean(1).mean(0)[2:],guess)\n",
    "    label=\"$T_1$ = %.4f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    if plot_guess: plt.plot(time_hist,exp_decay(time_hist,*guess),label=\"guess\")\n",
    "    plt.legend()\n",
    "\n",
    "except:\n",
    "    print('fit failed')\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'decay.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            'BX_mT':BX,\n",
    "            'BY_mT':BY,\n",
    "            'BZ_mT':BZ,\n",
    "            'gauss_duration':gauss_duration,\n",
    "            'rabi_amps':rabi_amps,\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(BZ), overwrite=True)\n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0cec63d-bcc4-4a12-93f2-7872fc8181f8",
   "metadata": {},
   "source": [
    "## Rabi amplitude with Gaussian and state prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052906d8-f4f9-4dea-ab94-954e6cb1e4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_range = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "freq_range[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ad820e-79e1-42a3-aa6e-b907ef67bbcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8af5f3c-e5d5-4340-8038-b0df175f98aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "experiment_name='Spin_detection_calibration_Rabi_amplitudeGauss'\n",
    "time_stamp=get_timestamp()\n",
    "directory = make_exp_directory(path,experiment_name+\"/\"+time_stamp)\n",
    "\n",
    "sim_and_plot=True\n",
    "excecute=True\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "param_type=int\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 60000\n",
    "N = 30\n",
    "stream_I = False\n",
    "\n",
    "gauss_duration = 160_000//4\n",
    "###################### prep params ######################\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "flattop_prep_duration = 2_000_000//4\n",
    "t_wait_prep = int(3e6//4)\n",
    "amplitude_prep_pulse = 0.25\n",
    "N_preparation = 1\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 \n",
    "\n",
    "#########################################################\n",
    "\n",
    "amp_param_init=0.\n",
    "amp_param_fin=0.005\n",
    "n_step_amp = 11\n",
    "rabi_amps = np.linspace(amp_param_init,amp_param_fin,n_step_amp)\n",
    "amp_param_step=np.diff(rabi_amps)[0]\n",
    "\n",
    "# rabi_amps=np.arange(amp_param_init,amp_param_fin+  amp_param_step//2,amp_param_step)\n",
    "\n",
    "cycle_time_estimated=17  #in us\n",
    "Integration_time=3000 #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "print(N_iterations)\n",
    "Measurement_time=2400 * 1000000 # in seconds * us /s\n",
    "N_repetition = Measurement_time//Integration_time\n",
    "print(N_repetition)\n",
    "\n",
    "filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "############################# re-zero frequency #############################\n",
    "spacing = 56\n",
    "tolerance = 5\n",
    "\n",
    "repeat_measurement=0\n",
    "N=30\n",
    "directory = \"\"\n",
    "filename = \"\"\n",
    "print(\"Current centre frequency = %.1f kHz\"%centre_freq)\n",
    "res,freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 5)\n",
    "excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,0,repeat_measurement, freq_range,Photon_IF,max_shift=40,plot = True, fit_double_lorentz = False)\n",
    "\n",
    "############################# Main sequence ############################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    n = declare(int)\n",
    "    j = declare(int)\n",
    "    amplitude_param = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "    update_frequency(spin_element, Photon_IF+centre_freq*1e3+1e3*spacing/2)\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        with for_(amplitude_param, amp_param_init, amplitude_param < amp_param_fin+amp_param_step/2, amplitude_param + amp_param_step):\n",
    "            \n",
    "            ################## Preparation #################\n",
    "            ## Prepare the nucleus in desired state using N_preparation sideband pulses followed by a wait time\n",
    "            update_frequency(spin_sticky_element, prep_freq)\n",
    "            with for_(n, 0, n < N_preparation, n + 1):\n",
    "                align()\n",
    "                flattop_pulse(amplitude_prep_pulse, flattop_prep_duration, switch_duration_extra)\n",
    "                wait(t_wait_prep, spin_element)\n",
    "            wait(t_wait_prep*5, spin_element) \n",
    "            align()\n",
    "            play(spin_gauss_pulse*amp(amplitude_param), spin_element, duration = gauss_duration) \n",
    "            align()\n",
    "            wait(int(waiting_time_spin/4), readout_element)\n",
    "            align()\n",
    "            save(j, index_stream)\n",
    "\n",
    "            measure_SMPD(p_stream, N_iterations,waiting_time, accumulate=False)\n",
    "            \n",
    "                \n",
    "        wait(int(10e6//4),spin_element)\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_step_amp).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f42c40c-02b8-4f0f-8003-ed4495b15d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name='Spin_detection_calibration_Rabi_amplitudeGauss'\n",
    "directory = make_exp_directory(path,experiment_name+\"\\\\\"+time_stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fe8dc6e-a458-4aa1-9779-a5b750696193",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = experiment_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d75cc9-f80e-47f2-8886-99fed90e0d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(directory+'_RabiAmplitudeGaussStatePrep.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c122879-396b-4ffa-bc67-9a2acd8f7e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "fullpath = \"Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Spin_detection_calibration_Rabi_amplitudeGauss\\\\20240118195848_\\\\_RabiAmplitudeGaussStatePrep\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c83d2d8-53c5-4ce2-8a2a-aaa8d88625fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "plot_guess = False\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "print(click_array.shape)\n",
    "\n",
    "%matplotlib inline\n",
    "integration_index=10\n",
    "integration_index_bg=0\n",
    "excess=(click_array.mean(0)[:,:integration_index].sum(-1)-0*click_array.mean(0)[:,-integration_index_bg:].mean(-1)*integration_index)*(time_hist[1]-time_hist[0])\n",
    "background = click_array.mean(0)[:,-integration_index_bg:].mean(-1)*(time_hist[1]-time_hist[0])\n",
    "\n",
    "def rabi_fit(t,f,a):\n",
    "    return a*(1-np.cos(2*np.pi*f*(t)))\n",
    "\n",
    "def rabi_fit_heating(t,f,a,b):\n",
    "    return a*(1-np.cos(2*np.pi*f*(t)))+b*t\n",
    "\n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "try:\n",
    "    guess=[0.5/rabi_amps[np.argmax(excess)],max(excess-excess[0])/4,(excess[-1]-excess[0])/rabi_amps[-1]]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating,rabi_amps,excess-excess[0],guess)\n",
    "    label_rabi=\"$\\pi$ pulse amp = %.5f\" %(1/popt[0]/2)\n",
    "    fine_rabi_amps = np.linspace(rabi_amps[0],rabi_amps[-1],100)\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=\"guess\")\n",
    "    plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*popt),'-',label=label_rabi)\n",
    "except:\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=\"guess\")\n",
    "    print('fit failed')\n",
    "\n",
    "plt.plot(rabi_amps,excess-excess[0],'o')\n",
    "plt.title('average number %s \\n cycle time %.1f us \\n Spin_const_amp =  %.3f \\n duration_pulse = %.3f us'%(average_number, cycle_time*1e3, Spin_Gauss_amplitude, 1e-3*gauss_duration*4))\n",
    "plt.xlabel('Rabi amp (A.U.)')\n",
    "plt.ylabel('integrated excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'Rabi.pdf')\n",
    "\n",
    "############### Plot Background ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(rabi_amps,background,'-o')\n",
    "plt.title('average number %s \\n cycle time %.1f us '%(average_number, cycle_time*1e3))\n",
    "plt.xlabel('Rabi amp (A.U.)')\n",
    "plt.ylabel('background counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'background.pdf')\n",
    "\n",
    "############### Plot decay ###############\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(time_hist,click_array.mean(1).mean(0),'-o')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('count rate (/ms)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT.pdf')\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist[2:], click_array.mean(1).mean(0)[2:],[1,0.1,5])\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    plt.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    plt.legend()\n",
    "\n",
    "except:\n",
    "    print('fit failed')\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'decay.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            'BX_mT':BX,\n",
    "            'BY_mT':BY,\n",
    "            'BZ_mT':BZ,\n",
    "            'gauss_duration':gauss_duration,\n",
    "            'rabi_amps':rabi_amps,\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(BZ), overwrite=True)\n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2635691-74a5-4e06-88d1-472316d247e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['axes.formatter.useoffset'] = False\n",
    "sns.set(font_scale=1.5, style='ticks')\n",
    "sns.set_style({\"xtick.direction\": \"in\",\"ytick.direction\": \"in\",'ytick.right': True,'xtick.top': True})\n",
    "sns.set_palette(colors)\n",
    "############### Plot Rabi ###############\n",
    "plt.figure(figsize=(6.5,5))\n",
    "try:\n",
    "    guess=[1.5/rabi_amps[np.argmax(excess)],max(excess-excess[0])/4,(excess[-1]-excess[0])/rabi_amps[-1]]\n",
    "    popt,popc=sp.curve_fit(rabi_fit_heating,rabi_amps,excess-excess[0],guess)\n",
    "    label_rabi=r\"$\\tau_{\\pi} = 80~\\mathrm{\\mu s}$, amp = %.4f\" %(1/popt[0]/2)\n",
    "    fine_rabi_amps = np.linspace(rabi_amps[0],rabi_amps[-1],100)\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=\"guess\")\n",
    "    plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*popt),'-',label=label_rabi)\n",
    "except:\n",
    "    if plot_guess: plt.plot(fine_rabi_amps,rabi_fit_heating(fine_rabi_amps,*guess),'-',label=\"guess\")\n",
    "    print('fit failed')\n",
    "\n",
    "plt.plot(rabi_amps,excess-excess[0],'o')\n",
    "plt.xlabel('Rabi amplitude (A.U.)')\n",
    "plt.ylabel('Excess counts')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'Rabi_presentation_plot.pdf')\n",
    "\n",
    "np.savetxt(directory+filename+'_'+str(np.round(BZ,3))+'mT_'+'Rabi_presentation_plot_data.txt',np.transpose([rabi_amps,excess]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb3d661-6a4e-4f5c-84aa-c14d872c4415",
   "metadata": {},
   "source": [
    "# Ramsey interleaved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19d4700-6351-4fd0-b8fc-6e6b5fa4a663",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Ramsey parameters #######################\n",
    "\n",
    "amplitude_pulse_ramsey = 0.079 # Pi pulse amplitude\n",
    "gaussian_pulse_length_ramsey = 5000//4\n",
    "\n",
    "waiting_time= 1000\n",
    "waiting_time_spin= 80000\n",
    "\n",
    "t0 = 5_000//4\n",
    "tf = 200_000//4 \n",
    "dt = 7_500//4\n",
    "times_ramsey = np.arange(t0, tf + dt//2, dt)*4 \n",
    "ramsey_detuning=0e-6 #GHz 0.2e-4\n",
    "n_step_duration=len(times_ramsey)\n",
    "\n",
    "N = 10 \n",
    "cycle_time_estimated=17 #in us\n",
    "Integration_time=2000  #in us\n",
    "N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='prepared_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int) \n",
    "    meas_angle = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(k, 0, k < 4, k+1):\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=True)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            # If k==0, we flip a and b\n",
    "            with if_(k==0):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            # If k==1, we flip a\n",
    "            with if_(k==1):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            # If k==2, we flip b\n",
    "            with if_(k==2):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            # If k==3, we don't pulse at all, we just read out\n",
    "             \n",
    "            wait(int(40e6//4))\n",
    "            align()\n",
    "\n",
    "            # Ramsey sequence  \n",
    "            update_frequency(spin_element, Photon_IF + centre_freq*1e3 +delta_freq)\n",
    "            with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "                #play('ON',fsv_trigger)\n",
    "                with for_each_(meas_angle, [0, 0.25]):\n",
    "                    align()\n",
    "                    reset_frame(spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse_ramsey*0.5), spin_element, duration=gaussian_pulse_length_ramsey) \n",
    "                    wait(t, spin_element)\n",
    "                    frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t) + meas_angle, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse_ramsey*0.5), spin_element, duration=gaussian_pulse_length_ramsey) \n",
    "                    align()\n",
    "                    wait(int(waiting_time_spin/4), readout_element)\n",
    "                    align()\n",
    "\n",
    "                    measure_SMPD(rabi_stream,N_iterations,waiting_time, accumulate=False)\n",
    "\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        \n",
    "        rabi_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(2).buffer(n_step_duration).buffer(4).map(FUNCTIONS.average(4)).save_all('clicks')\n",
    "        rabi_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "501c6d6c-f0ce-4c2d-bc03-9ca01d05e681",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600)\n",
    "\n",
    "fig,ax=plt.subplots(2,1, figsize=(8,12))\n",
    "ax[0].clear()\n",
    "ax[1].clear()\n",
    "    \n",
    "\n",
    "display.clear_output(wait=True)\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "time_step = time_hist[1] - time_hist[0]\n",
    "click_arrays=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    \n",
    "\n",
    "skip_points=0\n",
    "integration_index=10\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "click_arrays=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "readout_freq_vals =np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "lorentz_centers = []\n",
    "for i in range(4):\n",
    "    def loss(arg):\n",
    "        data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "        return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "    \n",
    "    click_array = click_arrays[:,i]\n",
    "    excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "\n",
    "    guess=[150, -readout_freq_vals[i]*1e-3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    print(result.x)\n",
    "    lorentz_centers.append(result.x[1]*1e3)\n",
    "\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    freq=np.fft.fftfreq(len(times_ramsey),d=(times_ramsey[1]-times_ramsey[0]))\n",
    "    sorting_order=np.argsort(freq)\n",
    "    freq=np.sort(freq)\n",
    "    fft_value = np.fft.fft(excess[:,0]-linear_background+1j*(excess[:,1]-linear_background))\n",
    "    \n",
    "    x = 1e6*freq\n",
    "    y = np.abs(fft_value[sorting_order])\n",
    "    guess = [x[np.argmax(y)],(max(x)-min(x))/8, max(y)-min(y), min(y)]\n",
    "    #est,std,fine,data_fit = fit_function(guess, lorentz, x, y)\n",
    "    ax[0].plot(x,y,'o',color= colors[i],label=labels[i])\n",
    "    ax[0].plot(fine,data_fit,'-',color= colors[i])\n",
    "    ax[0].set_xlabel('Frequency (kHz)')\n",
    "    ax[0].set_ylabel('|FFT(counts)|')\n",
    "    ax[0].axvline(readout_freq_vals[i],color=\"k\",linestyle = \"--\")\n",
    "    \n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "\n",
    "    ax[1].plot(times_ramsey*1e-3,excess[:,0]-linear_background+0.2*(3-i),'.-',color=colors[i])\n",
    "    ax[1].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit+0.2*(3-i),\"--\",color=colors[i],label=labels[i]+f\" fit_freq={np.round(result.x[1]*1e3,1)}kHz\" )\n",
    "    ax[1].set_xlabel('Ramsey time (µs)')\n",
    "    ax[1].set_ylabel('Arb. u.')\n",
    "    # print(f\"Frequency = {result.x[1]*1e3}kHz\")\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "A1_val = ((lorentz_centers[2] - lorentz_centers[0]) + (lorentz_centers[3] - lorentz_centers[1]))/2\n",
    "A2_val = ((lorentz_centers[1] - lorentz_centers[0]) + (lorentz_centers[3] - lorentz_centers[2]))/2\n",
    "print(f\"A1 = {np.round(A1_val,3)} kHz, A2 = {np.round(A2_val,3)} kHz\")\n",
    "\n",
    "try:\n",
    "    plt.savefig(directory+filename+'decay.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure motherfucker\")\n",
    "        \n",
    "    \n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': click_array,\n",
    "            'time_axis': time_axis,\n",
    "            # 'theta': theta,\n",
    "            # 'pump_detuning':pump_detuning,\n",
    "            # 'cavity_freq':cavity_freq,\n",
    "            'BY_mT':BY,\n",
    "            'BZ_mT':BZ,\n",
    "            'amplitude_pulse':amplitude_pulse,\n",
    "            'ramsey_detuning':ramsey_detuning,\n",
    "            'Ramsey_time':times_ramsey,\n",
    "            'n_step_duration':n_step_duration\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "print(fullpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a47ccea2-a672-42c4-879d-fd7f46fe9511",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b10ea27f-2f6d-484a-acdd-86356f03c8a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "centre_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09bf9f89-906b-448c-875e-559aa1954879",
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_points=0\n",
    "integration_index=10\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "click_arrays=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "readout_freq_vals =np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "plt.figure(figsize=(8,6))\n",
    "for i in range(4):\n",
    "    click_array = click_arrays[:,i]\n",
    "    excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "    \n",
    "    guess=[10, ramsey_detuning*1e3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    freq=np.fft.fftfreq(len(times_ramsey),d=(times_ramsey[1]-times_ramsey[0]))\n",
    "    sorting_order=np.argsort(freq)\n",
    "    freq=np.sort(freq)\n",
    "    fft_value = np.fft.fft(excess[:,0]-linear_background+1j*(excess[:,1]-linear_background))\n",
    "    plt.plot(1e6*freq,np.abs(fft_value[sorting_order]),'-o',color= colors[i],label=labels[i])\n",
    "    plt.xlabel('Frequency (kHz)')\n",
    "    plt.ylabel('|FFT(counts)|')\n",
    "    plt.axvline(readout_freq_vals[i],color=\"k\",linestyle = \"--\")\n",
    "#plt.title(f\"A1 = {spacing1}KHz, A2 = {spacing2}kHz\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a002469-7ce2-45e3-9b03-4d10f05800f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_points=0\n",
    "integration_index=10\n",
    "freq_guesses = [-25, -5, 5, 30] ## in KHz\n",
    "plt.figure(figsize= (8,6))\n",
    "for i in range(4):\n",
    "    # plt.figure(figsize=(5,3))\n",
    "    click_array = click_arrays[:,i]\n",
    "    excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "    \n",
    "    def loss(arg):\n",
    "        data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "        return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "    \n",
    "    guess=[10, readout_freq_vals[i]*1e-3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(), 0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "    # plt.plot(times_ramsey*1e-3,excess[:,0]-linear_background,'-o',color='red')\n",
    "    # plt.plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "    # plt.plot(times_ramsey*1e-3,excess[:,1]-linear_background,'-o',color='black')\n",
    "    # plt.plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "    # plt.xlabel('Ramsey time (µs)')\n",
    "    # plt.ylabel('Counts')\n",
    "    # plt.legend()\n",
    "    # plt.show()\n",
    "    plt.plot(times_ramsey*1e-3,excess[:,0]-linear_background+0.2*(3-i),'.',color=colors[i])\n",
    "    plt.plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit+0.2*(3-i),\"--\",color=colors[i],label=labels[i]+f\" fit_freq={np.round(result.x[1]*1e3,1)}kHz\" )\n",
    "    # print(f\"Frequency = {result.x[1]*1e3}kHz\")\n",
    "plt.xlabel('Ramsey time (µs)')\n",
    "plt.ylabel('Arb. u.')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db52793b-dae8-4469-b8dd-f92d33f1b8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "[27, 4.2, -8.6, -31.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9540573d-9edf-4522-8c57-ffd2f1f45690",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Echo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8de1ad09-1522-415d-8c07-1a12fe7d9f36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "bd596b43-a04d-4ba8-ae96-d39380664f89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:49:48.236187Z",
     "iopub.status.busy": "2024-03-26T15:49:48.235186Z",
     "iopub.status.idle": "2024-03-26T15:51:09.711295Z",
     "shell.execute_reply": "2024-03-26T15:51:09.709284Z",
     "shell.execute_reply.started": "2024-03-26T15:49:48.236187Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current centre frequency = 38.0 kHz\n",
      "5980.592965482646 38.009681599030934 5 15 False\n",
      "Keeping previous centre frequency offset: 38.0 kHz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+xOXM88FXSBUWFh52/8HtPXr0qPE5S0pKNGTIEJWUlOjqq6/WrbfeavmZYDCoCy64QMuWLVO/fv30yiuvKD09vcbXBAAAAAAkh7gsz/3795fH49GmTZv0ySefHLJ/3rx5kqRzzz23RucrLS3V0KFDtWnTJl122WV64IEHLD9jmqYuu+wyLVy4UL169dIbb7wRWagMAAAAAJBa4rI8p6ena/LkyZKkSZMmRZ5xlqT7779fRUVFGjRokPr06RPZ/o9//EP5+fmaNm1atXP5/X4NHz5cn332mc4//3w98cQTMgzDMsM111yjZ599Vvn5+Vq8eLGOOuqo2Hw5AAAAAEDCictnniXp5ptv1pIlS7Rq1Sp17txZAwYM0JYtW1RQUKDmzZtr9uzZ1Y7fs2ePNmzYoJKSkmrbb7rpJq1evVpOp1NpaWm6/PLLD3u9p556KjJ+9dVX9be//U2S1LZtW11//fWH/czUqVOVn59fh28JAAAAAEgEcVueMzMztXTpUs2cOVNz5szRggUL1KxZM40fP14zZsxQbm5ujc5TWloqSQqFQpozZ84Rj/txeT74GUl6++23j/iZ8ePHU54BAAAAIAUYpmmadodIFT6fTx6PR16vV2632+44kiQzHFZgZ9VsvSuntYwariQOAAAAAD8nbIZVcqCqa7Ru1FoOI/66Rm06WtzOPKNhGA6H0nPb2B0DAAAAQJJxGA61aZw8XSP+qj8AAAAAAHGGmecUZ1ZW6tsHH5Iktbjmahm8xxoAAABADARCAf3t46qFmK868Sq5nC6bE9UNM88pzgwGtXf2bO2dPVtmMGh3HAAAAABJIhAO6KnPn9JTnz+lQDhgd5w6ozwDAAAAAGCB8gwAAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCB8gwAAAAAgAXe85zijMxMdXhtYWQMAAAAALGQmZap+SPmR8aJjvKc4gyHQxmdO9sdAwAAAECScRgOdWraye4YMcNt2wAAAAAAWGDmOcWZlZXa8/g/JUnHXPF/MtLTbU4EAAAAIBkEQgE98dkTkqSJJ0yUy+myOVHdUJ5TnBkMas/DD0uSjr58AuUZAAAAQEwEwgE9+umjkqTxx49P+PLMbdsAAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIFXVaU4IyND7V56MTIGAAAAgFjIcGbo+eHPR8aJjvKc4gynU1knnGB3DAAAAABJxulwqvsx3e2OETPctg0AAAAAgAVmnlOcWVmpvc88I0lqdvHFMtLTbU4EAAAAIBkEQgE9u/5ZSdJvjvuNXE6XzYnqhvKc4sxgUN/+9V5JUtOLLqI8AwAAAIiJQDig+9feL0m6oOsFCV+euW0bAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACzwqqoUZ2Rk6Nh//zsyBgAAAIBYyHBmaPbQ2ZFxoqM8pzjD6VSjvr+wOwYAAACAJON0OHVyq5PtjhEz3LYNAAAAAIAFZp5TnBkIqPTFFyVJTc8/X4bLZXMiAAAAAMkgEA5o3lfzJEljuoyRy5HYXYPynOLMQEC7ZtwhSTpq9GjKMwAAAICYCIQC+kvBXyRJIzuOTPjyzG3bAAAAAABYoDwDAAAAAGCB8gwAAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCBV1WlOCM9XbmPPRoZAwAAAEAspDvT9fCZD0fGiY7ynOKMtDQ1Of10u2MAAAAASDJpjjQNzB1od4yY4bZtAAAAAAAsMPOc4sxAQN7XXpckec79lQyXy+ZEAAAAAJJBIBzQG1+/IUka3mG4XI7E7hqU5xRnBgIqufFGSZJ72FDKMwAAAICYCIQCuuX9WyRJZ+ednfDlmdu2AQAAAACwQHkGAAAAAMAC5RkAAAAAAAuUZwAAAAAALFCeAQAAAACwQHkGAAAAAMACr6pKcUZ6uto8+EBkDAAAAACxkO5M172D7o2MEx3lOcUZaWlyDxtmdwwAAAAASSbNkaah7YbaHSNmuG0bAAAAAAALzDynODMY1L4lSyRJTc46S0YafyQAAAAA1F0wHNQ7W9+RJJ157JlKcyR210js9Kgzs7JSO665VpLUtXAt5RkAAABATFSGKvXH5X+UJBWMK0j48sxt2wAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABgIbHXCkedGS6XWv/lL5ExAAAAAMSCy+nSjP4zIuNEZ5imadodIlX4fD55PB55vV653W674wAAAABASqtNR+O2bQAAAAAALHDbdoozg0HtX7lSktT4tNNkpPFHAgAAAEDdBcNBrdq5SpLUL6ef0hyJ3TUSOz3qzKys1Pbf/V6S1LVwLeUZAAAAQExUhio16Z1JkqSCcQUJX565bRsAAAAAAAuUZwAAAAAALFCeAQAAAACwQHkGAAAAAMAC5RkAAAAAAAuUZwAAAAAALCT2WuGoM8PlUstbbo6MAQAAACAWXE6Xbux7Y2Sc6AzTNE27Q6QKn88nj8cjr9crt9ttdxwAAAAASGm16Wjctg0AAAAAgAVu205xZigk/0drJUnZJ/WR4XTanAgAAABAMgiFQyr8tlCS1LtFbzkdid01KM8pzqyo0NZLL5UkdS1cKyM72+ZEAAAAAJJBRahCExZNkCQVjCtQtiOxuwa3bQMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABV5VleKMtDS1uP6PkTEAAAAAxILL4dKUPlMi40RnmKZp2h0iVfh8Pnk8Hnm9XrndbrvjAAAAAEBKq01H47ZtAAAAAAAscJ9uijNDIZV/8YUkKbNbNxlOp82JAAAAACSDUDik9XvXS5KOa3acnI7E7hqU5xRnVlSoeOz5kqSuhWtlZGfbnAgAAABAMqgIVeiiNy6SJBWMK1C2I7G7BrdtAwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFXlWV4oy0NB0zaVJkDAAAAACx4HK49Puev4+ME51hmqZpd4hU4fP55PF45PV65Xa77Y4DAAAAACmtNh2N27YBAAAAALDAfbopzgyHVblpkyQpvWNHGQ7+PgUAAABA3YXNsL7+/mtJUoejOshhJHbXoDynOLO8XF+fO0KS1LVwrYzsbJsTAQAAAEgG5cFyjV44WpJUMK5A2a7E7hqJXf0BAAAAAGgAlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACzwqqoUZ6SlqdmECZExAAAAAMSCy+HS+OPHR8aJzjBN07Q7RKrw+XzyeDzyer1yu912xwEAAACAlFabjsZt2wAAAAAAWOA+3RRnhsMK7CyRJLlyWstw8PcpAAAAAOoubIZVcqCqa7Ru1FoOI7G7BuU5xZnl5dp01lmSpK6Fa2VkZ9ucCAAAAEAyKA+Wa9jLwyRJBeMKlO1K7K6R2NUfAAAAAIAGQHkGAAAAAMAC5RkAAAAAAAuUZwAAAAAALFCeAQAAAACwQHkGAAAAAMACr6pKdWlpajruosgYAAAAAGIhzZGmC7peEBknOsM0TdPuEKnC5/PJ4/HI6/XK7XbbHQcAAAAAUlptOhq3bQMAAAAAYCHx585RJ6ZpKlRaKklyNm0qwzBsTgQAAAAgGZimqdKKqq7RNCPxuwblOcWZZWX6b7/+kqSuhWtlZGfbnAgAAABAMigLlmnQ3EGSpIJxBcp2JXbX4LZtAAAAAAAsUJ4BAAAAALBAeQYAAAAAwALlGQAAAAAAC5RnAAAAAAAsUJ4BAAAAALDAq6pSXVqaPKNGRcYAAAAAEAtpjjSN6DgiMk50hmmapt0hUoXP55PH45HX65Xb7bY7DgAAAACktNp0NG7bBgAAAADAQuLPnaNOTNOUWVYmSTKysmQYhs2JAAAAACQD0zRVFqzqGllpid81mHlOcWZZmTb07qMNvftESjQAAAAA1FVZsEx95/RV3zl9IyU6kVGeAQAAAACwQHkGAAAAAMAC5RkAAAAAAAuUZwAAAAAALFCeAQAAAACwQHkGAAAAAMBCXJfnsrIyTZ8+XV26dFFmZqZycnI0YcIE7dixo8bn+P777zVnzhxddNFFat++vdLT09WkSRP17dtXDz30kAKBwBE/GwqF9MADD+iEE05QVlaWmjdvrvPPP1/r16+PxdeLD06nmgwdqiZDh0pOp91pAAAAACQJp8OpIXlDNCRviJyOxO8ahmmapt0hDqe8vFxnnHGG1qxZo9atW2vAgAEqLi7WBx98oObNm2vNmjXq0KGD5Xluvvlm3XnnnTIMQ7169VKXLl20e/duvf/++6qoqNBpp52mRYsWKTs7u9rnwuGwxowZo/nz5+uoo47SmWeeqT179ui9995TVlaWli5dql/84he1+k4+n08ej0der1dut7tWnwUAAAAAxFZtOlrczjzfcccdWrNmjU499VR99dVXmjt3rgoKCnTfffdp9+7dmjBhQo3O06hRI91www0qLi5WYWGhXnjhBb3zzjv67LPPdOyxx2rlypW64447Dvnc7NmzNX/+fHXu3Flffvml5s2bp2XLlumll16S3+/X//t//0/BYDDWXxsAAAAAEIficua5srJSLVq0kNfrVWFhoU488cRq+3v27KmioiJ99NFH6tOnT9TXef755zVu3Di1a9dOmzdvrravW7duWr9+vebPn69Ro0ZV2zdy5EgtXLhQ8+bN03nnnVfj6zHzDAAAAADxI+Fnnt9//315vV517NjxkOIsSWPGjJEkvfbaa3W6Ts+ePSVJO3furLZ98+bNWr9+vbKysjR8+PB6u348CPv9Wp9/nNbnH6ew3293HAAAAABJwh/w64R/n6AT/n2C/IHE7xpxWZ4//fRTSVLv3r0Pu//g9qKiojpd5+uvv5YktWrV6rDX7969u1wuV71dHwAAAACQGOKyPG/dulWSlJube9j9B7dv2bKlTtd56KGHJFXdhm3H9QEAAAAAiSHN7gCHs3//fkk6ZAXsgxo1aiRJ2rdvX9TXeOyxx7RkyRIdddRRmjp1ar1cv6KiQhUVFZHffT5f1HkBAAAAAPaJy5nn+rZixQpdffXVMgxDs2fPVk5OTr1cZ+bMmfJ4PJGftm3b1st1AAAAAAD1Ky7Lc+PGjSVJ/iMsYHXgwAFJUpMmTWp97nXr1mnkyJGqrKzUQw89pNGjR9fb9adNmyav1xv52bZtW63zAgAAAADsF5e3bR977LGSpO3btx92/8HteXl5tTrv5s2bdfbZZ6u0tFS33Xabrrzyynq9fkZGhjIyMmqVMS6EQ9KWVdL+XVLjllJeP8nhtDsVAAAAANgmLsvzwVdIFRYWHnb/we09evSo8TlLSko0ZMgQlZSU6Oqrr9att95qef1169YpEAgcsuJ2NNePW06nGg0aGBnri4XSW3+SfD96fZc7Rxp2t9RthD0ZAQAAACQcp8OpAW0GRMaJzjBN07Q7xE9VVlaqRYsW8nq9+vjjj9WrV69q+3v27KmioiJ99NFH6tOnj+X5SktLNWjQIH322We67LLL9K9//UuGYfzsZ7p166b169dr/vz5GjVqVLV9I0eO1MKFCzVv3jydd955Nf5etXkBty2+WCi9eImkn/6R+OHf1flPU6ABAAAAJI3adLS4fOY5PT1dkydPliRNmjQp8oyxJN1///0qKirSoEGDqhXnf/zjH8rPz9e0adOqncvv92v48OH67LPPdP755+uJJ56wLM6SNGXKFEnSDTfcoG+//Tay/ZVXXtHChQvVqVOnQ15xldDCoaoZ50OKs/637a2pVccBAAAAQIqJy9u2Jenmm2/WkiVLtGrVKnXu3FkDBgzQli1bVFBQoObNm2v27NnVjt+zZ482bNigkpKSattvuukmrV69Wk6nU2lpabr88ssPe72nnnqq2u8TJkzQm2++qfnz5ys/P19nnnmm9uzZo+XLlysrK0vPPvus0tLi9l9f7W1ZVf1W7UOYkm9H1XHtBzRYLAAAAACIB3Hb/jIzM7V06VLNnDlTc+bM0YIFC9SsWTONHz9eM2bMUG5ubo3OU1paKkkKhUKaM2fOEY/7aXl2OBx66aWX9NBDD2n27Nl6/fXX1ahRI5133nn685//rG7dukX93eJJ2O/XV/1Pk8JBdTnXkCPN4i7+/bsaJhgAAACAhOYP+HX6i6dLkpadv0zZrmx7A9VRXD7znKzi8ZnnsN+vDb2rbn/vOqbEujxf+jozzwAAAAAs+QN+9Z3TV5JUMK4gLstzwj/zDJs0aa3I4mCHMCR3m6rXVgEAAABAiqE843+GzPhh8NMC/cPvw+7ifc8AAAAAUhLlGf+T/8uq11G5W1ff7s7hNVUAAAAAUlrcLhgGm3QbIeUPr1pVe/8uqXHLqlu1mXEGAAAAkMIozziUw8miYAAAAADwI5TnVOdwKPvkkyNjAAAAAIgFh+HQSS1PiowTHa+qakDx+KoqAAAAAEhVvKoKAAAAAIAYojwDAAAAAGCB8pziwn6/vjq1n746tZ/Cfr/dcQAAAAAkCX/Ar4EvDNTAFwbKH0j8rsGCYVCotNTuCAAAAACSUGlF8nQNZp4BAAAAALBAeQYAAAAAwALlGQAAAAAAC5RnAAAAAAAsUJ4BAAAAALDAatupzuFQZvfukTEAAAAAxILDcOj4o4+PjBOdYZqmaXeIVOHz+eTxeOT1euV2u+2OAwAAAAAprTYdLfHrPwAAAAAA9YzyDAAAAACABcpziguXlWnj4DO1cfCZCpeV2R0HAAAAQJIoC5Zp6LyhGjpvqMqCid81WDAs1ZmmAjt3RsYAAAAAEAumaWrngZ2RcaKLeXlev369Pv/8c7Vt21Z9+/aN9ekBAAAAAGhwUd22PXfuXA0ePFgFBQXVtl9//fXq3r27LrjgAvXr10+jR49WKBSKSVAAAAAAAOwSVXl+9tln9cknn+jEE0+MbFu1apXuu+8+NWnSRBdeeKHatWunhQsX6rnnnotZWAAAAAAA7BBVeV63bp169Oih9PT0yLZnnnlGhmHoxRdf1HPPPacPP/xQjRs31qxZs2IWFgAAAAAAO0RVnr/99lu1adOm2ralS5eqRYsWOvvssyVJzZo108CBA7Vx48a6pwQAAAAAwEZRLRiWlZUln88X+b2kpERfffWVzj///GrHHXXUUSotLa1bQtQvw1B6p46RMQAAAADEgmEY6ujpGBknuqjKc4cOHbRixQp9//33Ouqoo/Tcc8/JMIzIrPNB33zzjVq0aBGToKgfjqwsdXz9dbtjAAAAAEgyWWlZWjBqgd0xYiaq27bHjx8vn8+nPn366LzzztPNN9+sxo0ba+TIkZFjAoGAPvroI3Xp0iVmYQEAAAAAsENUM88TJ07U0qVL9fLLL2vz5s1q1KiRHn/8cR199NGRY15//XV5vV4NHjw4ZmEBAAAAALBDVOXZ5XLppZdeUnFxsXbv3q38/Hw1adKk2jHt27fX/Pnzdcopp8QkKOpHuKxMm8eOlSS1f+klObKybE4EAAAAIBmUBct00esXSZKe/9XzykpL7K4RVXk+qF27dmrXrt1h9/Xq1Uu9evWqy+nREExTlRs3RcYAAAAAEAumaWqTd1NknOiieubZ6XTq8ssvtzxu4sSJSkurUz8HAAAAAMB2UZVn0zRr/DcHyfA3DAAAAACA1BZVea4pr9erjIyM+rwEAAAAAAD1rsb3VG/durXa7/v37z9k20HBYFAbNmzQ4sWL1bFjx7olBAAAAADAZjUuz+3atZNhGJHfX375Zb388ss/+xnTNDVx4sTo06HhhUPSllXS/l1S45ZSXj/J4bQ7FQAAAADYqsbleeDAgZHyvHz5crVo0UL5+fmHPTY9PV05OTkaMWKERo8eHZukqB+GIVdOTtV4w5vS0lsk387/7XfnSMPulrqNsCcfAAAAgIRkGIZyGuVExonOMKNY0cvhcGj8+PGaPXt2fWRKWj6fTx6PR16vV2632+441X2xUHrxEkk//ePwwx/y85+mQAMAAABIKrXpaFG9R2rz5s1q3LhxVOEQh8Ih6a0/6dDirB+2GdJbU6X84dzCDQAAACAlRbXadl5eno4++uhYZ4Fdtqyqfqv2IUzJt6PqOAAAAABIQVHNPB+0bNkyvffeeyopKVFFRcVhjzEMQ//617/qchnUo3B5ubZM/rNUeozyBu+R4+f+ROzf1WC5AAAAACS28mC5xr81XpL01LCnlJmWaW+gOoqqPHu9Xo0cOVIrVqyQ1SPTlOc4Fw6rfNMOSemqer75Z/7zbNyygUIBAAAASHRhM6zPv/s8Mk50UZXnP/3pT3rvvffUqVMn/e53v1OXLl3UpEmTWGdDgztSeTaqVt3O69fQgQAAAAAgLkRVnl999VW1bNlSa9asUbNmzWKdCbb6aYH+YbXtYXexWBgAAACAlBXVgmFer1f9+/enOCebXz8huVtX35Z9tDTmKV5TBQAAACClRVWeO3furAMHDsQ6C+yW/0tp6Ewp+5j/bfPvkRZPq3oPNAAAAACkqKjK85VXXqlly5Zp48aNsc4DO335pvTS+KrC/GO+EunFSyjQAAAAAFJWVOX5t7/9ra666ioNGjRITz75pLZv3x7rXGhAzqZN5WzaVHr7Fh1+wbAftr01VQqHGjIaAAAAgATWNKOpmmY0tTtGTBim1bumDsPprFo4yjRNGYbx8xcwDAWDwejSJRmfzyePxyOv1yu32213nOo2r5D+/Svr4y59XWo/oP7zAAAAAEA9q01Hi2q17bZt21qWZiSY/btiexwAAAAAJJGoynNxcXGMY8B2jVvG9jgAAAAASCJRlWckj3B5ubZN/D9JptqekCOHv0SHf+7ZkNw5Ul6/Bk4IAAAAIBGVB8v1+yW/lyQ9etajykzLtDlR3VCeU104LP+HH1aNr50hLfytJEPVC/QPt+gPu0tyOBs4IAAAAIBEFDbD+mjXR5FxoouqPD/99NO1Ov6SSy6J5jJoaPm/lDKflt76k+Tb+b/t7pyq4txthH3ZAAAAAMBGUZXn8ePH12jBsIOrcVOeE0i3EVL+cGnLqqrFwRq3rLpVmxlnAAAAACksqvI8ffr0w5bncDisbdu2afny5dq8ebPGjx+vvLy8OodEA3M4eR0VAAAAAPxIVOX5tttu+9n9wWBQU6ZM0dy5c/XhwedpAQAAAABIUI76OGlaWpoeeOABZWVlaerUqfVxCQAAAAAAGky9rbbtdDrVp08fvf322/V1CcSIkZVldwQAAAAASSgrLXm6Rr2+quqbb77RgQMH6vMSqCNHdrbyPy60OwYAAACAJJPtytYH/+8Du2PETL3cth0Oh/X3v/9dq1evVo8ePerjEgAAAAAANJioZp4HDx58xH379+/X5s2btXfvXjkcDt16661RhwMAAAAAIB5EVZ6XLVv28ydNS9Npp52m6dOn68wzz4zmEmgg4YoKbb/qKklS7t/+JkdGhs2JAAAAACSDilCFrl16rSTpgTMeUIYzsbtGVOV58+bNR9yXnp6uY445Ri6XK+pQaEChkA4sfy8yBgAAAIBYCIVDWrFjRWQsp82B6iiq8pyXlxfrHAAAAAAAxK16WTAMAAAAAIBkUqfyXFRUpCuuuELdunWTx+ORx+NRt27d9Lvf/U5FRUWxyggAAAAAgK2iLs8PPfSQTjrpJM2aNUtffvml9u3bp3379unLL7/UP//5T5100kl66KGHYpkVAAAAAABbRFWe3377bV177bVKT0/Xtddeq48//lilpaX6/vvv9cknn+i6665TRkaGpkyZonfeeSfWmQEAAAAAaFBRlef7779faWlpWrx4se6991717NlTHo9HbrdbPXr00F//+lctXrxYDodD9913X6wzAwAAAADQoAzTNM3afujoo49W79699fbbb//scUOGDFFhYaG+++67qAMmE5/PJ4/HI6/XK7fbbXccAAAAAEhpteloUc08+/1+NW/e3PK45s2by+/3R3MJAAAAAADiRlTluW3btlq9erWCweARjwkGg1q9erXatm0bdTgAAAAAAOJBVOV55MiR2rJliyZMmKDvv//+kP0+n08TJ07U1q1bNWrUqDpGRH0KV1Ro+9XXaPvV1yhcUWF3HAAAAABJoiJUoSnLpmjKsimqCCV+14jqmee9e/fq5JNPVnFxsRo3bqxhw4apXbt2kqQtW7borbfeks/nU4cOHfThhx+qadOmsc6dkOLxmeew368NvftIkroWrpUjO9vmRAAAAACSgT/gV985fSVJBeMKlO2Kv65Rm46WFs0FmjVrphUrVuiKK67QG2+8oZdeeumQY4YPH67HH3+c4gwAAAAASHhRlWdJysnJ0WuvvabNmzdr5cqV2rlzZ2T7aaedpvbt28csJAAAAAAAdoq6PB/Uvn17ijIAAAAAIKlFtWBYOByWz+dTIBA44jGBQEA+n0/hcDjqcAAAAAAAxIOoyvMDDzygpk2bavny5Uc8Zvny5WratKn+/ve/Rx0OAAAAAIB4EFV5nj9/vtq2bauzzjrriMecddZZys3N1csvvxx1OAAAAAAA4kFUzzz/97//1UknnWR5XPfu3VVYWBjNJdBAjKwsdS1cGxkDAAAAQCxkpWWpYFxBZJzooirPXq9XHo/H8jiPx6PS0tJoLoEGYhiGDN7tDAAAACDGDMOIy3c7Ryuq27Zbt26toqIiy+OKiorUokWLaC4BAAAAAEDciKo8Dx48WOvXr9fcuXOPeMyLL76oL774QmeccUbU4VD/wpWV2jl1mnZOnaZwZaXdcQAAAAAkicpQpW5aeZNuWnmTKkOJ3zWiKs/XX3+90tPTdckll2jy5MkqKirSgQMHdODAARUVFWny5Mm6+OKLlZ6eruuvvz7WmRFLwaC8CxbIu2CBFAzanQYAAABAkgiGg1q4aaEWblqoYDjxu0ZUzzzn5+fr6aef1qWXXqpHH31Ujz76aLX9pmkqMzNTTz75pLp37x6ToAAAAAAA2CWqmWdJGjt2rIqKinTFFVeoU6dOysjIUEZGhjp16qTf//73+vTTT3XBBRfEMisAAAAAALaIaub5oE6dOumRRx6JVRYAAAAAAOJS1DPPAAAAAACkCsozAAAAAAAWKM8AAAAAAFio0zPPSHxGVpY6r3o/MgYAAACAWMhKy9LyC5ZHxomO8pziDMNQWrNmdscAAAAAkGQMw1CzzOTpGty2DQAAAACABWaeU1y4slLf3nWXJKnF1KlypKfbnAgAAABAMqgMVeqeD++RJN1w8g1KdyZ214j5zLPX69VHH32kb775JtanRn0IBlU653mVznleCgbtTgMAAAAgSQTDQc3dMFdzN8xVMJz4XSOq8rx48WJNmDBBH3/8cbXtf//739WqVSv17dtXubm5uvbaa2MSEgAAAAAAO0VVnmfNmqWXXnpJnTt3jmz77LPPdM011ygUCumUU06R2+3W3/72N7366qsxCwsAAAAAgB2iKs+FhYXq1auXGjduHNn21FNPRf75/vvva+3atUpPT9cjjzwSk6AAAAAAANglqvK8a9cu5ebmVtv2zjvv6KijjtKFF14oSWrfvr0GDRqk9evX1z0lAAAAAAA2iqo8O51OlZeXR37fu3ev1q1bpwEDBsjh+N8pmzdvrt27d9c9JQAAAAAANoqqPLdr106rVq1SIBCQJL3yyisyTVNDhgypdtx3332no48+uu4pAQAAAACwUVTveb7gggt00003aeDAgerXr5+efPJJuVwujRo1KnKMaZpau3atunbtGqusqAdGZqY6LlkSGQMAAABALGSmZeqt896KjBNdVOX5qquu0quvvqqCggIVFBTI4XDor3/9q9q0aRM55t1339Xu3bv1u9/9LmZhEXuGw6H03DbWBwIAAABALTgMh9o0Tp6uEVV5btSokVatWqX33ntPu3fvVq9evaq9tkqqei76gQce0LnnnhuToAAAAAAA2MUwTdO0O0Sq8Pl88ng88nq9crvddseRJJmVlfr2wYckSS2uuVpGerrNiQAAAAAkg0AooL99/DdJ0lUnXiWX02VzokPVpqNFtWDYz1m/fr3mzZungoKCWJ8a9cAMBrV39mztnT1bZjBodxwAAAAASSIQDuipz5/SU58/pUA4YHecOouqPM+dO1eDBw8+pCBff/316t69uy644AL169dPo0ePVigUiklQAAAAAADsElV5fvbZZ/XJJ5/oxBNPjGxbtWqV7rvvPjVp0kQXXnih2rVrp4ULF+q5556LWVgAAAAAAOwQVXlet26devToofQfPR/7zDPPyDAMvfjii3ruuef04YcfqnHjxpo1a1bMwgIAAAAAYIeoyvO3335b7bVUkrR06VK1aNFCZ599tiSpWbNmGjhwoDZu3Fj3lAAAAAAA2Ciq8pyVlSWfzxf5vaSkRF999ZUGDRpU7bijjjpKpaWldUsIAAAAAIDNoirPHTp00IoVK/T9999Lkp577jkZhhGZdT7om2++UYsWLeocEgAAAAAAO6VF86Hx48dr8uTJ6tOnj3r16qU33nhDjRs31siRIyPHBAIBffTRRzrppJNiFhaxZ2RmqsNrCyNjAAAAAIiFzLRMzR8xPzJOdFGV54kTJ2rp0qV6+eWXtXnzZjVq1EiPP/64jj766Mgxr7/+urxerwYPHhyzsIg9w+FQRufOdscAAAAAkGQchkOdmnayO0bMRFWeXS6XXnrpJRUXF2v37t3Kz89XkyZNqh3Tvn17zZ8/X6ecckpMggIAAAAAYBfDNE3T7hCpwufzyePxyOv1yu122x1HkmRWVmrP4/+UJB1zxf/J+NHrxwAAAAAgWoFQQE989oQkaeIJE+VyumxOdKjadLSoZp5/bO/evVq7dq327NmjvLw89evXr66nRAMyg0HtefhhSdLRl0+gPAMAAACIiUA4oEc/fVSSNP748XFZnmsjqtW2JWn37t0aN26cWrVqpWHDhuk3v/mNZs2aFdk/a9YsNWvWTCtXroxJUAAAAAAA7BJVed67d6/69eunF154Qd27d9cf/vAH/fTu71//+tfat2+f5s2bF5OgAAAAAADYJaryfOedd2rTpk2aPn26CgsL9fe///2QY5o1a6YePXpo+fLldQ4JAAAAAICdoirPCxYsUJcuXXTbbbf97HEdO3bUjh07orkEAAAAAABxI6ryvGPHDvXs2dPyOMMw5PP5orkEAAAAAABxI6ry7Ha7VVJSYnncpk2b1Lx582guAQAAAABA3IjqVVUnn3yy3n33XW3evFnt27c/7DGffvqpPvnkE40ZM6ZOAVG/jIwMtXvpxcgYAAAAAGIhw5mh54c/Hxknuqhmnq+88kpVVFRo9OjRWr9+/SH7N27cqIsvvlimaWry5Ml1Don6YzidyjrhBGWdcIIMp9PuOAAAAACShNPhVPdjuqv7Md3ldCR+14iqPA8bNkw33HCDioqK1L17d+Xn58swDC1atEg9e/bUcccdp3Xr1unGG2/UaaedFuvMAAAAAAA0KMP86Quaa+Gll17SnXfeqaKiomrb8/Pzdcstt+iiiy6qc8Bk4vP55PF45PV65Xa77Y4jSTIrK7X3mWckSc0uvlhGerrNiQAAAAAkg0AooGfXPytJ+s1xv5HL6bI50aFq09HqVJ4P2r17t4qLixUOh5Wbm6s2bdrU9ZRJKR7Lc9jv14befSRJXT/6QI7dn0j7d0mNW0p5/aQkuL0CAAAAQMPzB/zqO6evJKlgXIGyXdk2JzpUbTpaVAuG/VTz5s1ZVTsZPPwLqWzn/35350jD7pa6jbAvEwAAAADEgaieed61a5cWLlyozZs3H/GYzZs3a+HChfr222+jDocGtu8nrx/zlUgvXiJ9sdCePAAAAAAQJ6Iqz/fff79Gjx6t8vLyIx5TVlam0aNH66GHHoo6HBpAOPQzO3+4o/+tqRbHAQAAAEByi6o8/+c//9Hxxx+v44477ojHdOvWTccff7zeeOONqMOhAWwrsDjAlHw7pC2rGiQOAAAAAMSjqMrzli1b1KVLF8vjOnfurK1bt0ZzCTSU/TW8rX7/rvrNAQAAAABxLKryHArV7BZewzBUUVERzSXQUBq3qOFxLes3BwAAAADEsahW2+7QoYNWr16tYDCotLTDnyIYDGr16tU69thj6xQQ9cvoNFDH/krSge9kOA731jKjatXtvH4NHQ0AAABAAstwZmj20NmRcaKLaub53HPP1TfffKOpU6fqSK+JnjZtmr755huNGMFrjuKZ4UpXowl3q1HLShkO46d7q/4x7C7e9wwAAACgVpwOp05udbJObnWynEnQJwzzSO33Z+zdu1c9e/bUzp071b17d11++eXq2LGjJGnTpk3617/+pXXr1qlVq1b69NNPdcwxx8Q8eCKqzQu4G9wXC6W3/iT5fvye5zZVxZn3PAMAAABIQrXpaFGVZ0lav369Ro8era+++kqGUX3G0jRNdenSRS+//LKOP/74aE6flOKxPJuBgEpffFGS1HTMeTJ2fli1OFjjllW3aifB3xABAAAAaHiBcEDzvponSRrTZYxcDpfNiQ7VIOVZqlo47JVXXtGSJUu0bds2SVLbtm111lln6de//rWczroVr7KyMs2cOVMvvPCCtm7dqmbNmmnYsGGaMWOG2rRpU+PzLF++XMuWLdMHH3ygDz74QHv27FFeXp6Ki4t/9nNfffWVZs6cqXfffVclJSXKyMhQfn6+xo0bp0mTJik9Pb1W3ycey3PY79eG3n0kSV0L18qRnW1zIgAAAADJwB/wq++cvpKkgnEFynbFX9dosPJcn8rLy3XGGWdozZo1at26tQYMGKDi4mJ98MEHat68udasWaMOHTrU6Fy9evXSp59+Wm2bVXletWqVhgwZIr/fr+OOO07du3eX1+vVihUrVFZWpkGDBmnJkiVHXDDtcCjPAAAAAFJFspXnqBYMW7VqVY2Pfeihh6K5hO644w6tWbNGp556qr766ivNnTtXBQUFuu+++7R7925NmDChxuc6++yzdccdd2jRokX6/PPPa/SZyZMny+/3a+bMmfriiy/04osvatGiRdq4caM6dOig5cuX65lnnonquwEAAAAAEktU5XnQoEG69dZbFQ6Hj3jMN998o6FDh2rKlCm1Pn9lZaX+8Y9/SJIefvhhNW7cOLJvypQp6tGjh5YvX661a9fW6Hz33HOPbrrpJp199tlq1qyZ5fH79+/Xxx9/rOzsbN1www3V9uXk5Gjy5MmSpA8//LCmXwkAAAAAkMCiKs/NmzfXHXfcof79+2vTpk2H7J8/f75OOOEEvf322+rfv3+tz//+++/L6/WqY8eOOvHEEw/ZP2bMGEnSa6+9VvvwNeByueRwWP+rOfroo+vl+gAAAACA+BJVef7ss880YsQIFRQUqFevXvrXv/4lSfL7/frtb3+rMWPGyOfz6c4779Ty5ctrff6Dzyf37t37sPsPbi8qKoomvqWMjAwNHDhQfr9f99xzT7V9O3fu1MMPPyyXy6WLL764Xq4PAAAAAIgvUZXno48+WvPnz9fjjz8u0zT1f//3f/rVr36lnj17avbs2eratatWr16tadOmHfIaq5rYunWrJCk3N/ew+w9u37JlSzTxa+Sxxx5T27ZtNW3aNHXr1k3nn3++hg0bpk6dOsk0Tb3xxhvq0qVLvV0fAAAAABA/ar5U9GFMnDhRAwYM0CmnnKL//Oc/kqTzzjtPzz77rDIyMqI+7/79+yVJ2UdY+blRo0aSpH379kV9DStdu3bVypUrNXr0aBUWFmr9+vWSJMMwdMYZZ9To/dUVFRWqqKiI/O7z+eotb7SM9HTlPvZoZAwAAAAAsZDuTNfDZz4cGSe6qGaeD9qyZYt++9vfyufzKSMjQ6Zp6q233kqKVajfffdd9ezZU4FAQO+++658Pp82b96sm266SU8++aT69++v3bt3/+w5Zs6cKY/HE/lp27ZtA6WvOSMtTU1OP11NTj9dRi1euwUAAAAAPyfNkaaBuQM1MHeg0hyJ3zWiLs/PPPOMevbsqVWrVmn48OEqLi7W448/Lkm64oorNHr0aH333XdRnfvg6tp+v/+w+w8cOCBJatKkSVTnt7J3716NHTtWgUBA//nPf3TGGWeoSZMmateunWbMmKFJkyapuLhY995778+eZ9q0afJ6vZGfbdu21UteAAAAAED9iqo8X3jhhRo/frwCgYAefvhhvfbaa2rRooUmTpyowsJCnXTSSXr11VfVo0cPLVq0qNbnP/bYYyVJ27dvP+z+g9vz8vKiiW/pjTfe0N69e3XKKaeoTZs2h+wfO3asJOm999772fNkZGTI7XZX+4k3ZiCg71+Zr+9fmS8zELA7DgAAAIAkEQgHtGDjAi3YuECBcOJ3jajmzl988UWdeOKJeu6555Sfn19tX+fOnbVq1Sr9+c9/1syZMzV8+HAFg8Fanb9nz56SpMLCwsPuP7i9R48eUaS3drCcezyew+4/uL20tLRert+QzEBAJTfeKElyDxsqw+WyOREAAACAZBAIBXTL+7dIks7OO1suR2J3jahmnm+44QatWbPmkOJ8kNPp1O2336733ntP7dq1q/X5+/fvL4/Ho02bNumTTz45ZP+8efMkSeeee26tz10TrVq1kiR9/PHHCoVCh+z/8MMPJSmq7wYAAAAASDxRlee77rpLrhrMUJ566qmRdzbXRnp6uiZPnixJmjRpUuQZZ0m6//77VVRUpEGDBqlPnz6R7f/4xz+Un5+vadOm1fp6PzVs2DBlZGRo8+bNuuWWWxQOhyP7NmzYoOnTp0uSxowZU+drAQAAAADiX52XPNu7d6/Wrl2rPXv2KC8vT/369au2/+BrpWrr5ptv1pIlS7Rq1Sp17txZAwYM0JYtW1RQUKDmzZtr9uzZ1Y7fs2ePNmzYoJKSkkPONWvWLM2aNUuSFPjhud6SkhKdcsopkWMeeeQR9e7dW5LUunVr3Xvvvbrqqqs0c+ZMzZ07VyeeeKK+++47rV69WhUVFfrlL3+p8ePHR/XdAAAAAACJJerVtnfv3q1x48apVatWGjZsmH7zm99ECqpUVVibNWumlStXRnX+zMxMLV26VLfccouys7O1YMECbdmyRePHj1dhYaE6dOhQ43Nt375dBQUFKigoiDwvXVlZGdlWUFBwyDuYJ0+erHfffVejRo2S3+/Xq6++qsLCQp144ol6+OGHtXDhQqXxaicAAAAASAmGaZpmbT+0d+9e9e3bV5s2bVKvXr3Uv39/Pfzwwxo/fnxkRnjv3r1q2bKlJk2apAcffDDWuROSz+eTx+OR1+uNm5W3w36/NvSuuv29a+FaObKzbU4EAAAAIBn4A371ndNXklQwrkDZrvjrGrXpaFHNPN95553atGmTpk+frsLCQv39738/5JhmzZqpR48eWr58eTSXAAAAAAAgbkR13/GCBQvUpUsX3XbbbT97XMeOHbVs2bJoLoEGYqSnq82DD0TGAAAAABAL6c503Tvo3sg40UVVnnfs2KGRI0daHmcYxiHPEiO+GGlpcg8bZncMAAAAAEkmzZGmoe2G2h0jZqK6bdvtdh92Veuf2rRpk5o3bx7NJQAAAAAAiBtRleeTTz5ZH374oTZv3nzEYz799FN98skn6t+/f9ThUP/MYFC+t96S7623ZAaDdscBAAAAkCSC4aAWFS/SouJFCoYTv2tEVZ6vvPJKVVRUaPTo0Vq/fv0h+zdu3KiLL75Ypmlq8uTJdQ6J+mNWVmrHNddqxzXXyqystDsOAAAAgCRRGarUH5f/UX9c/kdVhhK/a0RVnocNG6YbbrhBRUVF6t69u/Lz82UYhhYtWqSePXvquOOO07p163TjjTfqtNNOi3VmAAAAAAAaVFTlWZLuuusuzZ07VyeccIK++uormaapkpISffbZZ+rcubOee+45zZgxI5ZZAQAAAACwRVSrbR80duxYjR07Vrt371ZxcbHC4bByc3PVpk2bWOUDAAAAAMB2dSrPBzVv3pxVtQEAAAAASSvq27YBAAAAAEgVlGcAAAAAACzE5LZtJC7D5VLrv/wlMgYAAACAWHA5XZrRf0ZknOgM0zRNu0OkCp/PJ4/HI6/XK7fbbXccAAAAAEhptelo3LYNAAAAAIAFbttOcWYwqP0rV0qSGp92mow0/kgAAAAAqLtgOKhVO1dJkvrl9FOaI7G7RmKnR52ZlZXa/rvfS5K6Fq6lPAMAAACIicpQpSa9M0mSVDCuIDXK89atW+t0kWOPPbZOnwcAAAAAwE41Ks/t2rWTYRhRXcAwDAWDwag+CwAAAABAPKhReR44cOAh5bmiokJr1qyRJDVt2lR5eXmSqmap9+7dK8Mw1LdvX2VkZMQ4MgAAAAAADatG5XnZsmXVft+3b58GDx6s7t27669//auGDh1abf/ixYt1ww03KBAIaNGiRTELCwAAAACAHaJ6VdUtt9yiTZs26d133z2kOEvS2WefrSVLlmjTpk266aab6hwSAAAAAAA7RVWeX3nlFQ0ePFjHHHPMEY855phjNHjwYM2fPz/qcAAAAAAAxIOo1grfvXt3jRYBCwaD2rNnTzSXQAMxXC61vOXmyBgAAAAAYsHldOnGvjdGxonOME3TrO2HjjvuOG3fvl1ffPGF2rZte9hjtm3bpm7duik3N1fr16+vc9Bk4PP55PF45PV65Xa77Y4DAAAAACmtNh0tqtu2L7/8ch04cECDBg3S008/rfLy8si+iooKPfPMMxo0aJD8fr8uv/zyaC4BAAAAAEDciGrmORwO6//9v/+nuXPnRl5h1bx5c0lVt3RLkmmaGjt2rJ5//nk5HFF19KQTjzPPZigk/0drJUnZJ/WR4XTanAgAAABAMgiFQyr8tlCS1LtFbzkd8dc16n3m2eFw6Pnnn9fzzz+v0047TS6XS99++62+/fZbuVwunXbaaZozZ47mzp1LcY5zZkWFtl56qbZeeqnMigq74wAAAABIEhWhCk1YNEETFk1QRSjxu0ZUC4YddMEFF+iCCy5QMBjUd999J0k6+uijlZZWp9MCAAAAABBXYtJy09LS1LJly1icCgAAAACAuFOn8hwMBvXGG2/ogw8+0J49e9S3b19NmDBBkrRz507t2bNH3bp1YyYaAAAAAJDQom61K1eu1G9+8xtt27ZNpmnKMAwFAoFIeV69erXOP/98vfTSS/r1r38ds8AAAAAAADS0qFbz+uKLLzRs2DCVlJToyiuv1IsvvqifLtp97rnnKjs7Wy+//HJMggIAAAAAYJeoZp5nzJih8vJyvfnmmzr77LMPe0x6erp69+6tjz/+uE4BAQAAAACwW1TleenSpfrFL35xxOJ8UJs2bfTpp59GFQwNw0hLU4vr/xgZAwAAAEAsuBwuTekzJTJOdFG1pe+//15t27a1PO7AgQMKBALRXAINxEhP19GXX253DAAAAABJxuV06bLul9kdI2aieua5RYsW2rhxo+Vx69evr1HJBgAAAAAgnkVVngcPHqxPPvlES5cuPeIx8+fP18aNGzVkyJCow6H+maGQyj77TGWffSYzFLI7DgAAAIAkEQqHtG7POq3bs06hcOJ3jajK89SpU5Wenq5Ro0bp0Ucf1TfffBPZV1paqtmzZ+vyyy9Xo0aNNGXKlJiFReyZFRUqHnu+iseeL7Oiwu44AAAAAJJERahCF71xkS564yJVhBK/a0RVnvPz8/X8888rHA5r8uTJatOmjQzD0L///W8dc8wxmjhxoioqKvTcc8+pffv2sc4MAAAAAECDiqo8S9KoUaO0bt06XXnllcrPz1dmZqbS09PVoUMHXXHFFSoqKtKIESNimRUAAAAAAFvU6d1EeXl5evDBB2MUBQAAAACA+BTVzPPTTz+tVatWWR63Zs0aPf3009FcAgAAAACAuBFVeR4/frxmzZpledy//vUvXXZZ8rzXCwAAAACQmqJ+5rkmwuGwDMOoz0sAAAAAAFDv6vTMs5Wvv/5abre7Pi+BOjLS0nTMpEmRMQAAAADEgsvh0u97/j4yTnQ1bku33357td8/+eSTQ7YdFAwGtWHDBr333nsaMmRI3RKiXhnp6Wp+5WS7YwAAAABIMi6nS3/o9Qe7Y8SMYZqmWZMDHQ6HDMOQaZqRf1pp0aKF3nzzTfXu3bvOQZOBz+eTx+OR1+tlRh4AAAAAbFabjlbjmecnn3xSkmSapiZMmKDTTjtNl19++WGPTU9PV05Ojk455RRlZGTUIjoamhkOq3LTJklSeseOMhz1+hg8AAAAgBQRNsP6+vuvJUkdjuogh5HYXaPGM88/dsYZZ+icc87RDTfcUB+ZklY8zjyH/X5t6N1HktS1cK0c2dk2JwIAAACQDPwBv/rO6StJKhhXoGxX/HWNepl5/rGlS5dGFQwAAAAAgESU2PPmAAAAAAA0gKjfTWSapp577jm9+uqr+u9//6t9+/YddhExwzC06YdnagEAAAAASERRlefKykoNHz5c77777hFX3a7pityIH/7KoByOCjm2rZaxf5fMxi0Vbnuq5HAqy+WUYRh2RwQAAAAAW0R12/Z9992nd955R7/61a/03//+VxdffLEMw1BFRYXWr1+v2267TY0aNdL111+vcDgc68yoJ3/6y936fma+Mp8bqYxX/0+Zz43U9zPzde1tM1QWCNkdDwAAAABsE9XM89y5c9WsWTPNmTNHjRo1kuOH1xu5XC517dpV06dP1xlnnKEzzjhDXbt21YQJE2IaGvXjIdfDcqj63QKttFePuh5U4MteUo/R9gQDAAAAAJtFNfO8ceNG/eIXv1CjRo2qTvJDeQ6F/jc7OWDAAPXv31+PPPJIDGKivhhpaWp22Xg17SE5HKYcP7kz22FU3YKfvuQmKczsMwAAAICacTlcGn/8eI0/frxcDpfdceosqvLsdDrl8Xgivx8s0bt37652XJs2bbRhw4Y6xEN9M9LT1fL8fmrVbaccziMcI1OGb4e0ZVXDhgMAAACQsFxOl6476Tpdd9J1cjlTtDy3adNG27dvj/zeqVMnSdKaNWuqHVdUVKTGjRvXIR4axP5dNTtuw5v1mwMAAAAA4lRU5fmUU07RunXrVFFRIUn65S9/KUm65ppr9NZbb+mzzz7TlVdeqfXr16tv376xS4uYM8NhVR5wqnK/U5aLo695RPpiYYPkAgAAAJDYwmZYO/bv0I79OxQ2E38h6ajK83nnnafMzEwtXrxYUtXM8zXXXKOtW7dq+PDh6tWrlx5++GFlZ2frnnvuiWlgxJZZXq5N42/UptdbygxZvYrKkN6ayrPPAAAAACyVB8s17OVhGvbyMJUHy+2OU2dRrbY9fPhwlZSUVNt233336eSTT9aCBQtUWlqqLl266KqrrlLnzp1jEhTxwJQOPvvcfoDdYQAAAACgwURVno/kwgsv1IUXXhjLU6IhnTxR+vif1sfV9BlpAAAAAEgSUd223axZMw0aNCjWWWC3LkNrdlzjlvWbAwAAAADiTFTlORgMKjc3N9ZZYLe2fSV3jqQjPftsSO42Ul6/hkwFAAAAALaLqjwff/zx2rFjR6yzwG4OpzTs7h9++WmB/uH3YXfpiC+EBgAAAIAkFVV5vvLKK7Vy5UqtXLky1nlgt24jpPOfltytq29351Rt7zbCnlwAAAAAYKOoFgw77bTT9Nvf/lZDhw7Vb3/7W5177rk69thjlZmZedjjjz322DqFRD1KS1PTcRdFxpKqCnL+8KpVtffvqnrGOa8fM84AAAAAaizNkaYLul4QGSc6wzRNs7YfcjgcMgxDpmnKMH7+3cCGYSgYDEYdMJn4fD55PB55vV653W674wAAAABASqtNR4uq/g8cONCyNAMAAAAAkCyiKs/Lli2LcQzYxTRNhUpLJUnOpk35SxEAAAAAMWGapkorqrpG04zE7xqJf+M56sQsK9N/+/WXJHUtXCsjO9vmRAAAAACSQVmwTIPmDpIkFYwrULYrsbtGVKttAwAAAACQSqKaeb799ttrdFx6erqOPvpo9erVSyeffHI0lwIAAAAAwHZRlefbbrvtkPvVDy7a/ePtP16Nu2vXrpo1a5b69esXbVYAAAAAAGwRVXl+8sknVVBQoMcee0x5eXk677zzIu9y3rZtm15++WUVFxfriiuuUNu2bfXee+9p8eLFGjp0qD788EPl5+fH9EsAAAAAAFCfoirP3bt31+9//3tNnz5dt9xyi5xOZ7X9d999t2bMmKG77rpLK1as0I033qgHHnhA1113ne6++249+eSTMQkPAAAAAEBDMMyD91vXwvDhw7Vt2zYVFRX97HE9evRQbm6u3nzzTZmmqY4dOyocDqu4uDjavAmtNi/gbihhv18beveRVLXatoPVtgEAAADEgD/gV985fSXF72rbteloUc08r169Wuecc47lcSeccILefPNNSVXPQnfv3l2LFy+O5pKoL2lp8owaFRkDAAAAQCykOdI0ouOIyDjRRfUNgsFgjWaPi4uLFQqFIr9nZGQoMzMzmkuinjjS05Vz10y7YwAAAABIMunOdN152p12x4iZqN7zfNJJJ2nNmjWaO3fuEY+ZO3euVq9eXe0VVVu2bFHLli2juSQAAAAAALaJaub51ltv1VlnnaVx48Zp9uzZGjNmjNq2bSvpf6ttv/3220pLS9Ott94qSfr222/18ccf69JLL41detSZaZoyy8okSUZW1iGvIAMAAACAaJimqbJgVdfISkv8rhHVgmGS9Morr2jixIkqLS097DufmzVrpieeeEKjR4+WJO3cuVMrVqxQnz591KlTp7onT0AsGAYAAAAgVbBg2A9+/etfa8iQIXrxxRf1/vvvq6SkRJLUunVr9e/fX2PHjq128ZycHF1wwQXRXg4AAAAAANvUacmzJk2a6PLLL9fll18eqzwAAAAAAMSdqBYMAwAAAAAgldSpPC9evFijR49WmzZtlJGRoQkTJkT2LVq0SFOmTNHOnTvrHBIAAAAAADtFXZ6vvvpqnXPOOXr11Ve1b98+BQKBavtbt26tBx988GdfZwUAAAAAQCKIqjw//fTT+vvf/64+ffqosLBQPp/vkGN69Oihtm3b6rXXXqtzSAAAAAAA7BTVgmGPPvqojjrqKL3xxhtq3rz5EY/r0aOHPvvss6jDoQE4nWoydGhkDAAAAACx4HQ4NSRvSGSc6KIqz+vWrdOgQYN+tjhLksfj0a5du6IKhobhyMhQ7kMP2h0DAAAAQJLJcGbo/tPvtztGzET9zLNhGJbH7Ny5U1lZWdFeAgAAAACAuBBVee7cubMKCwsPWSTsx/bt26dPPvlExx9/fNThAAAAAACIB1GV57Fjx6qkpERTp0494jHTpk2T1+vVhRdeGHU41L+w36/1+cdpff5xCvv9dscBAAAAkCT8Ab9O+PcJOuHfJ8gfSPyuEdUzz9dcc41eeOEFPfjgg1q1apVGjhwpSdq0aZMeeOABzZ8/XytXrlTv3r01ceLEmAYGAAAAAKChRVWes7KytGTJEo0fP17/+c9/9MEHH0iSVqxYoRUrVkiShgwZomeffVbp6emxSwsAAAAAgA2iKs+S1Lx5c73xxhv69NNPtXjxYhUXFyscDis3N1dDhgzRL37xi1jmBAAAAADANlGX54N69uypnj17xiILAAAAAABxKaoFwwYPHqx77rnH8rh7771XgwcPjuYSAAAAAADEjahmnpctW6Z27dpZHrdhwwYtX748mksAAAAAABA36nzb9s8pLy9XWlq9XgJ15XSq0aCBkTEAAAAAxILT4dSANgMi40RXb83W5/Np1apVat26dX1dAjHgyMjQsY8/bncMAAAAAEkmw5mhR856xO4YMVPj8tyhQ4dqv8+bN0/Lli077LHBYFC7du1SMBjU5MmT6xQQAAAAAAC71bg8FxcXR8aGYWj//v3av3//YY91uVzKycnRiBEjNHPmzDqHBAAAAADATjUuz+FwODJ2OBwaP368Zs+eXS+h0HDCfr++6n+aJKnL+yvlyM62OREAAACAZOAP+HX6i6dLkpadv0zZrsTuGlE98/zkk0+qU6dOsc4Cm5hlZXZHAAAAAJCEyoLJ0zWiKs+XXnpprHMAAAAAABC36rTatt/v10cffaSSkhJVVFQc8bhLLrmkLpcBAAAAAMBWUZfn6dOn64EHHpDf7z/iMaZpyjAMyjMAAAAAIKFFVZ7vuece3XHHHXI6nRo+fLi6dOmiJk2axDobAAAAAABxIary/MQTTygrK0srVqxQ7969Y50JAAAAAIC4ElV53rZtmwYPHkxxTgYOh7JPPjkyBgAAAIBYcBgOndTypMg40UVVnlu1aqVGjRrFOgts4MjMVN4zT9sdAwAAAECSyUzL1JPDnrQ7RsxEVf8vvPBCLVu2TAcOHIh1HgAAAAAA4k5U5fm2227TcccdpxEjRmjjxo2xzgQAAAAAQFyJ6rbtX/7ylwqHw1q2bJmOO+445eXlKTc3V47DPDNrGIbeeeedOgdF/Qj7/dp45lmSpE7vLJEjO9vmRAAAAACSgT/g17CXh0mS3jrvLWW7ErtrRFWely1bFhmHQiF9/fXX+vrrrw97rGEYUQVDwwmVltodAQAAAEASKq1Inq4RVXnevHlzrHMAAAAAABC3oirPeXl5sc4BAAAAAEDcSvyXbQEAAAAAUM8ozwAAAAAAWKjRbdtOpzPqCxiGoWAwGPXnAQAAAACwW43Ks2maUV+gLp9FA3A4lNm9e2QMAAAAALHgMBw6/ujjI+NEZ5i02wbj8/nk8Xjk9XrldrvtjgMAAAAAKa02HS3x6z8AAAAAAPWM8gwAAAAAgAXKc4oLl5Vp4+AztXHwmQqXldkdBwAAAECSKAuWaei8oRo6b6jKgonfNWq0YBiSmGkqsHNnZAwAAAAAsWCapnYe2BkZJzpmngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsMBq26nOMJTeqWNkDAAAAACxYBiGOno6RsaJzjCTYc3wBOHz+eTxeOT1euV2u+2OAwAAAAAprTYdjdu2AQAAAACwQHkGAAAAAMAC5TnFhcvKtOlXv9KmX/1K4bIyu+MAAAAASBJlwTKNWjBKoxaMUlkw8bsGC4alOtNU5cZNkTEAAAAAxIJpmtrk3RQZJzpmngEAAAAAsEB5BgAAAADAAuUZAAAAAAALcV2ey8rKNH36dHXp0kWZmZnKycnRhAkTtGPHjlqdZ/ny5frzn/+s4cOHq3nz5jIMQ+3atavRZ/fv368///nP6tGjhxo3biyPx6Pu3btr0qRJ2r9/fxTfCgAAAACQaOJ2wbDy8nINHjxYa9asUevWrTVy5EgVFxfrySef1Ouvv641a9aoQ4cONTrX1VdfrU8//bTWGTZv3qwzzzxTmzdvVocOHXTOOeeooqJCGzZs0COPPKJp06apcePGtT4vAAAAACCxxG15vuOOO7RmzRqdeuqpWrx4caSk3n///bruuus0YcIELVu2rEbnOvvsszV27FidfPLJys3N1fHHH2/5mYqKCp1zzjnaunWrHnvsMV1xxRXV9q9bt07NmjWr9feKO4YhV05OZAwAAAAAsWAYhnIa5UTGic4w43DN8MrKSrVo0UJer1eFhYU68cQTq+3v2bOnioqK9NFHH6lPnz61Ovc333yj1q1bKy8vT8XFxUc87p577tGf/vQnXX/99brnnnui+RqH8Pl88ng88nq9crvdMTknAAAAACA6telocfnM8/vvvy+v16uOHTseUpwlacyYMZKk1157rd4yPPHEE5KkK6+8st6uAQAAAABIDHF52/bB55N79+592P0HtxcVFdXL9bdt26aNGzcqNzdXbdu21fvvv6+FCxfK6/Wqffv2Ou+889SpU6d6uTYAAAAAIP7EZXneunWrJCk3N/ew+w9u37JlS71c/4svvpAk5eTkaNKkSXrkkUeq7b/55pt111136brrrquX6zekcHm5tvzmYklS3rPPyJGZaXMiAAAAAMmgPFiu8W+NlyQ9NewpZaYldteIy9u2D74CKjs7+7D7GzVqJEnat29fvVy/tLRUklRYWKjHHntMt912m7Zt26aSkhLdfffdkqQ//vGPeuONN372PBUVFfL5fNV+4k44rPJ161S+bp0UDtudBgAAAECSCJthff7d5/r8u88VNhO/a8RlebZb+IcSGQwGdcUVV+jWW29Vbm6uWrVqpRtuuEHXXnutJOkvf/nLz55n5syZ8ng8kZ+2bdvWe3YAAAAAQOzFZXk++Foqv99/2P0HDhyQJDVp0qRery9Jl1122SH7D24rKChQeXn5Ec8zbdo0eb3eyM+2bdtiHxYAAAAAUO/i8pnnY489VpK0ffv2w+4/uD0vL69erv/j87Zr1+6Q/Qe3hUIh7d27VzkH35P8ExkZGcrIyKiPiAAAAACABhSXM889e/aUVPXM8eEc3N6jR496uX5+fr4yf1g46+Dzzz+2d+/eyPjHs9QAAAAAgOQUl+W5f//+8ng82rRpkz755JND9s+bN0+SdO6559bL9TMyMjR06FBJ0rJlyw7Zv3z5cklShw4dLF+kDQAAAABIfHFZntPT0zV58mRJ0qRJkyLPOEvS/fffr6KiIg0aNEh9+vSJbP/HP/6h/Px8TZs2LSYZbrjhBknSjBkz9NVXX0W2b968Wbfccosk6Xe/+11MrmU3Z9OmcjZtancMAAAAAEmmaUZTNc1Ijq4Rl888S1XvUl6yZIlWrVqlzp07a8CAAdqyZYsKCgrUvHlzzZ49u9rxe/bs0YYNG1RSUnLIuWbNmqVZs2ZJkgKBgCSppKREp5xySuSYRx55RL1794783q9fP02fPl233367TjzxRPXv319Op1Pvv/++9u3bp3POOUdTpkypj6/eoBzZ2eqyepXdMQAAAAAkmWxXtt678D27Y8RM3JbnzMxMLV26VDNnztScOXO0YMECNWvWTOPHj9eMGTOUm5tb43Nt375dBQUF1bZVVlZW23a4dzD/+c9/Vs+ePfXggw9qzZo1CgaD6tq1qy699FJNnjxZTqcz+i8IAAAAAEgYhmmapt0hUoXP55PH45HX6+VZaQAAAACwWW06Wlw+84yGEy4v15aLL9GWiy9R+GfeWQ0AAAAAtVEeLNdlb12my966TOXBxO8acXvbNhpIOCz/hx9GxgAAAAAQC2EzrI92fRQZJzpmngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsMBq25CRlWV3BAAAAABJKCstebqGYZqmaXeIVFGbF3ADAAAAAOpXbToat20DAAAAAGCB8gwAAAAAgAXKc4oLV1Ro6xVXaOsVVyhcUWF3HAAAAABJoiJUoT8s+YP+sOQPqgglftdgwbBUFwrpwPL3ImMAAAAAiIVQOKQVO1ZExnLaHKiOmHkGAAAAAMAC5RkAAAAAAAuUZwAAAAAALFCeAQAAAACwQHkGAAAAAMAC5RkAAAAAAAuGaZqm3SFShc/nk8fjkdfrldvttjsOAAAAAKS02nQ0Zp4BAAAAALBAeQYAAAAAwALlOcWFKyq0/eprtP3qaxSuqLA7DgAAAIAkURGq0JRlUzRl2RRVhBK/a1CeU10opH2LFmnfokVSKGR3GgAAAABJIhQO6e0tb+vtLW8rFE78rkF5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALaXYHgL2MrCx1LVwbGQMAAABALGSlZalgXEFknOgozynOMAwZ2dl2xwAAAACQZAzDULYreboGt20DAAAAAGCB8pziwpWV2jl1mnZOnaZwZaXdcQAAAAAkicpQpW5aeZNuWnmTKkOJ3zUoz6kuGJR3wQJ5FyyQgkG70wAAAABIEsFwUAs3LdTCTQsVDCd+16A8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAICFNLsDwF5GVpY6r3o/MgYAAACAWMhKy9LyC5ZHxomO8pziDMNQWrNmdscAAAAAkGQMw1CzzOTpGty2DQAAAACABWaeU1y4slLf3nWXJKnF1KlypKfbnAgAAABAMqgMVeqeD++RJN1w8g1KdyZ212DmOdUFgyqd87xK5zwvBYN2pwEAAACQJILhoOZumKu5G+YqGE78rkF5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALvOc5xRmZmeq4ZElkDAAAAACxkJmWqbfOeysyTnSU5xRnOBxKz21jdwwAAAAAScZhONSmcfJ0DW7bBgAAAADAAjPPKc6srNS3Dz4kSWpxzdUy0tNtTgQAAAAgGQRCAf3t479Jkq468Sq5nC6bE9UNM88pzgwGtXf2bO2dPVtmMGh3HAAAAABJIhAO6KnPn9JTnz+lQDhgd5w6ozwDAAAAAGCB8gwAAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCB8gwAAAAAgAXe85zijMxMdXhtYWQMAAAAALGQmZap+SPmR8aJjvKc4gyHQxmdO9sdAwAAAECScRgOdWraye4YMcNt2wAAAAAAWGDmOcWZlZXa8/g/JUnHXPF/MtLTbU4EAAAAIBkEQgE98dkTkqSJJ0yUy+myOVHdUJ5TnBkMas/DD0uSjr58AuUZAAAAQEwEwgE9+umjkqTxx49P+PLMbdsAAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIFXVaU4IyND7V56MTIGAAAAgFjIcGbo+eHPR8aJjvKc4gynU1knnGB3DAAAAABJxulwqvsx3e2OETPctg0AAAAAgAVmnlOcWVmpvc88I0lqdvHFMtLTbU4EAAAAIBkEQgE9u/5ZSdJvjvuNXE6XzYnqhvKc4sxgUN/+9V5JUtOLLqI8AwAAAIiJQDig+9feL0m6oOsFCV+euW0bAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACzwqqoUZ2Rk6Nh//zsyBgAAAIBYyHBmaPbQ2ZFxoqM8pzjD6VSjvr+wOwYAAACAJON0OHVyq5PtjhEz3LYNAAAAAIAFZp5TnBkIqPTFFyVJTc8/X4bLZXMiAAAAAMkgEA5o3lfzJEljuoyRy5HYXYPynOLMQEC7ZtwhSTpq9GjKMwAAAICYCIQC+kvBXyRJIzuOTPjyzG3bAAAAAABYoDwDAAAAAGCB8gwAAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCBV1WlOCM9XbmPPRoZAwAAAEAspDvT9fCZD0fGiY7ynOKMtDQ1Of10u2MAAAAASDJpjjQNzB1od4yY4bZtAAAAAAAsMPOc4sxAQN6FC6XvNspzcjsZR+VIef0kh9PuaAAAAAASWCAc0BtfvyFJGt5huFwOl82J6obynOLMz15VyU23SJLcY0pkpJmSO0cadrfUbYTN6QAAAAAkqkAooFver+oaZ+ednfDlmdu2U9kXC6VXJh663VcivXhJ1X4AAAAAAOU5ZYVD0lt/kmQeZucP296aWnUcAAAAAKQ4ynOq2rJK8u38mQNMybej6jgAAAAASHGU51S1f1dsjwMAAACAJEZ5TlWNW8b2OAAAAABIYpTnVJXXr2pVbRlHOMCQ3G2qjgMAAACAFMerqlKVwykNu1vGC5eoTb9SSaYMx8HFw34o1MPu4n3PAAAAAKKS7kzXvYPujYwTnWGa5uGWW0Y98Pl88ng88nq9crvddsep8sXCqlW3f7x4mLtNVXHmPc8AAAAAklhtOhozz6mu2wgpf3jVqtr7d1U945zXjxlnAAAAAPgRynOKM4NB7VuyRJLU5KxRMtL4IwEAAACg7oLhoN7Z+o4k6cxjz1SaI7G7RmKnR52ZlZXacc21kqSuhWspzwAAAABiojJUqT8u/6MkqWBcQcKXZ1bbBgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALib1WOOrMcLnU+i9/iYwBAAAAIBZcTpdm9J8RGSc6wzRN0+4QqcLn88nj8cjr9crtdtsdBwAAAABSWm06GrdtAwAAAABggdu2U5wZDGr/ypWSpMannSYjjT8SAAAAAOouGA5q1c5VkqR+Of2U5kjsrpHY6VFnZmWltv/u95KkrvP/LiP4vdS4pZTXT3I47Q0HAAAAIGFVhio16Z1JkqSCcQWUZySR58ZIaT88Au/OkYbdLXUbYW8mAAAAAIgDPPOc6r588/DbfSXSi5dIXyxs2DwAAAAAEIcoz6ksHJLevuUIO3+YgX5ratVxAAAAAJDCKM+pbMsqaV/JzxxgSr4dVccBAAAAQAqjPKey/btiexwAAAAAJCnKcypr3DK2xwEAAABAkmK17VSW10/GUa3Vso9XkinDYf7kAKNq1e28fnakAwAAAJDAXE6Xbux7Y2Sc6CjPqczhlPHLu9Vs/yWH2WlU/WPYXbzvGQAAAECtuRwuXZR/kd0xYobbtlNdtxHS+U9L7tbVt7tzqrbznmcAAAAAYOY51ZmhkPz7Wkn9Ziv7mEoZZburnnHO68eMMwAAAICohcIhFX5bKEnq3aK3nAneLyjPKc6sqNDWSy+VJHUtXCsjO9vmRAAAAACSQUWoQhMWTZAkFYwrULYjsbsGt20DAAAAAGCB8gwAAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCB8gwAAAAAgAVeVZXijLQ0tbj+j5ExAAAAAMSCy+HSlD5TIuNEZ5imadodIlX4fD55PB55vV653W674wAAAABASqtNR4vr27bLyso0ffp0denSRZmZmcrJydGECRO0Y8eOWp1n+fLl+vOf/6zhw4erefPmMgxD7dq1q9U5Kisr1a1bNxmGoTRmaAEAAAAgpcRtCywvL9fgwYO1Zs0atW7dWiNHjlRxcbGefPJJvf7661qzZo06dOhQo3NdffXV+vTTT+uU5y9/+Yu+/PLLOp0jHpmhkMq/+EKSlNmtmwyn0+ZEAAAAAJJBKBzS+r3rJUnHNTtOTkdid424Lc933HGH1qxZo1NPPVWLFy9W48aNJUn333+/rrvuOk2YMEHLli2r0bnOPvtsjR07VieffLJyc3N1/PHH1yrL+vXrNXPmTE2cOFH//Oc/a/tV4ppZUaHisedLkroWrpWRnW1zIgCpKBgMKhgM2h0DSGoOh0Mul0uGYdgdBUCKqAhV6KI3LpIkFYwrULYjsbtGXD7zXFlZqRYtWsjr9aqwsFAnnnhitf09e/ZUUVGRPvroI/Xp06dW5/7mm2/UunVr5eXlqbi42PJ40zQ1cOBAffXVV/ryyy/VrFkzOZ3OqP5PXjw+8xz2+7Whd9W/w66Fa+WgPANoQH6/X3v27NGBAwfsjgKkBJfLpSZNmuiYY46Rk7vNANQzf8CvvnP6SvqhPLvir2vUpqPF5czz+++/L6/Xq44dOx5SnCVpzJgxKioq0muvvVbr8lxbjz/+uFauXKlnnnlGTZs2rddrAUAqqays1LZt2+RyudS6dWtlZGQwIwbUE9M0FQqFtH//fn3//fcqKytT27ZtKdAAUAtxWZ4PPp/cu3fvw+4/uL2oqKhec5SUlGjq1Kk688wz9Zvf/KZerwUAqebbb7+V0+lUXl4e/wceaCCNGzeWx+PR1q1btWfPHrVs2dLuSACQMOJyte2tW7dKknJzcw+7/+D2LVu21GuOyZMnq7y8XI888ki9XgcAUo1pmvL7/fJ4PBRnoIFlZWXJ7XZr3759isOn9wAgbsXlzPP+/fslSdlHeP62UaNGkqR9+/bVW4ZXX31Vr7zyim699VZ16dIlqnNUVFSooqIi8rvP54tVPABIaIFAQKFQSFlZWXZHAVJSkyZN9P333ysQCCg9Pd3uOACQEOJy5tlu+/bt0+TJk9WlSxdNmzYt6vPMnDlTHo8n8tO2bdsYpgSAxBUOhyWJWWfAJgf/u3fwv4sAAGtxOfN88LVUfr//sPsPrsrapEmTern+jTfeqO3bt2vJkiXKyMiI+jzTpk3TlClTIr/7fL64K9BGWpqOmTQpMgaAhsQCYYA9+O8egIbgcrj0+56/j4wTXVy2pWOPPVaStH379sPuP7g9Ly+vXq7/2muvKTMzUzNmzNCMGTMO2R8KhXT66adLkh588EH16tXrsOfJyMioU/luCEZ6uppfOdnuGAAAAACSjMvp0h96/cHuGDETl+W5Z8+ekqTCwsLD7j+4vUePHvWWoby8XMuXLz/i/oP7vv/++3rLAAAAAACID3H5zHP//v3l8Xi0adMmffLJJ4fsnzdvniTp3HPPrZfrFxcXyzTNw/5IVc8JHfz94Ax0ojLDYVX897+q+O9/ZfLcEwDY7m9/+5uOP/74yHuvTz/9dI0fP16GYWjZsmV2xwMAoMbCZlgbSzdqY+lGhc3E7xpxWZ7T09M1eXLVrcSTJk2KPOMsSffff7+Kioo0aNAg9enTJ7L9H//4h/Lz8+u0wFcqMsvL9fW5I/T1uSNklpfbHQcAUtorr7yiq6++WiUlJRoxYoQuvfRSDRs27IjHt2vXjmdXAQBxqzxYrtELR2v0wtEqDyZ+14jL27Yl6eabb9aSJUu0atUqde7cWQMGDNCWLVtUUFCg5s2ba/bs2dWO37NnjzZs2KCSkpJDzjVr1izNmjVLUtXrUSSppKREp5xySuSYRx55RL17967HbwQAwM9bsGCBpKo7rAYPHhzZXlJSoqlTp0bWBAEAAA0vbstzZmamli5dqpkzZ2rOnDlasGCBmjVrpvHjx2vGjBnKzc2t8bm2b9+ugoKCatsqKyurbeMdzAAAux1cELNDhw7Vtrdu3VqtW7e2IxIAAPhBXN62fVBWVpZuv/12bdy4URUVFSopKdGTTz552OJ82223yTRNPfXUU0fc93M/NX122TRNBYPBOn4zAAD+57bbbpNhGFq6dKkkqX379jIMI/Kc80+feV62bJkMw9CWLVskKXKsYRhq166dTd8CAIDkFrczzwAApIpevXrp0ksv1VtvvaVdu3bpvPPOU+PGjSVJrVq1OuT4Vq1a6dJLL9W8efN04MABXXrppZF9xxxzTIPlBgAglVCeAQBxxzRNlQVCdseosSyXs04Ld40aNUqjRo3S6aefrl27dunee+/92Rnk/Px8PfXUU1q2bJkOHDhw2LuuAABAbFGeAQBxpywQUrfpi+yOUWNf3D5U2en8TyoAAMmM/6VPcUZamppNmBAZAwAAAEAsuBwujT9+fGSc6GhLKc5IT1fLG663OwYAVJPlcuqL24faHaPGslxOuyMAABB3XE6XrjvpOrtjxAzlGQAQdwzD4DZoAAAQV/h/JinODIcV2FkiSXLltJbhiOu3lwEAAABIEGEzrJIDVV2jdaPWchiJ3TUozynOLC/XprPOkiR1LVwrIzvb5kQAgJpKT0+XJAWDQaWxbgUAIM6UB8s17OVhkqSCcQXKdiV210js6g8AQArLycmRJG3YsMHmJAAAJD/+mhoAgAQ1YsQILV++XGeeeabOOOMMNWrUSMccc4zuuusuu6MBAJB0KM8AACSoq666SqWlpXr++ef18ssvKxAIKC8vj/IMAEA94LZtAADixLJly2Saptq1a1dt+1NPPSXTNHX66adX256WlqYZM2Zo48aNqqyslGmaKi4ubrC8AACkEsozAAAAAAAWKM8AAAAAAFjgmedUl5ampuMuiowBAAAAIBbSHGm6oOsFkXGiS/xvgDpxpKer1fTpdscAAAAAkGTSnem6+ZSb7Y4RM9y2DQAAAACABWaeU5xpmgqVlkqSnE2byjAMmxMBAAAASAamaaq0oqprNM1I/K5BeU5xZlmZ/tuvvySpa+FaGdnZNicCAAAAkAzKgmUaNHeQJKlgXIGyXYndNbhtGwAAAAAAC5RnAAAAAAAsUJ4BAAAAALBAef7/7d15WFRl+wfw7wzLDIuAAioqIoK45r4jClaCZoprhgtolkumlWtvb2m+bWpumdnPVDQrzSVNLNdSNPfd190QXHBHBNmGZe7fH7wzOc6wGTLAfD/XNVfMs5xznzmP0D3Pc84hIiIiIiIiKgCTZyIiIiIiIqICMHkmIiIiIiIiKgCTZ0tnbQ3n0FA4h4YC1nxyGRFRaTVt2jQoFAosX77c3KEQEREVirXSGj18eqCHTw9YK8t+rlH2j4D+EaWtLap9/pm5wyAiohIQFxcHb29vdOrUCbt37zZ3OEREVM7ZWtnikw6fmDuMYsOZZyIiIiIiIqICcObZwokIJD0dAKCws4NCoTBzREREREREVB6ICNKzc3MNO+uyn2tw5tnCSXo6LjZvgYvNW+iTaCKickebA8TuBf67Lve/2hxzR5SnTZs2oV27drC3t4erqyv69OmDS5cumWx78uRJTJo0CS1atIC7uztUKhVq166N0aNH4+bNmwZtp02bBm9vbwBAdHQ0FAqF/hUREaFvt3fvXowZMwaNGzdGxYoVYWdnh3r16mHKlCl4+PDhszpsIiIqh9Kz09HmxzZo82MbfRJdlnHmmYiIyrdzm4Ctk4Hkx5JJp2pAyAygQQ/zxWXCN998g1GjRkGhUCAgIAAeHh44ePAgWrdujZdfftmo/eeff47169ejcePG6NChA4DchHrRokXYuHEjjh49imrVqgEAmjZtij59+mD9+vWoUqUKQkJC9NvR9QWAiRMn4tSpU2jcuDGef/55ZGRk4Pjx45gxYwY2b96MgwcPwtHR8Rl/EkRERKUPk2ciIiq/zm0C1gwBIIblybdyy/t/V2oS6KtXr+Kdd96BjY0NoqKiEBwcDADIysrC0KFD8f333xv1GTFiBObPn48qVaroy7RaLT7++GNMnToV//73v7Fs2TIAQGhoKJo2bYr169ejXr16ed61e+rUqWjfvj2cnZ31ZRqNBmPHjsXixYsxZ84cfPjhh8V45ERERGUDl20TEVH5pM3JnXF+MnEG/i7bOqXULOFetmwZMjIy8Oqrr+oTZwCwsbHB/PnzYW9vb9QnKCjIIHEGAKVSiQ8//BDVq1fHpk2bihxH165dDRJnAFCpVJg3bx6sra3xyy+/FHmbRERE5QFnnomIqHy6ut9wqbYRAZLjc9t5B5RYWHnZu3cvAGDAgAFGda6urujSpQs2btxoVJeQkIBNmzbhzJkzePjwIXJycr8MyMrKQkJCAh48eIBKlSoVKZb4+HhERUXhwoULSE5OhlarBQDY2tri8uXLRTwyIiKi8oHJMxERlU8pd4q33TOmu8GXl5eXyfpatWoZla1atQpvvPEGUlJS8tzuo0ePipQ8z5kzB1OmTEFWVlah+xAREVkCLtsmIqLyybFKwW2K0q6UuXr1KiIiIpCZmYl58+bh8uXLSEtLy30EoQjatWsHIPcxIYV18OBBjB8/Hvb29li+fDni4uKQkZGh36aHh8ezOhwiIqJSjzPPls7KChV019ZZWZk3FiKi4uTVPveu2sm3YPq6Z0VuvVf7ko7MJA8PD1y8eBFXr15FgwYNjOqvXr1q8P63335DZmYmJkyYgHHjxhm1v3LlSpFj2LBhAwDgk08+QXh4uEFdeno6bt++XeRtEhGR5bJSWuFFrxf1P5d1nHm2cEqVCjXmz0ON+fOgVKnMHQ4RUfFRWuU+jgoAoHii8n/vQz7PbVcKBATkXne9Zs0ao7oHDx5g+/btBmWJiYkAgBo1ahi137NnD+7cMV6ObmtrCwDIzs42GUN+21y7dm2RZrGJiIhUVirMCZyDOYFzoLIq+7kGk2ciIiq/GvTIfRyV0xPLjZ2qlarHVAHA0KFDoVKp8MMPP2Dnzp368qysLLzzzjtITU01aO/n5wcA+P777w3q4uPjMXLkSJP7cHNzg42NDWJiYvQ3FjO1zaVLlxpc83zu3DlMnjz56Q+OiIioHFAIv0YuMcnJyXB2dkZSUhKcnJzMHU7BtDm5d6FNuZN7TaBX+1IzQ0NEZVtGRgZiY2Ph7e0NtVr97HdYRn6fLVy4EGPGjIFSqUTHjh1RtWpVHDx4EImJiejevTt++OEHREZG6q91bt68Oc6ePYuqVavC398fGRkZ2LVrF5o2bQoA2L9/P2JjYw1uNtajRw9ERUWhYcOGaN68OWxtbeHv74+hQ4ciISEBjRo1wu3bt+Ht7Y1WrVrhwYMHiI6ORmhoKA4fPoyrV69yBrocKPF/g0REpVRRcjTOPFs4bVoazterj/P16kOblvZ3xblNwLxGwIruwPrXcv87r1FuORFRWaO0yn0c1XN9c/9bChNnAHjzzTexYcMGtGrVCocOHcK2bdvQpEkTHDx4EL6+vgZtbW1tsXfvXowaNQpqtRqbN2/G+fPn8dZbb2HHjh2wsbExuY8lS5Zg8ODBSEhIwI8//oilS5ciOjoaQO4jsY4cOYKwsDBkZmZi06ZNiI+Px3/+8x+sWrXqmR8/ERGVL2lZaXhuxXN4bsVzSMtKK7hDKceZ5xJUGmeetWlpuNi8BQCg7vFjUNrb5ybIa4bA+AY7/7tGsJQtdSSisoezXkTmxX+DRFQS0rLS0ObHNgCAQ2GHYG9jb+aIjHHmmZ6eNgfYOhmm70z7v7KtU3LbERERERERWQgmz2To6n4g+WY+DQRIjs9tR0REREREZCGYPJOhFONHm/yjdkREREREROUAk2cy5FileNsRERERERGVA0yeyZBX+9znn+puDmZEAThVz21HRERERERkIazNHQCZmZUVHDp11P8MpRUQMuN/d9tWwPDGYf9LqEM+L7WPeSEiIiIiotLBSmmFgOoB+p/LOibPFk6pUqHm//2fYWGDHrmPo9o62fDmYU7VchNnPqaKiIiIiIgKoLJS4esXvjZ3GMWGyTOZ1qAHUO+l3Ltqp9zJvcbZqz1nnImIiIiIyCIxeaa8Ka0A7wBzR0FERERERGR2vGGYhdOmpeFCs+a40Kw5tGlp5g6HiIiIiIjKibSsNLT+oTVa/9AaaVllP9fgzDNB0tPNHQIREREREZVD6dnlJ9fgzDMRERERERFRAZg8ExERERERERWAyTMRERERERFRAZg8ExERlRIKhQK1atUyKIuLi4NCoUBgYKBZYipvdu/eDYVCgYiICHOHQkREZQyTZyIiIiIiIqIC8G7blk6phH2rVvqfiYiodKlevTrOnz8Pe3t7c4dSLrRu3Rrnz5+Hs7OzuUMhIir3lAolWlZpqf+5rGPybOGUajW8Vn5n7jCIiCgPNjY2qFevnrnDKDfs7e35eRIRlRC1tRqRIZHmDqPYlP30n4iIqBzL65rn5cuXQ6FQYNq0abh27RrCwsLg7u4OOzs7tGzZElFRUXlu8/z584iIiICnpydUKhWqVKmCAQMG4OzZs0WKLTAwEAqFAnFxcfjpp5/QqlUr2Nvbo3r16pg0aRIyMzMBADExMXj11VdRuXJl2NvbIygoCKdPnzba3sOHD7FgwQIEBwfDy8sLKpUKrq6uCAkJwY4dO4za//bbb1AoFPDx8cGjR48M6kQEwcHBUCgU+Oyzz/TleV3zPG3aNCgUCixfvhzHjh1D165d4eLigkqVKqF///64ceMGACA1NRWTJk1CrVq1oFar0ahRI6xbt84otsfPT0Gfnc7j5zo1NRXvvvsuPD09YWdnh+bNmxuc07Vr16JNmzZwcHBAlSpVMHbsWKSnl59nqRIRlUZMnomIiMqwuLg4tGrVCocPH8bzzz+PZs2a4dixYwgNDcX27duN2m/cuBHNmjXDihUr4Obmhh49esDb2xtr1qxB69atsWfPniLHMH/+fAwaNAguLi4ICQlBZmYmZs2ahddffx2XL19G27ZtcfLkSXTu3Bm+vr7YvXs3goKCcOfOHYPtHDx4EGPHjsWlS5dQt25d9OrVC3Xr1sX27dsRHByMZcuWGbTv1q0b3nzzTVy5cgVjx441imn79u3o2LEjJk+eXOhjOXToEPz9/XHv3j0EBwfD1dUVa9euxfPPP4+kpCQEBQVhxYoVaNWqFdq1a4dz586hf//+2LZtW5E/t7xkZmbi+eefxw8//IC2bduibdu2OHXqFHr16oWdO3di7ty5CAsLQ4UKFRAcHIycnBwsWLAAw4cPL7YYiIjIBKESk5SUJAAkKSnJ3KHo5aSmysW27eRi23aSk5pq7nCIyEKkp6fLuXPnJD09Pd92qZmpeb4ysjMK3TY9K/2p26ZlpeXZNi0rrXg+kP8BIF5eXgZlsbGxAkA6depkUB4ZGSkABICMHz9ecnJy9HVz584VABIQEGC0LQcHB3F0dJQdO3YY1G3ZskVsbGzE09NTNBpNoeLt1KmTABBHR0c5cuSIvvzWrVtSpUoVUSgUUr9+fZkyZYpotVoREdFqtTJ48GABIB9++KHB9q5cuSIHDhww2s/x48fFxcVFnJyc5NGjRwZ1aWlpUr9+fQEga9euFRGR06dPi0qlEmdnZ4mLizNov2vXLgEg4eHhBuVTp07Vf56LFi3Sl2dmZsoLL7wgAKRBgwbSuXNnSUlJ0dcvWbJEAEjHjh0Ntqc7P1OnTs33s4uNjdWX6c41AKP96Lbn6+srFStWNPi84+PjpXLlygJAYmJiTO7vSYX9N0hE9E+kZqZKwKoACVgVIKmZpTPXKEqOxmueCTmJieYOgYjIpDY/tsmzLqB6AL5+4Wv9+8A1gUjPNr1stWWVlgbXXIWsD0GixvTvvoauDbG6+2r9+9CNobiZetNkWx9nH2wM3ZjfITxz3t7e+PTTT6F87KaPY8aMwfTp03Hw4EFkZmbC1tYWADBv3jykpqZiwYIFeOGFFwy2ExISglGjRuHLL7/Er7/+il69ehU6hrfffhstW7bUv69atSrCwsIwd+5caDQaTJ8+HQqFAkDu47gmTJiAlStXIjo62uhYvL29jbbfrFkzvPnmm/jkk0+wa9cuvPzyy/o6Ozs7/QztiBEj0KxZMwwcOBAajQZLly6Fl5dXoY8DADp06ICRI0fq39vY2OCtt97Czp07ceHCBWzYsAEODg76+oiICEyZMgUHDhxAVlYWbGxsirQ/U5RKJRYtWmSwnyFDhmDixIn466+/8O9//9vg865WrRoGDhyIuXPnYs+ePahdu/Y/joGIqLjk9fe2LOKybSIiojIsMDBQnxzrWFtbw9vbG1lZWUhISNCX65Zx9+7d2+S2AgICAACHDx8uUgxdunQxKtMlcIGBgUYJpa7u1q1bRv1ycnKwfft2TJs2DSNGjEBERAQiIiKwa9cuAMDly5eN+jRr1gwff/wxHjx4gObNm+O///0vXn31VQwcOLBIx1HQsdSqVQt+fn4GdVZWVvDy8kJWVhbu379f5P2ZYmo/SqVS/0VAfjGa+kyJiKh4cOaZiIhKrUNhh/Kss1JaGbzf3X93nm2ffDzG1j5bC912Y+hGiIjJtrrZVHOqUaOGyfIKFSoAADQajb5Md3Oq6tWr57vNoiaBprbn6OhYYN3jsQHAjRs30L17d5w6dSrPfT15YzCd8ePH46effsKxY8fg4eGBr7/+2mS7ghT1WB6vf/J4nlZB+ynKZ0pERMWHyTMREZVa9jaFf7bxs2prZ21X6Lbm8Phy7YJotVoAQHh4eL7t2rTJe7l8UWMoSnzDhw/HqVOn0KdPH0yaNAl169ZFhQoVoFQqsXjxYowYMSLPLzLOnTuHM2fOAAASEhIQFxeHpk2bFuk4Coq3KMdSEN25eJr9FGccRERUeEyeiYiILESNGjUQExOD2bNnw9XV1dzhGEhNTcWOHTtQpUoV/PTTT7CyMlxZcOXKlTz7ajQa/XXOgwYNwvfff4+BAwfi2LFjUKvVzzp0k3RL6VNSUkzWX79+vSTDISKiYsCvLomIiCzEiy++CADYsGGDmSMxlpSUBK1WCw8PD6PEOSsrK9+Y33vvPZw+fRoDBgzAypUrMWTIEJw7dw4TJ0581mHnycPDAwBw6dIlo7pLly7h2rVrJR0SERH9Q0yeLZ1SCXWjRlA3agRwGRgRUbk2fvx42NnZYcKECfj555+N6jUaDdatW4cbN26UeGyVK1eGs7Mzzpw5g3379unLc3JyMHnyZJNJKAD8/vvvmDdvHjw9PbFo0SIAwIIFC1CrVi189dVX2Lo17+vbn6VWrVrB3t4eW7ZswbFjx/Tl9+/fx/Dhw/Ndtk1EVF4oFUo0dG2Ihq4Nje4pUhaV/SOgf0SpVsN73Vp4r1sLpZmWthERUcnw9fXFqlWrkJWVhT59+qBOnTro0aMHXn31VXTs2BGurq7o169fsd01uiisra0xadIkZGdno1OnTujSpQsGDBgAX19ffPPNN3jzzTeN+iQmJiI8PBwKhQLfffcdXFxcAABOTk5YuXIllEolhg4dapbjcXR0xIQJE5CdnY0OHTogJCQEXbt2hZ+fH3JyctCuXbsSj4mIqKSprdVY3X01VndfDbV12c81mDwTERFZkJ49e+L06dMYPXo0FAoFduzYgV9//RV3797Fyy+/jDVr1qBBgwZmie1f//oXVqxYgcaNG2Pfvn3YuXMnmjRpgoMHDxo811hn5MiRiI+Px/jx4xEYGGhQ16FDB0yePBm3b9/G8OHDS+gIDE2bNg2zZs1CjRo18Mcff+DMmTMYNmwYduzYYfR4MSIiKv0UktdtK6nYJScnw9nZGUlJSXBycjJ3OEREZpORkYHY2Fh4e3ub7YZORJaM/waJiHIVJUfjzLOF06an46/Oz+Ovzs9Dm55u7nCIiIiIiKicSM9OR/C6YASvC0Z6dtnPNfioKksngqybN/U/ExERERERFQcRwc3Um/qfyzrOPBMREREREREVgMkzERERERERUQGYPBMREREREREVgMkzERERERERUQF4wzD6mzYHiN0LpNwBHKsAXu0BpZW5oyIiIiIiIjI7Js+WTqGAra8PoHkEfN0aSLv5d51TNSBkBtCgh/niIyIiIiKiMkmhUMDH2Uf/c1nH5NnCKe3s4DPzDWDNECDtidvHJ9/KLe//HRNoIiIiIiIqEjtrO2wM3WjuMIoNr3m2dNocYOtkAKaeu/a/sq1TctsRERERERFZKCbPlu7qfiD5Zj4NBEiOz21HRERERERkoZg8WzhtwnXE/OaOmN/coc3O5zqElDslFxQREREREZV56dnpCN0YitCNoUjPTjd3OP8Yr3m2dA6VkZlsU3A7xyrPPhYiIiIiIio3RAQxSTH6n8s6Js+WzrNNAQ0UuXfd9mpfIuEQERERERGVRly2bekMnuP85LLt/70P+ZzPeyYiskARERFQKBTYvXu3uUMhIiIyOybP9LcKVQ3fO1XjY6qIiIiIiIjAZdv0uDcPA/dO5t4czLFK7lJtzjgTERERERFx5pkeo7QCvAOA5/rm/peJMxFRibt+/TrGjh0LPz8/2NnZoVKlSmjZsiU++ugjJCcn69uJCFatWoXOnTujYsWKUKvVqF+/PqZNm4a0tDSj7QYGBkKhUCAuLg4bN25E27Zt4eDggEqVKuHVV1/FjRs3DNorFAqsWLECABAUFASFQqF/xcXFAQCmTZsGhUKB5cuX4/Dhw+jevTtcXV2hUChw8uRJ/bYOHTqEfv36wcPDA7a2tqhRowaGDx+Oa9euFf8HSERE9IwwebZ0CgVsqlWDTbVqgCKfR1UREdEzt3fvXjRu3BgLFixAVlYWXn75Zfj7+yMpKQnTpk3DlStXAABarRYDBw5EWFgYjhw5gqZNm6Jbt25ITU3FRx99hKCgIKSnm34kyNdff42+ffvCzs4O3bp1g6OjI1avXo3OnTsb9AkPD4ePjw8AIDg4GOHh4fqXo6OjwTb37NmDDh06IC4uDl26dEHHjh2hVCr1+2vfvj1+/vlneHl5ITQ0FK6urli6dClatmyJ8+fPP4uPkoiISgGFQoFqDtVQzaEaFOUh1xAqMUlJSQJAkpKSzB0KEZFZpaeny7lz5yQ9Pd3coZQaCQkJ4u7uLgBk1qxZkpOTY1C/f/9+uXPnjoiIzJw5UwBIYGCg3Lp1S99Go9HIa6+9JgBk8uTJBv07deokAMTe3l7279+vL09NTZX27dsLAFm6dKlBn/DwcAEgu3btMhnz1KlTBYAAkBkzZhjVHzhwQKysrKR69epy9OhRg7olS5YIAGnTpk3BHw4VO/4bJCLKVZQcjTPPRERUamnT0vJ+aTSFb5uR8fRt09PzbpvH7O7TWLJkCe7du4eQkBBMmDBBP3Or065dO1SuXBnZ2dmYOXMmHBwcsHr1alSt+vfNHm1tbbFgwQJUrVoVixcvhlarNdrPO++8g3bt2unf29vb49133wWQO4P8NJ577jlMnDjRqPzzzz9HTk4OvvnmG7Ro0cKg7rXXXkOPHj1w6NAhnDhx4qn2S0REVJJ4wzAiIiq1LjZvkWedQ6eOqPl//6d/f8m/AySPZNa+VSt4rfxO//6v519ATmKiybbqRo3gvW6t/v2Vl7oj6+ZNk21tfX3gs3lzvsdQWDt37gQAjBgxIt92x48fx/379/Hiiy+iSpUqRvV2dnZo0aIFfv31V1y+fBl169Y1qO/SpYtRHz8/PwDArVu3nir27t27Gy3H02q1+P3332Fvb4/g4GCT/QICArBp0yYcPnwYzZo1e6p9ExERlRQmzxZOm5GBq4MGAwC8vl8JpVpt5oiIiCzT9evXAUB/nXFedDfr2rFjR4HXj92/f98oea5Ro4ZRuwoVKgAANE/M5hdWzZo1Te47JSUFQO6MeEFxEhFR+ZORnYGIrREAgOUhy6G2Ltu5BpNnS6fVIuPMGf3PRESlSd3jx/KutDJ8IoDfvj/zbvvEEmjf33cWum3tXzcDIqbbmuHmJ7ql2L6+vvD398+3raurq1HZk8vBi4PaxBevujgdHR3Rp0+ffPs3bNiw2GMiIiLz04oWZxPO6n8u65g8ExFRqaW0tzd/Wzu7Qrf9Jzw9PXHhwgXExMTgueeey7Odbua4Xr16WL58eYnE9jTc3NygVquhVCoRGRlZPu6ySkREFo03DCMiIioFXnjhBQDA4sWL823XqlUrODs7Izo6Gg8ePHimMemWW2dnZxe5r7W1NQIDA5GcnIzff/+9uEMjIiIqcUyeiYiISoHhw4fDzc0NW7Zswbx58yBPLBU/ePAg7t69C5VKhUmTJuHRo0fo3bu3/tnPj4uPj8fKlSv/cUzVqlUDAFy8ePGp+r///vtQKpUYOnQodu/ebVSfkpKCZcuW5flMaiIiotKEy7aJiIhKgUqVKmHt2rXo0aMH3nnnHXz55Zdo1aoV0tPTcf78efz11184ceIEKleujClTpuDChQtYuXIl6tevj2bNmsHb2xuZmZm4ePEizp07h8aNG2Pw4MH/KKaXX34Z06dPx4QJE7Bjxw64ubkBAGbMmGHyeuondejQAQsXLsSYMWMQFBSERo0awc/PDzY2NoiLi8PJkyeh0WjQu3dv2JXQ8ngiIqKnxZlnIiKiUiIwMBCnTp3CyJEjISLYuHEj9u3bB2dnZ0yfPl1/J26lUonvvvsOv/zyC1588UXExsZi/fr1+PPPP6FWqzFx4kQsW7bsH8fTokULfP/992jQoAG2b9+OpUuXYunSpXj06FGhtzFy5EgcPXoU4eHhePToETZv3oxt27YhJSUFAwcOxObNm+Hs7PyPYyUiInrWFPLkujB6ZpKTk+Hs7IykpCQ4OTmZOxwAgDYtDX89n3udne/vO4t0Ex0ioqeVkZGB2NhYeHt7m7xTMxE9W/w3SEQlIS0rDSHrQwAAW/tshb1N6cs1ipKjcdm2hVPa28PvwH5zh0FEREREROWMvY099gzYY+4wig2XbRMREREREREVgMkzERERERERUQGYPFs4bUYGrg4egquDh0CbkWHucIiIiIiIqJzIyM7A0K1DMXTrUGRkl/1cg9c8WzqtFmlHjuh/JiIiIiIiKg5a0eLonaP6n8s6zjwTERERERERFYDJMxEREREREVEBmDwTEZHZiIi5QyCySPy3R0RUdEyeiYioxFlZWQEAsrKyzBwJkWXSaDQAAGtr3v6GiKiwmDwTEVGJs7GxgUqlQlJSEmfAiEpYTk4OHjx4AAcHBybPRERFwN+YBIWdnblDICIL5Obmhvj4eNy4cQPOzs6wsbGBQqEwd1hE5ZKIICcnB+np6UhKSoJWq4WHh4e5wyIiC2BnXX5yDYXwK/8Sk5ycDGdnZyQlJcHJycnc4RARmV1ycjLu37+vX0JKRM+WlZUV7O3tUblyZdja2po7HCIisytKjsaZZyIiMhsnJyc4OTkhKysLOTk55g6HqFxTKpVc4UFE9A8weSYiIrOzsbGBjY2NucMgIiIiyhNvGGbhtBoNro0YgWsjRkDLZZNERERERFRMNDkajN45GqN3joYmp+znGpx5tnQ5OUiN3qP/mYiIiIiIqDjkaHOwN36v/mdYmTmgf4gzz0REREREREQFYPJMREREREREVAAmz0REREREREQFYPJMREREREREVAAmz0REREREREQF4N22S5CIAACSk5PNHMnftGlpSPnfXbaTk5OhzM42c0RERERERFQepGWlISf971wj26b05Rq63EyXq+VHIYVpRcXixo0b8PT0NHcYRERERERE9Jjr16+jRo0a+bZh8lyCtFotbt68iQoVKkChUJglhuTkZHh6euL69etwcnIySwxU+nBckCkcF/QkjgkyheOCTOG4IFNK47gQETx69AjVqlWDUpn/Vc1ctl2ClEplgd9mlBQnJ6dSM2Cp9OC4IFM4LuhJHBNkCscFmcJxQaaUtnHh7OxcqHa8YRgRERERERFRAZg8ExERERERERWAybOFUalUmDp1KlQqlblDoVKE44JM4bigJ3FMkCkcF2QKxwWZUtbHBW8YRkRERERERFQAzjwTERERERERFYDJMxEREREREVEBmDxbiPT0dHz44Yfw8/ODWq1GtWrVMGzYMMTHx5s7NCqkY8eO4fPPP0fv3r1Ro0YNKBSKQj0vfPny5WjdujUcHR1RqVIldOvWDfv378+3z759+9CtWzdUqlQJjo6OaN26Nb777rt8+9y4cQNDhw5FtWrVoFar4efnh6lTpyIjI6NIx0mFl5aWho0bN+K1115D3bp1oVar4eDggCZNmmD69OlISUnJsy/HRfk2Z84c9O7dG3Xq1IGzszNUKhW8vLwwZMgQ/Pe//82zH8eF5UhISEDlypWhUCjg6+ubb1uOi/IrMDBQ//8Tpl5bt2412Y9jwjLcu3cPEyZMQN26dWFnZ4dKlSqhefPmmDhxosn2UVFR6NSpk/4xVIGBgfj111/z3cfZs2fRr18/uLu7w87ODs899xzmzZsHrVabZ5/ExESMGzcOXl5e+r9vb7/9Nh4+fPhPDrdwhMq99PR0adu2rQAQDw8P6d+/v7Ru3VoAiLu7u8TExJg7RCqEnj17CgCjV37GjRsnAMTOzk569uwpwcHBYm1tLVZWVrJhwwaTfdatWydWVlaiUCikU6dO0qdPH3FxcREAMn78eJN9Ll++LG5ubgJAGjVqJP3795fatWsLAPH395eMjIx/evhkwrfffqsfB/Xr15d+/fpJcHCwVKhQQQBIvXr15M6dO0b9OC7KP1dXV1Gr1dK6dWvp1auX9OrVS/z8/ASA2NjYSFRUlFEfjgvLEh4eLgqFQgCIj49Pnu04Lsq3Tp06CQDp06ePhIeHG71Onz5t1IdjwjIcPXpUXF1dBYA0bNhQXnnlFenatat4eXmJlZWVUfu5c+cKALG2tpaQkBDp2bOn2NnZCQBZsGCByX3s379f36Z169bSv39/qVq1qgCQfv36iVarNepz79498fX1FQBSu3Zt6d+/vzRs2FAAiJ+fnyQkJBT7Z/E4Js8W4P333xcA0q5dO3n06JG+fPbs2QJAOnXqZL7gqNA+//xz+eCDD2TTpk1y69YtUalU+SbPO3bsEADi6uoqly5d0pfv379fbG1txcXFRRITEw36JCQkiJOTkwCQ9evX68tv376t/0W1a9cuo335+/sLABk7dqy+LCsrS3r16iUAZOrUqU993JS35cuXyxtvvCHnzp0zKL9586Y0a9ZMAMirr75qUMdxYRn+/PNPSU9PNypfuHChAJAqVapIVlaWvpzjwrLs3LlTAMgbb7yRb/LMcVH+6ZLn2NjYQrXnmLAMd+/eFTc3N7G3t5dffvnFqP7QoUMG7y9cuCBWVlaiUqlk//79+vKLFy+Kq6urWFtby+XLlw36ZGZmire3twCQOXPm6MsfPXok7dq1EwASGRlptO+BAwcKAOndu7fB37G33npLAEh4ePhTHnXhMHku5zQajTg7OwsAOX78uFF948aNBYAcPXrUDNHRP1FQ8ty1a1cBIHPnzjWqGzt2rACQL774wqB8xowZAkB69uxp1Ofnn38WANK9e3eD8kOHDgkAqVy5stG3wLdv3xYbGxupWLGiwS84evb2798vAESlUolGo9GXc1yQj4+PAJBTp07pyzguLEdaWpr4+PhIgwYN5NKlS/kmzxwX5V9Rk2eOCcswatQoASALFy4sUvtx48YZ1c2ZM0cAyJgxYwzKf/rpJwEgTZo0Mepz7Ngx/YqDx928eVOUSqXY2trK7du3DeoyMjLE3d1drKysTK66Ky5Mnsu5P/74I98/jNOnT+e3d2VUfslzWlqavv769etG9Xv27DG56qBjx44CQFauXGnUR6PRiFqtFrVabTCj9eGHHwoAee2110zG0rlz5zy/VaZnJzU1Vb+k++bNmyLCcUG56tWrJwDk/PnzIsJxYWkmT54sCoVC9uzZI7GxsXn+PwLHhWUoSvLMMWEZ0tLSpEKFCuLg4CBpaWmF6lOzZk0BIHv37jWqu3btmgAQLy8vg/IhQ4YIAPnPf/5jcpu6JfqPj81ly5YJAHn++edN9hk2bFieM9bFhTcMK+dOnToFAGjevLnJel356dOnSywmevYuXrwIjUYDd3d31KhRw6g+r/Oe33ixtbVFo0aNkJGRgUuXLhWqT377omfrypUrAAAbGxtUqlQJAMcFAStXrsTFixdRp04d1KlTBwDHhSU5ffo0Zs+ejaFDhyIgICDfthwXlmXp0qUYPXo0xowZgy+//BLXrl0zasMxYRmOHj2KR48eoVmzZrCzs8OWLVvw7rvvYvTo0Zg3bx5u3rxp0P7hw4f68dKsWTOj7Xl6esLNzQ1Xr15FcnKyvvxpznFpGBdMnss53WA29Uvu8fKrV6+WWEz07BV03h0cHODi4oLExEQ8evQIAJCcnIykpKR8+5kaLxxjpdP8+fMBACEhIVCpVAA4LizRrFmzEBERgX79+qFRo0YYMmQIPDw8sGrVKlhZWQHguLAUWq0Ww4cPh4uLC2bOnFlge44Ly/Lxxx9j0aJFWLhwIcaNGwdfX1/85z//MWjDMWEZzp07BwCoXLkyQkND0a1bN8ydOxeLFi3CO++8A19fX6xatUrfXneuKlasCAcHB5PbLK5zXBrGBZPnck73qBp7e3uT9bpBrvslR+VDQecdMD73jz/WqCjjhWOs9Pntt9+wdOlS2NjYGPzPD8eF5dm2bRtWrFiBdevW4ezZs/Dy8sKqVavQokULfRuOC8uwYMECHDlyBLNmzYKrq2uB7TkuLEPHjh2xcuVKxMTEIC0tDRcvXsQnn3wCa2trfPjhh/ovYgGOCUuRmJgIANi0aRO2bt2KhQsX4u7du4iLi8OECROQnp6O8PBwnDx5EsDTjYvC9Cut44LJMxFROXLhwgUMGjQIIoJZs2ahSZMm5g6JzGjnzp0QESQmJmLPnj2oU6cOOnXqhE8++cTcoVEJunbtGv7973+jU6dOiIiIMHc4VIpMnz4dgwYNQu3atWFnZwc/Pz/861//wsaNGwEA06ZNQ3p6unmDpBKle75ydnY2pk+fjtGjR8Pd3R1eXl6YNWsW+vXrh6ysLMyaNcvMkZoHk+dyztHREQCQlpZmsj41NRUAUKFChRKLiZ69gs47YHzudX3y62dqvHCMlR7x8fEICQlBYmIi3n33XYwbN86gnuPCcrm4uCAgIAC//fYbWrRogQ8++ABHjhwBwHFhCd58801kZmbim2++KXQfjgvL1qVLF7Rs2RIPHz7EoUOHAHBMWIrHz9nQoUON6nVl0dHRBu2LMi4K06+0jgsmz+VczZo1AQA3btwwWa8r9/LyKrGY6Nkr6Lynpqbi4cOHqFixov4XjJOTE5ydnfPtZ2q8cIyVDg8ePECXLl1w9epVDB06FF988YVRG44LsrGxwSuvvAIRQVRUFACOC0uwefNm2NvbY+TIkQgMDNS/BgwYACD3izdd2e3btwFwXBD0NxW8desWAI4JS6H7TO3t7eHu7m5UX6tWLQDA3bt3Afx9rhITE/XJ65OK6xyXhnHB5Lmc0y3ZPH78uMl6XXnjxo1LLCZ69urWrQuVSoV79+4hPj7eqD6v857feMnKysKZM2egVqvh5+dXqD757YuKT0pKCrp27Ypz586hd+/e+Pbbb6FQKIzacVwQALi5uQEA7t27B4DjwlI8fPgQ0dHRBi/djGJGRoa+LCMjAwDHBf197avuOlKOCcugu2N2eno6NBqNUf2DBw8A/D0L7OLiok9qT5w4YdT++vXruH//Pry8vODk5KQvf5pzXBrGBZPncs7f3x/Ozs6IiYnRX9j/uHXr1gEAXn755RKOjJ4lOzs7dO7cGQCwdu1ao/q8zvtLL71kUP+4zZs3IyMjAy+88ALUarVRn6ioKKNfsnfu3MHevXtRsWJF+Pv7/4MjorxoNBr07NkThw8fRnBwsMFdlJ/EcUHA30vtfHx8AHBcWAIRMfmKjY0FkDsWdGW6WSWOC8t279497N27F8Dfj//hmLAMNWvWRJMmTSAi+r8Xj9OVPf5YqvzO8dOMixMnTuDKlSto1KiR/ncSkPsEEaVSib179+pnvnU0Gg2ioqJgZWWFbt26FeZQn84ze4I0lRrvv/++AJD27dtLSkqKvnz27NkmH2ZPZYNKpZL8/gnv2LFDAIirq6tcunRJX75//35RqVTi4uIiiYmJBn0SEhLEyclJAMj69ev15Xfu3BFfX18BILt27TLal7+/vwCQcePG6cuysrKkd+/eAkCmTp36tIdJ+cjOzpZevXoJAAkICJDU1NQC+3BclH9//vmnbNmyRXJycgzKMzMz5csvvxSlUil2dnZy7do1fR3HhWWKjY0VAOLj42OynuOifNu3b59s2LBBsrOzDcpjY2P156NHjx4GdRwTluGHH34QAPLcc8/JzZs39eUnTpyQSpUqCQBZs2aNvvzChQtiZWUlKpVKDhw4oC+/dOmSuLq6irW1tVy+fNlgH5mZmeLt7S0AZM6cOfrylJQUadeunQCQyMhIo9gGDhwoAKRPnz6SlZWlLx87dqwAkPDw8GL4BPLG5NkCpKenS5s2bQSAeHh4SP/+/fXv3d3dJSYmxtwhUiFs3rxZ2rRpo38pFAoBYFC2efNmgz7jxo0TAGJvby89e/aUrl27irW1tVhZWcmGDRtM7mfdunWiVCpFoVBIUFCQ9O3bV1xcXASAvPvuuyb76H456n7RvvLKK1K7dm39lzYZGRnF/XGQiMybN08ACADp1auXhIeHm3zdu3fPoB/HRfkWGRkpAMTNzU2Cg4MlLCxMunTpIh4eHgJA1Gq1/PTTT0b9OC4sT0HJswjHRXmm+11RtWpV6datm4SFhYm/v7+o1WoBIA0bNpQ7d+4Y9eOYsAzh4eECQFxcXKRbt24SFBSkn7h5/fXXjdrPmTNHAIi1tbV07dpVevbsKXZ2dgJAvvzyS5P72Ldvn75NmzZtpH///vq/VX379hWtVmvU5969e+Lj46P/3fXKK69Io0aNBIDUqVNHEhISiv2zeByTZwuRlpYmH3zwgfj4+Iitra1UrVpVIiIi5Pr16+YOjQpJ90cuv5epb+giIyOlRYsWYm9vLy4uLhISEiL79u3Ld19//vmnhISEiIuLi9jb20vLli1l+fLl+fa5du2aRERESNWqVcXW1lZ8fX3lgw8+kPT09H9y2JSPqVOnFjgmAEhsbKxRX46L8uvKlSvyr3/9S/z9/cXDw0NsbGzEwcFBGjZsKG+99ZbRt/+P47iwLIVJnkU4Lsqrc+fOyahRo6R58+bi7u4u1tbW4uzsLG3btpXZs2dLWlpann05Jso/rVYrixcv1p9nBwcHadeuXb7nbNOmTRIQECCOjo7i6OgoAQEBEhUVle9+zpw5I3369BFXV1dRq9XSsGFDmTNnjtHqqcclJCTIW2+9JZ6enmJrayuenp4yduxYo1UPz4JCRKRQ67uJiIiIiIiILBRvGEZERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERUYhQKRb6vwMBAc4dIz9CqVavQokUL2NvbQ6FQoFatWvq6mJgY9OrVC25ublAqlVAoFNi9e7fZYi2Kv/76C7a2tpg4caJBeURERJGOY9q0aVAoFJg2bVq+7WrVqgWFQoG4uLinivfWrVuws7PD6NGjn6o/EZGlsjZ3AEREZHnCw8NNlterV6+EI6GScuTIEQwaNAhqtRpdunSBi4sL3NzcAABarRZ9+/bFyZMn0bZtW9SpUwdKpRJVq1Z95nFNmzYNH330ESIjIxEREfFU23jvvfdga2uLSZMmFW9wz4iHhwfeeOMNfP3113j77bfh5+dn7pCIiMoEJs9ERFTili9fbu4QqIRFRUVBq9ViwYIFGDZsmEFdXFwcTp48iYCAAOzZs8dMET6d48ePY926dRg7dizc3d3NHU6hTZo0CV999RU++OAD/PTTT+YOh4ioTOCybSIiInrmbty4AQCoXbt2kepKu0WLFgEAhgwZYuZIiqZ69eoICgrChg0bcOfOHXOHQ0RUJjB5JiKiUufxa0W3bduGoKAguLi4QKFQ4OHDh/p2W7duxUsvvQR3d3eoVCrUrl0b7777LhISEkxu98GDBxgzZgyqVasGtVqNBg0aYP78+RARo2twgb+vQc1rplx37akp58+fR0REBDw9PaFSqVClShUMGDAAZ8+eNWq7fPly/bWu165dQ1hYGNzd3WFnZ4eWLVsiKioqz8/q/PnzeO2111CrVi2oVCpUrlwZ/v7++OKLL5CdnQ0AaNSoERQKBS5evGhyG9evX4eVlRW8vb0hInnu63EJCQmYOHEi6tSpA7VajUqVKiEkJATbt283eWyRkZEAgKCgIP017rq6Tp06AQBWrFhh8vr3/fv3IzQ0FF5eXlCpVKhatSpat26NKVOmICUlxSi2Q4cOoV+/fvDw8ICtrS1q1KiB4cOH49q1awbtatWqhY8++ggAMHToUIPr7wtznXJKSgpWr16NOnXqoEWLFoX63AAgMzMT/fr1g0KhQO/evaHRaArdNy+BgYEF3lPgyWukw8LCkJWVxZUgRESFxGXbRERUav34449YsmQJWrZsia5duyImJkafrE6ZMgUzZsyAra0tWrVqBQ8PD5w6dQpz587Fpk2bsG/fPlSpUkW/rcTERHTo0AHnz59H1apV0bNnTzx48AATJkzAX3/9Vaxxb9y4EQMGDIBGo0HTpk3Rtm1bXL9+HWvWrEFUVBS2bNmCjh07GvWLi4tDq1atUKFCBTz//PO4du0aDhw4gNDQUGzZsgVdunQxaL927VoMHjwYGo0G9evXR69evZCUlISzZ89i4sSJGD58OFxcXDBixAiMHTsWS5YswaxZs4z2u2zZMmi1WgwfPjzPLwMeFx8fj44dO+LKlSuoWbMmQkNDce/ePezcuRPbtm3DnDlz8M477wAAfH19ER4ejj///BMxMTEIDg7WX8usq7t9+za2bdsGHx8fdOjQAcDf179HRUUhNDQUIoLWrVujffv2ePjwIS5fvowZM2Zg5MiRcHR01Mf29ddf46233gIAtGrVCgEBAbh48SKWLl2KTZs2ITo6GvXr1wcA9O3bFzt37sSpU6fg7+8PX19f/XYKc711dHQ0UlJSinSju5SUFPTq1Qs7d+7EsGHDsHjxYlhZWRW6f15CQkKMvvzR7W/9+vUAYLQfXdy//vorJk+e/I9jICIq94SIiKiEAJDC/OkJDw/Xt129erVR/Zo1awSANGrUSC5fvqwv12q18uGHHwoAeeWVVwz6jBw5UgBISEiIpKam6ssPHTokjo6OAkC8vLwM+kydOlUASGRkpMk4vby8jI4nNjZWHBwcxNHRUXbs2GFQt2XLFrGxsRFPT0/RaDT68sjISP3xjh8/XnJycvR1c+fOFQASEBBgsK1Lly6JWq0Wa2tr+eGHHwzqtFqtbNu2TTIyMkRE5OHDh2Jvby/u7u4G+xURycnJkZo1a4qVlZXEx8ebPM4nde/eXQBIWFiYwfb27t0r9vb2YmVlJSdOnDDoozunu3btMtrerl27BICEh4cb1XXs2FEAyLp164zqDh8+LMnJyfr3Bw4cECsrK6levbocPXrUoO2SJUsEgLRp08agvKBznJ/JkycLAFm8eLHJ+ieP+f79+9K6dWsBIBMnTjRqr4tl6tSp+e5XN+5iY2PzbafVaiU0NFQAyOjRo022cXNzE5VKJenp6flui4iIRLhsm4iISlxhl5W+9NJLeOWVV4z6f/LJJwByH330+Gyhbulz06ZNsW7dOty/fx8AkJqaihUrVkCpVOKrr76Cvb29vk/r1q3x5ptvFtuxzZs3D6mpqfjss8/wwgsvGNSFhIRg1KhRuH79On799Vejvt7e3vj000+hVP7953nMmDGoWLEiDh48iMzMTH353LlzkZGRgeHDhyMsLMxgOwqFAl26dIFKpQIAODs7Y8CAAbh37x5++eUXg7bbt2/HtWvX8NJLL6FatWoFHt+VK1ewefNmODo6YsGCBbC1tdXXdejQASNHjkROTg4WLlxY4LYK4969ewBg9FkC0M/S63z++efIycnBN998Y7SM+rXXXkOPHj1w6NAhnDhxolhiO336NACgbt26BbbVzdYfPnwYn332GWbOnJln248++ijf5ddXr14tVHwffPABNm7ciKCgIMyfP99km7p160Kj0eD8+fOF2iYRkSVj8kxERCUuPDzc5Ovx5bcA0KNHD6O+d+/exalTp1CnTh00atTIqF6hUMDf3x85OTk4duwYAODYsWNIT09Hy5Yt4ePjY9Tn1VdfLaYjg/6a3969e5usDwgIAAAcPnzYqC4wMNAgGQUAa2treHt7Iysry+Ba7p07dwIARowYUai4Ro4cCQD49ttvDcp17994441CbefPP/8EkPtFQKVKlYzqBw8eDADYu3dvobZXEF0SPHjwYBw5cgRardZkO61Wi99//x329vYIDg422Sa/z/5p3L17FwBQsWLFfNtdvnwZ/v7+uHDhAhYvXowpU6bk275JkyZ5/hsJDw+Hg4NDgbGtXr0an3zyCXx8fLBu3TpYW5u+Uk93DnVfUhARUd54zTMREZW4wt6gqGbNmkZlutnpy5cvF3h9rm7m+ebNmwAALy8vk+1MXSv6tHTxVa9evVCxPa5GjRom2+pmVx+/sdT169cBwOSXAaa0atUKzZs3x86dOxEbGwtvb2/cuXMHUVFRqFGjBkJCQgq1Hd1nmddnpiuPj48v1PYK8umnn+K///0voqKiEBUVhYoVK6JDhw7o0aOH/rnRQO7nqbt52JNfQDzJ1Gf/NJKSkgDAYPbblNGjRyM7OxszZszA66+/XuB2Q0NDMW3atDzrd+/ejdTU1Dzrjx07hmHDhqFChQrYtGmTyS85dJycnADA4EZ8RERkGpNnIiIqtXSJ0eN0M49Vq1bNc4ZRJ69kubiYmgXVlYWHh+fbt02bNkZljy/XfhZGjhyJN954A0uXLsXHH3+MFStWICsrC8OGDSuWm1YBKNQNx4rC09MTR48exR9//IHNmzcjOjpan0jPnDkTBw4cgKurq/5zd3R0RJ8+ffLdZsOGDYslNmdnZwDAo0eP8m33yiuv4Mcff8TcuXPRo0cP/c3QnoXbt2+jZ8+e0Gg0WLNmDRo0aJBve90XAC4uLs8sJiKi8oLJMxERlSm62Vk3N7dCz2B7eHgAQJ7XiuZVrpvBNPU4pJycHNy+fdtkfDExMZg9ezZcXV0LFd/T8PT0xOXLlxETE4OmTZsWqk9YWBgmTJiAyMhITJs2DUuWLIFSqcRrr71W6P3qrovO6zMr7Mx7UVhbW6NLly76u41fvXoVw4YNwx9//IEZM2Zg5syZcHNzg1qthlKpRGRkZLEn8aZUrlwZQO4j0PIzfPhw+Pv7Y/To0ejcuTN2794NPz+/Yo9Ho9EgNDQU8fHxmDFjBrp3715gn8TERACAu7t7scdDRFTe8JpnIiIqU2rUqIF69erh3LlzuHTpUqH6tGjRAnZ2djh27BiuXLliVL969WqT/XRJt6n97Nq1C1lZWUblL774IgBgw4YNhYrtaeluoLV48eJC93FwcMCgQYNw8+ZNTJo0CZcvX0ZwcLDJ5fF50T1KauvWrSaX+n7//fcA/r6++Fnw8vLSP1rpzJkzAHIT7MDAQCQnJ+P3338v9LZ0X5DonoldFE2aNAGAPJ+f/bhRo0bhq6++wq1btxAUFFTsj0cDgNdffx2HDh3CoEGDMGnSpEL1uXDhAlQqlf7xXURElDcmz0REVOZ88MEH0Gq16NOnD06ePGlUn5CQYHBjLEdHRwwePBg5OTl46623kJ6erq87evQovvrqK5P70T2L+fvvvze4E3hsbCzGjh1rss/48eNhZ2eHCRMm4Oeffzaq12g0WLduHW7cuFGYQ83T22+/DbVajW+//RY//fSTQZ2IYMeOHQbXSOvobhw2d+5cACjUNbiPq127Nl566SU8evQI48aNM/gC4cCBA1i0aBGsrKyK7Q7mc+fONTnD/9tvvwHInYHXef/996FUKjF06FDs3r3bqE9KSgqWLVtmcP51M+mFSYCfpPuC4MiRI4Vq/+abb2L+/Pm4efMmgoKCEBMTU+R95mXmzJlYuXIl2rRpY3RTuLzExMQgISEBrVu3NnmJBBERGeKybSIiKnPCwsJw9uxZfPrpp2jRogWaNm0KHx8fiAhiYmJw+vRpODo6GiSGn332GaKjo/Hbb7/Bx8cHHTt2RGJiIv744w+MGDHC5KOVfHx8MGTIEHz33Xdo2rQpOnbsiLS0NBw8eBDdunVDWlqa0fJlX19frFq1CmFhYejTpw98fX1Rv359ODg4ID4+HsePH0dqaipOnDiR5w3CCsPPzw+RkZEYMmQIBgwYgOnTp6Nx48ZISkrCmTNncP36dSQmJuofV6Xz3HPPoX379ti/fz+qVq2Kl19+ucj7/r//+z8EBATgu+++Q3R0NNq1a4d79+5h9+7dyMnJwezZswu9lLwgH330ESZMmIAmTZqgTp06EBGcOnUKly5dQqVKlTBhwgR92w4dOmDhwoUYM2YMgoKC0KhRI/j5+cHGxgZxcXE4efIkNBoNevfuDTs7OwBAly5doFarMXfuXJw5cwbVqlWDQqHAxIkTC3wEVceOHeHo6GgyUc/L2LFjodVq8c477yAoKAjR0dHw9vZ+qs/mce+99x6A3Dt/674gedIXX3wBNzc3/Xtd3C+99NI/3j8RkUUw83OmiYjIggCQwvzpCQ8PFwCya9eufNtFR0dLv379pFq1amJjYyOurq7SuHFjGTNmjERHRxu1v3//vowaNUqqVq0qKpVK6tWrJ1988YVotVoBIF5eXkZ9NBqNTJkyRTw9PcXW1lZ8fHzk448/luzsbPHy8srzeP766y8ZPXq01KlTR9RqtVSoUEHq1q0rAwYMkDVr1ohGo9G3jYyMFAAydepUk9vq1KmTAJDY2FijulOnTsmgQYOkevXqYmNjI5UrVxZ/f3+ZPXu2ZGVlmdzev//9bwEg7733nsn6wrh//76MHz9efHx8xNbWVlxcXKRLly6ybds2k+3zO6e7du0SABIeHm5U991330lYWJjUrVtXKlSoIBUqVJAGDRrIu+++Kzdu3DC5rxMnTkh4eLh4eXnpY2vYsKEMGzZMNm/eLFqt1qD9tm3bxN/fXxwdHfVjtKCxp/P6668LADl8+HCRjvmLL77QjzndeZ06dWq+40BHN+4eHw+6uPN7PTl+OnfuLDY2NnL79u1CHSsRkaVTiIiUVKJORERUWikUCnh5eRkszy6PRAT169fHpUuX8Ndff6F27drmDqlMO3nyJJo1a4YxY8ZgwYIF5g6n0G7cuAEvLy/07dvXaNk/ERGZxmueiYiILMi6detw8eJFdOvWjYlzMWjatCn69euHZcuW4e7du+YOp9BmzZoFpVKJ6dOnmzsUIqIyg8kzERGRBRg+fDj69u2LwYMHw8rKCh999JG5Qyo3PvvsM2RlZWHmzJnmDqVQbt26hcWLF+P1118v8LpuIiL6G5dtExERofwv21YoFLC2tkadOnUwffp09O3b19whERERlSlMnomIiIiIiIgKwGXbRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAVg8kxERERERERUACbPRERERERERAX4f6h2km1oedAgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory created\n",
      "290\n",
      "20000\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse = 0.1\n",
    "gaussian_pulse_length = 4_800//4 # DEFINE HERE\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "\n",
    "\n",
    "for i in range (1):\n",
    "    res,freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 4)\n",
    "    excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "    centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,tolerance,repeat_measurement, freq_range,Photon_IF,plot = True)\n",
    "\n",
    "    # Define the two readout frequencies\n",
    "    freq_right = Photon_IF + centre_freq*1e3\n",
    "    \n",
    "    experiment_name='Spin_detection_electron_echo'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    ####QUA Ramsey\n",
    "    powerdBm = 5.5+10*np.log10((Spin_const_amp/0.15)**2)\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 80000\n",
    "    #duration_pio2=1500#2000\n",
    "    param_type=int\n",
    "    stream_I = False \n",
    "    N = 10 \n",
    "    \n",
    "    cycle_time_estimated=17 #in us\n",
    "    Integration_time=5000  #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    print(N_iterations)\n",
    "    Measurement_time=100 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    print(N_repetition)\n",
    "\n",
    "    t0 = 100_000//4\n",
    "    tf = 2_000_000//4 \n",
    "    dt = 60_000//4\n",
    "    times_ramsey = np.arange(t0, tf + dt//2, dt)*4 * 2 \n",
    "    ramsey_detuning=1e-6 #GHz 0.2e-4\n",
    "    n_step_duration=len(times_ramsey)\n",
    "\n",
    "    with program() as Spin_detection_Ramsey:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "\n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        t = declare(int)\n",
    "\n",
    "        meas_angle = declare(fixed)\n",
    "\n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "        \n",
    "        update_frequency(spin_element, freq_right)\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(10e6)//4,spin_element)####Wait for ten millisecond after each sequence\n",
    "            align()\n",
    "\n",
    "            with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "                with for_each_(meas_angle, [0, 0.25]):\n",
    "\n",
    "                    align()\n",
    "                    reset_frame(spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "                    \n",
    "                    wait(t, spin_element)\n",
    "                    \n",
    "                    frame_rotation_2pi(0.25, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "                    \n",
    "                    wait(t, spin_element)\n",
    "                    frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*t) + meas_angle, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "                    \n",
    "                    align()\n",
    "                    wait(int(waiting_time_spin/4), readout_element)\n",
    "                    align()\n",
    "\n",
    "                    I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                    with for_(i, 0, i < N_iterations, i + 1):\n",
    "\n",
    "                        with while_(I>I_threshold_reset):\n",
    "                            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                            play(qubit_pulse, qubit_element)\n",
    "                            align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                            I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                        wait(int(waiting_time/4), readout_element)\n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element)\n",
    "\n",
    "\n",
    "                        play(pump_pulse, pump_element) \n",
    "                        align(qubit_element,  readout_element,JPA_element, pump_element, spin_element)\n",
    "                        I = readout_block_I(readout_element, JPA_element, readout_pulse, JPA_pulse, I1, Q2, I)\n",
    "                        assign(click, I>I_threshold)\n",
    "\n",
    "                        save(click, p_stream)  # I is saved into I_stream\n",
    "\n",
    "        with stream_processing():\n",
    "\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(2).buffer(n_step_duration).map(FUNCTIONS.average(3)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_Ramsey)\n",
    "\n",
    "    res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d03dc58-cdc9-4689-9607-fe8ff44a9a0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "6277b749-b91a-487f-bb9e-fcd893c8439c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:53:15.764431Z",
     "iopub.status.busy": "2024-03-26T15:53:15.763431Z",
     "iopub.status.idle": "2024-03-26T15:53:17.015214Z",
     "shell.execute_reply": "2024-03-26T15:53:17.013226Z",
     "shell.execute_reply.started": "2024-03-26T15:53:15.764431Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255\n",
      "Change the magnetic field by 0.0000 mT\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for repeat_save in range(1):\n",
    "    # Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "    better_sleep(0)\n",
    "    \n",
    "    display.clear_output(wait=True)\n",
    "    fig,ax=plt.subplots(2,2, figsize=(10,10))\n",
    "    ax[0,0].clear()\n",
    "    ax[0,1].clear()\n",
    "    ax[1,0].clear()\n",
    "    ax[1,1].clear()\n",
    "    \n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    #time_hist=time_axis.reshape( int(time_axis.shape[0]/N),N).mean(-1)*1e-6\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    time_step = time_hist[1] - time_hist[0]\n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    #print('shape of click array:',click_array.shape)\n",
    "    \n",
    "    ax[1,1].plot(time_hist,click_array.mean(2).mean(1).mean(0),'-o')\n",
    "    ax[1,1].set_title('average number %s \\n cycle time %.1f us \\n BY = %.3f mT'%(average_number, cycle_time*1e3, BY))\n",
    "    ax[1,1].set_xlabel('time (ms)')\n",
    "    ax[1,1].set_ylabel('count rate (/ms)')\n",
    "    \n",
    "    guess=[10,0.07,0.11]\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist[1:], click_array.mean(2).mean(1).mean(0)[1:],guess)\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        ax[1,1].plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        ax[1,1].legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'decay.pdf')\n",
    "    except:\n",
    "        print('couldnt save cause your looking at the figure')\n",
    "    \n",
    "    skip_points=0\n",
    "    integration_index=10\n",
    "    excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "    \n",
    "    guess=[3000, ramsey_detuning*1e3 , np.max(excess[:])-np.min(excess[:]), excess[:].mean(),0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "    \n",
    "    label=\"%s, T_R= %.1f us \\n avg %d, BY = %.2f mT, BZ = %.3f mT \\n f= %.3f MHz, df= %.3f MHz\"%(time_stamp,result.x[0], average_number, BY, BZ,result.x[1], -(result.x[1]-ramsey_detuning*1e3))###minus sign on detuning is on purpose\n",
    "    \n",
    "    compensation = 12.5 * (result.x[1]*1e3 - ramsey_detuning*1e6) / 144e3\n",
    "    print('Change the magnetic field by %.4f mT'%(compensation))\n",
    "    \n",
    "    ax[0,0].plot(times_ramsey*1e-3,excess[:,0],'-o',color='red')\n",
    "    ax[0,0].plot(times_ramsey*1e-3,linear_background,color='red')\n",
    "    ax[0,0].set_title(label)\n",
    "    \n",
    "    ax[1,0].plot(times_ramsey*1e-3,excess[:,1],'-o',color='k')\n",
    "    ax[1,0].plot(times_ramsey*1e-3,linear_background,color='k')\n",
    "    \n",
    "    ax[0,1].plot(times_ramsey*1e-3,excess[:,0]-linear_background,'-o',color='red')\n",
    "    ax[0,1].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "    ax[0,1].plot(times_ramsey*1e-3,excess[:,1]-linear_background,'-o',color='black')\n",
    "    ax[0,1].plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "    ax[0,1].set_xlabel('Echo time (us)')\n",
    "    ax[0,1].legend()\n",
    "    \n",
    "    plot_guess=True\n",
    "    if plot_guess:\n",
    "        ax[0,0].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*guess)),label=label)\n",
    "        ax[1,0].plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*guess)),label=label)\n",
    "    \n",
    "    \n",
    "    plt.xlabel('time (us)')\n",
    "    plt.ylabel('click rate (ms-1)')\n",
    "    plt.grid()\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'Ramsey.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    \n",
    "    display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BY_mT':BY,\n",
    "                'BZ_mT':BZ,\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                'n_step_duration':n_step_duration\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24da094c-3e77-4185-8750-2e17985eb18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10,5),tight_layout=True)\n",
    "ax[0].set_title(r\"$T_{echo}$ = %.2f ms\"%(result.x[0]*1e-3))\n",
    "ax[0].plot(times_ramsey*1e-3,excess[:,0]-linear_background,'o',color='red')\n",
    "ax[0].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX',alpha=0.5)\n",
    "ax[0].plot(times_ramsey*1e-3,excess[:,1]-linear_background,'o',color='black')\n",
    "ax[0].plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY',alpha=0.5)\n",
    "ax[0].set_xlabel('Echo time (us)')\n",
    "ax[0].set_ylabel(\"Relative Count Rate (/ms)\")\n",
    "ax[0].grid()\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(time_hist,click_array.mean(2).mean(1).mean(0),'o')\n",
    "ax[1].grid()\n",
    "ax[1].set_ylim([0, max(click_array.mean(2).mean(1).mean(0))*1.1])\n",
    "ax[1].set_title('average number %s \\n cycle time %.1f us \\n BY = %.3f mT'%(average_number, cycle_time*1e3, BY))\n",
    "ax[1].set_xlabel('time (ms)')\n",
    "ax[1].set_ylabel('count rate (/ms)')\n",
    "\n",
    "guess=[10,0.07,0.11]\n",
    "popt, pcov = sp.curve_fit(exp_decay, time_hist[1:], click_array.mean(2).mean(1).mean(0)[1:],guess)\n",
    "label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "ax[1].set_title(label)\n",
    "ax[1].plot(time_hist,exp_decay(time_hist,*popt))\n",
    "ax[1].legend()\n",
    "print(directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "033beb49-77ec-460d-8f65-e2a38c1c5a78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "65501061-4b98-48ce-a7cb-0d7d3c11362a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-02T09:48:34.198725Z",
     "iopub.status.busy": "2023-10-02T09:48:34.198725Z",
     "iopub.status.idle": "2023-10-02T09:48:34.378762Z",
     "shell.execute_reply": "2023-10-02T09:48:34.378762Z",
     "shell.execute_reply.started": "2023-10-02T09:48:34.198725Z"
    }
   },
   "source": [
    "## Measure AC shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf824577-c60c-45a6-941e-5fc508cf2aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "def Complex_osc_decay(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T)**2-1j*(2*np.pi*f*t+2*np.pi*phi))+beta+1j*beta+(a+1j*a)*t\n",
    "\n",
    "def loss(arg):\n",
    "    data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "    return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse = 0.1\n",
    "gaussian_pulse_length = 4_800//4 # DEFINE HERE\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "ac_pump_amp = 0.1*0.2\n",
    "ac_pump_duration = 20_000//4\n",
    "freq_pump = Photon_IF+0.300e6\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "\n",
    "\n",
    "for i in range (1):\n",
    "    \n",
    "    res, freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 4)\n",
    "    excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "    centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,tolerance,repeat_measurement, freq_range,Photon_IF,plot = True)\n",
    "\n",
    "    # Define the two readout frequencies\n",
    "    freq_right = Photon_IF + centre_freq*1e3\n",
    "    \n",
    "    experiment_name='Spin_detection_electron_echo_ac_shift'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    ####QUA Ramsey\n",
    "    powerdBm = 5.5+10*np.log10((Spin_const_amp/0.15)**2)\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 100000\n",
    "    #duration_pio2=1500#2000\n",
    "    param_type=int\n",
    "    stream_I = False \n",
    "    N = 10 \n",
    "    \n",
    "    cycle_time_estimated=17 #in us\n",
    "    Integration_time=4000  #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    print(N_iterations)\n",
    "    Measurement_time=100 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    print(N_repetition)\n",
    "\n",
    "    t0 = 25_000//4\n",
    "    tf = 500_000//4 \n",
    "    dt = 25_000//4\n",
    "    times_ramsey = np.arange(t0, tf + dt//2, dt)*4 * 2 \n",
    "    ramsey_detuning=2.5e-6 #GHz 0.2e-4\n",
    "    n_step_duration=len(times_ramsey)\n",
    "    \n",
    "    \n",
    "\n",
    "    with program() as Spin_detection_Ramsey:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "\n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        t = declare(int)\n",
    "\n",
    "        meas_angle = declare(fixed)\n",
    "\n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "        \n",
    "        update_frequency(spin_sticky_element, freq_pump)\n",
    "        update_frequency(spin_element, freq_right)\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            align()\n",
    "            #wait(int(10e6)//4,spin_element)####Wait for ten millisecond after each sequence\n",
    "            align()\n",
    "            \n",
    "\n",
    "            with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "                with for_each_(meas_angle, [0, 0.25]):\n",
    "\n",
    "                    align()\n",
    "                    reset_frame(spin_element)\n",
    "                    play('ON',fsv_trigger)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "                    \n",
    "                    \n",
    "                    # wait(int(5000/4), spin_element)\n",
    "                    \n",
    "                    align()\n",
    "                    flattop_pulse(ac_pump_amp, t, switch_duration_extra)\n",
    "                    # wait(t-ac_pump_duration-switch_duration_extra-int(5000/4))\n",
    "                    \n",
    "                    align()\n",
    "                    frame_rotation_2pi(0.25, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "                    align()\n",
    "\n",
    "                    flattop_pulse(0, t, switch_duration_extra)\n",
    "                    \n",
    "                    align()\n",
    "                    frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*t) + meas_angle, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "                    \n",
    "                    align()\n",
    "                    wait(int(waiting_time_spin/4), readout_element)\n",
    "                    align()\n",
    "                    \n",
    "                    \n",
    "                    measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "\n",
    "\n",
    "        with stream_processing():\n",
    "\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(2).buffer(n_step_duration).map(FUNCTIONS.average(3)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "    res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94da080c-4879-44c1-b3c1-31ce974fa13a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for repeat_save in range(1):\n",
    "    # Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "    better_sleep(0)\n",
    "    \n",
    "    display.clear_output(wait=True)\n",
    "    fig,ax=plt.subplots(2,2, figsize=(10,10))\n",
    "    ax[0,0].clear()\n",
    "    ax[0,1].clear()\n",
    "    ax[1,0].clear()\n",
    "    ax[1,1].clear()\n",
    "    \n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    #time_hist=time_axis.reshape( int(time_axis.shape[0]/N),N).mean(-1)*1e-6\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    time_step = time_hist[1] - time_hist[0]\n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    #print('shape of click array:',click_array.shape)\n",
    "    \n",
    "    ax[1,1].plot(time_hist,click_array.mean(2).mean(1).mean(0),'-o')\n",
    "    ax[1,1].set_title('average number %s \\n cycle time %.1f us \\n BY = %.3f mT'%(average_number, cycle_time*1e3, BY))\n",
    "    ax[1,1].set_xlabel('time (ms)')\n",
    "    ax[1,1].set_ylabel('count rate (/ms)')\n",
    "    \n",
    "    guess=[10,0.07,0.11]\n",
    "    try:\n",
    "        popt, pcov = sp.curve_fit(exp_decay, time_hist[1:], click_array.mean(2).mean(1).mean(0)[1:],guess)\n",
    "        label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "        ax[1,1].plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "        ax[1,1].legend()\n",
    "    \n",
    "    except:\n",
    "        print('fit failed')\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'decay.pdf')\n",
    "    except:\n",
    "        print('couldnt save cause your looking at the figure')\n",
    "    \n",
    "    skip_points=0\n",
    "    integration_index=10\n",
    "    excess=(click_array.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "    \n",
    "    def loss(arg):\n",
    "        data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "        return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "    \n",
    "    guess=[2000, ramsey_detuning*1e3/2, np.max(excess[:])-np.min(excess[:]), excess[:].mean(),0,0]\n",
    "    result = sp.minimize(loss,guess)\n",
    "    linear_background = result.x[5] * times_ramsey*1e-3 + result.x[3]\n",
    "    times_ramsey_fit=np.linspace(times_ramsey[0],times_ramsey[-1],201)\n",
    "    linear_background_fit = result.x[5] * times_ramsey_fit*1e-3 + result.x[3]\n",
    "    \n",
    "    label=\"%s, T_R= %.1f us \\n avg %d, BY = %.2f mT, BZ = %.3f mT \\n f= %.3f kHz, df= %.3f kHz\"%(time_stamp,result.x[0], average_number, BY, BZ,result.x[1]*1e3, -(result.x[1]-ramsey_detuning*1e3)*1e3)###minus sign on detuning is on purpose\n",
    "    \n",
    "    compensation = 12.5 * (result.x[1]*1e3 - ramsey_detuning*1e6) / 144e3\n",
    "    print('Change the magnetic field by %.4f mT'%(compensation))\n",
    "    \n",
    "    ax[0,0].plot(times_ramsey*1e-3,excess[:,0],'-o',color='red')\n",
    "    ax[0,0].plot(times_ramsey*1e-3,linear_background,color='red')\n",
    "    ax[0,0].set_title(label)\n",
    "    \n",
    "    ax[1,0].plot(times_ramsey*1e-3,excess[:,1],'-o',color='k')\n",
    "    ax[1,0].plot(times_ramsey*1e-3,linear_background,color='k')\n",
    "    \n",
    "    ax[0,1].plot(times_ramsey*1e-3,excess[:,0]-linear_background,'-o',color='red')\n",
    "    ax[0,1].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "    ax[0,1].plot(times_ramsey*1e-3,excess[:,1]-linear_background,'-o',color='black')\n",
    "    ax[0,1].plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "    ax[0,1].set_xlabel('Echo time (us)')\n",
    "    ax[0,1].legend()\n",
    "    \n",
    "    plot_guess=False\n",
    "    if plot_guess:\n",
    "        ax[0,0].plot(times_ramsey_fit*1e-3,np.real(Complex_osc_decay(times_ramsey_fit*1e-3,*guess)),label=label)\n",
    "        ax[1,0].plot(times_ramsey_fit*1e-3,np.imag(Complex_osc_decay(times_ramsey_fit*1e-3,*guess)),label=label)\n",
    "    \n",
    "    \n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.ylabel('click rate (ms-1)')\n",
    "    plt.grid()\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'Ramsey.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    \n",
    "    display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': click_array,\n",
    "                'time_axis': time_axis,\n",
    "                'BY_mT':BY,\n",
    "                'BZ_mT':BZ,\n",
    "                'amplitude_pulse':amplitude_pulse,\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                'n_step_duration':n_step_duration\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c45b03a7-5fb8-4b72-b0bd-3d2db8d2d921",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "\n",
    "time_data=res.timestamp.fetch_all()\n",
    "time_axis=time_data-time_data[0]\n",
    "cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "#time_hist=time_axis.reshape( int(time_axis.shape[0]/N),N).mean(-1)*1e-6\n",
    "time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "time_step = time_hist[1] - time_hist[0]\n",
    "click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    \n",
    "fig = plt.figure(figsize=(10,8),tight_layout=True)\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "gs0 = gridspec.GridSpec(1, 2, figure=fig,wspace=0.25,hspace=0.3,width_ratios=[1,0.5])\n",
    "gs01 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[1],wspace=0,hspace=0.3)\n",
    "gs00 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs0[0],wspace=0,hspace=0.3)\n",
    "\n",
    "def loss(arg):\n",
    "    data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "    return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "\n",
    "axe_FFT = fig.add_subplot(gs01[1])\n",
    "tau = times_ramsey*1e-3\n",
    "freq=np.fft.fftfreq(len(tau),d=(tau[1]-tau[0]))\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,0]-linear_background)[1:len(tau)//2]),'r-o')\n",
    "axe_FFT.plot(freq[1:len(tau)//2],np.abs(np.fft.fft(excess[:,1]-linear_background)[1:len(tau)//2]),'k-o')\n",
    "axe_FFT.set(xlabel = 'freq (MHz)',ylabel = 'FFT')\n",
    "\n",
    "guess=[2000, ramsey_detuning*1e3/2, np.max(excess[:])-np.min(excess[:]), excess[:].mean(),0,0]\n",
    "result = sp.minimize(loss,guess)\n",
    "linear_background = result.x[5] * tau + result.x[3]\n",
    "tau_fit=np.linspace(tau[0],tau[-1],201)\n",
    "linear_background_fit = result.x[5] * tau_fit + result.x[3]\n",
    "\n",
    "label=\"%s, T_R= %.1f us \\n avg %s, BY = %s mT \\n f= %.3f MHz, df= %.3f MHz\"%(time_stamp,result.x[0], average_number, BY_field, result.x[1], result.x[1]-ramsey_detuning*1e3)\n",
    "\n",
    "axe_XX=fig.add_subplot(gs00[0])\n",
    "axe_XX.plot(tau,excess[:,0],'-o')\n",
    "axe_XX.plot(tau,linear_background)\n",
    "axe_XX.set_title(label)\n",
    "axe_XX.set(ylabel = 'excess clicks')\n",
    "\n",
    "axe_YY=fig.add_subplot(gs00[1])\n",
    "axe_YY.plot(tau,excess[:,1],'-o')\n",
    "axe_YY.plot(tau,linear_background)\n",
    "axe_YY.set(ylabel = 'excess clicks')\n",
    "\n",
    "axe_XX_YY=fig.add_subplot(gs00[2])\n",
    "axe_XX_YY.plot(tau,excess[:,0]-linear_background,'-o',color='red',alpha=0.5)\n",
    "axe_XX_YY.plot(tau_fit,np.real(Complex_osc_decay(tau_fit*1e-3,*result.x))-linear_background_fit,color='red',label='XX')\n",
    "axe_XX_YY.plot(tau,excess[:,1]-linear_background,'-o',color='black',alpha=0.5)\n",
    "axe_XX_YY.plot(tau_fit,np.imag(Complex_osc_decay(tau_fit*1e-3,*result.x))-linear_background_fit,color='black',label='XY')\n",
    "axe_XX_YY.legend()\n",
    "axe_XX_YY.set(xlabel = r'Ramsey time ($\\mu$s)', ylabel = 'excess clicks')\n",
    "\n",
    "guess=[1.79,0.07,0.11]\n",
    "try:\n",
    "    popt, pcov = sp.curve_fit(exp_decay, time_hist, click_array[:n,:,:,:].mean(2).mean(1).mean(0),guess)\n",
    "    label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "    axe_T1=fig.add_subplot(gs01[0])\n",
    "    axe_T1.plot(time_hist,click_array[:n,:,:,:].mean(2).mean(1).mean(0),'-o')\n",
    "    axe_T1.plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "    axe_T1.set(title = 'mean T1', xlabel = 'time (ms)', ylabel = r'rate $s^{-1}$')\n",
    "    axe_T1.legend()\n",
    "except:\n",
    "    print('fit failed')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7839c6-e695-47a8-8349-777c70ae4962",
   "metadata": {},
   "source": [
    "## AC Shift v power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e47715a-0968-4b47-a97b-0d169f019e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "def Complex_osc_decay(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T)**2-1j*(2*np.pi*f*t+2*np.pi*phi))+beta+1j*beta+(a+1j*a)*t\n",
    "\n",
    "def loss(arg):\n",
    "    data=np.array(excess[:,0])+1j*np.array(excess[:,1])\n",
    "    return np.sum(np.abs(data[:]-Complex_osc_decay(times_ramsey[:]*1e-3,*arg)))\n",
    "\n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse = 0.1\n",
    "gaussian_pulse_length = 4_800//4 # DEFINE HERE\n",
    "\n",
    "BX = MagnetX.get_supply_current()*FieldtoCurrentRatio_list[0]\n",
    "BY = MagnetY.get_supply_current()*FieldtoCurrentRatio_list[1]\n",
    "BZ = MagnetZ.get_supply_current()*FieldtoCurrentRatio_list[2]\n",
    "\n",
    "\n",
    "ac_amp_init=0\n",
    "ac_amp_fin =0.02\n",
    "ac_amp_step=0.001\n",
    "n_step_amp=int((ac_amp_fin -ac_amp_init)/ac_amp_step)+1\n",
    "ac_amps = np.linspace(ac_amp_init,ac_amp_fin,n_step_amp)\n",
    "\n",
    "freq_pump = Photon_IF+0.300e6\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "echo_time = int(5e5/4)\n",
    "\n",
    "for i in range (1):\n",
    "    \n",
    "    res, freq_range = run_freq_sweep(centre_freq,freq_step = 0.004e6,meas_time_secs = 4)\n",
    "    excess_array = analyse_freq_sweep(res,freq_range,repeat_measurement)\n",
    "    centre_freq = calc_new_centre_freq(excess_array,centre_freq,spacing,tolerance,repeat_measurement, freq_range,Photon_IF,plot = True)\n",
    "\n",
    "    # Define the two readout frequencies\n",
    "    freq_right = Photon_IF + centre_freq*1e3\n",
    "    \n",
    "    experiment_name='Spin_detection_ac_shift_v_power'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s_amp%.3f'%(experiment_name, amplitude_pulse)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    ####QUA Ramsey\n",
    "    powerdBm = 5.5+10*np.log10((Spin_const_amp/0.15)**2)\n",
    "    waiting_time= 1000\n",
    "    waiting_time_spin= 100000\n",
    "    #duration_pio2=1500#2000\n",
    "    param_type=int\n",
    "    stream_I = False \n",
    "    N = 10 \n",
    "    \n",
    "    cycle_time_estimated=17 #in us\n",
    "    Integration_time=4000  #in us\n",
    "    N_iterations = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "    print(N_iterations)\n",
    "    Measurement_time=100 * 1000000 # in seconds * us /s\n",
    "    N_repetition = Measurement_time//Integration_time\n",
    "    print(N_repetition)\n",
    "\n",
    "    n_points_detuning = 10\n",
    "\n",
    "\n",
    "    with program() as Spin_detection_Ramsey:\n",
    "        I = declare(fixed)\n",
    "        I1 = declare(fixed)\n",
    "        Q2 = declare(fixed)\n",
    "        click=declare(bool)\n",
    "        \n",
    "        \n",
    "        i = declare(int)\n",
    "        j = declare(int)\n",
    "        t = declare(int)\n",
    "        ac_amp = declare(fixed)\n",
    "\n",
    "        meas_angle = declare(fixed)\n",
    "\n",
    "        p_stream = declare_stream()\n",
    "        index_stream = declare_stream()\n",
    "        \n",
    "        update_frequency(spin_sticky_element, freq_pump)\n",
    "        update_frequency(spin_element, freq_right)\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "            save(j, index_stream)\n",
    "\n",
    "            align()\n",
    "            #wait(int(10e6)//4,spin_element)####Wait for ten millisecond after each sequence\n",
    "            align()\n",
    "            \n",
    "            with for_(ac_amp, ac_amp_init, ac_amp < ac_amp_fin+ac_amp_step//2, ac_amp + ac_amp_step):\n",
    "\n",
    "                with for_(t, 0, t < n_points_detuning, t + 1):\n",
    "\n",
    "                    with for_each_(meas_angle, [0, 0.25]):\n",
    "\n",
    "                        align()\n",
    "                        reset_frame(spin_element)\n",
    "                        play('ON',fsv_trigger)\n",
    "                        play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "                        wait(int(5e4/4), spin_element)\n",
    "\n",
    "                        align()\n",
    "                        flattop_pulse(ac_amp, echo_time, switch_duration_extra)\n",
    "                        wait(int(5e4/4), spin_sticky_element)\n",
    "\n",
    "                        align()\n",
    "                        frame_rotation_2pi(0.25, spin_element)\n",
    "                        play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "                        wait(int(5e4/4), spin_element)\n",
    "\n",
    "                        align()\n",
    "                        flattop_pulse(0, echo_time, switch_duration_extra)\n",
    "                        wait(int(5e4/4), spin_sticky_element)\n",
    "\n",
    "                        align()\n",
    "                        frame_rotation_2pi(Cast.mul_fixed_by_int(1/(2*np.pi), t) + meas_angle, spin_element)\n",
    "                        play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "\n",
    "                        align()\n",
    "                        wait(int(waiting_time_spin/4), readout_element)\n",
    "\n",
    "                        align()\n",
    "                        measure_SMPD(p_stream,N_iterations,waiting_time, accumulate=False)\n",
    "\n",
    "\n",
    "        with stream_processing():\n",
    "\n",
    "            p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(2).buffer(n_points_detuning).buffer(n_step_amp).map(FUNCTIONS.average(4)).save_all('clicks')\n",
    "            p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "            index_stream.save('interation')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "    \n",
    "    res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70bf8cf4-0525-446a-998d-e98fd1a22e77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8551b8da-7ca3-4d23-bf4f-7a1b32867bb4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def decay_oscillation(t,T,f,alpha,beta,phi,a):\n",
    "    return alpha/2*np.exp(-(t/T)**2)*np.cos(2*np.pi*f*t+2*np.pi*phi)+beta+a*t\n",
    "def fixed_oscillation(t, f, alpha, beta, phi):\n",
    "    return alpha/2 * np.cos(2*np.pi*(t*f + phi)) + beta\n",
    "\n",
    "dephase0 = []\n",
    "dephase1 = []\n",
    "for repeat_save in range(1):\n",
    "    # Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "    better_sleep(0)\n",
    "    \n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    \n",
    "    time_data=res.timestamp.fetch_all()\n",
    "    time_axis=time_data-time_data[0]\n",
    "    cycle_time=(time_axis[1:]-time_axis[:-1]).mean()*1e-6\n",
    "    #time_hist=time_axis.reshape( int(time_axis.shape[0]/N),N).mean(-1)*1e-6\n",
    "    time_hist=time_axis.reshape(N, int(time_axis.shape[0]/N)).mean(-1)*1e-6\n",
    "    time_step = time_hist[1] - time_hist[0]\n",
    "    click_array=np.array([sublist[0] for sublist in res.clicks.fetch_all()])/cycle_time\n",
    "    #print('shape of click array:',click_array.shape)\n",
    "    x_axis = np.arange(n_points_detuning)\n",
    "    \n",
    "    for ii, ac_amp in enumerate(ac_amps):\n",
    "        print(\"Now processing : %d\"%ii)\n",
    "        click_array_i = click_array[:,ii]\n",
    "        \n",
    "        fig,ax=plt.subplots(1,2, figsize=(15,4))\n",
    "        ax[1].plot(time_hist,click_array_i.mean(2).mean(1).mean(0),'-o')\n",
    "        ax[1].set_title('average number %s \\n cycle time %.1f us \\n BY = %.3f mT'%(average_number, cycle_time*1e3, BY))\n",
    "        ax[1].set_xlabel('time (ms)')\n",
    "        ax[1].set_ylabel('count rate (/ms)')\n",
    "\n",
    "        guess=[1,0.3,0.11]\n",
    "        try:\n",
    "            popt, pcov = sp.curve_fit(exp_decay, time_hist[1:], click_array_i.mean(2).mean(1).mean(0)[1:],guess)\n",
    "            label=\"$T_1$ = %.2f ms\"%(popt[0])\n",
    "            ax[1].plot(time_hist,exp_decay(time_hist,*popt),label=label)\n",
    "            ax[1].legend()\n",
    "        except:\n",
    "            print('fit failed')\n",
    "        try:\n",
    "            plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'decay.pdf')\n",
    "        except:\n",
    "            print('couldnt save cause your looking at the figure')\n",
    "\n",
    "        skip_points=0\n",
    "        integration_index=10\n",
    "        excess=(click_array_i.mean(0)[:,:,skip_points:integration_index].sum(-1))*time_step\n",
    "        \n",
    "        if ii==0:\n",
    "            guess0 = [1/n_points_detuning, excess.max() - excess.min(), excess.mean(),  0.5]\n",
    "            guess1 = [1/n_points_detuning, excess.max() - excess.min(), excess.mean(),  0.75]\n",
    "        else:\n",
    "            guess0 = popt0\n",
    "            guess1 = popt1\n",
    "            \n",
    "        bounds = np.array([\n",
    "            [1/n_points_detuning*0.5, 1/n_points_detuning*2],\n",
    "            [0,1],\n",
    "            [0,2],\n",
    "            [-np.inf,np.inf]\n",
    "        ])\n",
    "        try:\n",
    "            popt0, pcov0 = sp.curve_fit(fixed_oscillation, x_axis, excess[:,0], guess0, bounds=bounds.T)\n",
    "            popt1, pcov1 = sp.curve_fit(fixed_oscillation, x_axis, excess[:,1], guess1, bounds=bounds.T)\n",
    "        except Exception as e:\n",
    "            popt1 = guess\n",
    "            popt2 = guess\n",
    "            print(\"Error during fit : \", e)\n",
    "        \n",
    "        dephase0.append(popt0[-1])\n",
    "        dephase1.append(popt1[-1])\n",
    "        label0 = r\"$\\phi$ = %.3f\"%popt0[-1]\n",
    "        label1 = r\"$\\phi$ = %.3f\"%popt1[-1]\n",
    "        x_axis_fit = np.linspace(0, n_points_detuning-1, 201)\n",
    "\n",
    "        ax[0].plot(x_axis,excess[:,0],'-o',color='red')\n",
    "        ax[0].plot(x_axis_fit,fixed_oscillation(x_axis_fit, *popt0),'-',color='red', alpha=0.5, label=label0)\n",
    "        ax[0].plot(x_axis,excess[:,1],'-o',color='k')\n",
    "        ax[0].plot(x_axis_fit, fixed_oscillation(x_axis_fit, *popt1),'-',color='k', alpha=0.5, label=label1)\n",
    "        ax[0].set(\n",
    "            title = \"amplitude = %.3f\"%ac_amps[ii],\n",
    "            xlabel = \"Measurement step\",\n",
    "            ylabel = \"Count rate (/ms)\"\n",
    "        )\n",
    "        ax[0].legend()\n",
    "\n",
    "\n",
    "        try:\n",
    "            plt.savefig(directory+filename+'_'+str(np.round(BY,3))+'mT_'+'Ramsey.pdf')\n",
    "        except:\n",
    "            print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "        fullpath=directory+filename+'.hdf5'\n",
    "        datasets= {\n",
    "                    'click_array': click_array,\n",
    "                    'time_axis': time_axis,\n",
    "                    'BY_mT':BY,\n",
    "                    'BZ_mT':BZ,\n",
    "                    'amplitude_pulse':amplitude_pulse,\n",
    "                    'n_points_detuning':n_points_detuning,\n",
    "                    'ac_amps':ac_amps,\n",
    "                    \"echo_time\": echo_time,\n",
    "                    'n_step_duration':n_step_duration\n",
    "                    }\n",
    "\n",
    "        save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    \n",
    "    dephase0 = dephase0 - dephase0[0]\n",
    "    dephase1 = dephase1 - dephase1[0] + 0.25\n",
    "    plt.figure()\n",
    "    plt.plot(ac_amps**2, dephase0, 'o')\n",
    "    plt.plot(ac_amps**2, dephase1, 'o')\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"Detunned pulse amplitude ^ 2 (A.U.)\")\n",
    "    plt.ylabel(\"Dephasing (units of pi)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93ce3c50-7162-43d2-b056-e9be723b6052",
   "metadata": {},
   "outputs": [],
   "source": [
    "dephase0 = dephase0 - dephase0[0]\n",
    "dephase1 = dephase1 - dephase1[0]\n",
    "plt.figure()\n",
    "plt.plot(ac_amps**2, dephase0, 'o')\n",
    "plt.plot(ac_amps**2, dephase1, 'o')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Detunned pulse amplitude ^ 2 (A.U.)\")\n",
    "plt.ylabel(\"Dephasing (units of pi)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78085057-f12d-45b3-88b2-79feb44ceeac",
   "metadata": {},
   "outputs": [],
   "source": [
    "interpulse_delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f38c57c7-02a4-4359-bbcf-ba6971b03a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "interpulse_delay = (4*echo_time + 4*switch_duration_extra + 1e5) * 1e-6  # s\n",
    "phi_n0 = (dephase0 - dephase0[0])  # In units of pi!!\n",
    "phi_n1 = (dephase1 - dephase1[0])\n",
    "delta_phi = -(phi_n0+phi_n1)/2 / interpulse_delay                        # s-1\n",
    "\n",
    "kappa = 4.41e6            # s-1\n",
    "delta = 300e3 * 2 * np.pi # s-1\n",
    "T1 = 1.1e-3               # s\n",
    "\n",
    "OmegaR_05 = 50e3 * 2 * np.pi # Rabi frequency at 0.05 relative power. See notebook AC Stark shift\n",
    "\n",
    "g_0 = np.sqrt(kappa / T1) / 2            # s-1\n",
    "R = kappa * OmegaR_05 / (2 * g_0 * 0.05) # s-1\n",
    "epsilon = R * ac_amps                    \n",
    "\n",
    "chi = 2 * g_0 ** 2 / delta                # s-1\n",
    "nbar = epsilon**2 / (delta**2 + kappa**2/4) # photons\n",
    "\n",
    "vals =np.polyfit(nbar, delta_phi, 1)\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(nbar, delta_phi, 'o')\n",
    "plt.plot(nbar, np.polyval(vals, nbar))\n",
    "#plt.ylim([-0.2, 0.05])\n",
    "plt.grid()\n",
    "plt.title(r\"$\\chi_{fit}/2\\pi$ = %d Hz  |  $\\chi/2\\pi$ = $2g_0^2/2\\pi \\delta$= %d Hz\"%(vals[0]*1e3, chi/2/np.pi))\n",
    "plt.xlabel(r\"$\\bar{n}$\")\n",
    "plt.ylabel(r\"$\\Delta \\Phi / t$ (kHz)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2727b4ff-c7ff-4a38-aa41-6408c8179b29",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Sideband"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638e60f3-d815-44b0-ab92-311c4bca851c",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59214f1c-85f8-43b5-9eea-d570a96ff8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.800e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "\n",
    "n_step_freq = 21\n",
    "sideband_freqs = freq_sideband + np.linspace(-30e3, +30e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<1, m+1):\n",
    "                align()\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f81dadd5-0203-407e-884b-f0802fec6f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[1]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b144827-3141-4cda-9ccd-ade9c0eb276c",
   "metadata": {},
   "source": [
    "### vs amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba2ece6a-5e5f-4ce7-bcd1-d72cf4f4003b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "sideband_frequency_meas = 0.8e6\n",
    "rabi_amplitudes = np.linspace(0.1, 0.1, 1)\n",
    "\n",
    "for rabi_amplitude in rabi_amplitudes:\n",
    "    print(f'Measuring at {rabi_amplitude}')\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "    freq_electron = readout_freqs[-1]\n",
    "\n",
    "    ####################### Sideband Params #######################\n",
    "\n",
    "    freq_sideband = Photon_IF + centre_freq*1e3 + sideband_frequency_meas\n",
    "\n",
    "    #rabi_amplitude = 0.1\n",
    "    rabi_duration = int(120_000//4/rabi_amplitude*0.1)\n",
    "\n",
    "    n_step_freq = 21\n",
    "    sideband_freqs = freq_sideband + np.linspace(-15e3, +15e3, n_step_freq)\n",
    "    sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='sideband_spectro'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    ####################### Define preparation parameters #######################\n",
    "    prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "    ####################### Measurement time estimate #######################\n",
    "\n",
    "    N_repetition = 1e9\n",
    "\n",
    "    ####################### Run program #######################\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "            with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "                ################# Now play the Sideband sequence #################\n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(5e6//4))\n",
    "\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "\n",
    "                update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "\n",
    "    ########################## ANALYZE ###################################\n",
    "    better_sleep(3600)\n",
    "    \n",
    "    \n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    sideband_freqs = np.array(sideband_freqs)\n",
    "    x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "    xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "    ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans = kmeans)\n",
    "\n",
    "    p_data = pops[-1]\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: \n",
    "        ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "        sideband_frequency_meas = est[0]*1e3\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].legend()\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    plt.xlabel(\"Readout iteration\")\n",
    "    plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                '''\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                '''\n",
    "                'centre_freq': centre_freq,\n",
    "                'rabi_amplitude': rabi_amplitude,\n",
    "                'rabi_duration': rabi_duration,\n",
    "                'sideband_freqs': sideband_freqs,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "393d5e24-ec3c-4f2a-b89a-c07cd2d6669d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "sideband_frequency_meas = -0.784e6\n",
    "rabi_amplitudes = np.linspace(0.3, 0.4, 2)\n",
    "\n",
    "####################### sideband prep pulse params #######################\n",
    "freq_sb_blueprep = Photon_IF + centre_freq*1e3 + int(0.8e6)\n",
    "sb_pi_amplitude_blue_prep = 0.1\n",
    "sb_pi_duration_blue_prep = int(120_000//4)\n",
    "##########################################################################\n",
    "\n",
    "for rabi_amplitude in rabi_amplitudes:\n",
    "    print(f'Measuring at {rabi_amplitude}')\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "    freq_electron = readout_freqs[-1]\n",
    "\n",
    "    ####################### Sideband Params #######################\n",
    "\n",
    "    freq_sideband = Photon_IF + centre_freq*1e3 + sideband_frequency_meas\n",
    "\n",
    "    #rabi_amplitude = 0.1\n",
    "    rabi_duration = int(120_000//4/rabi_amplitude*0.1)\n",
    "\n",
    "    n_step_freq = 31\n",
    "    sideband_freqs = freq_sideband - np.linspace(-55e3, +5e3, n_step_freq)\n",
    "    sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='sideband_spectro'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    ####################### Define preparation parameters #######################\n",
    "    prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "    ####################### Measurement time estimate #######################\n",
    "\n",
    "    N_repetition = 1e9\n",
    "\n",
    "    ####################### Run program #######################\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "            with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                reset_frame(spin_sticky_element)\n",
    "                ################# Now prepare the opposite spin state #################\n",
    "                \n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                \n",
    "                # Extra unconditional sideband reset pulses on nuclear spin a should ensure any unwanted cross relaxation during readout is corrected for\n",
    "                update_frequency(spin_sticky_element, freq_sb_blueprep+delta_freq)\n",
    "                for i in range(10):\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                    flattop_pulse(sb_pi_amplitude_blue_prep, sb_pi_duration_blue_prep, switch_duration_extra)\n",
    "                wait(int(10e6//4))  \n",
    "\n",
    "                ################# Now play the Sideband sequence #################\n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(5e6//4))\n",
    "\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "\n",
    "                update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "\n",
    "    ########################## ANALYZE ###################################\n",
    "    better_sleep(3600)\n",
    "    \n",
    "    \n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    sideband_freqs = np.array(sideband_freqs)\n",
    "    x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "    xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "    ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans = kmeans)\n",
    "\n",
    "    p_data = pops[0]\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: \n",
    "        ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "        sideband_frequency_meas = est[0]*1e3\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].legend()\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    plt.xlabel(\"Readout iteration\")\n",
    "    plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                '''\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                '''\n",
    "                'centre_freq': centre_freq,\n",
    "                'rabi_amplitude': rabi_amplitude,\n",
    "                'rabi_duration': rabi_duration,\n",
    "                'sideband_freqs': sideband_freqs,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a09d94e-e9cc-407e-b099-c0bfcce53c49",
   "metadata": {},
   "source": [
    "### Red -> orange, sideband polarising Blue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31babd3-69ee-4d93-8851-dca1fefd7778",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.800e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "\n",
    "n_step_freq = 41\n",
    "sideband_freqs = freq_sideband + np.linspace(-40e3, +40e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin polarisation #################\n",
    "            # Run 3 rounds of sideband pumping at shifted frequencies in lieu of tested chirped pulses\n",
    "            sideband_pump_blue(centre_freq-10, delta_freq, enable_fsv_trigger = True)\n",
    "            sideband_pump_blue(centre_freq, delta_freq, enable_fsv_trigger = True)\n",
    "            sideband_pump_blue(centre_freq+10, delta_freq, enable_fsv_trigger = True)\n",
    "            #############################################################\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<1, m+1):\n",
    "                align()\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af3d451-f7bb-4476-bd43-2418a888ae96",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94821f4f-f0d5-4a2c-bcf4-05ea9c42d908",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[1]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de24abc-b972-4567-b7e1-0f7f9d6ea67f",
   "metadata": {},
   "source": [
    "### Red -> orange, sideband polarising Red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcc63e8e-fdf0-4c9a-bd0d-6776d60b3bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.800e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "\n",
    "n_step_freq = 41\n",
    "sideband_freqs = freq_sideband + np.linspace(-40e3, +40e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin polarisation #################\n",
    "            # Run 3 rounds of sideband pumping at shifted frequencies in lieu of tested chirped pulses\n",
    "            sideband_pump_red(centre_freq-10, delta_freq, enable_fsv_trigger = True)\n",
    "            sideband_pump_red(centre_freq, delta_freq, enable_fsv_trigger = True)\n",
    "            sideband_pump_red(centre_freq+10, delta_freq, enable_fsv_trigger = True)\n",
    "            #############################################################\n",
    "            \n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<1, m+1):\n",
    "                align()\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23a2b48-cf46-463c-a0b8-8881e29cefce",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(5*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[1]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f156bcdd-28c5-4604-ae8c-450b28c8781d",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ded7271-4baf-4e47-b5b9-6731770fe218",
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "993f9c1e-98b2-4081-85e6-c982e6bdf0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "rabi_amplitude = 0.1\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(800e3)\n",
    "\n",
    "rabi_durations = 1e3*(1+np.linspace(0,300,31))//4\n",
    "rabi_durations = [int(rabi_duration) for rabi_duration in rabi_durations]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_rabi'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    rabi_duration_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(rabi_duration_set, rabi_durations):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "             ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            play('ON',fsv_trigger)\n",
    "            update_frequency(spin_sticky_element, freq_sideband+delta_freq)\n",
    "            flattop_pulse(rabi_amplitude, rabi_duration_set, switch_duration_extra)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(rabi_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8a172a8-adbd-4fd1-91e4-76a5f8c8ce9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = 4*1e-3*np.array(rabi_durations)\n",
    "xlabel = \"Rabi durations [us]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [0.02,100,0.5,1,1*np.pi]\n",
    "y = pops[-1]\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  y)\n",
    "except Exception as e: \n",
    "    print(\"fit failed\", e)\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "    \n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "ax[1].plot(fine,data_fit)\n",
    "plt_label = \"$T_{hp} = %.2f(%i)$ ms \"%(T_half_period,1e2*T_err)+ r\"$T_\\pi = %.2f(%i)$ ms \"%(T_pi,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))+r\"$T_{\\pi/2} = %.2f(%i)$ ms\"%(T_pio2,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            \n",
    "            'rabi_durations': rabi_durations,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbd445ba-c0bd-4d33-8900-800d6a4216ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "112/8000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac27ed9-2d3f-4fcd-a16c-304af8144512",
   "metadata": {},
   "source": [
    "prepare_stream, click_acc, freq_electron+delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3# Raman Superduper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a019867-8ad1-4e58-95de-917930a32bc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-24T18:44:15.083776Z",
     "iopub.status.busy": "2024-01-24T18:44:15.082775Z",
     "iopub.status.idle": "2024-01-24T18:44:15.286779Z",
     "shell.execute_reply": "2024-01-24T18:44:15.284783Z",
     "shell.execute_reply.started": "2024-01-24T18:44:15.083776Z"
    }
   },
   "source": [
    "### vs amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "151095f9-6e01-403a-a7f5-35d9c1a0b8be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "sideband_frequency_meas = 0.8e6\n",
    "rabi_amplitudes = np.linspace(0.1, 0.4, 4)\n",
    "freq_fits = 1e3*np.array([799.80764263, 780.13891173, 740.95719916, 712.94286472])\n",
    "\n",
    "for rabi_amplitude, freq_fit in zip(rabi_amplitudes, freq_fits):\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "    freq_electron = readout_freqs[-1]\n",
    "\n",
    "    ####################### Sideband Params #######################\n",
    "\n",
    "    freq_sideband = Photon_IF + centre_freq*1e3 + freq_fit\n",
    "\n",
    "    rabi_durations = 1e3*(1+np.linspace(0,250,21))//4 * 0.1 / rabi_amplitude\n",
    "    rabi_durations = [int(rabi_duration) for rabi_duration in rabi_durations]\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='sideband_rabi'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    ####################### Define preparation parameters #######################\n",
    "\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "    N_repetition = 1e9\n",
    "\n",
    "    ####################### Run program #######################\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "\n",
    "        freq_set = declare(int)\n",
    "        rabi_duration_set = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "            with for_each_(rabi_duration_set, rabi_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "\n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                reset_frame(spin_sticky_element)\n",
    "\n",
    "                 ################# Now play the Sideband sequence #################\n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(5e6//4))\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                update_frequency(spin_sticky_element, freq_sideband+delta_freq)\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration_set, switch_duration_extra)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(rabi_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    x = 4*1e-3*np.array(rabi_durations)\n",
    "    xlabel = \"Rabi durations [us]\"\n",
    "\n",
    "    ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    guess = [0.02,100,0.5,1,1*np.pi]\n",
    "    y = pops[-1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  y)\n",
    "        T_half_period = 0.5/est[0]\n",
    "        T_min = 0.5/(est[0]+std[0])\n",
    "        T_max = 0.5/(est[0]-std[0])\n",
    "        T_err = T_half_period-T_min\n",
    "        T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "        T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "        T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "        T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "        plt_label = \"$T_{hp} = %.2f(%i)$ ms \"%(T_half_period,1e2*T_err)+ r\"$T_\\pi = %.2f(%i)$ ms \"%(T_pi,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))+r\"$T_{\\pi/2} = %.2f(%i)$ ms\"%(T_pio2,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))\n",
    "    \n",
    "    except Exception as e: \n",
    "        print(\"fit failed\", e)\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "        plt_label = \"fit failed\"\n",
    "\n",
    "\n",
    "    ax[1].plot(fine,data_fit)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'rabi_durations': rabi_durations,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b5855a0-a777-4695-8d13-a7196b0b9283",
   "metadata": {},
   "outputs": [],
   "source": [
    "array([-798.19304169, -786.74252085, -763.87977026, -728.01578446])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b4fe18c-2a22-416f-9d03-be5383c03f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "rabi_amplitudes = np.linspace(0.1, 0.4, 4)\n",
    "freq_fits = 1e3*np.array([-798.19304169, -786.74252085, -763.87977026, -728.01578446])\n",
    "\n",
    "####################### sideband prep pulse params #######################\n",
    "freq_sb_blueprep = Photon_IF + centre_freq*1e3 + int(0.8e6)\n",
    "sb_pi_amplitude_blue_prep = 0.1\n",
    "sb_pi_duration_blue_prep = int(120_000//4)\n",
    "##########################################################################\n",
    "\n",
    "for rabi_amplitude, freq_fit in zip(rabi_amplitudes, freq_fits):\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "    freq_electron = readout_freqs[-1]\n",
    "\n",
    "    ####################### Sideband Params #######################\n",
    "\n",
    "    freq_sideband = Photon_IF + centre_freq*1e3 + freq_fit\n",
    "\n",
    "    rabi_durations = 1e3*(1+np.linspace(0,500,21))//4 * 0.1 / rabi_amplitude\n",
    "    rabi_durations = [int(rabi_duration) for rabi_duration in rabi_durations]\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='sideband_rabi'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    ####################### Define preparation parameters #######################\n",
    "\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "    N_repetition = 1e9\n",
    "\n",
    "    ####################### Run program #######################\n",
    "\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "\n",
    "        freq_set = declare(int)\n",
    "        rabi_duration_set = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "            with for_each_(rabi_duration_set, rabi_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "\n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                \n",
    "                ################# Now prepare the opposite spin state #################\n",
    "                \n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                \n",
    "                # Extra unconditional sideband reset pulses on nuclear spin a should ensure any unwanted cross relaxation during readout is corrected for\n",
    "                update_frequency(spin_sticky_element, freq_sb_blueprep+delta_freq)\n",
    "                for i in range(10):\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                    flattop_pulse(sb_pi_amplitude_blue_prep, sb_pi_duration_blue_prep, switch_duration_extra)\n",
    "                wait(int(10e6//4))  \n",
    "                \n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                reset_frame(spin_sticky_element)\n",
    "\n",
    "                 ################# Now play the Sideband sequence #################\n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                wait(int(5e6//4))\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                update_frequency(spin_sticky_element, freq_sideband+delta_freq)\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration_set, switch_duration_extra)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(rabi_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    x = 4*1e-3*np.array(rabi_durations)\n",
    "    xlabel = \"Rabi durations [us]\"\n",
    "\n",
    "    ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    guess = [0.02,100,0.5,1,1*np.pi]\n",
    "    y = pops[-1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  y)\n",
    "        T_half_period = 0.5/est[0]\n",
    "        T_min = 0.5/(est[0]+std[0])\n",
    "        T_max = 0.5/(est[0]-std[0])\n",
    "        T_err = T_half_period-T_min\n",
    "        T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "        T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "        T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "        T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "        plt_label = \"$T_{hp} = %.2f(%i)$ ms \"%(T_half_period,1e2*T_err)+ r\"$T_\\pi = %.2f(%i)$ ms \"%(T_pi,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))+r\"$T_{\\pi/2} = %.2f(%i)$ ms\"%(T_pio2,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))\n",
    "    \n",
    "    except Exception as e: \n",
    "        print(\"fit failed\", e)\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "        plt_label = \"fit failed\"\n",
    "\n",
    "\n",
    "    ax[1].plot(fine,data_fit)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'rabi_durations': rabi_durations,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c39b82-f48b-4abd-8ca7-89aeababff35",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Pumping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77c7afe0-457b-4ba2-9bd5-7ce60244534a",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_sideband"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d17c5d3-7bfd-4e07-bb2f-07ea734f28b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 - int(0.790e6)\n",
    "rabi_amplitude = 0.3\n",
    "rabi_duration = int(1_000_000//4)\n",
    "chirp_rate = calc_chirp_rate(0,60e3,rabi_duration*4)\n",
    "\n",
    "n_step_pumps = 11\n",
    "n_pumping = np.linspace(0, 50, n_step_pumps)\n",
    "n_pumping = [int(f) for f in n_pumping]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_pumping'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    n_p = declare(int) \n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(n_p, n_pumping):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[0]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[0]+delta_freq, freq_sideband_prep_red+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            \n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            reset_frame(spin_element)\n",
    "            \n",
    "            with for_(m, 0, m<n_p, m+1):\n",
    "                align()\n",
    "                #update_frequency(spin_element, freq_sideband+delta_freq)\n",
    "                chirped_square_pulse(rabi_amplitude, spin_element, rabi_duration,freq_sideband+delta_freq , chirp_rate = chirp_rate)\n",
    "                align()\n",
    "                wait(int(1e6//4))\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_pumps).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f77b937-90d6-4332-9fbb-bef17045eb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = np.array(n_pumping)\n",
    "xlabel = \"Pumping steps\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans=kmeans_standard)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b':| raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e4bea7-d634-4763-8db1-f3307da7ad82",
   "metadata": {},
   "source": [
    "### Pre-prepare using sideband pulses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea34d615-f07c-4ea6-9ca6-e3af27b2a5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 - int(0.790e6)\n",
    "rabi_amplitude = 0.3\n",
    "rabi_duration = int(10_000_000//4)\n",
    "chirp_rate = calc_chirp_rate(0,60e3,rabi_duration*4)\n",
    "\n",
    "n_step_pumps = 11\n",
    "n_pumping = np.linspace(0, 10, n_step_pumps)\n",
    "n_pumping = [int(f) for f in n_pumping]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_pumping'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    n_p = declare(int) \n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(n_p, n_pumping):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            #chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=True)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            \n",
    "            #wait(int(20e6/4))\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                   prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                   readout_freqs[0]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[0]+delta_freq, freq_sideband_prep_red+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            \n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            reset_frame(spin_element)\n",
    "            \n",
    "            with for_(m, 0, m<n_p, m+1):\n",
    "                align()\n",
    "                chirped_square_pulse(rabi_amplitude, spin_element, rabi_duration,freq_sideband+delta_freq , chirp_rate = chirp_rate)\n",
    "                align()\n",
    "                wait(int(1e6//4))\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_pumps).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3c541d-5d23-4d19-9779-47cafb9fd772",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[0:]])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = np.array(n_pumping)\n",
    "xlabel = \"Pumping steps\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans=kmeans_standard)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b':| raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb799fab-dc0a-46ae-bde2-0354c0102ad5",
   "metadata": {},
   "source": [
    "### Adiabatic limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16cdabc5-d653-4e5c-9dfc-064b7aa4cd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 - int(0.790e6)\n",
    "rabi_amplitude = 0.3\n",
    "rabi_duration = int(10_000_000//4)\n",
    "chirp_rate = calc_chirp_rate(0,60e3,rabi_duration*4)\n",
    "\n",
    "n_step_pumps = 6\n",
    "n_pumping = np.linspace(0, 5, n_step_pumps)\n",
    "n_pumping = [int(f) for f in n_pumping]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_pumping'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    n_p = declare(int) \n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(n_p, n_pumping):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=True)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            \n",
    "            wait(int(20e6/4))\n",
    "            # click_acc = nuclear_spin_RO(\n",
    "            #        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "            #        readout_freqs[0]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            # )\n",
    "            # raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[0]+delta_freq, freq_sideband_prep_red+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            \n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            reset_frame(spin_element)\n",
    "            \n",
    "            with for_(m, 0, m<n_p, m+1):\n",
    "                align()\n",
    "                chirped_square_pulse(rabi_amplitude, spin_element, rabi_duration,freq_sideband+delta_freq , chirp_rate = chirp_rate)\n",
    "                align()\n",
    "                wait(int(1e6//4))\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_pumps).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b084b68-1389-4830-8619-a13c9f548108",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[0:]])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = np.array(n_pumping)\n",
    "xlabel = \"Pumping steps\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans=kmeans_standard)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b':| raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0dd884c-1d50-4cf2-8616-891aa684cd19",
   "metadata": {},
   "source": [
    "### vs freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d6d7df-2369-405e-87a8-06798f0b3409",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.7650e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "chirp_rate = calc_chirp_rate(0,50e3,rabi_duration*4)\n",
    "\n",
    "n_step_freq = 2\n",
    "sideband_freqs = freq_sideband + np.linspace(-0e3, +0e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<10, m+1):\n",
    "                align()\n",
    "                chirped_square_pulse(rabi_amplitude, element, rabi_duration, chirp_rate = chirp_rate)\n",
    "                #flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4290b8e-de77-43a6-b1d0-7f52840effa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e6c53d2-6744-4cc1-9d82-a78e23de13d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.775e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "\n",
    "n_step_freq = 11\n",
    "sideband_freqs = freq_sideband + np.linspace(-20e3, +20e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<10, m+1):\n",
    "                align()\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31599a2f-9389-463a-954f-81646a885845",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(20*60)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81980da4-3df6-49f5-b8f1-83753a651861",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_sideband = Photon_IF + centre_freq*1e3 + int(0.780e6)\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(120_000//4)\n",
    "\n",
    "n_step_freq = 11\n",
    "sideband_freqs = freq_sideband + np.linspace(-10e3, +10e3, n_step_freq)\n",
    "sideband_freqs = [int(f) for f in sideband_freqs]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, sideband_freqs):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_element, freq_set+delta_freq)\n",
    "            with for_(m, 0, m<10, m+1):\n",
    "                align()\n",
    "                flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "                wait(int(5e6//4))\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e45b1aa-3ffd-4620-bfc5-488058f60d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*60)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "sideband_freqs = np.array(sideband_freqs)\n",
    "x = (sideband_freqs - Photon_IF)*1e-3-centre_freq\n",
    "xlabel = \"Frequency [kHz]\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[1]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'sideband_freqs': sideband_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0602b422-3e20-4afb-a4a0-aa844083b110",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## continous vs time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae4ef248-fda0-4b42-b6f8-c6b846324c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "freq_pump1 = Photon_IF + centre_freq*1e3 + int(0.804e6)\n",
    "freq_pump2 = Photon_IF + centre_freq*1e3 + int(0.775268e6)\n",
    "freq_pump3 = Photon_IF + centre_freq*1e3 + int(0.775946e6)\n",
    "rabi_amplitude = 0.028\n",
    "\n",
    "\n",
    "n_step_pumps = 11\n",
    "pumping_times = np.linspace(0, int(20_000_000//4), n_step_pumps)\n",
    "pumping_times = [int(f) for f in pumping_times]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_pumping'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    pump_duration_set = declare(int) \n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(pump_duration_set, pumping_times):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron+delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            sideband_pump('blue', centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            '''\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            update_frequency(spin_sticky_extra_element,  freq_pump1 + delta_freq)\n",
    "            update_frequency(spin_sticky_extra2_element, freq_pump2 + delta_freq)\n",
    "            update_frequency(spin_sticky_element,        freq_pump3 + delta_freq)\n",
    "\n",
    "            flattop_pulse(rabi_amplitude, pump_duration_set, switch_duration_extra, element=spin_sticky_extra_element)\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            #flattop_pulse(rabi_amplitude, pump_duration_set, switch_duration_extra, element=spin_sticky_extra2_element)\n",
    "            #wait(int(5e6//4))\n",
    "            #align()\n",
    "            #flattop_pulse(rabi_amplitude, pump_duration_set, switch_duration_extra)\n",
    "            #wait(int(5e6//4))\n",
    "            #align()\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_pumps).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556e2fc2-42c4-42e2-ade7-8244012f753d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = np.array(pumping_times)*4e-6\n",
    "xlabel = \"Pumping duration (ms)\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs, kmeans=kmeans_standard)\n",
    "\n",
    "p_data = pops[-2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b':| raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7581bfcb-a765-4b03-9a7e-c3099d185d43",
   "metadata": {},
   "source": [
    "### vs freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f7dad36-7daf-40e1-9e7d-7626972576f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Sideband Params #######################\n",
    "\n",
    "\n",
    "rabi_amplitude = 0.1\n",
    "rabi_duration = int(20_000_000//4)\n",
    "\n",
    "n_steps_freq = 21\n",
    "freq_pump = Photon_IF + centre_freq*1e3 - int(0.775e6) + np.linspace(-30, 30, n_steps_freq)*1e3\n",
    "freq_pump = [int(f) for f in freq_pump]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='sideband_spectro'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "prep_freq = Photon_IF-0.785e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set = declare(int)\n",
    "    n_p = declare(int) \n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        #with for_(freq_set, sideband_freq_init, freq_set < sideband_freq_fin+sideband_freq_step//2, freq_set + sideband_freq_step):\n",
    "        with for_each_(freq_set, freq_pump):\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron+delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "\n",
    "            wait(int(5e6//4))\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            reset_frame(spin_sticky_element)\n",
    "\n",
    "\n",
    "            ################# Now play the Sideband sequence #################\n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            update_frequency(spin_sticky_element,        freq_set   + delta_freq)\n",
    "            \n",
    "            flattop_pulse(rabi_amplitude, rabi_duration, switch_duration_extra)\n",
    "            \n",
    "            wait(int(5e6//4))\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        \n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_steps_freq).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fcde696-ae75-4b8b-b97b-356b39725993",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "x = (freq_pump - ( Photon_IF + centre_freq*1e3 ))*1e-3\n",
    "xlabel = \"Frequency of the 2nd pump tone (kHz)\"\n",
    "\n",
    "ax, pops = basic2plot(data, data_prep, x, xlabel, readout_freqs)\n",
    "\n",
    "p_data = pops[2]\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, 'k', label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend()\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            '''\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b':| raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            '''\n",
    "            'centre_freq': centre_freq,\n",
    "            'rabi_amplitude': rabi_amplitude,\n",
    "            'rabi_duration': rabi_duration,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2d078a-24ef-42f5-a031-25e36b9a35c8",
   "metadata": {},
   "source": [
    "# Raman"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b199fb-47f9-460a-a224-b88b60c06e40",
   "metadata": {},
   "source": [
    "## Spectroscopy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bce75f0-9400-4a0f-be5c-80cad87b62c7",
   "metadata": {},
   "source": [
    "### On A (B up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40ad170-160d-4ab1-8c70-1d9a64942dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-20e3)\n",
    "# nuclear_spin_freq_a  = int(808.41e3)\n",
    "# nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "# raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "# raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "# detuned_electron_amplitude_a = 0.05\n",
    "# detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "# detuned_sideband_amplitude_a = 0.05\n",
    "# detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "# ramp_time       = int(0.4e6/4)\n",
    "\n",
    "\"\"\"\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.201e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "#nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-0.3e3,0.3e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = np.linspace(808_000, 811_000, 151)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a/np.sqrt(2), detuned_sideband_amplitude_a_prep/np.sqrt(2), int(raman_pi_duration_a_prep*6), ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "763a5813-053d-467b-a71b-0818810b7442",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_a_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_a_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_a_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_a_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = labels[2]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "946195f1-074b-4710-a304-6c04a74f3742",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-20e3)\n",
    "# nuclear_spin_freq_a  = int(808.41e3)\n",
    "# nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "# raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "# raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "# detuned_electron_amplitude_a = 0.05\n",
    "# detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "# detuned_sideband_amplitude_a = 0.05\n",
    "# detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "# ramp_time       = int(0.4e6/4)\n",
    "\n",
    "\"\"\"\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.201e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "#nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-0.3e3,0.3e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = np.linspace(808_000, 811_000, 101)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a_prep, int(raman_pi_duration_a_prep), ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92bf8137-3ceb-439d-a771-857a5208867d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2.5*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_a_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_a_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_a_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_a_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = labels[2]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec6d7d6-5b46-4c09-99b2-1210757ca234",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-20e3)\n",
    "# nuclear_spin_freq_a  = int(808.41e3)\n",
    "# nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "# raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "# raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "# detuned_electron_amplitude_a = 0.05\n",
    "# detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "# detuned_sideband_amplitude_a = 0.05\n",
    "# detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "# ramp_time       = int(0.4e6/4)\n",
    "\n",
    "\"\"\"\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.201e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "#nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-0.3e3,0.3e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = np.linspace(808_000, 811_000, 101)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b_prep, int(raman_pi_duration_b_prep), ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a7808c-629d-41ed-996e-4a17065716f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2.5*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_a_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_a_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_a_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_a_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = labels[2]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00fb243-8189-4b87-8376-ee239849eefc",
   "metadata": {},
   "source": [
    "### On A (B down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f2536ba-2231-4ad3-96ad-0db75e3da3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-20e3)\n",
    "# nuclear_spin_freq_a  = int(808.41e3)\n",
    "# nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "# raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "# raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "# detuned_electron_amplitude_a = 0.05\n",
    "# detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "# detuned_sideband_amplitude_a = 0.05\n",
    "# detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "# ramp_time       = int(0.4e6/4)\n",
    "\n",
    "\"\"\"\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.201e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-0.3e3,0.3e3,21,nonlinearity=3)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a829f6c-4414-493d-a5d5-55941a23600d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0.5)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_a_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_a_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_a_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_a_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = labels[3]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            # 'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            # 'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            # 'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            # 'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            # 'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            # 'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            # 'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            # 'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bc74d7-10fd-47f9-bdda-c47fc6797138",
   "metadata": {},
   "source": [
    "### On A (B interleaved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d334032-4d2a-486f-a820-85280546d5f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "2700*6/3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e7ed02f-8255-4c56-8b1e-3b8beb14f08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "data_all = []\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-10e3)\n",
    "\n",
    "raman_detunings = raman_detuning+ sinhspace(-125e3,25e3,7,nonlinearity=2)\n",
    "# nuclear_spin_freq_a  = int(808.41e3)\n",
    "# nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "# raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "# raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "# detuned_electron_amplitude_a = 0.05\n",
    "# detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "# detuned_sideband_amplitude_a = 0.05\n",
    "# detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "# ramp_time       = int(0.4e6/4)\n",
    "\n",
    "\"\"\"\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.201e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-0.15e3,0.15e3,8,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "for raman_detuning in raman_detunings:\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "        l = declare(int)           # Index for switch case\n",
    "    \n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "            with for_(l, 0, l<2, l + 1):\n",
    "                with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "                    ################# Raman experiment #################\n",
    "                    # Sweep number of readout pulses\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    align()\n",
    "                    chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                    align()\n",
    "                    wait(int(20e6/4))\n",
    "                    align()\n",
    "    \n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                    gaussian_pulse_length = 5000//4\n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                    save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "    \n",
    "                    align()\n",
    "                    with switch_(l):\n",
    "                        with case_(0):\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                        with case_(1):\n",
    "                            wait(100)\n",
    "                        with default_():\n",
    "                            pass\n",
    "                    ################# Now play the Raman spectroscopy sequence #################\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    align()\n",
    "    \n",
    "                    Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                    # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                    with switch_(l):\n",
    "                        with case_(0):\n",
    "                            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,0,1,0])\n",
    "                        with case_(1):\n",
    "                            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,1,0,1])\n",
    "                        with default_():\n",
    "                            pass\n",
    "                    \n",
    "                    #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).buffer(2).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    \n",
    "    better_sleep(1*3600)\n",
    "    res = job.result_handles\n",
    "\n",
    "    ###########################################################\n",
    "    \n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "    \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "    \n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38412610-eb35-4fe2-aa70-cd551c3e0d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(60)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_a_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_a_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_a_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_a_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = labels[3]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+center_freq)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e3fd1a-bdf3-4109-a971-06032451a5f8",
   "metadata": {},
   "source": [
    "### On B (A down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2138,
   "id": "7f9c123a-cfdf-4097-bf13-5eb176991889",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:19:09.483687Z",
     "iopub.status.busy": "2024-04-01T14:19:09.483687Z",
     "iopub.status.idle": "2024-04-01T14:19:12.546857Z",
     "shell.execute_reply": "2024-04-01T14:19:12.544856Z",
     "shell.execute_reply.started": "2024-04-01T14:19:09.483687Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b_prep + sinhspace(-0.3e3,0.3e3,11,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_b_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2140,
   "id": "8963ef97-367a-4933-b2e9-0bd16ddace54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:21:52.126119Z",
     "iopub.status.busy": "2024-04-01T14:21:52.126119Z",
     "iopub.status.idle": "2024-04-01T14:21:53.499444Z",
     "shell.execute_reply": "2024-04-01T14:21:53.497444Z",
     "shell.execute_reply.started": "2024-04-01T14:21:52.126119Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 11, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_spectroscopy\\\\20240401161910_raman_spectroscopy.hdf5'"
      ]
     },
     "execution_count": 2140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_b_prep*4*1e-9)\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].vlines([(nuclear_spin_freq_b_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_b_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfb7a436-efe5-4498-8692-7b18c1d855a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "-int(354)/1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc427492-3d8d-4cc2-b374-da1163587507",
   "metadata": {},
   "outputs": [],
   "source": [
    "est[0]*1e3 - nuclear_spin_freq_b_bare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a112cbe3-7f57-4518-9f65-baabbbec7280",
   "metadata": {},
   "source": [
    "### On B (A interleaved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a35bb054-e82d-44dc-8db1-54106111cff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "pump_steps_after_tracking = 60\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = raman_detuning_b_prep\n",
    "raman_detunings = raman_detuning+ sinhspace(-0e3,0e3,1,nonlinearity=2)\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b_prep + sinhspace(-0.15e3,0.15e3,8,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=150\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "for raman_detuning in raman_detunings:\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "        l = declare(int)           # Index for switch case\n",
    "    \n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "            with for_(l, 0, l<2, l + 1):\n",
    "                with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "                    ################# Raman experiment #################\n",
    "                    # Sweep number of readout pulses\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    align()\n",
    "                    chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                    align()\n",
    "                    wait(int(20e6/4))\n",
    "                    align()\n",
    "    \n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                    gaussian_pulse_length = 5000//4\n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                    save(delta_freq, delta_freq_stream)\n",
    "                    \n",
    "                    align()\n",
    "                    chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking, enable_fsv_trigger=False)\n",
    "                    align()\n",
    "                    wait(int(20e6/4))\n",
    "                    align()\n",
    "                    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "    \n",
    "                    align()\n",
    "                    with switch_(l):\n",
    "                        with case_(0):\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                        with case_(1):\n",
    "                            wait(100)\n",
    "                        with default_():\n",
    "                            pass\n",
    "                    ################# Now play the Raman spectroscopy sequence #################\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    align()\n",
    "    \n",
    "                    Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "    \n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "            \n",
    "                    with switch_(l):\n",
    "                        with case_(0):\n",
    "                            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,0,0])\n",
    "                        with case_(1):\n",
    "                            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "                        with default_():\n",
    "                            pass\n",
    "                    \n",
    "                    #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).buffer(2).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    better_sleep(0.2*3600)\n",
    "    res = job.result_handles\n",
    "\n",
    "    ###########################################################\n",
    "    \n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "    \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "    \n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9cd6fbf-1207-409d-b257-1fcb56b8d217",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a5fadf-4a43-4c63-a277-2678b6da8a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all_b = data_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfc71694-c6d2-468a-b998-39f7631cc033",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data_all = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "fftx, ffty = plot_flattop_ft(raman_pi_duration_b_prep, nuclear_spin_freq_b_prep)\n",
    "num_sweeps = data_all.shape[1]\n",
    "states_to_fit = [1,3]\n",
    "results = []\n",
    "############################### extract populations #################################\n",
    "for j in range(num_sweeps):\n",
    "    data = data_all[:,j]\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = pops[states_to_fit[j]]\n",
    "        results.append(p_data)\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(x[0],x[-1])\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    ax[1].plot(x, p_data, \"o\", label = labels[3]) \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    \n",
    "    fftx, ffty = plot_flattop_ft(raman_pi_duration_b_prep, nuclear_spin_freq_b_prep)\n",
    "    ax[1].plot(fftx/1e3,ffty/5,\"k--\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+f\"fig{j}_\"+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "plt.plot(x,results)    \n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dae6222-cf43-4fab-b3e8-5e9b251753bf",
   "metadata": {},
   "source": [
    "### Knill pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b1c747-9422-4f60-9d85-002f16581d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a_prep +sinhspace(-0.4e3,0.4e3,21,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + freq_set) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            frame_rotation_2pi(0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(-0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(-0.25, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f969874-cb81-475f-8b4c-21f64f44ce44",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(4*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_b_prep*4*1e-9)\n",
    "\n",
    "#ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "for (k,pop) in enumerate(pops):\n",
    "    ax[1].plot(x, pop, \"o\", label = labels[k]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "798d5f80-1af4-42e9-b24c-8e36ce566883",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a_prep +sinhspace(-0.4e3,0.4e3,21,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + freq_set) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)            # Detuned Electron frequency\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            frame_rotation_2pi(0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(-0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(-0.25, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            frame_rotation_2pi(0.0833, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,0,1,0])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7de2cb5-bfe5-4e54-8eaf-28547d8d77d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(4*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p2\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_b_prep*4*1e-9)\n",
    "\n",
    "#ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "for (k,pop) in enumerate(pops):\n",
    "    ax[1].plot(x, pop, \"o\", label = labels[k]) \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891e102c-3dea-4d69-976c-7d25980d764e",
   "metadata": {},
   "source": [
    "### On B (A up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e64606fb-1b2d-43a6-ad0f-a21847096669",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b_prep + sinhspace(-0.5e3,0.5e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=200\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0f032f1-e502-4e54-b8d6-ef005a023f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p1\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "pulse_bandwidth = 1/(raman_pi_duration_b_prep*4*1e-9)\n",
    "\n",
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_b_prep\n",
    "center_freq = nuclear_spin_freq_b_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "#ax[1].vlines([(nuclear_spin_freq_b_prep+pulse_bandwidth/2)/1e3,(nuclear_spin_freq_b_prep-pulse_bandwidth/2)/1e3],0,1,linestyle=\"--\",color=\"black\")\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].plot((full_freq+nuclear_spin_freq_b_prep)/1e3,full_pulse/(5*max(full_pulse)),\"k--\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_ylabel(\"P\")\n",
    "# ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "# ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c1ef8eb-3c94-4963-b240-4545c206321f",
   "metadata": {},
   "outputs": [],
   "source": [
    "amplitude = 1\n",
    "cos_ramp_time = ramp_time_prep\n",
    "top_wait_time = raman_pi_duration_a_prep\n",
    "center_freq = nuclear_spin_freq_b_prep\n",
    "around_pulse = 10e6\n",
    "vals = np.concatenate(([0]*int(around_pulse/4),chirp_cos_raise(cos_ramp_time,amplitude,0)[0], [amplitude]*top_wait_time, chirp_cos_raise(cos_ramp_time,-amplitude,0)[0]+amplitude,[0]*int(around_pulse/4)))\n",
    "times = np.linspace(0,len(vals)*4*1e-6,len(vals))\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.plot(times,vals)\n",
    "plt.xlabel(\"Pulse time (ms)\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(3,3))\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(times),d=times[1]-times[0])\n",
    "fft_y = np.abs(np.fft.rfft(vals))\n",
    "plt.plot(fft_x,fft_y)\n",
    "plt.xlim(0,500)\n",
    "plt.xlabel(\"Hz\")\n",
    "\n",
    "plt.figure(figsize=(3,3))\n",
    "full_pulse = np.concatenate((np.flip(fft_y),fft_y))\n",
    "full_freq = np.concatenate((-np.flip(fft_x),fft_x))\n",
    "plt.plot(full_freq+nuclear_spin_freq_b_prep,full_pulse)\n",
    "plt.xlim(nuclear_spin_freq_b_prep-1000,nuclear_spin_freq_b_prep+1000)\n",
    "plt.xlabel(\"Hz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d9162ed-8d49-45d2-8c7a-d81d2e94e2d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d952ccde-3110-43c0-952c-06d22407da62",
   "metadata": {},
   "outputs": [],
   "source": [
    "1/(raman_pi_duration_a_prep*4*1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a80c62dd-0f5b-44b2-80d7-49c45f262450",
   "metadata": {},
   "outputs": [],
   "source": [
    "est"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba3e5d75-05be-43de-872b-d98a38e8675b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*1800)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0])} kHz width = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0180dfcf-fe15-449f-b5a4-9be2f3ec0755",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.458e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_duration = int(5.5e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b + sinhspace(-0.3e3,0.3e3,21,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            #Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf48d761-d62d-4711-86cd-5b514ac76b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(0,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf18ef10-148a-4d38-9359-8760ad4fe0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spectroscopy(data):\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    ############################### Fit ###############################\n",
    "    \n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "    ############################### Plotting ###############################\n",
    "    \n",
    "    fig,ax=plt.subplots(2,2,figsize=(18,8))\n",
    "    print(np.shape(ax))\n",
    "    for i in range(len(readout_freqs)): ax[0,0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0,0].set_ylabel(\"Mean counts\")\n",
    "    ax[0,0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0,0].legend()\n",
    "    \n",
    "    ax[0,1].set_ylim(0,1)\n",
    "    ax[0,1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0,1].set_ylabel(\"Population\")\n",
    "    ax[0,1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "    ax[0,1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[0,1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "    if plot_guess: ax[0,1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[0,1].set_ylabel(\"P\")\n",
    "    ax[0,1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0,1].legend()\n",
    "    \n",
    "    bins=np.arange(0,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[1,0].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[1,0].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[1,0].legend()\n",
    "    ax[1,0].set_ylabel(\"instances\")\n",
    "    ax[1,0].set_xlabel('counts')\n",
    "    \n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[1,1].plot(1e-3*delta_freq)\n",
    "    ax[1,1].set_xlabel(\"Readout iteration\")\n",
    "    ax[1,1].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return(est[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93382231-eb1b-4cb5-bf69-864ef35aace8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nchunks = 10\n",
    "chunksize = len(data)//nchunks\n",
    "centres = []\n",
    "\n",
    "for chunk in range(nchunks):\n",
    "    data_chunk = data[chunk*chunksize:(1+chunk)*chunksize]\n",
    "    print(data.shape,data_chunk.shape)\n",
    "    centres.append(plot_spectroscopy(data_chunk))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d801dca-378b-4c4f-a6fd-4a512f7fb488",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['axes.formatter.useoffset'] = False\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(np.round(centres,3)*1e3-np.mean(centres)*1e3,\"o-\")\n",
    "plt.xlabel(\"chunk #\")\n",
    "plt.ylabel(f\"Nuclear spin frequency - {np.mean(centres):.3f} kHz (Hz)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1a4305-e5a8-4463-8343-4a35257da740",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>100).mean(0)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(0,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d80b9e-5cc2-4748-8d7c-550c83ef8fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.arctan(51/33)/np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85802412-74b2-4d28-9643-3269360e61a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd1cbe15-204f-4374-af44-be21f948f4ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bee45cc2-1af8-4ae6-92db-7cec617ef746",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a81c0a6-a6e7-4931-8ee6-1bffe7fcbad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "flip_b = False\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.90e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_detuning = 350e3# 1.4e6\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.1\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.3\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "ramp_time       = int(6e6/4)\n",
    "raman_pulse_duration = int(20e6//4)\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b + np.linspace(-0.5e3,0.5e3,51)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "print(nuclear_spin_frequencies)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.778e6)\n",
    "threshold = 80 # threshold of counts above which we consider preparation to have been sucessful\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_index = -1\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = 1e6\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "        \n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            if flip_b:\n",
    "                align()\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "                wait(int(5e6//4))\n",
    "            \n",
    "            # Raman_pulse_cos(freq_set, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "            Raman_pulse_cos(freq_set, freq_electron, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration,ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#################################################################################################################################################################################\n",
    "\n",
    "better_sleep(3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8309a347-ae99-43c2-91c6-8a11c69ce060",
   "metadata": {},
   "outputs": [],
   "source": [
    "#better_sleep(3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-2]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "\n",
    "    \n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "# \n",
    "# ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "    'click_array': data,\n",
    "    'data_prep': data_prep,\n",
    "\n",
    "    'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "    'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "    'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "    'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "    'raman_pi_duration_a': raman_pi_duration_a,\n",
    "    'raman_pi_duration_b': raman_pi_duration_b,\n",
    "    'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "    'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "    'ramp_time': ramp_time,\n",
    "    'raman_pulse_duration': raman_pulse_duration,\n",
    "    'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "    'readout_freqs': readout_freqs,\n",
    "    'prep_freq': prep_freq,\n",
    "    'raman_detuning':raman_detuning,\n",
    "    'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "    'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "    't_wait_prep': t_wait_prep,\n",
    "    'N_ROcycle': N_ROcycle,\n",
    "    'delta_freq': delta_freq\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e7c4e0-8806-451c-8022-cf792d738ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#better_sleep(3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "\n",
    "    \n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "# \n",
    "# ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "    'click_array': data,\n",
    "    'data_prep': data_prep,\n",
    "\n",
    "    'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "    'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "    'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "    'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "    'raman_pi_duration_a': raman_pi_duration_a,\n",
    "    'raman_pi_duration_b': raman_pi_duration_b,\n",
    "    'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "    'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "    'ramp_time': ramp_time,\n",
    "    'raman_pulse_duration': raman_pulse_duration,\n",
    "    'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "    'readout_freqs': readout_freqs,\n",
    "    'prep_freq': prep_freq,\n",
    "    'raman_detuning':raman_detuning,\n",
    "    'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "    'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "    't_wait_prep': t_wait_prep,\n",
    "    'N_ROcycle': N_ROcycle,\n",
    "    'delta_freq': delta_freq\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc47576-cd1a-412e-bb49-028bf4f6332f",
   "metadata": {},
   "source": [
    "### vs electron amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94b8a7d6-009c-414a-86cf-5ef424786f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(2, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "# Old asymmetric pulse params with short ramp times for comparison with Will's simulations, sweeping amplitude ratio keeping total amplitude fixed\n",
    "\n",
    "raman_detuning = 1400e3\n",
    "nuclear_spin_freq_a  = int(808.78e3)\n",
    "nuclear_spin_freq_b = int(810.717e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_b = 0.2\n",
    "detuned_sideband_amplitude_b = 0.8\n",
    "\n",
    "ramp_time       = int(10e3//4)\n",
    "\n",
    "# nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-0.1e3,0.1e3,11,nonlinearity=2)\n",
    "# nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "    # Active tracking (dangerous! but sometimes useful)\n",
    "    nuclear_spin_freq_a\n",
    "    nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-0.1e3,0.1e3,11,nonlinearity=2)\n",
    "    nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "    ####################### Save params #######################\n",
    "    # Define save params every step in the loop or we will overwrite all our previous data\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    detuned_electron_amplitude_a = 0.1636*factor\n",
    "    detuned_sideband_amplitude_a = 0.4000/factor\n",
    "    raman_pulse_duration = int((9.13e6//4))\n",
    "    print(\"Nuclear spin A spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 1, 0, 1])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: \n",
    "        ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "        \n",
    "        ########## ACHTUNG!!! This is where the tracking freuency update is happeing!!! ##########\n",
    "        nuclear_spin_freq_a = est[0]*1e3\n",
    "        ##########################################################################################\n",
    "        \n",
    "    if plot_guess: \n",
    "        ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af04ec49-a610-4f39-ad4d-53b38a537c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(2, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "# Old asymmetric pulse params with short ramp times for comparison with Will's simulations\n",
    "\n",
    "raman_detuning = 1400e3\n",
    "nuclear_spin_freq_a  = int(808.866e3)\n",
    "nuclear_spin_freq_b = int(810.653e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.1636\n",
    "#detuned_electron_amplitude_b = 0.1636*2\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.4\n",
    "#detuned_sideband_amplitude_b = 0.4*2\n",
    "\n",
    "ramp_time       = int(10e3//4)\n",
    "\n",
    "# raman_pulse_duration = int(9.13e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b + sinhspace(-0.25e3,0.25e3,21,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "    ####################### Save params #######################\n",
    "    # Define save params every step in the loop or we will overwrite all our previous data\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    detuned_electron_amplitude_b = 0.1636*2*factor\n",
    "    detuned_sideband_amplitude_b = 0.4000*2/factor\n",
    "    raman_pulse_duration = int((14.9e6//4)/factor)\n",
    "    print(\"Nuclear spin B spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 0, 1, 1])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(1000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e590e8-08c6-4591-ba12-d39b909a9b75",
   "metadata": {},
   "source": [
    "### vs both amplitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "605c8055-c443-4e93-b940-bc11d9237bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(0.9, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "# Old asymmetric pulse params with short ramp times for comparison with Will's simulations\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.37e3)\n",
    "nuclear_spin_freq_b = int(810.656e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-1.5e3, 1.5e3, 21,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "    ####################### Save params #######################\n",
    "    # Define save params every step in the loop or we will overwrite all our previous data\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    detuned_electron_amplitude_a = 0.05*np.sqrt(factor)\n",
    "    detuned_sideband_amplitude_a = 0.05*np.sqrt(factor)\n",
    "    raman_pulse_duration = int((0.7e6//4)/factor)\n",
    "    print(\"Nuclear spin A spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 1, 0, 1])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(1000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260e2268-7d6e-43b7-8192-c58e5047bca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(1, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "# Old asymmetric pulse params with short ramp times for comparison with Will's simulations\n",
    "\n",
    "raman_detuning = 1400e3\n",
    "nuclear_spin_freq_a  = int(808.866e3)\n",
    "nuclear_spin_freq_b = int(810.653e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.1636\n",
    "#detuned_electron_amplitude_b = 0.1636*2\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.4\n",
    "#detuned_sideband_amplitude_b = 0.4*2\n",
    "\n",
    "ramp_time       = int(10e3//4)\n",
    "\n",
    "# raman_pulse_duration = int(9.13e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b + sinhspace(-0.25e3,0.25e3,21,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "    ####################### Save params #######################\n",
    "    # Define save params every step in the loop or we will overwrite all our previous data\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    detuned_electron_amplitude_b = 0.1636*2*np.sqrt(factor)\n",
    "    detuned_sideband_amplitude_b = 0.4000*2*np.sqrt(factor)\n",
    "    raman_pulse_duration = int((14.9e6//4)/factor)\n",
    "    print(\"Nuclear spin B spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 0, 1, 1])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(1000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ddd8f02-66a5-47e6-b1e3-981c9406b4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "data_all.append(data)\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d83aa97-cb34-4573-b203-4d3848fce1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(1, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a = int(808.387e3)\n",
    "nuclear_spin_freq_b = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_duration = int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-1e3,1e3,21,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "    ####################### Save params #######################\n",
    "\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    detuned_electron_amplitude_a = 0.05*np.sqrt(factor)\n",
    "    detuned_sideband_amplitude_a = 0.05*np.sqrt(factor)\n",
    "    raman_pulse_duration = int((0.58e6//4)/factor)\n",
    "    print(\"Nuclear spin A spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(3000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2dd9c7c-66ef-410c-a621-7ba1a87ddeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(1, 0.5, 6)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a = int(808.387e3)\n",
    "nuclear_spin_freq_b = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(0.5e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_duration = int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-1e3,1e3,21,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "\n",
    "    detuned_electron_amplitude_b = 0.05*np.sqrt(factor)\n",
    "    detuned_sideband_amplitude_b = 0.05*np.sqrt(factor)\n",
    "    raman_pulse_duration = int((0.58e6//4)/factor)\n",
    "    print(\"Nuclear spin B spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(3000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62177342-9227-4f1a-8981-fa84b3cea13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "# data_all.append(data)\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p3)],(max(x)-min(x))/4,-1,1]\n",
    "try: est,std,fine,data_fit = fit_function(guess, lorentz, x, p3)\n",
    "except: print(\"fit failed\")\n",
    "    \n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].errorbar(x, p3, p3.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'data_prep': data_prep,\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b': detuned_electron_amplitude_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'ramp_time': ramp_time,\n",
    "            'raman_pulse_duration': raman_pulse_duration,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'raman_detuning':raman_detuning,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle,\n",
    "            'delta_freq': delta_freq\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d1454b4-dc81-4ffc-ac3f-b2e58ea2d7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687fcfdd-1795-4260-a158-385373711807",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all_6 = [data_all_5[0],data_all_5[1],data_all_5[2],data_all_5[3],data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6eb9eee-48c7-4d4a-aa1c-43e0916d0243",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors = np.linspace(1.2,0.8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b61b7fd6-4c2e-4960-8641-0bb20a82064f",
   "metadata": {},
   "outputs": [],
   "source": [
    "centres = []\n",
    "for i, data in enumerate(data_all_6[:-1]):\n",
    "    x = 1e-3*(nuclear_spin_freq_a + sinhspace(-0.08e3,0.08e3,11,nonlinearity=0))\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    ############################### Fit ###############################\n",
    "    \n",
    "    guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/4,-1,1]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p3)\n",
    "        centres.append(est[0])\n",
    "    except: print(\"fit failed\")\n",
    "        \n",
    "    ############################### Plotting ###############################\n",
    "    \n",
    "    plt.figure(figsize = (10,4))\n",
    "    \n",
    "    plt.title(\"\")\n",
    "    plt.ylim(0,1)\n",
    "    plt.errorbar(x, p3, p3.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    plt.plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "    plt.ylabel(\"P\")\n",
    "    plt.xlabel('Nuclear spin drive frequency (kHz)')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7795f24e-73e6-415b-ba3a-2b89d9e3ecd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "factors = np.linspace(1.2,0.8, 5)\n",
    "\n",
    "nuc_freq_b_func = interp1d(factors,centres)\n",
    "plt.figure(figsize = (6,5))\n",
    "plt.plot(factors, centres,\"o\", label = \"data\")\n",
    "plt.plot(factors, nuc_freq_b_func(factors), label = \"interpolation\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Amplitude correction factor\")\n",
    "plt.ylabel(\"Nuclear spin b frequency (kHz)\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+'nuc_b_freq_vs_sideband_rescaling.pdf')\n",
    "np.savetxt(directory+filename+'_'+'nuc_b_freq_vs_sideband_rescaling.txt',np.transpose([factors, centres]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a1db9a2-ad6b-4c8d-9658-a2a9a6902089",
   "metadata": {},
   "source": [
    "### vs detuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c823011e-2f60-4935-b417-b24d79ee0e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_detunings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4f82189-33f1-45e0-9f11-191b8fc20018",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "\n",
    "nuclear_spin_freq_a  = nuclear_spin_freq_a_prep\n",
    "nuclear_spin_freq_b = nuclear_spin_freq_b_prep\n",
    "\n",
    "raman_pi_duration_a =  int(3.79e6//4)\n",
    "raman_pi_duration_b = raman_pi_duration_b_prep\n",
    "\n",
    "detuned_electron_amplitude_b = detuned_sideband_amplitude_b_prep\n",
    "detuned_sideband_amplitude_b = detuned_sideband_amplitude_b_prep\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "\n",
    "ramp_time       = ramp_time_prep\n",
    "\n",
    "nuclear_spin_frequencies = int(808.79e3) + sinhspace(-0.5e3,0.5e3,11,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "raman_detunings =  -int(809.791e3/2-40e3) + sinhspace(-10e3,10e3,11,nonlinearity=0)\n",
    "raman_detunings = [int(x) for x in raman_detunings]\n",
    "\n",
    "\n",
    "\n",
    "for raman_detuning_swept in raman_detunings[:]:\n",
    "    ####################### Define the readout frequen\n",
    "\n",
    "    # Active tracking (dangerous! but sometimes useful)\n",
    "    #nuclear_spin_freq_a\n",
    "\n",
    "    ####################### Save params #######################\n",
    "    # Define save params every step in the loop or we will overwrite all our previous data\n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "    \n",
    "    ####################### Adjust amplitudes #######################\n",
    "    print(\"Nuclear spin A spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_swept, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 1, 0, 1])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: \n",
    "        ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "        \n",
    "        ########## ACHTUNG!!! This is where the tracking freuency update is happeing!!! ##########\n",
    "        nuclear_spin_freq_a = est[0]*1e3\n",
    "        ##########################################################################################\n",
    "        \n",
    "    if plot_guess: \n",
    "        ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "311928eb-b979-4c17-9035-1c5217ab2a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "\n",
    "nuclear_spin_freq_a  = nuclear_spin_freq_a_prep\n",
    "nuclear_spin_freq_b = nuclear_spin_freq_b_prep\n",
    "\n",
    "raman_pi_duration_a =  raman_pi_duration_a_prep\n",
    "raman_pi_duration_b = raman_pi_duration_b_prep\n",
    "\n",
    "detuned_electron_amplitude_b = detuned_sideband_amplitude_b_prep\n",
    "detuned_sideband_amplitude_b = detuned_sideband_amplitude_b_prep\n",
    "\n",
    "detuned_electron_amplitude_a = detuned_electron_amplitude_a_prep\n",
    "detuned_sideband_amplitude_a = detuned_electron_amplitude_a_prep\n",
    "\n",
    "ramp_time       = int(10000//4)\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-2e3,2e3,41,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "# data_all list can be optionally saved later, to have the whole sweep data in one file\n",
    "data_all = []\n",
    "raman_detuning =  -int(809.791e3/2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "####################### Define the readout frequen\n",
    "\n",
    "# Active tracking (dangerous! but sometimes useful)\n",
    "#nuclear_spin_freq_a\n",
    "\n",
    "####################### Save params #######################\n",
    "# Define save params every step in the loop or we will overwrite all our previous data\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "\n",
    "####################### Adjust amplitudes #######################\n",
    "\n",
    "print(\"Nuclear spin A spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "\n",
    "            # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a_prep, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0, 1, 0, 1])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d164e6-8cae-415d-b40c-f0454abeef01",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "data_all.append(data)\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: \n",
    "    ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "\n",
    "    ########## ACHTUNG!!! This is where the tracking freuency update is happeing!!! ##########\n",
    "    nuclear_spin_freq_a = est[0]*1e3\n",
    "    ##########################################################################################\n",
    "\n",
    "if plot_guess: \n",
    "    ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e04474ef-cabe-4228-aa6a-27b95727ba90",
   "metadata": {},
   "outputs": [],
   "source": [
    "Photon_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce3b40c5-a088-44da-8381-cc73c04da407",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "data_all.append(data)\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: \n",
    "    ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "\n",
    "    ########## ACHTUNG!!! This is where the tracking freuency update is happeing!!! ##########\n",
    "    nuclear_spin_freq_a = est[0]*1e3\n",
    "    ##########################################################################################\n",
    "\n",
    "if plot_guess: \n",
    "    ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db81b99-b3dd-4cc1-9cff-800828721be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "raman_detunings = 1e3*np.linspace(1400,1400,1)\n",
    "raman_detunings = [int(x) for x in raman_detunings]\n",
    "\n",
    "for raman_detuning_swept in raman_detunings[:]:\n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.745e6)\n",
    "\n",
    "    \n",
    "    ####################### Raman pulse params #######################\n",
    "    \n",
    "    nuclear_spin_freq_a  = int(808.90e3)\n",
    "    nuclear_spin_freq_b = int(810.79e3)\n",
    "\n",
    "    raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "    raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "    detuned_electron_amplitude_a = 0.01\n",
    "    detuned_electron_amplitude_b = 0.2\n",
    "\n",
    "    detuned_sideband_amplitude_a = 0.15\n",
    "    detuned_sideband_amplitude_b = 0.8\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_duration = int(40e6//4)\n",
    "    \n",
    "    nuclear_spin_frequencies = nuclear_spin_freq_b + sinhspace(-0.2e3,0.1e3,31, nonlinearity = 0)\n",
    "    nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "    print(nuclear_spin_frequencies)\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    \n",
    "    freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.778e6)\n",
    "    threshold = 80 # threshold of counts above which we consider preparation to have been sucessful\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_index = -1\n",
    "    \n",
    "    ####################### Electron spin readout parameters #######################\n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Measurement time estimate #######################\n",
    "    N_repetition = 1e6\n",
    "    readout_freqs = [readout_freqs[-1]]\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "            \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                Raman_pulse_cos(freq_set, freq_electron+delta_freq, raman_detuning_swept, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                #Raman_pulse_cos(freq_set, freq_electron, raman_detuning_swept, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration,ramp_time)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    #################################################################################################################################################################################\n",
    "    \n",
    "    better_sleep(1800)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 1\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    \n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    \n",
    "    \n",
    "    timing1 = timing\n",
    "    data1 = data\n",
    "    data_prep1 = data_prep\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    \n",
    "    fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    \n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    \n",
    "    \n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    \n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "    \n",
    "    bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[3].plot(1e-3*delta_freq)\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning_swept': raman_detuning_swept,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    " \n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8caf142-20ca-4113-991b-2a4fae1a69cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning_swept': raman_detuning_swept,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36622deb-5dec-4b1c-a144-f4acbea210da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_fwhm(x_data, y_data, invert = False):\n",
    "    # Baseline Correction\n",
    "    if invert: y_data=-y_data\n",
    "    \n",
    "    peak_index = np.argmax(y_data)\n",
    "    baseline = np.mean(np.concatenate([y_data[:int(0.1 * len(y_data))], y_data[int(0.9 * len(y_data)):]]))\n",
    "    corrected_y = y_data - baseline\n",
    "\n",
    "    # Find Peak and Half Maximum\n",
    "    peak_value = corrected_y[peak_index]\n",
    "    half_max = peak_value / 2\n",
    "\n",
    "    # Find Nearest Points to Half Maximum\n",
    "    left_idx = np.where(corrected_y[:peak_index] <= half_max)[0][-1]\n",
    "    right_idx = np.where(corrected_y[peak_index:] <= half_max)[0][0] + peak_index\n",
    "\n",
    "    # Calculate FWHM\n",
    "    fwhm = x_data[right_idx] - x_data[left_idx]\n",
    "    return fwhm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d074f30f-26ad-433a-be29-d881abe34563",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "\n",
    "timing1 = timing\n",
    "data1 = data\n",
    "data_prep1 = data_prep\n",
    "############################### extract populations #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "#guess = [x[np.argmin(p_data.mean(0))],estimate_fwhm(x, p_data.mean(0), invert = False),-0.8,0.8]\n",
    "\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "# \n",
    "# ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "\n",
    "bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[3].plot(1e-3*delta_freq)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'data_prep': data_prep,\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b': detuned_electron_amplitude_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'ramp_time': ramp_time,\n",
    "            'raman_pulse_duration': raman_pulse_duration,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'raman_detuning':raman_detuning,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle,\n",
    "            'delta_freq': delta_freq\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca046dd-beab-42ee-ad35-e044a60e4533",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "raman_detunings = 1e3*np.linspace(200,2000,10)\n",
    "raman_detunings = [int(x) for x in raman_detunings]\n",
    "\n",
    "for raman_detuning_swept in raman_detunings[:]:\n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman pulse params #######################\n",
    "    \n",
    "    nuclear_spin_freq_a  = int(808.90e3)\n",
    "    nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "    raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "    raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "    detuned_electron_amplitude_a = 0.1\n",
    "    detuned_electron_amplitude_b = 0.05+0.1*1e-3*raman_detuning_swept/1000\n",
    "\n",
    "    detuned_sideband_amplitude_a = 0.3\n",
    "    detuned_sideband_amplitude_b = 0.15\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_duration = int(20e6//4)\n",
    "    \n",
    "    nuclear_spin_frequencies = nuclear_spin_freq_b + np.linspace(-2.5e3,0.5e3,151)\n",
    "    nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "    print(nuclear_spin_frequencies)\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    \n",
    "    freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.778e6)\n",
    "    threshold = 80 # threshold of counts above which we consider preparation to have been sucessful\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_index = -1\n",
    "    \n",
    "    ####################### Electron spin readout parameters #######################\n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Measurement time estimate #######################\n",
    "    N_repetition = 1e6\n",
    "    readout_freqs = [readout_freqs[-1]]\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "            \n",
    "                raman_preparation_b(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                # Raman_pulse_cos(freq_set, freq_electron+delta_freq, raman_detuning_swept, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron+delta_freq, raman_detuning_swept, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    #################################################################################################################################################################################\n",
    "    \n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 1\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    \n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    \n",
    "    \n",
    "    timing1 = timing\n",
    "    data1 = data\n",
    "    data_prep1 = data_prep\n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data.mean(0))],(max(x)-min(x))/8,-0.8,0.8]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data.mean(0))\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "    \n",
    "    fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    \n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    \n",
    "    \n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    \n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "    \n",
    "    bins=np.arange(50,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[3].plot(1e-3*delta_freq)\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning_swept': raman_detuning_swept,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    " \n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a2eedc-a76a-4fac-9226-2d09f885167a",
   "metadata": {},
   "source": [
    "### VS both amplitudes (Symmetric Raman Detuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b63cffac-6e32-4fde-954b-3110e5a77936",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "factors = np.linspace(1, 0.5, 1)\n",
    "factors = [1.5, 0.5]\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-20e3)\n",
    "nuclear_spin_freq_a = int(808.777e3)\n",
    "nuclear_spin_freq_b = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.70e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.40e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.025*1.25\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.025*1.25\n",
    "\n",
    "ramp_time       = int(1.2e6/4)\n",
    "raman_pulse_duration = int(3.70e6//4)\n",
    "nuclear_spin_frequencies = np.linspace(808_000, 811_000, 101)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "\n",
    "data_all = []\n",
    "\n",
    "for factor in factors:\n",
    "\n",
    "    detuned_electron_amplitude_b = 0.05*np.sqrt(factor)\n",
    "    detuned_sideband_amplitude_b = 0.05*np.sqrt(factor)\n",
    "    raman_pulse_duration = int((0.58e6//4)/factor)\n",
    "    print(\"Nuclear spin B spectroscopy\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "        freq_set  = declare(int)\n",
    "\n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "\n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pulse_duration, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "                #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "        with stream_processing():\n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    #################################################################################################################################################################################\n",
    "    better_sleep(5*3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    # data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "\n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "    ############################### extract populations #################################\n",
    "\n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    ############################### Fit ###############################\n",
    "\n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "\n",
    "\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "\n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[2].plot(1e-3*delta_freq)\n",
    "    ax[2].set_xlabel(\"Readout iteration\")\n",
    "    ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac11ce9-1411-4d5b-99da-621786682310",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(4*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],2)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989b7a81-5ea2-4c61-8de7-07e33bc73bae",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a4dea77-69d3-4ea0-a07e-13ab7085bf8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_sideband_prep_blue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23f2216d-b61e-4790-8e5c-b32cd8b01eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_sideband_prep_red"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c2af60-d899-4c69-a725-0bdaacbb1038",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-30T15:18:39.517708Z",
     "iopub.status.busy": "2024-01-30T15:18:39.517708Z",
     "iopub.status.idle": "2024-01-30T15:18:39.719636Z",
     "shell.execute_reply": "2024-01-30T15:18:39.719525Z",
     "shell.execute_reply.started": "2024-01-30T15:18:39.517708Z"
    }
   },
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e8d2b8-4e30-42f5-a106-11467ef7fb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.376e3)\n",
    "nuclear_spin_freq_b = int(810.656e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.1636*2\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.4*2\n",
    "\n",
    "ramp_time       = int(0.6e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,9,2))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[0]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[0]+delta_freq, freq_sideband_prep_red+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq - (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb14a5b-0aaf-408f-9296-4780598abc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9b98a4-6a8d-489e-baaa-52dda3100c31",
   "metadata": {},
   "source": [
    "### Rabi on a (b down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878715eb-ead4-4012-9bbd-99b7acc8e2af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentz_filter(delta, kappa):\n",
    "    return 1 / np.sqrt(1+4*delta**2/kappa**2)\n",
    "\n",
    "k = 0.700\n",
    "x = np.linspace(-10, 10, 1001)\n",
    "y = lorentz_filter(x, k)\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(x, y)\n",
    "plt.vlines(1.4, 0, lorentz_filter(1.4, k), color='r', linestyle='--', label=f'F($\\Delta$=1.4MHz)={lorentz_filter(1.4, k):.2f}')\n",
    "plt.vlines(2.8, 0, lorentz_filter(2.8, k), color='b', linestyle='--', label=f'F($\\Delta$=2.8MHz)={lorentz_filter(2.8, k):.2f}')\n",
    "plt.xlabel(r'$\\delta$ (MHz)')\n",
    "plt.ylabel(r'Filtering factor')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b3513d3-4a4f-480c-be8f-36fb58260f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_detuning = 130e3\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "\n",
    "detuned_electron_amplitude_a = 0.02\n",
    "detuned_sideband_amplitude_a = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ca69dca-b2cd-4e1c-8c33-e565e5c1bfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentz_filter(delta, kappa):\n",
    "    return 1 / np.sqrt(1+4*delta**2/kappa**2)\n",
    "\n",
    "def rabi_freq_from_amp(amp, freq):\n",
    "    return 106.8 * amp/0.05 * lorentz_filter(freq, 700)\n",
    "\n",
    "omega_1 = rabi_freq_from_amp(detuned_electron_amplitude_a, raman_detuning*1e-3)\n",
    "omega_2 = rabi_freq_from_amp(detuned_sideband_amplitude_a, raman_detuning*1e-3+nuclear_spin_freq_a*1e-3)\n",
    "\n",
    "ratio_1 = omega_1/(raman_detuning*1e-3)\n",
    "ratio_2 = omega_2/(raman_detuning*1e-3+nuclear_spin_freq_a*1e-3)\n",
    "\n",
    "print(f'Allowed rabi frequency: {omega_1:.0f} kHz | Sideband pulse, allowed transition eq. rabi frequency: {omega_2:.0f} kHz')\n",
    "print(f'Allowed rabi ratio: {ratio_1:.3f} | Sideband pulse, allowed transition eq. rabi ratio: {ratio_2:.3f}')\n",
    "print(f'For maximal contrast multiply detuned_electron_amplitude_a by a factor {ratio_2/ratio_1:.3f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5982d4cc-5be5-49a9-ba02-2ce506e61cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "ramp_time*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2037,
   "id": "9e677f0a-d44e-408f-9a62-117f2f50ffcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T10:09:42.385204Z",
     "iopub.status.busy": "2024-03-31T10:09:42.384204Z",
     "iopub.status.idle": "2024-03-31T10:09:45.912085Z",
     "shell.execute_reply": "2024-03-31T10:09:45.910144Z",
     "shell.execute_reply.started": "2024-03-31T10:09:42.385204Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[250, 113838, 176826, 212580, 234362, 250250, 266137, 287919, 323673, 386661, 500250, 613838, 676826, 712580, 734362, 750250, 766137, 787919, 823673, 886661, 1000250]\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "raman_pulse_durations = (1e3+1e6*np.concatenate((sinhspace(0,2,11, nonlinearity = 3), sinhspace(2,4,11, nonlinearity = 3)[1:])))//4\n",
    "#raman_pulse_durations = (1e3+1e6*np.linspace(0,5,11))//4 \n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            # play('ON',fsv_trigger)\n",
    "            # align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2048,
   "id": "acb218f1-c6df-4c2e-bc06-78deb835a2da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-31T14:49:57.853890Z",
     "iopub.status.busy": "2024-03-31T14:49:57.852894Z",
     "iopub.status.idle": "2024-03-31T14:50:00.325077Z",
     "shell.execute_reply": "2024-03-31T14:50:00.324076Z",
     "shell.execute_reply.started": "2024-03-31T14:49:57.853890Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(450, 21, 4)\n",
      "[0.10480651041273642, 100, 0.4, 0.9, 3.141592653589793]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Rka8SRWmgrav7GNvUGtuZ47i6oFYOao2jXVNr7AoczImIvEFABNBukPSqzlAiBerKMF0ZrC+nAoYrwIW/pFd1Sg0Q0alamK42MJnaX9rGOje5fBNE+Wxw9vf3x+jRo7F+/Xr8+OOPmD9/vs36FStWAAAmTZpks3zChAnYsWMHVqxYUWMe53Xr1qG8vBwTJ050eg5nIiIissNkqFZrfHVATpP62Tmi8rMTiisG5QprB2hqHxyUiIhcRBNYMdd8gu1yk0H62W4zMFmKFLJNZUDeCemF1dV2EqRWP5HxQNqfkDM0Az48jzMAbN68GePGjUOLFi2we/dudOnSBQCwe/dujBo1Cv7+/jh37hzCwsKs+1y+fBkdOnSATqfDTz/9hGnTpgEAcnNzcd111+HMmTPYunUrRo4c6XQ5OY8zERE1Se6c81UUq/U1thOMdU7UGge3dlxr3Ni+c0REJC+LBShKtx2QrDJY19LNRqcXEfpaMedxro9ffvkFL7/8svV7g8EAABg8eLB12fPPP48JEyYAAMaOHYt58+ZhyZIlSEhIwLhx42AwGLBp0yaIooilS5fahGYAiIiIwBdffIGZM2dixowZGDlyJFq0aIHNmzejsLAQCxYsqFdoJiIiapIq+47VGJjqdecHpjLppSmaaoTjNOlfQ+1TNwIA1AG1B+OwdlVN9oiIqHlQKKp+zncdX7VcFIEruVKAPvw9kPSN7EVrVsE5Ly8Pe/furbG8+rK8vDybdYsXL0ZCQgI++OADbNq0CRqNBmPHjsXzzz+PoUPtz484ffp07NixA6+88gr27NkDg8GA7t274+GHH8asWbNce1FERERyq63vmC5bWj7zf1J4FkWg9JKdeY3TgMvnpNFV62pKF9zGQa1xS9YaExGR9LsgOFp6QfBIcG7WTbW9BZtqExFRk2ExA4t72tY0X03lJw3cUpgmDe7iiDqwjlpjjglCRET1YP09lQ1AZFNtIiIi8oAzmx2HZgAwlQO5xyq+EaQm3LUNxBUYyVpjIiJyHYVS6ja0/C4AAuQaJIzBmYiIyFeJolRrnLEPyNgrvS4mO7fvdfOBvncAobGsNSYiInl1nyx1G+I8zkRERORyJj2QfaQqJGfslUbMbojOY4HILq4tHxERkbO6Twa6TQCSNwGv3ej20zE4ExERNVdX8oDMitrk9L3Ahb8As952G4UaaN0HiB0ExA4E2vYHvhhv7TtWU0XT7Dj7A2gSERHJRqEE2svz+4jBmYiIqDmwWIC8k7a1yZdTa24X0KIqJMcOAtr0rTmtU619xyr6Kie+5rr5nImIiLwAgzMREZE30hcDWQel/snpe4DMA4C+qOZ2UddUheTYQUCLTnUP1lW971iNeZxfc34eZyIiomaCwZmIiKipE0WgML1iEK89Um1yzjFAtNhupw4EYvpXheSYAYB/eMPOWdl3LG2X1A86KFpqns2aZiIi8kEMzkRERE2NyQBcPCLVJGfslQLzlYs1twttV602eSAQ3RNQuvBXu0IJdBjuuuMRERF5KQZnIiIiTyvJr1abvA/IOmRnEC+V7SBesYOkptNERETkdgzOREREcrJYgPyUitrkihGvL5+tuZ1/RFVIbjfY/iBeREREJAsGZyIiInfSXwGyDlSF5Iz9tQzi1a2iJnmw84N4ERERkSwYnImIiFzFZhCviimhcpLtDOIVIM2X3G5w4wfxIiIiIrdjcCYiImqoykG8rHMn7wOKs2tuFxpbNdK1OwbxIiIiIrfib20iIiJnWQfxqgjJFw4BpnLbbRQqoFXvitrkgUDMQCC0rWfKS0RERC7B4ExERGRP5SBelSE5Yy9w6UzN7fzDq9UmD5IG8dIEyF9eIiIichsGZyIiIqBiEK+DVSE5cx9QbmcQr8h4oF21oNyiMwfxIiIiauYYnImIyPeIIlCUYTuI18VkQDTbblc5iFdlSI4ZAAREeKbMRERE5DEMzkRE1PyZDMDFo9UG8drrYBCvgVcN4qWWv7xERETUpDA4ExFR02UxA2m7gCs5QFA0EDcUUCjr3q/kktTUurJ/ctbB2gfxqgzJsYM4iBcRERHZxeBMRERN0/E1wPonbGuGQ9oAia8D3SdXLbNYgPxT1Qbx2lPHIF4VIblNPw7iRURERE5hcG5CSg4cQPCIERCUTtSmEBE1Z8fXAMvvAiDaLtdlS8tHPiXVPKfXMYiXtdn1ICCyCwfxIiIiogYRRFEU696M3Emn0yE0NBT7OndBWNu2iH7maYSMH+/pYhEReYbFDCzuCeguOL+Pyl8auKsyKMdcy0G8iIiIfEBllioqKkJISIjbzsMa5ybGlJODrHnzgSWLGZ6JyDed/9O50Nx+ONBtojQ1FAfxIiIiIjdicG5qRBEQBOS8ugjBY8aw2TYR+QZDKZC6DTi1Hji2yrl9+s8Ges1wY6GIiIiIJAzOTZEownTxIkoPHETgoIGeLg0RkXvoLgCnNgApG4Bz22uOel2XoGj3lIuIiIjoKgzOTVjOq68icPBgaDp3grZTZ2g7d4LSje32iYjcShSB7CQpKJ9aD2Qftl0f2g6ITwQ6jwPWzqsYTdveMByCNLp23FAZCk1ERETE4Nyk6VNSoE9JsVmmatkS2s6doOnUGdpOnSq+7gRVeLiHSklE5ICxDEjdLgXlU7/ZTi0FQRrQq2siEH8j0LJ71ajXN75eMaq2ANvwXLE+8TXn5nMmIiIicgEG5yZK2aIFIh99BMbUc9CfPQv9mTMwXbwIU24uTLm5KNm1u8b21iDduXNVDXVEBAROv0JEciq+KIXkUxuAs1sBU1nVOnUg0GmUFJS7jAeCWto/RvfJwMz/ARuetB0oLKSNFJqrz+NMRERE5GacjqoJqD4dVZBK+ltGWzujapuvXIHh7Fnoz5yVwvTZMzCcOQtjVlatx1aGhVU19bbWUHeGqmUUAzURuYYoAhePVvRXXg9cOGS7PiRGaoLd9Uag/TBA7ef8sS1mIG0XcCVH6tMcN5Q1zURERGQl13RUDM5NQGPncbaUlkKfeg76M6dtgrUxI0P6D60dipAQm6belTXUqlatGKiJqG7GcuD8H1JQPvUboMu0Xd+mn1Sr3DURaNWrqgk2ERERkQsxOPuQypt9YcsWtBoxwmVTUFnKy2E4d64iSJ+RQvXpMzCkpwMWi919FIGBFUG6E7SdO1trqNVtWkNQKFxSLiLyUldybZtgG0uq1qn8pSbYXROBrjcAwa08V04iIiLyGQzOPkSum13JYjDAcO48DGfP2Db7Pp8GmEx29xH8/aHt2LFqYLLOUrhWx8Rwrmmi5koUgZxj0sBeKRuArIOwGagruHXVwF4drgfU/h4rKhEREfkmBmcfIndwro1oNMKQng796TNVNdRnzsJw7hxEo9HuPoJWC03HjjWafWvaxUJQcew5Iq9j0gPnd1bNr1yUbru+dUJVE+zWfdgEm4iIiDyKwdmHNJXgXBvRZIIhI8N2YLIzZ2BITYWo19vdR1CroWnfvtoc1BXNvtu1g6DRyHwFRORQST5weqPUX/ns74DhStU6lR/QYUTF4F6J0qjWRERERE0Eg7MPaerBuTai2QxjVlZVH2prs++zEMvK7O+kUkETF2dbQ925MzTt20Oh1cp7AUTNSX1GnxZFIO9kxcBeG4CMfbBpgh0ULfVT7noj0HEkoAmQ4wqIiIiI6o3B2Yd4a3CujWixwJSdDf2ZMzWmzrKUlNjfSaGAJjYWmi5Vc1BrO3WCpkMHKPzZb5LIoeNrgPVPAMXZVctC2gCJr1fNd2wyAGl/Vk0ZVZhme4xWvaSgHJ8ItO4LcDBAIiIi8gIMzj6kuQXn2oiiCFNOjtRvuvrAZGfOwKLT2d9JEKCOibGZg1rbuTO0HTtAERjomnKZzSg9cBCmvDyooqIQMKA/Bzwj73F8DbD8LtjUGAMAKvoeD5wr1UKf/R3QV3vOlBrbJtihMXKVmIiIiMhlGJx9iK8E59qIoghTXl61PtQVzb7PnIG5sLDW/dRt2lTrQ11RQ92pE5TBwU6fW7dxI3JeXQTTxYvWZapWreo9lzaRR1jMwOKegO6Cc9sHRgFdbpDCcsdRgDbIveUjIiIicjMGZx/i68HZEdPly1WjfFfrQ23Oz691H1V0dLU5qCvmo+7UCcrQUJvtdBs3ImvefKm/Z3UVowS3XbLYa8Mza9F9xLk/gC8n1r1dr5lSzXPb/myCTURERM0Kg7MPYXCuP1NBAQypqTVqqE25ubXuo4yKlGqnO3WCpmMH5H/4IcyXC+xvLAhQRUej85bNXhc4WYvuA8wmIH03sPNd4OyWuref/jnQa4b7y0VEREQkMwZnH8Lg7DpmnQ76s2dtp846ewamC9l172xHyORJ0HbsCEGtgaDVQtBqoNBUfK3RQNBoodBqpK8dLZOplq8516K7VX1GpPYUkx5I3Q6cWAOk/AqUXnJ+31nrgA7D3Vc2IiIiIg9hcPYhDM7uZ75SAkNqVR/qkl27oT9xQr4CqNVQqNUV4VsK0wqtplog10LQqKHQam1CurRdLcsqQnrlflCrkTX/MZgv1RKovLgW3a2cGZHaU/RXgDObgBNrgVMbAUNx1Tr/cGlQr1O/AWUFqDk4GAAI0rXMP9r0/hBARERE5AJyZSmV245M1IQogwLh37s3/Hv3BgCU7N2H9Fmz6twvaOxYqMLDIRr0sBgMEPUGiHo9RIMBFoMeosEofW9dZoBokLaBxVJ1IKMRFqMRKC111yXWTRRhungRpQcOInDQQM+VoympbURqXba0fOb/5A/PpZel6aJOrgPObAHM+qp1wa2BbhOBayYCcdcBSnW1axBgex0Vo2onvsbQTERERNRIDM7kkwIG9IeqVSuYcnJqNmsGrLWzMUsWN7h2VjSZIOr1NmFaNBhg0eulAG4wQDTUsawymFcP7tZj2S4zXbrkcNC0SqX79yNg4LUQKppv+yyLGdjwJOzX1IoABGDDU0C3Ce4PnrpsKSifWAuc3wmI5qp14R2AayYB10y2P7hX98lSwN/wpO3o2iFtpNDs6VpzIiIiomaATbWbADbV9gxrf2DANjx7aX9gZ2vRAUDToQPCpk9DyOTJULds6eaSNVHOjkg99iWg311AQIRzx3W2v/Sls1VhOXO/7bronhVheRLQsrv1M+mS8xIRERE1I+zj7EMYnD2nOY1ALZrNODNmbO216AAEf3+IogiUl0sLlEoEDR+O0OnTEDxiBASNRsYSe9iR5cDP9zq/fYvOQMy1QMwA6d+WPQDlVY12HPWXvmYSkHNMCson1gK5x2z3jRlYEZYnAhEdG35dRERERD6EwdmHMDh7VnOa89iZWvTAodeh+LcNKPzpZ5QdOmTdRBkRgdBJkxA6fRr8unaVsdQekLYLWPMIcOlM3dsGtwGKL9Rcrg4AWidUBemyAmDtPNhv+g0gsCVQUm26NEEpjXTdbaL0CmndkCshIiIi8mkMzj6EwZlcqT616PrUcyha+TOKVq2GKS/PutyvVy+pKfdNN0HpbZ9JR02WL50FNr0gNZEGUHNAreqqjUhdVghkHQSyDkjNqjMPAvqi+pdNqQU6j5FqlrsmOt/8m4iIiIjsYnD2IQzO5Gr1rUUXTSZc2bkTRT/9jOKtWwGTCQAgaLUIHjcOYdOnIWDQINnmo26w2ppKj34euHgU2PdfwGIEBAXQb5Y02NaaRyo2tDMidW2jalsswKXTFSH6AJC6DSg4V3f5bvsR6OpdXQCIiIiImjIGZx/C4ExNienyZRStWYOin36G/vRp63J1mzYInTYNoVOnQhPT1oMlrEVtU0tdrfM4YPzLQMtrqvarMSJ12/qNSH10BfDTPXVvN/1zoNcM545JRERERHVicPYhDM7UFImiiPLkYyj8+Sfo1v0CS3GxdV3AkMEImzYdwePGQuHnZ7ufJ/qMW8zA4p624fdqChVwy3f2a3wbOyK1syN0z1on9WsmIiIiIpeQK0s18XaX8tm/fz9mzpyJNm3aQK1WIywsDMOHD8fSpUth728LZrMZ7777Lnr16gV/f39ERUVh5syZOHHihAdKT+R6giDAv1dPtH7xRXT5YwfavPUWAocOAQQBpbv34MK//oXTw69H9ksvoezoUYiiCN3GjTgzajTSZ83ChccfR/qsWTgzZix0GzfW+/yi0YCS1Z+h6OMXULL6M4hGQ+0bp+1yHJoBwGIC1P721ykqBurqNUP6t77TOMUNlZqEo7ZpowSpFjtuaP2OS0RERERNAmucAfz000/4+9//DrPZjH79+qFz587Iy8vDH3/8AZPJhNtuuw3ffPONdXuLxYIZM2Zg5cqVCAsLw5gxY5Cfn48dO3bA398fW7duxcCBA50+P2ucyZsYs7JQuGoVin5eCWNWlnW5qnVrmLKza+7QgHmxdV+8ipwP/wdTSVUQVQUC0Q/diZC7n5EWiCKQdxJI3Q4kfQtcPFz3gd3ZVNraVByoV39pIiIiImowNtWWiclkQtu2bZGbm4tvvvkGt912m3XdiRMnMGzYMFy+fBm///47Ro0aBQD47LPPcO+996JLly74448/EB0dDUAK4DNmzEDnzp1x4sQJqFQqu+e8GoMzeSPRYkHpvn0o/Oln6H77DTA4qBEGoGzRAu2WLYMqLBSK4GAIWi0EoWYNre6LV5H1xv8qvqu+XvpR1XZWf4TEB0oDcl3JqV+h3d1U2hX9pYmIiIjIaQzOMklOTkavXr0QHx+PkydP1lg/b948vPfee3j99dfxxBNPAAC6d++OEydOYOXKlZg6darN9lOmTMGaNWuwYsUKTJ8+3akyMDiTtyveuhWZDzxYv53UaigDA6EIDoYiOAjKoGAIgYEo/WMrRJMI+82eRagCzOg8MReCAoDKD2g3BOhwPbDnI6AkH6JFRGmeBqZyJVR+ZgREGSAoqk0tVd9m2PXV2P7SREREROQ0ubKUc1WizZhWq3VquxYtWgAAzp07hxMnTsDf3x8TJkyosd2MGTOwZs0arF271ungTOTtLCWlTm0n+PlB1OulZtZGI8yFhTAXFtrbsrYjwFSqQknAOAT9/VEgZiCgrhicrEVn6N6ei5xDITCVVQVVlb8Z0f10CPnna/IE2Mr+0kRERETUbPh8cO7YsSM6deqElJQUfPvttzWaan/99dcIDw/HzTffDAA4fFjqR9mzZ0+o1eoax+vXrx8A4MiRIzKUnqhpUEVFObVd7L8fQUArwHLhBCxZp2DOSYXl0kVYjALMRgElF7UoOhdY53Eyvj6JoLTvEXR9FgKHXw9NTFvoMv2Q9Wc4rp6OylSmlJb/3Q8h3RtydfXjkVHFiYiIiMitfD44K5VKfPnll5g4cSJuv/12vP322+jSpQtyc3Pxxx9/oHv37li2bBkiIiIAAOnp6QCAmJgYu8erXJ6WlibPBRA1AQH9EqAKBEwljptYBxx4DIICUEJ6qRUAogD4hQKR8VBdVKLonBPPjsmMK1u34srWrQAAdfv2MOXmVqy0c35BQM6rixA8ZoxbQ6xu40bkvLoIposXrctUrVoh+pmnnR4YjYiIiIiaHp8PzgBw3XXXYfv27bj55ptx6NAhHDp0CACg0Wgwbtw4dOzY0brtlStXAAABAQF2jxUYKNWWFVeb8/Zqer0eer3e+r1Op2v0NRB5kpC5F9EJl6vV+NYc1Cu6rw5CcEugdW8gsqvtKzASEAQEGA1QrezjOIAHCoj54nuU7N6DKzv/QNlfSTCeP++4gKII08WLKD1wEIGDnB/xvj50Gzcia958qRl6NaacHGl5PUYVJyIiIqKmhfM4A/juu+8wcOBAxMbGYu/evbhy5QpOnTqF2bNn4+2338bo0aNtgm5jLVq0CKGhodZXbGysy45NJLvCDGDX+wiJLUfb6wqg8rfYrFYFmNH2ugKExJYDiYuAO36S/h0wB2h/HRAUZZ2ySlBrEP3QnRV7Xj1uYUUAf+hO+Pfpg8j770P7r79G1927EH7nnXCGKS+vMVdaK9FsRs6ri2qEZmmltCzn1UUQzWa3nJ+IiIiI3Mvna5xPnz6NWbNmoWXLlli3bh2CgoIAAF26dMGnn36KCxcuYN26dfjiiy/wwAMPWNeXltofDKmkpAQAEBwcXOs5n376aSxYsMD6vU6nY3gm7yKKQMY+aSTrE2sBUQqEIbHlCG5bbmdU64r9gqLrPHTlPM05H34FU0nVclWgYDuPcwVlSAiCx45FwVdf1XlsZ/ti11fpgYM2zbNrkKHGm4iIiIjcx+eD8/fffw+j0YjExERrKK5u5syZWLduHXbs2IEHHngA7dq1AwBkZmbaPV7l8ri4uFrPqdVqnR7Nm6hJMRmA46ulwHzhUNXy9sOBnGNAWQEEhYjA6KvndK6YDipuqFOnCbn7GQTf+ThKf/0fTBfSoWrTDgE33QVBrbG7fcCA/lC1agVTTo79Wl8AEASUp6QgoH8/CE7OsV4X0WRCyd69yP/4E6e2z337bYRNuxkBgwZB07693XmsiYiIiKjp8fngXBl0Q0ND7a6vXF5QUAAA6NOnDwBp/mej0VhjZO3K/tG9e/d2S3mJPKLkEnBwKbD/M6A4W1qm1AK9/wYMegBo1RM4vgZYfhekvsnVw2tFOEys33RQglqDwCn/cG5bpRLRzzwt9SUWhFqbTOe++iqKfvwR0c8+i8DBg6pW1WMkbNFiQVlSEnTrfoHut99gvnTJ6WsqP3IEFytG3Fe1aoXAQYMQMGQwAgcPhrpVK6ePQ0RERETy8vng3KriP6sHDhywu37//v0AgPbt2wMAOnTogGuuuQYnTpzAL7/8gqlTp9psv2LFCgDApEmT3FNgIjnlngD2fAwc+QEwlUvLgqKBa/8B9J8j9U+u1H0yMPN/wIYnAd2FquUhbaTQ3H2yW4saMn48sGSx/VGtn3oS5sJC5L27GPrTp5E+ezaCExMR/a/HUXbsWJ0jYYuiCP3Jk9D98guKfv0VpgvZ1m2VYWEIGj8OVzZvgbmgwH5oFwQoIyIQdustKNu3H2V//QXTxYsoWr0aRatXAwA0cXHWEB0wcCBUFSP5ExEREZHnCaJYW7tG33Do0CH0798fAPDRRx/hgQcesK7bs2cPxo4di5KSEmzatAljx44FAHz22We499570aVLF+zcuRMtW7YEAPz888+YPn06OnfujBMnTkDlZHNQnU6H0NBQFBUVISQkxMVXSFRPFgtwZrPUHDt1a9XyVr2BIQ8BPW4GVA66GljMQNou4EqOFLLjhtarprmxHNUemwsLkffe+yj4/nvpOlUqwGSqeZCKJtTRzz4Lc1EhdL/8CkNqqnW1IiAAwePGImTCBAQOGQJBra4aVRuwDc8Vx2pbbVRtS3k5yg4dQsmevSjZuwflR5Ol8lSj7dZNqpEePAgB114LpZ2uJM5eNxEREVFzJVeW8vngDAD/+te/8NZbbwEAevToge7du+PChQvYvXs3LBYL5s6di08//dS6vcViwYwZM7By5UqEh4djzJgxyM/Px/bt2+Hn54etW7di0KBBtZ2uBgZnahIMJUDSt8DeT4BLZ6RlggLoNgEY/CDQbog1BHq78pQUXHz5FZTV0tLEHkGjQdCIEQiZMAFBI0dA4edXY5uGzuNsLi5G6f4DKN27ByW790B/6pTtBkol/Hv2RMDgwQgcMhj+CQk25+f80UREROSrGJxltnLlSnzyySc4ePAgioqKEBwcjISEBNx777249dZba2xvNpuxZMkSfPHFFzh79iwCAwMxatQovPTSS+jevXu9zs3gTB5VmAHs/y9wcBlQXiQt04YA/e4CBt4LhLf3ZOnc5srevciYNbvO7fx69UL47bcheMwYKB2Mll/JFTW/pkuXULpvH0p270HJ3j0wpqXbrBc0Gvj37YvAIYMBQUDe4iU1m4jbqekmIiIiam4YnH0IgzO5XF3NpUURyNwvNcc+vsY6nRTCOwCDHwASbgO0dYdEb1a07hdcePzxOrdr89ZbCJ04QYYS1c544QJK9uy11kibcnOd21EQoIqORuctm9lsm4iIiJolubKUzw8ORtTsHF8DrH+iavRroGKArteB+BurppPKOli1vsP1UnPsLuNl7Y/sSc7O6eyuuZ/rQ92mDcKm3YywaTdDFEUYzp1H6d49KPp1PcoqBjC0q2L+6OJt2xAyZox8BSYiIiJqZhiciZoT65RQVzUk0WUDy+8E/MKA8kJp2dXTSfmYOud+rqitDRjQX/7COSAIArQdO0DbsQMUwSGOg3OFrIceRl779vBPSJBefROg7dyZtdBERERETmJwJmouLGZpKqirQzNQtay8EAiIAgbNrTmdlI+xnfsZV009XTGq9jNPN+lwWZ/acMP58zCcP4+iVasAAIrAQPj17gX/hAQE9O0L/z59oKxlPnsiIiIiX8c+zk0A+zhTg5mNQFEGUHAeOL0Z2PNh3fvcsRLoPNrtRfMW3jwitWg248yYsXXWmnf4aQXKk5NRmpSEsqQklB8+AktpaY3NNR07VtRK94F/QkWttEIhw5UQERERNQwHB/MhDM5UK1EEruQChWlAQZoUkAvPV3ydBugyAdFS11FsTf8c6DXDHaX1Wt48B3J95o+uJJrN0J85g7K/klD2118oS0qCIS2txrEVQUHw793b2rzbv08fKPkzioiIiJoQBmcfwuDs4/RXKoLx+WrhuPLfdMBYs2bQhsoPCIuTppDKqru/K2atAzoMd0HBqalwRa25qaAAZUlJKEs6LP179ChEe7XSnTtJzbsr+ktrOnZkrTQRERF5DIOzD2Fw9rC6pm5qLLNJqhm2F4wL0oDS/DoOIAAhbaX5lMPjpH/D4qq+D4qWahctZmBxT2kgMLv9nAVpdO35R31m5Gxf4upac9Fkgv706YownYTSpKQa80kDgCIkpKpWOiEB/n16OzXfNREREZErMDj7EAZnDzq+RhpQS3ehalnl1E3dJzt3DFEESvKrheHztuG4KKtqnuTa+IfbhuHq4Tg0FlBpnL+e5XdVFqzaCqnZLmb+z/nrIrqK6dIllB0+LDXxTkpCWXIyxLIy240EAdqKWmmpiXdfaNq3Z600ERERuQWDsw9hcPaQ2qZushcyDSVSs+naao2NJY7PpdQCYe1qrzX2c+Foxnb/GNAWSHyNoZlcSjSZUJ6SYtPE25iRUWM7RWgo/Pv0tjbx9uvdG8qgIOfO4cX9z4mIiMj9GJx9CIOzB1ibNV+ofRu1P9CyhxSYS3LrOKAABLd20Jy6FSBnjZu7m58T1cKUn2/TvLs8+RjE8nLbjQQB2i5dqmqlExKg6dAeQsWAZpW8ecRzIiIikgeDsw9pzM02Go0wm+toBky2zCbg5Dpg6yv1208dDIS2kWpvQ9pUvGKkZUGtAbXWPeVtIKVSCbVa7elikI8TjUaUn0yxhumypCQYs7JqbKcMDYVfQh9p0LG+fWHKy8OFJ56sOc2Wg9HCiYiIyPcwOPuQhtxsnU6H/Px86PV6N5fOy4kiYDECZoM057HJAFgM9ue8tUcbDGgCAYUKELyvj6ZWq0VkZCRbMlCTYszNtWneXZ6cDNFgcP4AFfNTd96ymc22iYiIfJxcwVnltiOT2+h0OmRlZSEoKAiRkZFQq9U1mjj6JFEETHrAVAYYywFTuRSUYQGgrHj5VWysqFheh7A4KTh7GVEUYTQaUVRUhKyK2j2GZ2oq1C1bQj1+vLXGWDQYUH7ypLVG+srevbBculz7AUQRposXofv1V4RMnMiff0REROR2rHFuAur7V5LU1FSo1WrExMT47n8YRVEKxsZS6WUolcKyvTAsKAB1QMXLX/pXpQFyjku10bVRqIHoHtamod5IFEVkZmbCaDSiY8eOni4OkVOK1v2CC48/7tS2ipAQ+PfsAb8ePeDXoyf8evaEum0b3/3ZSERE5GNY40x2GY1G6PV6REZG+s5/DGuE5DLAWAbnQ7LWfvgNjQEKztV+3tAYrw7NACAIAkJDQ5GVlQWj0cg+z+QVVFFRzm2oVMKi06Fk126U7NpdtTgsDH49e8KvZw/495TCtCo62nd+ZhIREZHLMTh7mcqBwJptAGpQSPavCsu1hWR7/MMAdACKMm1rnhVqKTT7hzX+epqAys+K2Wxuvp8balYCBvSHqlUrmHJy7I9HUNHHudP6X2E4dw5lyckoTz6G8uRklJ86BXNhIUp27kTJzp3WXZSRkfDr0R3+FbXSfj17QN2ypYxXRURERN6MwdlLNYuaEzlDcm38w6Q5lA1XpMHDlGpAE+T1Nc3VNYvPCvkUQalE9DNPI2vefOlZrB6eKz7P0c88DYW/P/y6d4df9+7ATGm1Ra+H/tQplCcno+zYMZQnH4P+9GmY8/NRsn0HSrbvsB5K1bKlbc10jx5QtWgh45USERGRt2BwJnnYhOSyij7JMofk2giCNHo2ETUZIePHA0sW15zHOTra4TzOCq0W/r16wb9XL4RXLLOUl0N/8iTKKmuljyVDfzYVptxcXPn9d1z5/feq47dpLdVK9+ghheoe3aEKD7d7LiIiIvIdDM7kevUOydUCsrtDMhF5jZDx4xE8ZgxKDxyEKS8PqqgoBAzoX+8pqBR+fvBPSIB/QoJ1maWkBOUnT9rUTBvOnYPpQjaKL2SjeNMm67bqmBj49ewpDULWsyf8uneHkqPUExER+RQGZ3Ko1GBC9xd+AwAc//cNCNBc9ZGxF5JNZYDIkExEjScolQgcNNDlx1UEBiKgf38E9O9vXWa+cgXlx49b+0uXHUuGMS0dxsxMGDMzUbxhg3VbTVxcVa10zx7w694DyiDvm7qOiIiInCNbcC4tLUV+fj5atGiBwMCq/1wUFBTg9ddfR3JyMtq1a4d//vOf6NSpk1zFovoQRSkcMyQTUTOkDApC4MCBCBxYFdTNRUUoP35cGoDs2HGUJyfDmJkJQ1oaDGlp0P36q7ShIEDToYPNSN5+3bpBERDgoashIiIiV5ItOL/88st44403sG/fPvSv+Au/Xq/H4MGDcebMGVROJ71ixQocPnwYrVu3lqtoZE9lTXJpcdWynGT7n5gmEJKXLVuGOXPmYOnSpZg9e3a99xdFEStWrEBCQgK6dOni+gK64BxHjx5F7969MX/+fLz77rtuKCERXU0ZGorAIUMQOGSIdZmpoMAaosuPJaMs+RhM2dkwpKbCkJoK3Zq10oYKBbSdOlX0le4B/549oO3WDQo/Pw9dDRERETWUbMH5999/R6dOnayhGQC+/vprnD59GqNHj8aTTz6JX375Be+99x7effddvPHGG3IVjRw1tzZWq00WLYCg8nhIdjVRFPHQQw/h448/RnR0NLZu3YprrrmmyZ2jV69e6NChA9asWcPgTORBqvBwBA27DkHDrrMuM+Xno/zYMWt/6fLkZJhyc6E/fRr606dRtHKltKFSCW2XLtVG8u4JbXxXKDQaD10NEREROUO24Jyeno5+/frZLFuzZg0EQcDSpUsRGxuLcePGYcOGDVi/fj2Ds7s0pE9ypchuQECAV4dkex5++GF8/PHHAICcnByMHj0aW7duRbdu3ZrcOaZMmYLFixcjOTkZPXv2dFn5iKhxVJGRCBoxAkEjRliXGXNyUX6sqr90efIxmC9dgv7kSehPnkTRip+kDdVq+HXtWtFnWgrU2i5dIHDedSIiIodEsxklBw7Ici7ZgnNBQQHCwsKs34uiiJ07d6J3796IjY21Lu/Tpw9+++03uYrVvFlDckW/5Ib0STaaAZyVtlF7d82yPQ8//DA++ugjxMXFIS0tDe3atUNGRoY12MbHxzepc1QG5zVr1jA4EzVx6uiWUEe3RPDoUQCk33umixelmunkZGvNtLmwUArYx44By6V9BY0G2m7dpJG8e0h9prWdOkJQcUxPIiIiANBt3IicVxehMCtLlvPJ9hu4VatWOHfunPX7gwcPoqCgAHfeeafNdkIzC2b1cn4X0HMcoKjfVCsAGhmS/QGVn91QbLaI1q/3nbuM4V2ioFQ0j3v0xhtv4MMPP0Tfvn3x2WefoX///hg3bhz69++PBx98EGPHjkVKSgoCGjG4j6vPMWzYMISHh2P16tV45plnGlwuIpKfIAhQt24NdevWCB47FoAUpo1ZF6r1l5YCtaW4GOVHjqD8yJGq/f384Netm3Ukb/+ePaHp0KHe03MRERF5O93GjciaN1/KQDKRLTgnJCRg3bp1WLVqFcaMGYOXX34ZgiBg4sSJNtudPn0abdq0katYTct3M4GotkDi60D3ybVvJ4qAsRywlFY0ua5odt3IkHy1DcnZeHHNMev3s5fuR+tQP7w4qTsSe3r/4G133303jh49iiVLlkCn01mXP/DAA7BYLAgODm5UaHbHOVQqFSZMmIBvvvkG2dnZHESPyMsJggBNTFtoYtoiJPEGABVhOj3dZiTv8mPHYCkpQVlSEsqSkqr2DwiAX/dr4F9RK+3Xswc0cXEQFAoPXREREZF7iWYzcl5dJGtoBmQMzk888QTWrVuH6dOnA5D+Y5CQkIDRo0dbt8nJycHhw4dx6623ylWspkeXDSy/C5j5Pyk8W8zApTPAhb+AC0nA5Uyg0yygoBxQXRV+BQWg8gc09Q/JV9uQnI0Hvj6Eqz+OF4vK8cDXh/DxHf28PjxHRkbiq6++AgCbUAsADz30UJM9x5QpU/D1119j7dq1mDt3bqPLSERNiyAI0MTFQRMXh9AJEwAAosUCw/k0lB9LlvpMJx9D+fHjEEtLUXbgIMoOHLTurwgKkvpLV4zk7dezJ9SxsQ1q0SWazSg9cBCmvDyooqIQMKA/a7iJiMhjRLMZug0bYLp4UfZzyxachw4dipUrV+Ktt95Cfn4++vfvj1dffRWKan8V/+677xAcHIzExES5itUEVUTVVQ8Auz8ELh4FjCVVq4NigY53AlBI4bhaSBaVWpSZLLaHMprrXQKzRcSLa47VCM2VhxQALFxzHNd1jmx0s21/tdK3m+c3QGJiIrRaLVavXs3gTOQjBIUC2o4doO3YAaGTJgGQ/vNgSE21Gcm7/MQJWK5cQenevSjdu9e6vyI0FP49ulv7S/v37AFVmzYOf/5W9h2r/p8TVatWiH7maYSMH+++iyUiIp8liiLMBQUwZmbCmJkJQ4b0rzErE4bMLBizswGj0SNlk3WUkUmTJmFSxS98e+bPn4/58+fLV6CmzHAFyNgjfa0OAFr1BtokAK2uBdStgagugL+/zS5lBhO6v+D+gdVEABd15ei1cGOjj3X83zcgQMPBbuojKCgIo0aNwpYtW1BSUoLAwEBPF4mIPEComNpK26ULMHUqAEA0maA/e7aiVlrqL60/eRKWoiKU7NqNkl27rfsrw8NtRvL269kTquhoCIJQa98xU06OtHzJYoZnIiJqEEtpKQwVwdiYmVnxdZb1e0tpqeMDKJWAuf6Vg40lW2L53//+h86dO2Po0KEOt9uzZw9OnTqFu+66S6aSNWED7gEG3gtEdq0aMKy8HDh3rtmNbk3107t3b2zYsAEpKSk1pnkjIt8lqFTwi4+HX3w8wiq7RhkM0J85YzOSd/mpUzAXFKBk506U7Nxp3V8ZGQm/7t1RdvCg/b5joggIAnJeXYTgMWPYbJuIiGoQjUYYs7OvCsUZUo1xZibMly/XeQxVy5ZQx8ZCE9MW6rYxUMfEQB3TFprYWChbtMDZ8TfAlJPTPAcHmz17NmbPnl1ncP7888/xxRdfMDgDQI+bgZbXOL25v1qJ4/++odGn3XfuMmYv3V/ndsvmXIuBHSIadS5/Nf/T1RAbN25EVFQUEhISPF0UImriBI0Gft27w697d2CmtMyi10N/6pRtzfSZMzDn56Nkxw7HB6yYViv///4PgQMHQhkWJr1CQzldFhGRDxBFEaa8vGqB2LbG2HjxImCxM2hxNYrQUGjatq0IxFWhWN02Buq2baDQah3uH/3M01ILKBkrE5vcbziLxcI+rxCAkDZAnOM/MtTYSxBc0ux5eJcotA71w8Wicrv9nAUArUL9mtXUVN4kPT0dSUlJmD17ts0YAUREzlJotfDv1Qv+vXohvGKZpawM5SdPouCH5dCtWlXnMfKXvIf8q48bHAxleHhFmA6tCtUVL9VV3yvDwiD4+/P3PhFRE2PW6Wo0ozZkZkhfZ2VB1Osd7i9otVC3bSsF4pjYqnBcEZSVISGNKl/I+PHAksXS6NrNbR5nZ6WmpiKkkW+kd6v4z0Piaw2bz9kFlAoBL07qjge+PgQBsAnPlf+1eXFSd4ZmD1mzZg0AaXRtIiJXUfj7I6BvX4gGo1PBWd2pI2AywVxQCEvFrAGW4mJYiothTE93+ryCRlMjTDt8hYdBGRLCZuJERI1g0ethzLogDbqVkWFTY2zIyoKlqMjxARQKqFu1sq0xjomBOiYW6pi2UEVGun1qxJDx4xE8Zgwubt8OjBnj1nMBbg7O//73v22+T0pKqrGskslkQkpKCnbs2IFx48a5s1hNW0gbKTQ7msdZBok9W+PjO/rhxTXHkKOr+otSq2Y0j7O3Wr16Nfz9/TGeA/MQkRsEDOgPVatWtfcdEwSooqPRac0aa3gVTSaYdTqYCwurXgWFtt/beYlGI0SDAabcXJhyc50vpCBAERJiU6ttrza7qvZbein8/Fz0LhERNW2i2QxTTo5tM+qsqlGqnfmZq4yIgDomRupnXBGIK2uM1a1bQ1CrZbgSxwSlEoEDBshyLrcG54ULF0IQBIiiCEEQkJSUhKSkJIf7tGzZEq+++qo7i9V03boc6DnOYzXNV0vs2RrXdY60jp69bM61bJ7tYUVFRdi+fTsSExMREBDg6eIQUTMkKJW2fceqh+eKJtXRzzxtU+MrqFRQRURAFeH8uBeiKEIsLYW5sBAmu8G6yG7YthQXA6IIS1ERLEVFMKbVo3bbz89OLbbjJuWKkBC315o4wrm0icgeURRhLiy0HZk6o7LGOBPGC3VP2yQEBEj9jGOvCsVtpbCs4MwtNtwanJcuXQpAurF33303hg0bhnvuucfuthqNBm3atMHgwYOhraMzeLPVfmiTCc2VqofkgR0iGJo9bP369TAajWymTURuVb3vmM08ztHRLpvHWRAECIGBUAQGQt22rdP7iUZjVe12QUGNYF0zhEsBHCYTxPJymC5etLmmOikUUIaEON+MvDJwu+D/MpxLm6hpc/cftiylpTBmZdWcyzgzE8aMjLqnbVKpoG7TxmZkak1sjLV5tTI8nGNM1INbg/OsWbOsX3/55Ze48cYbbZYRNRXt27eH6Obh7F1xjtWrV0OhUGDixIkuKhURkX2VfceaWm2noFZD1aIFVC1aOL2PKIqwlJQ43YTcWrtdUgJYLNbv61VOf/+qGuxwJ0J3WBgUwcHW/8RyLm2ips0Vf9gSjUYYL160HYQrI0OqMc7MgvnSpTqPoYqKkoJwbIxUY1wZkGPaQtWqlcd/Zjcnsg0OtnXrVrlORdQsGY1GrF+/HoMGDUJ0dLSni0NEPkBQKhE4aKCni9FogiBAGRQEZVAQEBPj9H6iwQBzUZGD2mw7QbyoCDCbIZaVwVRWBlN2NhyPPVuNUgllaCgUoaEwZmbWPpc2gJxX/oOgkSOh0Gicvh4icg1n/7AliiLM+fkVoTizxijVxosXAbPZ4bkUwcFSKL5qLmN1TAzUbdpw7AYZNblRtYnIvtTUVAwbNgy33HKLp4tCROQTBI0GqqgoqKKinN5HFEVYioudqtGuCuJFEEtLAbMZ5suXYb58uc7zmHJzkdInAcrIFlBFRkm18JGRUEVFQhUZCWVkJFQtqr5XhISwSSaxz7wLiGazNAWSgz9sXfjXE8hdvASmCxcglpc7PJ6g0VRM21TRjLqt7SjVytBQd1wGNYCswfn48eN44403sGPHDmRnZ8NgMNjdThAEmEwmOYtGtQjQqHD+tQmeLgYBiI+Px7p16zxdDCIickAQBKlPdEgI0K6d0/tZDAZr7bVu/Xpc+uSTuncSRZjz8mHOy6+zVltQq6GMqgjTkZHWkK1sURG8KwK2qkULWQYEag4BztuuwZN95r3hvRItFlh0OpgKCqpakhQUwFxYAHNBgXW5IT29znESRL0extRU6RtBgKpVq6qBtyoH4YqNhbptDFRR7p+2iVxDtuC8e/dujB07FmVlZQCAiIgItGrVSq7TExERETVZCo0GiuiWUEe3hLmw0Kng3GbJYmjbtYMpPx+mvHyY8vNhvlT1deXLotNBNBphupAN04XsOo8rBARUhevISKgiW0g12JGRVSG7hbSsIU3Fm8OgZw29Bk8FSE/2mffE/ba2/KgYQNAahgsKrGHYVDm4YOXywkLAYnFZGSLm3ovw6dOlaZvYpaJZEER3j4hUYeTIkdixYwfmz5+P5557DhH1mLKiudPpdAgNDUVRURFCQkIcblteXo5z586hQ4cO8GOfBqukpCSsWrUKU6dORUJCgqeL06TwM0NE5F1Esxlnxoytcy7tzls2OxW6LHo9zJcuVYXpvHyYLuXDXC1wmy5dgikvD2JFBYezFKGhVQG7RQupFrsyYEe2sK5TRkRAUCprDXCVU521dfOgZ64Irg29hsYEyMaU2/p5qq2WtJ6fp/pwxf22HdyvwPqyCcPVaodNFbXFaGDrVUVgoDQHfHg4lOFhUIWHQxkWXjFqfjhM+fnI/+CDOo/T7ssvm8UYEd6gPlmqMWQLzkFBQejatSsOHTokx+m8CoMzuRM/M0RE3scaOAC7c2m7K2BaSkqq1VZfgik/T6rJtgbuqgBe1xyxNhQKKMLDYSkqchhoVC1botPmTW4Z9MwloyCbzTgzZgxMF3Psb1BLCG1MgGxU4DaZULRuHbKferrOa1N37Ah1RAQEPz8IWi0UWi0ErRaCVgOFVlom+FUu96tYXu1rPz8IGi0UftJ+UKmQfsedMOXl1XpOZXg4Wj71JCxFRTVrgCsDcmFh/T5r1QgBAdK87NYgHG6dOk5l/b4qJCvDwur87Ln6D1vUeM0uOEdERCAxMRHffvutHKfzKgzO5E78zBAReaem3KRZFEUp7FgDdj5M+XlSwM6/ZNNU3Hz5cr2bwCojIiqag7eQ+mW3aCH1x27RQmo63kJqQq6KiHCqGWx9g6toMsGYnQ1Dero0PVB6BgzpadCfTJFGPK9D4OhRCOzfH+qYWKjbtEbmQw/DlJtrf2MHQas+5TYVFECfkgJ9SgrKU05JX585A1Hv9LjuTZqg1UIZESGF3rDqITjcNghXC8iumE/dHk/9YYvsa3bBefLkyUhPT0dSUpIcp/MqDM7kTvzMEBF5L28YVKkuotkMc0EBCn9eibx33nH58RUhIda5vZWRlSE7omIgtBZQhocj89F5MOfn136M4GCETJggTReUkQ5j1oUGN/VtqOiFCxE07DppSrKgIMBicdzEGlKzYr++fWFISam9ZlejAWoZkLe6yPnzoW0fB0t5OUS9AaK+HBa9HmK5HqJBD0u5HqJeeln0lV+Xw6I3QCwvt9nGotfDUlrqVE2xpktnaDt3rtYk+upm0lI4Vvj713ksOTXlP2z5mmYXnJOTkzF48GC8/vrreOihh+Q4pddgcCZ34meGiIiagpK9+5A+a1ad27V9bwk0cXEVg51dkmqwL+XDnH8JpsuXbb52Z7gVNBqoY2OlOXPbxUIT2w6W8jLkvV13+A+ZNAmwWGDMzET52bMQr1yp38mVSij8/WGp537qdu3gF98V2q7x0MZ3hV98PFRt2uDsuPGyNy129n57c1/g5vCHreZAruAs26jahw4dwpw5c/Doo49i+fLlGDduHGJiYqCoZfj1u+66S66iEREREZGbBQzoD1WrVnUGuOAxY6TwER/v8HiixQJzUZEUri9dlkYUz78k9cO+lA/zpcswXboEY3q6NFhUHYJGjULQ6FHQtIuDpl0sVNHRNaYJEs1mFHzzbZ3X0Oa1RdYA5WyAVISHSzW3ZWWA2ex0aA6dPh1hM6ZD26UrlEH2pxKLfuZpqWmxINhtWhz9zNMuD3zO3u+AAf1del45CUql14Z+qj/ZapwVCgUEQUD10wkVD2t1oihCEASYzWY5itUksMaZ3ImfGSIiaio80TfU1TWf9b2G+g4mZdHrYS4qwpU//sDFZ59zabnlblrMvsAkh2ZX4/zCCy/YDcpERERE5BtCxo8HliyuGeCio90W4Fxd81nfaxCUynrV+Cq0WihatkTY1KnIf/8Dl5Y7eMwYWZsWe+J+E7mLbDXOVDvWOJM78TNDRERNjdx9Q91R81nfa2hIjW9zqbFlX2Byp2Y3OBjVjsGZ3ImfGSIioqYxCnJDAmRTKDdRU9bsmmo3dXl5eXj99dexdu1apKenw9/fH+3bt8eYMWPw5ptv1th+7dq1eOutt/DXX38BAPr164d//etfmDBhgtxFJyIiIqI6eKKp8tUaMphUUyg3EclY4zx69GintxUEAVu2bHFjaWwdPHgQN9xwAy5duoQePXqgZ8+e0Ol0OH78ODIzM2G6aqqDxYsX47HHHoNKpcLYsWOh1WqxceNGlJWV4f3338fDDz9cr/OzxpnciZ8ZIiIiImquml2N87Zt2+rcpnLUbTkHEcvLy0NiYiLKysqwevVqTJ482Wb9vn37bL5PSUnB448/Dq1Wi61bt2LIkCEAgFOnTmHo0KF47LHHkJiYiM6dO8t2DUREREREROQ+9idRdoNz587ZfZ09exbbtm3DM888g4CAAPzrX/9CamqqXMXCiy++iPz8fLz55ps1QjMADBxo25xmyZIlMJvNuP/++62hGQC6du2KZ599FiaTCUuWLHF7uWVjKAEWhkovQ4mnS0NERERERCS7JjU42O+//44bbrgBy5cvx8033+z285WVlSE6OhoWiwV5eXnw9/evc5+4uDikp6fjjz/+wLBhw2zWZWRkoF27doiLi8P58+edLkeTbqptKAFebSN9/cwFQBPo/nOSS7GpNhERERE1V3I11ZatxtkZo0ePxoABA/Daa6/Jcr4DBw6guLgYffv2hb+/P9avX48FCxbgwQcfxOLFi3HhwgWb7QsLC5Geng4A6Nu3b43jxcbGIjIyEmlpadDpdLJcA0mWLVsGQRCwbNmyBu0viiJ+/PFHnD592rUFq6a0tBSrVq1CUlJSg/Y/evQoBEHAY4895tqCERERERGRQ00qOANATEwMjh07Jsu5jh8/DgBo2bIlpk6diptuugnvvvsuPv74Yzz22GPo3LkzvvvuO+v2laE5PDwcgYH2a15jYmIAAGlpaW4uPbmKKIp46KGHMHPmTAwfPhwnTpxwy3lyc3Nx880344MPPmjQ/r169UKHDh2wZs0aF5eMiIiIiIgcaVLBuaysDPv375etOWlBQQEAYM2aNdiwYQM+/PBD5Obm4vz583j88cdRVlaGWbNmWWsIr1y5AgAICAio9ZiVgbq4uLjWbfR6PXQ6nc2LPOfhhx/Gxx9/DADIycnB6NGjcfLkSQ+Xyr4pU6YgNTUVycnJni4KEREREZHPkC04p6en1/o6fvw4Vq1ahfHjxyMjIwOJiYmylMlisQAATCYT/v3vf+PBBx9EVFQU4uLi8Oabb+Jvf/sbjEaj3XmcG2PRokUIDQ21vmJjY116fJeymKu+Tttl+30z8PDDD+Ojjz5CXFwcAKBdu3bW8JySkuLh0tU0ZcoUAGCtMxERERGRjGQLzu3bt0eHDh3svnr16oXp06fjzz//RNeuXV0eVGsTFBRk/XrOnDk11lcu2759u832paWltR6zpEQaeTo4OLjWbZ5++mkUFRVZXxkZGfUvvByOrwE+rDaq+DczgMU9peXNwBtvvIEPP/wQffv2xc8//wwAGDduHD788ENkZ2dj7NixDu+1JwwbNgzh4eFYvXq1p4tCREREROQzZJvH+frrr691fmaNRoPWrVtjxIgRuPXWW2Vrql1ZyxgQEICoqKga69u3bw9A6psKSLWRgNTEu6SkxG4/58zMTJtj26PVaqHVahtVdrc7vgZYfheAqwZd12VLy2f+D+hec/oub3L33Xfj6NGjWLJkiU1z+QceeAAWiwXBwcEOm+V7gkqlwoQJE/DNN98gOzsbrVu39nSRiIiIiIiaPdmC87Zt2+Q6ldMqR8YuKyuDXq+vEWYvX74MoKqmOSwsDO3atUN6ejr++usvu9NR5efnIy4uzq1DoddKFAGjC2pILWZg/ROoEZqlkwAQgA1PAh1HAgpl486lDgBq+YOKu0VGRuKrr74CgBr9zB966CFPFMkpU6ZMwddff421a9di7ty5ni4OEREREVGzJ1twboratWuHPn364PDhw9i+fTvGjx9vs76yiXb1qacmTJiAjz/+GCtWrKgRnFesWAEAmDRpkptLXgtjadWcy24lAroLwGsu6JvNuaHrLTExEVqtFqtXr2ZwJiIiIiKSgUdG1TYYDNi3bx9WrlyJlStXYt++fTAYDJ4oCp544gkAwOOPP47s7Gzr8qSkJLz99tsAgPvvv9+6fN68eVAqlfjkk0+wZ88e6/LTp0/jP//5D1QqFebNmydT6ckXBQUFYdSoUdiyZYu1Tz0REREREbmPrDXO5eXleOGFF/Dpp59ap3aqFBQUhPvvvx8vvfSSbH2cAeC2227Dxo0b8eWXX6J79+4YOnQoysrKsGvXLuj1etx7773429/+Zt0+Pj4eb775JhYsWIDhw4dj3Lhx0Gg02LhxI8rKyvDee++hc+fOspXfhjpAqsFtrLRd0kBgdbl9BRA3tHHnUjetPsTeonfv3tiwYQNSUlLQr18/TxeHiIiIiKhZky046/V6jB07Frt37wYg/ce/ffv2EAQB58+fx+HDh/HWW2/hzz//xJYtW2QdPGvp0qW47rrr8Omnn2Lbtm0QBAH9+vXDfffdh1mzZtXY/rHHHkPnzp3x5ptv4o8//gAADBgwAE888QQmTpwoW7lrEATXNHvuNBoIaSMNBGa3n7Mgre80uvF9nH1AaWmp3UHGDAYDlEollMr6v4cbN25EVFQUEhISXFBCIiIiIiJyRLbg/O6772LXrl0YNmwYPvzwQ/Tq1ctmfXJyMh5++GH88ccfWLx4MZ588km5igZBEHDvvffi3nvvdXqfSZMmea4vs7splEDi6xWjaguwDc8VA3klvsbQ7IRt27bhlltuwU8//YS2bdtalxuNRsyYMQMBAQH45ptv6hWe09PTkZSUhNmzZ0Oh8EhvCyIiIiIinyLb/7q/++47REVF4ZdffqkRmgGgZ8+eWLduHSIjI/HNN9/IVSyqTffJ0pRTwa1sl4e0aRZTUcmlpKQEBQUFuPHGG7Fr1y4AgMlkwt///nesXbsWOp0OZrO5Xsdcs0aaR3vKlCkuLy8REREREdUkW3A+c+YMRo4cieDg4Fq3CQoKwsiRI3H27Fm5ikWOdJ8MPLSv6vvbVwDzjzI018OECROwYsUK6PV6zJ49GwDw7bffYuXKlbjhhhuwcuVKaDSaeh1z9erV8Pf3rzEKPBERERERuYdswVmlUqG0tO45hktLS6FS+fQsWU1L9ebYcUPZPLsBJk2ahB9//NH6vdFoxLhx47Bq1ap69+UvKirC9u3bMXbsWLv9pomIiIiIyPVkC869evXC77//jtTU1Fq3OXfuHH7//Xf07t1brmIRyWLy5MlYvnw51Go1xowZg9WrVzdo9Pj169fDaDSymTYRERERkYxkC8733XcfysrKMHLkSHz++ecoKyuzrisrK8PSpUsxcuRIlJeX28ybTCSH9u3bQxRFfPbZZ247x9SpU5GXl4cNGzbA39+/QcdYvXo1FAqFZ0dvJyIiIiLyMbK1ib7zzjuxc+dO/Pe//8XcuXMxd+5cREZGAgDy8/MBAKIo4r777sPtt98uV7GIZBUaGtrgfY1GI9avX49BgwYhOjrahaUiIiIiIiJHZO1M/Omnn2L8+PF47733sHfvXuTl5QEANBoNBg8ejEceeQTTp0+Xs0hUF00gsLDI06UgAKmpqRg2bBhuueUWTxeFiIiIiMinyD4K1/Tp0zF9+nSYTCZcunQJANCiRQsOCEZUh/j4eKxbt87TxSAiIiIi8jkeS6sqlYrNTYmIiIiIiKjJk3Ue5/feew/Jycm1bpOcnIz33nvP4cjbRPYkJCTgxRdfREJCgqeLQkREREREzYxsNc6LFy/Gp59+irNnz9a6TXBwMBYsWIDU1FQsXrxYrqJRM5CQkMDQTEREREREbiFbjfOWLVuQkJCAdu3a1bpNXFwcEhISsGnTJrmKRUREREREROSQbME5IyMDHTt2rHO7Tp06ITMzU4YSEREREREREdVNtuCsVCqh1+vr3E6v18NsNstQIiIiIiIiIqK6yRacu3btip07d6K0tLTWbUpLS7Fz50506dJFrmIREREREREROSRbcJ4xYwYuX76Mf/zjHygpKamxvrS0FPfeey8KCgowY8YMuYpFRERERERE5JBso2o/8sgj+Oqrr/DDDz9g69atuPXWW9GpUycAwNmzZ/Hdd98hNzcX8fHxmD9/vlzFIiIiIiIiInJItuAcEBCAzZs344477sDvv/+OxYsXQxAEAIAoigCAUaNG4auvvkJgYKBcxSIiIiIiIiJySLbgDACtWrXC5s2bsX//fmzevBkZGRkAgNjYWIwdOxbXXnutnMUhIiIiIiIiqpOswbnStddey5BMREREREREXkG2wcGIiIiIiIiIvBGDMzlUaixFry97odeXvVBqrH0qMSIiIiIiouaKwZmIiIiIiIjIAQZnahaWLVsGQRCwbNmyBu1fWlqKVatWISkpyaXlqk4URfz44484ffp0g/Y/evQoBEHAY4895uKSERERERGRIwzORAByc3Nx880344MPPnDL8UVRxEMPPYSZM2di+PDhOHHiRL2P0atXL3To0AFr1qxxQwmJiIiIiKg2DM5EMnj44Yfx8ccfAwBycnIwevRonDx5st7HmTJlClJTU5GcnOzqIhIRERERUS0YnMkhs8Vs/fpgzkGb78k5Dz/8MD766CPExcUBANq1a2cNzykpKfU61pQpUwCAtc5ERERERDJicKZabU7bjKlrplq/f3DLg7jhpxuwOW2z5wrlZd544w18+OGH6Nu3L37++WcAwLhx4/Dhhx8iOzsbY8eORWmp86OVDxs2DOHh4Vi9erW7ikxERERERFdRyXmy3NxcfPTRR9ixYweys7Oh1+vtbicIAs6ePStn0egqm9M2Y8G2BRAh2izPLc3Fgm0L8M7IdzA2bqyHSuc97r77bhw9ehRLliyBTqezLn/ggQdgsVgQHByMgIAAp4+nUqkwYcIEfPPNN8jOzkbr1q3dUWwiIiIiIqpGtuB84sQJjBgxApcuXYIoinXvQPUmiiLKTGWNPo7ZYsaifYtqhGYA1mWv7XsNg1oNglKhbNS5/FX+EAShUcdoyiIjI/HVV18BgE1wBoCHHnqoQcecMmUKvv76a6xduxZz585tdBmJiIiIiMgx2YLzv/71L+Tn52P69Ol4+umn0bVrVwQFBcl1ep9QZirDoG8HyXKunNIcDP1+aKOPs/e2vQhQO1/jSkBiYiK0Wi1Wr17N4ExEREREJAPZgvMff/yB+Ph4LF++vFnXMBK5W1BQEEaNGoUtW7agpKQEgYGBni4SEREREVGzJltwFkURCQkJDM1u5K/yx97b9jb6OAdzDuLBLQ/Wud1HYz5C/+j+jTqXv8q/Ufv7qt69e2PDhg1ISUlBv379PF0cIiIiIqJmTbbgPGDAAKSlpcl1Op8kCIJLmj0PbTMU0QHRyC3NtdvPWYCA6IBoDG0ztNF9nD2ttLTU7uBcBoMBSqUSSmXTvL6NGzciKioKCQkJni4KEREREVGzJ9t0VAsXLsT+/fuxdu1auU5JDaRUKPHUwKfsrhMgtRh4cuCTXh+at23bho4dO+LPP/+0WW40GjFjxgzcfvvtMJub3rzV6enpSEpKwoQJE6BQcEY5IiIiIiJ3k3U6qnnz5mHatGm47bbbMG7cOMTExNT6H//rr79ezqLRVcbGjcU7I9/Bon2LkFuaa10eHRCNJwc+2SymoiopKUFBQQFuvPFGfPLJJwAAk8mEv//971i7di1uvPFGmM3mJlfrvGbNGgDS6NpEREREROR+sgXnkSNHQhAEiKKIr776Cl9//bXD7ZtiTZ+vGRs3FoNaDbKOnv3RmI+aRfPsShMmTMCKFSswY8YMzJ49GwDw7bffwmg04oYbbsDKlSuh0Wg8W0g7Vq9eDX9/f4wfP97TRSEiIiIi8gmyBee77rqLA4N5oeohuX90/2YTmitNmjQJP/74I2bMmAFAaqY9btw4rFq1Clqt1sOlq6moqAjbt29HYmKi3b7ZRERERETkerIF52XLlsl1KqJ6mTx5MpYvX46ZM2fi+uuvx+rVq+Hn5+fpYtm1fv16GI1GNtMmIiIiIpKRrH2ciZqqqVOnIi8vD4GBgVCp3PNYtG/fHqJYc5Ty+li9ejUUCgUmTpzoolIREREREVFdGJyJKoSGhnq6CA4ZjUasX78egwYNQnR0tKeLQ0RERETkM2QPzunp6Vi7di1Onz6N4uJiuzVwgiDg888/l7toRE1aamoqhg0bhltuucXTRSEiIiIi8imyBud///vfePnll2GxWKzLKoNz5cBhoigyODchAeoAHJ111NPFIADx8fFYt26dp4tBRERERORz7E+i7AY//PADFi5ciNjYWPzf//0fxo0bBwD47bff8PHHH2PEiBEQRRELFizA77//LlexiIiIiIiIiBySrcb5o48+gkajwdatWxEXF4edO3cCgDVA33fffXj33XfxxBNPYOrUqXIVi5qJhIQEvPjii0hISPB0UYiIiIiIqJmRrcb5yJEjGDp0KOLi4gDYNs2u9NhjjyE+Ph6vvPKKXMWiZiIhIQELFy5kcCYiIiIiIpeTLTjr9Xq0atXK+n3lPLmFhYU22/Xp0wf79++Xq1hEREREREREDskWnFu3bo3c3Fzr923btgUAHDt2zGa7zMxMmM1muYpFRERERERE5JBswblXr15ISUmxfj9y5EiIoogXX3wRJSUlAIDly5fjjz/+QI8ePeQqFhEREREREZFDsgXnSZMmISsryzpi9nXXXYdRo0Zh69atCA8PR2RkJG699VYIgoDnn39ermIREREREREROSRbcL7jjjtw4sQJm8GbVq5ciblz5yIiIgLFxcXo3r07vvrqKyQmJspVLCIiIiIiIiKHZAvOWq0W8fHxiIiIsC4LCQnBJ598gosXL0Kv1+Po0aO47bbb5CqSXZcuXULLli0hCAI6d+7scNtly5Zh4MCBCAoKQkREBG666Sbs2rVLppISERERERGRHGQLzt7in//8J/Lz8+vcbv78+ZgzZw6Sk5MxduxYDBw4EJs2bcL111+PVatWub+gREREREREJAvZg7PJZMLq1avx7LPP4r777sMXX3xhXXfhwgUcOXIEJpNJ7mIBALZs2YIvv/wS9957r8PtNm/ejCVLlqBFixY4fPgwVq1ahQ0bNmDHjh1QKpWYM2dOjWm2iIiIiIiIyDvJGpx37tyJzp07Y9q0aVi0aBE+++wz7Ny507p+9+7d6Nu3L9asWSNnsQAAZWVluO+++9C9e3c8/vjjDrd95513AADPPfccunTpYl0+ZMgQ3H///SgsLMTnn3/u1vISERERERGRPGQLzsePH0diYiKys7PxyCOPYPny5RBF0WabSZMmISAgAD/99JNcxbJ66aWXkJqaik8++QRqtbrW7crKyqwjg8+YMaPG+spla9eudU9BiYiIiIiISFYquU708ssvo7y8HL/++ivGjx9vdxuNRoN+/frhr7/+kqtYAIAjR47g7bffxpw5czB8+HCcP3++1m1TUlKg1+sRFRWFmJiYGuv79etnPWZzYCktRUq//gCA+EMHoQgI8HCJiIiIiIiI5CVbjfPWrVsxcODAWkNzpbZt2+LChQsylQqwWCz4xz/+gbCwMLzxxht1bp+eng4AdkMzAAQGBiIsLAwFBQUoLi52aVmJiIiIiIhIfrLVOBcWFiI2NrbO7UpKSmA0GmUokeT999/H/v37sXTpUrRo0aLO7a9cuQIACHBQ8xoYGIjCwkIUFxcjODi4xnq9Xg+9Xm/9XqfTNaDkREREREREJAfZapxbtmyJM2fO1LndiRMnnArYrpCeno7nnnsOI0aMwOzZs2U5JwAsWrQIoaGh1pdc19ucLVu2DIIgYNmyZQ3av7S0FKtWrUJSUpJLy+XKcxw9ehSCIOCxxx5zbcGIiIiIiMgh2YLz6NGjkZSUhK1bt9a6zcqVK3HmzBmMGzdOljI99NBDMBgM+OSTT5zeJygoCIAUgmpTUlICAHZrmwHg6aefRlFRkfWVkZFRj1KTO+Tm5uLmm2/GBx980GTP0atXL3To0MEjo84TEREREfky2ZpqP/XUU/jhhx8wdepUvPbaa7j55put6woKCrBy5Uo8/vjjCAwMxIIFC2Qp07p16xAWFob777/fZnl5eTkAICsrCyNHjgQAfP/992jVqhXatWsHAMjMzLR7zJKSEhQWFiI8PLzW4KzVaqHVal10FeRLpkyZgsWLFyM5ORk9e/b0dHGIiIiIiHyCbDXO3bp1w3fffQeLxYKHH34Ybdu2hSAI+PLLLxEZGYl7770Xer0e33zzDTp06CBXsVBYWIjt27fbvPbu3QtACtCVyyrDdHx8PLRaLfLy8pCVlVXjeIcOHQIA9O7dW7ZrcCfRbLZ+XXrggM33JL8pU6YAAGudiYiIiIhkJFtwBoCpU6ciOTkZjzzyCLp16wY/Pz9oNBp07NgR9913H44cOYLJkyfLVh5RFO2+zp07BwDo1KmTdVn79u0BAP7+/hg9ejQA4Mcff6xxzBUrVgCQ5qT2drqNG5E6YaL1+4y59+HMmLHQbdzowVL5tmHDhiE8PByrV6/2dFGIiIiIiHyGrMEZAOLi4rB48WIcO3YMJSUlKCsrw+nTp/HRRx+hU6dOchenQSqbkr/yyis4ffq0dfnu3bvx6aefIiwsDPfcc4+niucSuo0bkTVvPky5uTbLTTk5yJo3n+HZQ1QqFSZMmID9+/cjOzvb08UhIiIiIvIJsgfn5mDs2LGYN28eLl26hISEBEydOhU33XQTrr/+ephMJixduhRhYWGyl0sURVhKSxv9MhcXI+eV/wCiaO8kAETk/OdVmIuLG30u0d45yKEpU6ZAFEWsXbvW00UhIiIiIvIJsg0O1twsXrwYCQkJ+OCDD7Bp0yZoNBqMHTsWzz//PIYOHeqRMollZUjp11+GE0k1z6euHdjoQ8UfOgjBwZzYVFNiYiK0Wi1Wr16NuXPnero4RERERETNntuCc8eOHRu8ryAIOHv2rAtLUz/t27d3qiZ09uzZss7/TARIU6KNGjUKW7ZsQUlJCQIDAz1dJCIiIiKiZs1twfn8+fMQBKFBTXEFQXBDiZo/wd8f8YcONvo4pQcOIGPufXVuF/t/nyJgwIBGnUvw92/U/r6qd+/e2LBhA1JSUtCvXz9PF4eIiIiIqFlze1Pt/v3744477sCUKVPgz5DkVoIguKTZc+B110HVqhVMOTn2+zkLAlTR0Qi87joISmWjz+dJpaWlCLDznhkMBiiVSihdcH3uOMfGjRsRFRWFhISERpePiIiIiIgcc9vgYN9//z0mTpyII0eOYMGCBejTpw+eeuopJCcno2XLloiOjnb4Is8RlEpEP/N0xTdXr5QWRD/ztNeH5m3btqFjx474888/bZYbjUbMmDEDt99+O8yNnLfaHedIT09HUlISJkyYAIWC4/sREREREbmb2/7XPXPmTKxevRrZ2dl4//330aNHD3z55ZcYP348YmNj8a9//QtJSUnuOj01Usj48Wi7ZDFUUS1tlquio9F2yWKEjB/voZK5TklJCQoKCnDjjTdi165dAACTyYS///3vWLt2LXQ6XaODszvOsWbNGgDS6NpEREREROR+gijjfEDnzp3D119/jW+//RYpKSkQBAHXXHMN7rzzTtx2222IjY2VqyhNik6nQ2hoKIqKihASEuJw2/Lycpw7dw4dOnSAn5+f28tmLi62jp4d+3+fNtnm2cuWLcOcOXOwdOnSeg3YtnbtWsyYMQOiKMJoNEKtVsNoNOKGG27A6tWrodVqG102V59j3Lhx+PPPP5Gfn2+3CfjV5P7MEBERERHJpT5ZqjFkbefZoUMHPP/88zhx4gT27t2LRx55BJcuXcIzzzyDa6+9Vs6ikJOqh+SAAQOaZGhujEmTJuHHH3+0fm80GjFu3DisWrXKJaHZ1ecoKirC9u3bMXbsWKdCMxERERERNZ7HOkjGxcWhY8eOaNOmDURRhMVi8VRRyMdNnjwZy5cvh1qtxpgxY7B69WqX18y66hzr16+H0WhkM20iIiIiIhm5fVTt6kpLS/Hzzz/jm2++wZYtW2A2mxEaGop7770Xd955p5xFIbIxdepU5OXlITAwECqVex4LV5xj9erVUCgUmDhxootLR0REREREtXF7cLZYLPjtt9/w9ddfY82aNSgtLYVGo8HkyZNxxx134KabboJGo3F3MYjqFBoa2qTPYTQasX79egwaNIgjzxMRERERychtwXnv3r345ptv8MMPPyAvLw+CIOD666/HHXfcgRkzZsgSUqjxFAEBuObkCU8XgwCkpqZi2LBhuOWWWzxdFCIiIiIin+K24DxkyBAIgoBevXrh8ccfx2233Ya2bdu663REzV58fDzWrVvn6WIQEREREfkctw8Odvz4cTz77LPo0KEDNBqNUy9XjWZMRERERERE1Fhu7eMsiiJMJpM7T0EEAEhISMCLL76IhIQETxeFiIiIiIiaGbcFZ04vRXJKSEhgaCYiIiIiIrfw2DzORERERERERN6AwZmIiIiIiIjIAQZnIiIiIiIiIgcYnImIiIiIiIgcYHD2UqIoeroI5CX4WSEiIiIiahwGZy+jVCoBAEaj0cMlIW9R+Vmp/OwQEREREVH9MDh7GbVaDa1Wi6KiItYkUp1EUURRURG0Wi3UarWni0NERERE5JXcNo8zuU9kZCSysrKQmZmJ0NBQqNVqCILg6WJREyKKIoxGI4qKinDlyhW0bdvW00UiIiIiIvJaDM5eKCQkBACQn5+PrKwsD5eGmjKtVou2bdtaPzNERERERFR/DM5eKiQkBCEhITAajTCbzZ4uDjVBSqWSzbOJiIiIiFyAwdnLqdVqhiMiIiIiIiI34uBgRERERERERA4wOBMRERERERE5wOBMRERERERE5ACDMxEREREREZEDDM5EREREREREDjA4ExERERERETnA4ExERERERETkAIMzERERERERkQMMzkREREREREQOMDgTEREREREROcDgTEREREREROQAgzMRERERERGRAwzORERERERERA4wOBMRERERERE5wOBMRERERERE5ACDMxEREREREZEDDM5EREREREREDjA4ExERERERETnA4ExERERERETkAIMzERERERERkQMMzkREREREREQOMDgTEREREREROcDgTEREREREROQAgzMRERERERGRAwzORERERERERA4wOBMRERERERE54PPBubS0FKtWrcI999yD+Ph4+Pn5ITAwEH369MG///1vXLlypdZ9ly1bhoEDByIoKAgRERG46aabsGvXLhlLT0RERERERO4miKIoeroQnvTZZ5/h3nvvBQBcc8016NmzJ3Q6HXbt2oXi4mJ069YN27dvR8uWLW32mz9/PpYsWQJ/f3+MHz8e5eXl2LJlC0RRxIoVKzB16lSny6DT6RAaGoqioiKEhIS48vKIiIiIiIiaLbmylM/XOKvVasydOxfHjx/H8ePHsXz5cmzYsAEpKSno27cvTp48ifnz59vss3nzZixZsgQtWrTA4cOHsWrVKmzYsAE7duyAUqnEnDlzUFhY6JHrISIiIiIiItfy+eA8a9YsfPrpp7jmmmtslrdu3RoffvghAODnn3+GwWCwrnvnnXcAAM899xy6dOliXT5kyBDcf//9KCwsxOeffy5D6YmIiIiIiMjdfD44O9KnTx8AgF6vx6VLlwAAZWVl+P333wEAM2bMqLFP5bK1a9fKVEoiIiIiIiJyJwZnB1JTUwFIzbkjIiIAACkpKdDr9YiKikJMTEyNffr16wcAOHLkiHwFJSIiIiIiIrdhcHZgyZIlAIDExERotVoAQHp6OgDYDc0AEBgYiLCwMBQUFKC4uFieghIREREREZHbqDxdgKbq119/xeeffw61Wo2XX37ZurxyeqqAgIBa9w0MDERhYSGKi4sRHBxcY71er4der7d+r9PpXFhyIiIiIiIiciXWONtx8uRJ3HHHHRBFEW+++aa1r7OrLFq0CKGhodZXbGysS49PRERERERErsPgfJWsrCwkJiaioKAACxYswLx582zWBwUFAQBKS0trPUZJSQkA2K1tBoCnn34aRUVF1ldGRoaLSk9ERERERESuxqba1Vy+fBnjx49HWloa5syZg7feeqvGNu3atQMAZGZm2j1GSUkJCgsLER4eXmtw1mq11j7TRERERERE1LSxxrnClStXcOONN+L48eOYNm0a/vvf/0IQhBrbxcfHQ6vVIi8vD1lZWTXWHzp0CADQu3dvt5eZiIiIiIiI3I/BGdJgXVOmTMG+fftwww034LvvvoNSqbS7rb+/P0aPHg0A+PHHH2usX7FiBQBg0qRJ7iswERERERERycbng7PZbMatt96K33//HcOHD8fPP/8MjUbjcJ8FCxYAAF555RWcPn3aunz37t349NNPERYWhnvuucet5SYiIiIiIiJ5+Hwf5w8++AArV64EAERGRuLBBx+0u91bb72FyMhIAMDYsWMxb948LFmyBAkJCRg3bhwMBgM2bdoEURSxdOlShIWFyXUJRERERERE5EY+H5wLCgqsX1cGaHsWLlxoDc4AsHjxYiQkJOCDDz7Apk2boNFoMHbsWDz//PMYOnSoW8tMRERERERE8hFEURQ9XQhfp9PpEBoaiqKiIoSEhHi6OERERERERF5Brizl832ciYiIiIiIiBxhcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJyJiIiIiIiIHGBwJiIiIiIiInKAwZmIiIiIiIjIAQZnIiIiIiIiIgcYnImIiIiIiIgcYHAmIiIiIiIicoDBmYiIiIiIiMgBBmciIiIiIiIiBxiciYiIiIiIiBxgcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJyJiIiIiIiIHGBwJiIiIiIiInKAwZmIiIiIiIjIAQZnIiIiIiIiIgcYnImIiIiIiIgcYHAmIiIiIiIicoDBmYiIiIiIiMgBBmciIiIiIiIiBxiciYiIiIiIiBxgcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJyJiIiIiIiIHGBwJiIiIiIiInKAwZmIiIiIiIjIAQZnIiIiIiIiIgcYnImIiIiIiIgcYHAmIiIiIiIicoDBmYiIiIiIiMgBBmciIiIiIiIiBxiciYiIiIiIiBxgcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJyJiIiIiIiIHGBwJiIiIiIiInKAwZmIiIiIiIjIAQZnIiIiIiIiIgcYnImIiIiIiIgcYHAmIiIiIiIicoDBmYiIiIiIiMgBBmciIiIiIiIiBxiciYiIiIiIiBxgcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJwboaysDC+88AK6du0KPz8/tGnTBnfffTeysrI8XTQiIiIiIiJyEQbnBiovL8fo0aPx8ssv48qVK5gyZQpiY2OxdOlS9O3bF6mpqZ4uIhEREREREbkAg3MDvfLKK9izZw+GDBmCU6dO4YcffsDevXvx9ttvIy8vD3fffbeni0hEREREREQuIIiiKHq6EN7GYDCgZcuWKCoqwqFDh9C3b1+b9X369MGRI0dw4MAB9O/fv87j6XQ6hIaG4qNtq3DPdROgUancVXSyw2Ay4dvD25Cuu4h2Ia1wW5+RXn8PPHlNrjp3Q4/TkP1c+X556r1vjp9joPlel7fg++95zfUeNIeflXL+vvH079bG4ueY3MVgMuHzP3/BgyOnoqioCCEhIW47F4NzA2zduhWjR49Gp06dcObMmRrrX375Zbzwwgt48cUXsXDhwjqPVxmcr/n4Gqg0LXBnl0fxr+F/c0PJ6Wpv/vEjvjr9HkRloXWZYA7z6nvgyWty1bkbepyG7OfK98tT731z/BwDzfe6vAXff89rrvegOfyslPP3jad/tzYWP8fkLpX3wGS4hBMPnHB7cOafRBrg8OHDAIB+/frZXV+5/MiRI/U+tkVRiC/P/hsA+NC52Zt//Ci91wpAqLbcm++BJ6/JVedu6HEasp8r3y9PvffN8XMMNN/r8hZ8/z2vud6D5vCzUs7fN57+3dpY/ByTu1S/B3JhjXMDLFiwAO+++y4ee+wxvPPOOzXWHz58GAkJCejXrx8OHjxY5/Gq1zgr/ZUQRUBhDsVvM9dCo+TfNtzBYDZh/I+TICqKIAg113vjPfDkNbnq3A09TkP2c+X75an3vjl+joHme13egu+/5zXXe9AcflbK+fvG079bG4ufY++6Lm9y9T0wl5llqXFmcG6AuXPn4r///S+effZZvPLKKzXWnzlzBl26dEGXLl1w6tSpGuv1ej30er31e51Oh9jYWGtwJiIiIiIiorrJFZw5qrYHLFq0CKGhodZXbGysp4tEREREREREtWD7gQYICgoCAJSWltpdX1JSAgAIDg62u/7pp5/GggULrN9X1jhf7f74V3FL75GNLC3Z8/2Rbfgk5Zk6t/Ome+DJa3LVuRt6nIbs58r3y1PvfXP8HAPN97q8Bd9/z2uu96A5/KyU8/eNp3+3NhY/x951Xd7E2Xvgamyq3QCLFy/GY489hr/97W9Yvnx5jfW//PILJk6ciJtvvhk///xzncez38c5DAdmbeWQ9m5iMJkw4H+jYFEUOuif4l33wJPX5KpzN/Q4DdnPle+Xp9775vg5BprvdXkLvv+e11zvQXP4WSnn7xtP/25tLH6Oveu6vMnV94BNtZuwPn36AAAOHTpkd33l8t69e9f72JV/xriz66N82NxIo1Lhzi6PAqh6zyt56z3w5DW56twNPU5D9nPl++Wp9745fo6B5ntd3oLvv+c113vQHH5Wyvn7xtO/WxuLn2Pvui5v4ugeuBPvaANcd911CA0NxdmzZ5GUlISEhASb9StWrAAATJo0qd7HVpjDcGdXzv8mh8r3+Oo5+Lz5Hnjymlx17oYepyH7ufL98tR73xw/x0DzvS5vwfff85rrPWgOPyvl/H3j6d+tjcXPMblL9XtgwSVZzsmm2g303HPP4T//+Q+GDh2KjRs3IjAwEADwzjvv4J///CdGjBiBbdu2OXWsyqbaH21bhXuum8C/UMnMYDLh28PbkK67iHYhrXBbn5Fefw88eU2uOndDj9OQ/Vz5fnnqvW+On2Og+V6Xt+D773nN9R40h5+Vcv6+8fTv1sbi55jcxWAy4fM/f8GDI6dyOqqmqry8HCNHjsTevXvRunVrDB8+HGlpadi7dy+ioqKwZ88edOzY0aljVQZnd99sIiIiIiKi5kSuLMU+zg3k5+eHrVu34vnnn0dAQABWrVqFtLQ0zJ49G4cOHXI6NBMREREREVHTxhrnJoA1zkRERERERPXHGmciIiIiIiKiJoDBmYiIiIiIiMgBBmciIiIiIiIiBxiciYiIiIiIiBxgcCYiIiIiIiJygMGZiIiIiIiIyAEGZyIiIiIiIiIHGJyJiIiIiIiIHFB5ugAEiKIIQJq8m4iIiIiIiJxTmaEqM5W7MDg3AcXFxQCA2NhYD5eEiIiIiIjI+xQXFyM0NNRtxxdEd0dzqpPFYsGFCxcQHBwMQRA8XRyqhU6nQ2xsLDIyMhASEuLp4pADvFfegffJO/A+eQ/eK+/A++QdeJ+8Q+V9On78OOLj46FQuK8nMmucmwCFQoGYmBhPF4OcFBISwh+gXoL3yjvwPnkH3ifvwXvlHXifvAPvk3do27atW0MzwMHBiIiIiIiIiBxicCYiIiIiIiJygMGZyElarRYvvvgitFqtp4tCdeC98g68T96B98l78F55B94n78D75B3kvE8cHIyIiIiIiIjIAdY4ExERERERETnA4ExERERERETkAIMzERERERERkQMMzuSzysrK8MILL6Br167w8/NDmzZtcPfddyMrK6tex2nfvj0EQaj1dfLkSTddQfN38OBBvPbaa5g2bRpiYmKs72lDFRQUYN68eYiLi4NWq0VcXBzmz5+PwsJC1xXaR7nyXvGZco/S0lKsWrUK99xzD+Lj4+Hn54fAwED06dMH//73v3HlypV6H5PPlOu5+j7xeXKvd955B9OmTUOXLl0QGhpqfQ7uuusuHD16tN7H4zPlHq68T3ym5HPp0iW0bNkSgiCgc+fODTqGK58pDg5GPqm8vByjRo3Cnj170Lp1awwfPhznz5/Hvn37EBUVhT179qBjx45OHat9+/ZIS0vDrFmz7K5ftGgRWrdu7cri+4ypU6di9erVNZY35MdWfn4+hgwZgjNnzqBjx44YMGAAjh07hmPHjqFr167YvXs3IiIiXFFsn+TKe8Vnyj0+++wz3HvvvQCAa665Bj179oROp8OuXbtQXFyMbt26Yfv27WjZsqVTx+Mz5R6uvk98ntwrMjISJSUl6N27N9q2bQsAOHbsGE6dOgW1Wo2ff/4ZEydOdOpYfKbcx5X3ic+UfGbPno3//e9/EEURnTp1wpkzZ+q1v8ufKZHIBz377LMiAHHIkCFicXGxdfnbb78tAhBHjBjh9LHi4uJEPkru8dprr4nPP/+8uGbNGjE7O1vUarUNfq9vv/12EYA4bdo00Wg0Wpc/8sgjIgBx1qxZLiq1b3LlveIz5R7Lli0T586dKx4/ftxm+YULF8S+ffuKAMRbb73V6ePxmXIPV98nPk/utXPnTrGsrKzG8g8//FAEIEZHR9s8H47wmXIfV94nPlPy2Lx5swhAnDt3rghA7NSpU72P4epnijXO5HMMBgNatmyJoqIiHDp0CH379rVZ36dPHxw5cgQHDhxA//796zxe5V8e+Si5n5+fH/R6fb3f6+zsbMTExEClUiE9PR3R0dHWdXq9HrGxsbh8+TIuXLjgdC0OOdbQewXwmfKE3bt3Y+jQodBqtdDpdNBoNA635zPlGfW9TwCfJ0/q3Lkzzp49i8OHD6N3794Ot+Uz5Tn1uU8Anyk5lJWVoVevXtBqtVi1ahW6du1a7xpndzxT7ONMPufPP/9EUVEROnXqVCM0A8CMGTMAAGvXrpW7aOQmGzZsgMViwfDhw21+cAKAVqvFpEmTYDab8euvv3qohESe1adPHwDSfyYuXbpU5/Z8pjyjvveJPEutVgOAU3/g4DPlOfW5TySPl156Campqfjkk0+s96e+3PFMqRpUEiIvdvjwYQBAv3797K6vXH7kyJF6HffNN9/E2bNnodVq0aNHD9x8882IiopqXGHJJZy551988UW97zm5F58p+aSmpgKQ/gPpTH8vPlOeUd/7VB2fJ3l99dVXSElJQZcuXdClS5c6t+cz5Rn1vU/V8ZlyjyNHjuDtt9/GnDlzrGMQNYQ7nikGZ/I56enpAICYmBi76yuXp6Wl1eu4TzzxhM33jz32GN5//33cfffdDSgluZK77jm5F58p+SxZsgQAkJiYCK1WW+f2fKY8o773qTo+T+715ptv4tixYygpKcGJEydw7NgxtGnTBt999x2USmWd+/OZkkdj71N1fKZcz2Kx4B//+AfCwsLwxhtvNOpY7nim2FSbfE7lVB4BAQF21wcGBgIAiouLnTre5MmT8fPPPyMtLQ2lpaVITk7GggULoNfr8Y9//MPuSMMkL1ffc3IvPlPy+vXXX/H5559DrVbj5ZdfdmofPlPya8h9Avg8yeW3337Dl19+iRUrVuDYsWOIi4vDd99959RYKQCfKbk09j4BfKbc6f3338f+/fvx5ptvokWLFo06ljueKQZnokZ67733cPPNN6Ndu3bw9/dHjx498Pbbb+Pjjz+GKIp48sknPV1EIq/CZ0o+J0+exB133AFRFPHmm29a+9BS09KY+8TnSR6bN2+GKIooKCjAjh070KVLF4wYMQL/+c9/PF00qsYV94nPlHukp6fjueeew4gRIzB79mxPF8cuBmfyOUFBQQCA0tJSu+tLSkoAAMHBwY06zz333IOWLVsiJSWlwf0zyDXkuufkXnymXCsrKwuJiYkoKCjAggULMG/ePKf35TMln8bcJ0f4PLlHWFgYhg8fjl9//RX9+/fH888/j/3799e5H58peTX0PjnCZ6pxHnroIRgMBnzyyScuOZ47nikGZ/I57dq1AwBkZmbaXV+5PC4urlHnUSgU6NSpEwBpSHzyHLnuObkXnynXuXz5MsaPH4+0tDTMmTMHb731Vr325zMlj8beJ0f4PLmXWq3G3//+d4ii6NQsHXymPKO+98kRPlONs27dOgQEBOD+++/HyJEjra9bbrkFgPRHxMplFy9erPN47nimODgY+ZzKJm6HDh2yu75yuTNz+dWloKAAQFU/CvIMOe85uRefqca7cuUKbrzxRhw/fhzTpk3Df//7XwiCUK9j8JlyP1fcp7rweXKvyMhIAEBeXl6d2/KZ8pz63Ke68JlqnMLCQmzfvt3uuvLycuu68vLyOo/llmdKJPIxer1eDA0NFQGIf/31V431vXv3FgGIBw4caNR5kpOTRUEQxICAAFGv1zfqWCTRarViQ35sXbhwQVQoFKJGoxFzcnJs1pWXl4tRUVGiUqmssY4arqH3yhE+U41XXl4ujh49WgQg3nDDDQ1+H/lMuZer7pMjfJ7cb9asWSIA8c0336xzWz5TnlOf++QInyn3OHfunAhA7NSpU732c8czxeBMPunZZ58VAYhDhw4Vr1y5Yl3+9ttviwDEESNG2Gz//vvvi/Hx8eJTTz1ls/yXX34Rt2zZUuP4hw8fFq+55hoRgPjoo4+65Rp8UV1hrLb7JIqiePvtt4sAxOnTp4tGo9G6/NFHHxUBiLNmzXJHkX1WQ+8Vnyn3MZlM4s033ywCEIcPHy6WlJTUuQ+fKfm58j7xeXKvnTt3iuvXrxfNZrPNcoPBIL733nuiQqEQ/f39xfT0dOs6PlPyc+V94jMlv7qCs5zPFJtqk0967rnnsHnzZuzatQtdunTB8OHDkZaWhr179yIqKgpffPGFzfb5+flISUmp0Wdl3759eOmllxAXF4c+ffogICAAqampOHToEEwmE0aOHInXXntNzktrVn755RebaVcMBgMAYPDgwdZlzz//PCZMmACg9vsEAIsXL8aePXvw008/oVu3bhgwYACOHTuG5ORkdOnSBe+8846br6Z5c9W94jPlPh988AFWrlwJQGqa+OCDD9rd7q233rI2XeQzJT9X3ic+T+51+vRpzJkzB5GRkejfvz9atGiB/Px8HD16FNnZ2fDz88OyZcsQGxtr3YfPlPxceZ/4TDU9cj5TDM7kk/z8/LB161YsWrQI3377LVatWoWIiAjMnj0bL7/8cq2TpV/thhtuQEZGBvbv348///wTRUVFCAkJwbBhw3D77bdjzpw5UCqVbr6a5isvLw979+6tsbz6Mmf7JEVGRmLfvn1YuHAhVq1ahZUrVyI6OhqPPvooXnrpJYSFhbmq2D7JVfeKz5T7VPa9A2ANZvYsXLjQGsgc4TPlHq68T3ye3GvEiBF45plnsH37dhw5cgT5+fnQaDRo3749ZsyYgUcffRSdO3d2+nh8ptzDlfeJz5R3cfUzJYiiKLqnqERERERERETej9NRERERERERETnA4ExERERERETkAIMzERERERERkQMMzkREREREREQOMDgTEREREREROcDgTEREREREROQAgzMRERERERGRAwzORERERERERA4wOBMRUZMjCILNS6FQICwsDMOHD8dnn30GURQbfY6RI0dCEAScP3++8QV2gYULF9a47uDgYMTGxmL8+PFYuHBhkylrY7Vv3x6CIHi6GLUqLCxEixYt8Le//c3TRbFatWoVBEHA8uXLPV0UIiKfpPJ0AYiIiGoza9YsAIDZbMbZs2fx559/YufOndiyZQu+++47D5fOPfr06YOEhAQAQHl5OXJycrB3715s2rQJL7/8MubNm4fXXnsNGo3GswWtxfnz59GhQweMGDEC27Zt83RxGuQ///kPCgoK8OKLL3q6KFZTpkxBnz598Mwzz+Dmm2+GWq32dJGIiHwKgzMRETVZy5Yts/l+06ZNuOmmm/D999/j9ttvx8SJEz1TMDeaOnUqFi5caLPMZDLh+++/x/z58/Huu+8iJycH33zzjWcK6AJbtmyB0Wj0dDHsys7Oxvvvv49JkyahZ8+eni6OlSAIeOqpp3Drrbfis88+wwMPPODpIhER+RQ21SYiIq8xbtw43HnnnQCkpqu+QqVS4Y477sDOnTsRFBSEb7/9FmvWrPF0sRqsU6dO6Natm6eLYdcXX3wBvV6Pu+66y9NFqWHKlCkIDg7GJ5984umiEBH5HAZnIiLyKn379gUAZGRkAJCaBguCgJEjR9rdvrLv8NW117VJS0vDAw88gK5duyIgIAARERHo0aMH7rvvPqSkpNTYPiMjAw8//DA6deoEPz8/REREYOLEidi1a1eDrs+Rbt26Yf78+QCA9957z2adoz7btb1H1d+bffv2YeLEiWjRogUEQUBSUhIAICkpCU888QT69++PqKgoaLVadOzYEQ8++CAuXLhQ43gdOnQAAGzfvt2mv/bs2bOt2znq47x7925MmTLFeq727dvbPRcgtUgQBAELFy5Eeno6brvtNkRFRcHf3x8DBgzA2rVrHbybNYmiiM8//xzBwcGYMGFCjfXV36+DBw/ixhtvRFhYGCIiIjBz5kxkZmYCAEpKSvDEE0+gffv28PPzQ8+ePbFixQq759y1axemTp2KuLg4aLVatGrVCgMHDsRTTz2FK1eu2Gzr7++PqVOn4siRI9i7d2+9ro2IiBqHwZmIiLxKcXExAECr1br82BkZGejXr5+1Ru+mm27CiBEjoNVq8d///he7d++22X737t3o06cPPvzwQ6jVakyYMAE9e/bEb7/9huuvvx4//PCDy8t4yy23AJACl8FgcMkxd+zYgWHDhuH8+fMYP348rr/+eigU0n8RXnvtNbz77rsAgGHDhuGmm26CKIr4+OOPMWDAAJtAm5CQgOnTpwMAoqOjMWvWLOtr2LBhdZbj66+/xvDhw7FmzRrEx8dj2rRp0Gq1+Pjjj9GvXz+cPHnS7n7nz5/Htddei3379mHMmDHo27cvDh48iKlTp2Ljxo1Ovw/Hjx/HuXPnMHjwYPj5+dW63d69e3HdddchLy8PN9xwA1q0aIEff/wRY8aMQVFREUaNGoUvv/wS1157LYYMGYLjx49j5syZ+O2332yOs3btWuv1tm7dGtOmTUPfvn1x+fJlvP7668jPz69x7so/fvzyyy9OXxcREbmASERE1MQAEO39irJYLOKQIUNEAOKzzz4riqIonjt3TgQgjhgxwu6xXnzxRRGAuHTpUpvlI0aMEAGI586dsy574YUXRADiww8/XOM4aWlp4pkzZ6zfFxUVia1btxaVSqX49ddf22y7f/9+MTw8XAwKChJzc3OduubKcr744osOtzObzaJWqxUBiCkpKQ6vp1Jt71HlOQGIr7/+ut3z/f777+LFixdrlOGll14Sgf9n777Dm67ePo6/M7oXZbXsrQgCspW9RTao4AL54RZFRFHBR8UJKgqo4EIBN4iyt2yRjQxBNpbVxeqgu83zx7ctlK50JoXP67pyQc735ORODwm5exa2//3vf3Y919WqVauWqX9Pnjxp8/DwsFksFtuCBQsyPNfIkSNtgK1Zs2YZHjNjxoz0+F944QVbcnJy+rVJkybZAFvbtm2zjeNan3/+uQ2wjR07NsvrV/+8Pv/88/TyhIQEW5cuXWyArV69erZOnTrZoqOj069Pnz7dBtjatWuXob127drZANvcuXMzPde2bdtskZGRmcr37duXZVsiIlK0NOIsIiJOLzk5mSNHjjBs2DA2b96Mm5sb//vf/wr9ecLDwwHo0qVLpmtVq1alVq1a6fe//fZbgoODGTlyJA8++GCGus2aNeO1114jOjqaH374oVBjNJvN+Pv7A3Dx4sVCabNBgwaMHj06y2sdO3YkICAgUwyvv/46lSpVKrS11tOnTyc2NpaBAwfSp0+fDM81YcIEKlasyI4dO9i0aVOmx9aoUYP33nsvfZQc4JlnnsHf358tW7bYPTK/d+9eAG6++eYc67Vp04Ynn3wy/b6LiwvPPvssAAcPHuTzzz/Hy8sr/frQoUMpW7YsmzdvzrApWk7/3po3b46Pj0+m8rS14WlT6UVEpHhoV20REXFaWa2D9fHxYdasWRmS2MLStGlTAMaOHYvFYqFLly7ZTtlNmwI8YMCALK+3bdsWgG3bthV6nLbUc6wL6yzkXr165djW+fPnWbhwIf/88w+XLl0iOTkZgMTERM6fP8+FCxcoXbp0gWLYuHEjQKZfQoAxLf/ee+9lypQpbNy4kdatW2e43qFDh0zHc1mtVmrUqMGuXbs4f/48FSpUyDWGsLAwgPRfTGSnW7dumcpq1qwJGOu3b7rppgzXLBYL1apVY+fOnZw7dy49lqZNm/Lvv/8yePBgXnvtNZo2bZoh+c+K1WrFx8eHyMhIEhISnPZYMhGR640SZxERcVpp5zibzWZ8fX1p0KABAwYMyDWxya+hQ4eycuVK5syZQ+/evXF3d6d58+Z0796dYcOGERgYmF43bROua5O4a2W1TrUgUlJS0keaC5qspqlatWq2137++Wcef/zxTBtVXS0qKqrAsaStla5evXqW19PKz5w5k+la5cqVs3xM2ohtfHy8XTFERERkeFx2KlWqlKnM29s722tXX786lvfee499+/axaNEiFi1ahL+/P23atKFPnz489NBD2f7SxtfXl6ioKC5dukT58uVzf2EiIlJgSpxFRMRp2bsTdk5SUlLsrmuxWJg9ezavvPIKCxYsYM2aNWzdupWNGzcyYcIEli9fTqtWrTK0e88992SYlnutwj52af/+/SQkJODp6Zltknmt3H4G2SVoQUFB6bthT548mZ49e1KpUiU8PDwAaNWqFZs3b04fAS9KOY2I5zZKay8/Pz/gygZ0+Xm+vMRSpUoVduzYwZo1a1i8eDHr169PT6I/+OADNm/eTJkyZTI9Li3BL1WqlN3PJSIiBaPEWURESrS0qarZjYimHVuVF40bN6Zx48aMGzeOyMhIxo0bx6RJkxg5cmT61OvKlStz6NAhXnnllfQp3sUhbafuNm3aYLVe+W88p59Dfn4GAEuXLiUhIYEXX3yR5557LtP148eP56vdrFSsWJFDhw4RFBRE/fr1M11PG+HPbkS3MKSN3l64cKHInuNaVquVbt26pU//DgoKYtiwYaxZs4b333+fDz74IEP9xMREoqOj8fX11TRtEZFipM3BRESkRCtbtixWq5UTJ06QlJSU4VpiYiLr168vUPu+vr6MHz8ek8nEP//8k17etWtXAObNm1eg9vPi4MGDTJ48GSBTIpu2bvbw4cOZHrdq1ap8PV/alPCspkJv2LCB0NDQTOVpydy1fZGbtDXhP//8c6ZrCQkJ/PrrrxnqFYVGjRoBZHled3GpVq0aL7/8MkCGf29p0o7kuu2224ozLBGRG54SZxERKdFcXV254447uHDhAlOnTk0vT0pK4oUXXuDEiRN2t/X9999nmawsW7YMm81GlSpV0sueeOIJypcvzwcffMBXX32VaTp0UlISK1asyLK9vEpKSuLHH3+kbdu2XL58mSFDhtCjR48Mddq3bw/ARx99RExMTHr5mjVr0pPtvErb5OqHH37g8uXL6eVnzpzJsKv01cqWLYuLiwvHjh1L30TMHo888ggeHh788ssvGc4oTklJYezYsZw5c4amTZvmuqa8INKS8u3btxfZc1xt0qRJhISEZCpfunQpQIZ/b2nSZjyk9beIiBQPTdUWEZES7/XXX+fOO+9k5MiRzJ49m8DAQHbu3ElMTAwPP/wws2bNsqud3377jSFDhlCrVi0aNGiAh4cHJ06cYOvWrZjNZt555530uqVKlWLBggX07t2bJ554gnfeeYdbb70Vf39/QkJC2LVrF5cuXWLevHnceuutdr+W+fPnp09LjouLIzw8nB07dhAZGYnZbOaFF15g/PjxmR53//3388EHH/DXX39xyy230Lx5c06fPs327dsZNWoUEydOtDuGNH369KF+/frs2LGD2rVr07p1a+Li4li7di233XYbrVq14q+//srwGFdXV7p3786iRYto1KgRTZo0wdXVldatW+d4hFjVqlX58ssvGTp0KL1796Z169ZUqVKFXbt2cejQIQICAgr9aK9r3XLLLdSoUYOtW7cSFxeX7drvwvLmm2/y4osv0qhRI+rUqYPNZmPPnj0cPnyY0qVL8+KLL2Z6zLp16wDo2bNnkcYmIiIZacRZRERKvC5durBw4UKaN2/Orl27WL9+Pbfffjvbt2+3ewMtgFGjRjF8+HB8fHzYuHEj8+bNIywsjEGDBrF161buvffeDPVvv/129u3bx0svvYSvry/r169n/vz5BAUF0b59e2bOnJnlGb052bNnD7NmzWLWrFksXryYQ4cO0bJlS8aNG8fx48eZOHEiLi4umR7n4eHB6tWruf/++4mKimLp0qUkJycze/Zshg8fnqcY0ri6urJx40aeeuop3N3dWbx4Mf/++y/PPvssq1atyjIOMM5kHjx4MOfPn+enn37im2++sWvK/ODBg9m4cSO9evXi33//Ze7cucTGxvLUU0+xc+fOQt9o7Vomk4lHH32UqKgoFi1aVKTPBfDpp59y3333ERMTw7Jly1i+fDlWq5VRo0axd+9e6tSpk6F+bGws8+fPp2HDhrRs2bLI4xMRkStMtuLYClNERESkBAgJCaFGjRp06dKlWJLnvPj555954IEHmDZtGk899ZSjwxERuaEocRYRERG5yksvvcTEiRPZs2cPDRo0cHQ4ANhsNho3bkx0dDQHDhzQjtoiIsVMU7VFRERErjJ27Fj8/f158803HR1KugULFrBnzx7ee+89Jc0iIg6gEWcRERERERGRHGjEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURERERERCQHSpxFREREREREcqDEWURKnPPnz2Mymey6Pfjgg44ON5N///2X++67jzp16uDt7Y2vry+NGzfmk08+ISEhIU9thYSEMHz4cKpVq4abmxsVKlSgd+/enDx5Mr3Of//9l+3P59FHHy3sl1eoSkJfF0Z/JicnM2HCBGrXro2bmxu1a9dmwoQJJCcnZ6gXHR3NuHHj6N27NxUqVMBkMjF06NAieFVFoyT0Z14UtD/y+vgzZ87w+OOPU7NmTTw8PKhevTpDhgzh8OHDBX8xxeDgwYM59nlwcLBD47P3fZidU6dO8fDDDxMQEICbmxv16tXjk08+wWazZVnfns9vEXEeVkcHICKSH99//32G+2PGjOHy5ct88sknGcobNmxYnGHZ5dSpU1y4cIH77ruPypUrk5yczKZNmxg5ciRr1qxh/vz5drVz5MgR2rVrh5ubG8OGDaNKlSqcP3+erVu3cvHiRapWrZqhft++fbnnnnsylNWuXbuwXlaRcfa+Loz+fPbZZ/n888/53//+R6tWrdi0aRNjxozh1KlTTJ06Nb3euXPnePPNN6lQoQLNmjVj8eLFRfjKioaz92deFLQ/8vL4Cxcu0Lx5c+Lj43nqqaeoUaMGR48e5fPPP2fRokXs27ePypUrF/QlFam///4bgBdeeIHbbrstwzWr1UqFChUcENUV9r4PsxIcHEzLli2Jjo7mmWeeoWrVqqxevZrnnnuOkydPMnHixAz18/r5LSJOwCYiUsIlJSXZ3N3dbZ06dXJ0KAUyfPhwG2A7ePBgrnVTUlJszZs3t9122222yMjIHOueOHHCBtheffXVwgrVYUpSX9vbn3v37rWZTCbbiBEjMpSPGDHCZjKZbHv37k0vi4uLs50+fdpms9lsiYmJNsD28MMPF3rsxaUk9WdWCtofeXn8tGnTbIBt4cKFGcrnzp1rA2yTJk3Kz0soVqNHj7YBtv/++8/RoWSSl/dhVp599lmbyWSy/fnnnxnKn3rqKZvZbLb9+++/6WV5+fwWEeehqdoiUuIdPHiQuLi4TCMYJU316tUBuHTpUq51165dy/bt23nrrbfw8fEhLi7OrmnBsbGxxMbG5jm2mTNnYjKZWL58Oa+//jqVK1fGy8uLO++8M31a4aeffkrt2rVxd3enZcuW7NmzJ0MbMTExjB07ljp16uDh4YG/vz+NGzfOdSTnaiWpr+3tz19++QWbzcbIkSMzlI8cORKbzcbs2bPTy9zc3KhUqVKB4nKWvoSS1Z9ZKWh/5OXxERERAJlGZStWrAiAp6dnrm04uu93795NuXLlqFatml2vuTjl5X2YlfXr11OzZk1at26doXzw4MGkpKTw008/pZfl9/NbRBxLU7VFpMRL+2JX1F++U1JSuHDhgl11XVxc8PPzy7FOTEwMMTExXL58mW3btvHBBx9QoUIFu6aoLl++HIBSpUrRrl07Nm7ciMlkomXLlnz88cfccccdmR4zZcoU3n33XcCYov3888/z9NNP2/V60owdOxZ3d3deeuklTp8+zaRJk+jfvz8DBw7kp59+Yvjw4URGRvLBBx9w9913c+jQISwWCwBPP/00P/30E08++SQNGzbk8uXL7N+/n40bNzJ8+HC7nr8w+9pZ+nPHjh0EBARQo0aNDOU1atSgfPny7Nixw64Y88rRfQnF9969VmH3fXHo1KkTYEwnnjhxItWrV+fo0aOMGjWKm266ifvuu8/uthzV93///TcNGzbk3LlzGcrd3d3x9vbO40/EUFh9WdD3YXx8fJa/vPDy8gJg+/bt6WX5+fwWESfg2AFvEZGCS5v+t2fPniJ9nrQpz/bc2rdvn2t7b7zxRobHNG/e3Pb333/bFUvfvn1tgK1cuXK2Pn362GbPnm2bNm2arWLFijZ3d/cM0wqDgoJsnTt3tn322We2hQsX2r744gtbkyZNbIDtxRdftOv5ZsyYYQNszZo1syUmJqaXjxw50gbYatSoYYuJiUkv/+CDD2yAbcWKFellpUqVsj399NN2PV92CrOvnaU/b731VluTJk2yvNa4cWPbrbfemuW1/E7Vdpa+tNmK7717rcLue5ut4FPn7Xn8tGnTbP7+/hnia9eune3cuXN2PYcj+/7UqVPZ/oxffvnlPLeXprD6Mr/vwzR9+/a1ubm52c6ePZuhfPLkyTbA1qBBgwx17f38FhHnoRFnESnxdu/ejZubG7fcckuW12vUqMGPP/5Iq1atCvQ8gYGBrFq1yq66/v7+udYZMmQIbdq04fz586xZs4Z//vnHrmnaYOzGC1CvXj0WLFiQXt6xY0duvfVW3n77bebMmQNA1apV+eOPPzI8/tFHH6V9+/Z8/PHHPPnkk9SqVcuu533sscewWq/819G6dWsmT57M4MGD8fDwSC9PGzE5duxYelmpUqXYsmULQUFB+Z6qmVtf54Wz9GdMTAw+Pj5ZXnN3dycyMtKuGPPK0X0JhdufeVHYfV9cKleuTMuWLenatSu1a9dm3759fPjhh/Tt25cVK1akj27mxhF9v3v3bgDeeecdWrZsmeFaQfq/sPqyoO/DZ599loULF9KvXz8mTpxIlSpVWLt2LW+88QZWq5WYmJj0unn5/BYR56HEWURKvD179lCvXj1cXFwyXYuMjCQoKIj69esX+Hnc3d3p0qVLgdtJU7NmTWrWrAnAoEGDmDRpEt26dWPPnj25fpFM+3I7ePDgDOV169alZcuWrF+/PsfHWywWXn75Zfr06cPq1avtTpyv/aJcqlQpgEw7wKaVXz2FctKkSQwePJjq1atz66230qlTJ+655x7atm1r13NDzn2dV87Sn56ensTHx2d5LS4uLkMiU5gc3ZdQsP48d+4ct9xyC6dPn+bpp5/mjz/+4MKFC9SsWZO3336bPn36ZPvYwu774rBgwQLuuecedu3aRYMGDQDo06cPzZs3584772Tq1Km89NJLdrXliL5P21H7vvvus/vzxh6F1ZcFfR927tyZGTNmMGrUKNq1awcYn9MTJ05k3LhxGZLygn5+i4hjaHMwESnRgoODCQsLy3aN5L59+6hQoUKhrFFMTk4mJCTErpu9a+6u9sADD5CYmMgPP/yQa920DYUCAgIyXatQoQIXL17MtY20L8/XrjfMSdo6R3vLbVedX9qvXz9OnDjBjBkzaNq0Kb/++ivt2rXjySeftOu5c+vrvHKW/qxYsSJnzpzJ8tqZM2cKvBlYdhzZl1Dw/ly2bBldu3YlKSmJKlWqsH79eiIiIpgwYQIPPPBAjmcbF3XfF4XJkydTp06d9KQ5Tbdu3fDx8WHDhg12t+WIvt+9ezdeXl7pv1wqLIXVl4XxPnz44YcJDg5m+/btbNq0iZCQEIYOHcq5c+e4+eab0+sVxue3iBQ/Jc4iUqKlTf9r1KhRltf37t3LTTfdxAsvvEBAQADly5dn3rx56ddnz55Ns2bNeOihh6hUqRIVKlTIdvfUU6dOUaFCBbtuAwYMyPNrSdvt2p4vTc2bNwfg9OnTWcZZvnz5XNs4evQokPWXt6JStmxZhg4dysyZMzl58iQDBw7kyy+/5Pjx47k+Nre+TkhIoFSpUpjNZry9vfH29sZqteLu7s6vv/6aqb6z9GfTpk0JDQ3lxIkTGcpPnDhBWFgYTZs2zfNzF4eC9CXk3J/29OWSJUvo2bMnXl5ejBs3jurVq2M2m7nrrru46aabMmzGdK2i7vuicPbsWZKTkzOV22w2kpOTSUpKKrZY8tP3f//9N/Xr18dkMmVbJ6/vYSi8viys96GrqyvNmjWjVatW+Pr6snLlSmw2G927d0+vUxif3yJS/DRVW0RKtLQv39mNWu3du5e///6bF154gQ8++IDJkyfz7rvv0r9///Tr+/btY9SoUXz//fdMnz6dJ554grvvvjvDGkAovLV0YWFhWX4xmjZtGkCG9X+JiYkcO3YMPz+/DMfQ9O3blxEjRjB9+nQeffTR9Fi3b9/O9u3bGTZsWI7PFxsbyzvvvIOLiwvdunWz6zUVRHJyMlFRUelTPwGsVisNGjRgzpw5dv2yILe+dnV15dNPP+Xrr79OH31r0qQJ48aNy3LarrP056BBg5gwYQKTJ09mypQp6XUnT56MyWRi0KBBdsVYXAqjLyHn/sytL5OSkvjjjz/Sf8ZXCw8P599//81xeYYj1jhn9162V926dVm0aBGbN2/OsOvyb7/9RkxMDM2aNSuUOHOS376PiIjgxIkT6TuDZyev72EovL7My/vQ3r6MiIjg1VdfpXr16hken5fPbxFxHkqcRaREsydxfumll+jVqxdgbMZytX379jF8+HAeeOABwJhq9/jjjxMSEkLlypUz1C2stXRPPPEE58+fp0OHDlSpUoVLly6xYsUKVq9eTZs2bXjwwQfT6545c4ZbbrmFhx9+mJkzZ6aXly1blvfee4+RI0fSvn177rvvPs6dO8eUKVMoW7Ysb7zxRqbn69SpE5UrV+bs2bPMmjWL48ePM378eKpUqVLg15SbqKgoKlasSL9+/bjtttsoU6YMBw8eZOrUqdSvX9+u6bq59TUY/Zk2lTUpKYkDBw5kmtqaxln6s1GjRjz++ON88sknREVF0bp1azZt2sSMGTN44oknMh1n9dlnn3Hp0iVSUlIA49/4O++8AxhrXu05zqwgCqMvIff+zKkv//rrL+rWrUvp0qUzPCYpKYmHHnqIQYMG5RhHYa5xtrc/snsv2/v4l19+mWXLltGtWzeefvppatWqxb59+/jqq68IDAzkmWeeKZTXk5P89n1aX2f3XrxaXt7DUHh9mZf3YVZ9GRISQvfu3enXrx9VqlQhODiY6dOnc+HCBVatWpVhjXRePr9FxHkocRaREm3Pnj1Ur149yzXMNpuNf/75hy+++CK97J9//smQPO/du5cxY8ak3w8LC8NisWT6Ql6Y7rvvPmbOnMk333xDeHg4bm5u1K1blw8//JBnn30200h3dp577jnKlCnDxx9/zOjRo/H09OTOO+/MlAz36tWLWbNm8fnnn3PhwgW8vb1p0qQJkyZNynEDpcLk6enJiBEj+OOPP1i+fDkxMTFUrlyZJ598kjFjxmS7tvJqOfV1mn379tGvXz8ADh48iIuLC9WrVy+kV5G1wujPzz77jKpVqzJ9+nR+/PFHKlWqxLvvvpvlZk8TJ04kKCgo/f7ff/+dvvFS5cqVizxxLoy+hNz7M6e+XLJkSfovw9KkpKSkb7b01Vdf5e/F5UNB+8Pex7dq1YodO3bw1ltvMXv2bM6ePUvp0qUZOHAg77zzDoGBgYX90jLJb9+nvR57E+fifg+nycv78Fre3t7Url2bb775hrCwMPz9/enatStvvPEGtWvXzlTf3s9vEXEeJtvVOz6IiFxHTpw4Qb169YiKikpPXh588EGaNm3KqFGjiIyMxM/Pj+PHj1OjRg3AmJa3dOlSVq5c6cjQJZ8qV67Mr7/+yh133MGPP/7IZ599xubNmx0dluRDTn1566238vPPP6cnYjabjWHDhnH8+HGWLVuGp6enI0OXAtB7WESclTYHE5Hr1t69e6lfv36GEb/du3enb0a0b98+LBYLP/30E8nJyaxatYrx48fz7rvvOipkKYCLFy9y9uxZbr31VsAYrbp6J1spOXLqy6CgIKKiojKMXj711FP8+++/LF68WElzCab3sIg4MyXOInLd2rt3b4Y1d3FxcRw+fDi9bO/evdx///38/fff+Pn5MXr0aH766af0HU+lZNm3bx/Vq1dPPy+1YsWKLFmyhNWrVzs4MsmrnPoybTftNEFBQXz55Zfs3r2bChUqpO/G/N577zkqfMknvYdFxJlpqraI3LCefvppatSowejRox0diojYqWfPnjz99NMZkmcREZGiphFnEblh7d27l1tuucXRYYhIHrRv3z7XY41EREQKm0acReSGVapUKXbs2JHljqciIiIiImmUOIuIiIiIiIjkQFO1RURERERERHJgzb2KFLWUlBTOnj2Lj48PJpPJ0eGIiIiIiIiUCDabjaioKCpWrIjZXHTjwjdM4rxz505WrVrFtm3b2LZtG2fOnAGMH3R+XLx4kXHjxjF//nxCQkIIDAykf//+jBs3jlKlSuWprbNnz1KlSpV8xSEiIiIiInKjO3XqFJUrVy6y9m+YNc79+vVjwYIFmcrz8/LPnTvHHXfcwdGjR6lZsybNmjVj//797N+/n5tuuonNmzdTunRpu9uLiIigVKlSnDp1Cl9f3zzHI8UjMTGRlStX0q1bN1xcXBwdjuRAfVUyqJ9KBvVTyaG+KhnUTyWD+qlkSExMZP78+Tz66KNcunQJPz+/InuuG2bE+Y477qBhw4Y0b96c5s2bU716deLj4/PV1siRIzl69CgDBgxg9uzZWK3Gj3HEiBF8+umnjBo1ipkzZ9rdXtr0bF9fXyXOTiwxMRFPT098fX31Aerk1Fclg/qpZFA/lRzqq5JB/VQyqJ9KhrR+Aop8yesNkzi//PLLhdJOcHAwP//8M66urkybNi09aQb48MMP+eWXX/jhhx/44IMPKF++fKE8p4iIiIiIiDiOdtXOo+XLl5OSkkLbtm0JCAjIcM3NzY3evXuTnJzM0qVLHRShiIiIiIiIFCYlznm0Z88eAJo0aZLl9bTyvXv3FltMIiIiIiIiUnSUOOfRyZMnAbLdsS2tPCgoqNhiEhERERERkaJzw6xxLizR0dEA6YvQr+Xl5QVAVFRUtm3Ex8dn2JgsMjISMBa3JyYmFlaoUsjS+kZ95PzUVyWD+qlkUD+VHOqrkkH9VDKon0qG4uwfJc4OMH78eN58881M5StXrsw2IRfnsWrVKkeHIHZSX5UM6qeSQf1UcqivSgb1U9ExmUxYLJYCt2O1Wlm7dm0hRCR5lZKSQkpKiqPDyECJcx55e3sDEBMTk+X1y5cvA+Dj45NtG2PGjGHUqFHp9yMjI6lSpQrdunXTcVROLDExkVWrVtG1a1cdS+Dk1Fclg/qpZFA/lRzqq5JB/VR0bDYbYWFh6bM5C9pWXFwc7u7uRX7MkWTNYrFQpkwZfH19s+2DxMREFixYUCzxKHHOo6pVqwJw+vTpLK+nlVerVi3bNtzc3HBzc8tU7uLiog/QEkD9VHKor0oG9VPJoH4qOdRXJYP6qfAFBwcTFRVFQEAAnp6eBUp4U1JSiI6OxtvbG7NZ20IVJ5vNRlJSEpGRkYSHh5OYmEiFChUcHZYS57xq1KgRALt27cryelp5w4YNiy0mEREREZEbWXJyMpcuXaJ8+fKUKVOmwO2lpKSQkJCAu7u7EmcH8fHxwc3NjXPnzlG+fPlCmX5fEPpXkEfdu3fHbDazceNGwsLCMlyLj49n0aJFWCwWevTo4aAIRURERERuLGmbRGm/oOuLl5cXNpvNKTZpU+Kcjc8++4y6desyZsyYDOUVKlTg/vvvJyEhgaeffpqkpKT0ay+99BLh4eE89NBDlC9fvrhDFhEREZGcpCTDiY2wb67xZ0qyoyOSQqb1yNcXZ+rPG2aq9pIlS3j77bfT7yckJABw++23p5e99tpr9OzZE4Bz585x6NAhgoODM7U1efJktmzZwm+//UbdunVp1qwZ+/fv559//qFOnTp8/PHHRfxqRERERCRPDiyE5S9D5NkrZb4Vofv7UK+P4+ISkRLhhkmcw8PD2bp1a6byq8vCw8Ptaqts2bJs27aNcePGMX/+fObNm0dAQAAjRozgzTffpFSpUoUVtoiIiIgU1IGFMGcIYMtYHhlslA/8TsmziOTohkmchw4dytChQ+2uP27cOMaNG5ft9dKlS/PJJ5/wySefFDw4ERERESkaKcnGSPO1STOklplg+StQtyeYHbv5kIg4L61xFhEREZHrV9BfGadnZ2KDyDNGPWeltdkiDnfDjDiLiIiIyA0oOrRw6xU3rc2Wa3To0AGAdevWOTSOG41GnEVERESkeBXnCKp3QOHWK05pa7OvHTFPW5t9YKFj4sorjZg73JEjR/j666+x2bJaslB8bU6cOBEfHx/i4uIKLY7iohFnERERESk+xT2CWq2V0X5kMFmvczYZ16u1ylu7KcnG9O7oUCPprtaqcNdIXy9rszVi7nAHDhygc+fOhISEsHnzZqZPn47ZXLDx0/y22b9/f0aPHs2qVavo3bt3gWIobkqcnUnCZUjI4oPPZAEX94z1smMyg4tHPuvGkPWHM4AJXD3zVzcxFmwp2cfh6pXPunFgy+E3lnmp6+IJaefEJcVDSlLmOomJWJLj4erfqmVXN43VA9I+RJISICWHw9vzVNf9yn+SeambnAjJCdnXtbiBxZqPukmQHJ9DXVewuOS9bkoyJOXwG0mzC1hdM9dN66uEy2BzyaJuCiTF2tlubnWtYHUz/m6zQWJM4dTN0/v+eviMyOHfWkn5jMhP3ZLwGXH1+8nsfX18RuRat4R+RmT12Zdd3WzbLeLPiGx3tz4LcwbDgK+hbq/C/x7R5U34/TEjjkwx2qDLuMz/PnL6jDiwEP54HaLDrpR5lYUWj0NgI6Ot5ATjfZIUn/p347PAjJka4Qcwbz9rPHdW8VqscOmkfWuz98+HWh3BvVTunxEpyXByK8RdBJ8KRrKfklx03yP2z0v9uV8jrb8Hfm8kz4XxGZEQb/wsr/552rL5+aYxmYx/x1nVTbtvSyE5KYVtQRcJi0qgvI87Lar7YzHlMMqaU7uFVRdyGbk3gdnM3r176dKlC+fPnwdg5syZJCUmMuPbb7BYLBnq2tvu3n/+saPNrNutVaM6DRo0YN7vv9O7Z4+MTV/9C6C0GFKSjZ9JQgyY0+LK+D3CnJLDv8lCpMTZmXx0M7hlcch3nW7w4K9X7n9YO/v/TKu1gf8tuXJ/cgOIOZ913YqN4fF1V+5PbQkRJ7OuW64uDL/qOK+vO0L4wazr+lWF5/dduT/jLjj7d9Z1PcvAS8ev3P/hHgj6M+u6Lp7w6lXnas8ZDEdWZl0XYFzElb/PexwOLMi+7tizV/6DXDQS9vyU+emBXkBip7bgWsEoXDEWtk/Pvt3n9oJ/NePva96Cvz7Nvu7TW6D8LcbfN34E6ydkX/exNVCpqfH3rZ/Dqtezr/vwYqjR1vj7zpmw9MXs6z4wB2660/j73jmw4Ons6947E+r3N/5+cBH8OjT7un2nQeMHjb8fWw0/Dcy+bo+J0CL1P9mgv2BWr+zrdn0LWj9n/D14N3zdCbjSV+y9qm77V6DjGOPv5w7BtNvJVqtnods7xt8jTsGUhtnXbf4o9PzI+HvMefiwVvZ1Gz0A/T83/p4YA+9VzL5uvb7G8Shpcqpbwj8jLN/3NvovKyXoMyLd6GPGF2i4Lj4jMryfrpPPiCxdB58RWX72gfN8Rjz1Vw4jqKl+fwz8qsDz/1wpK6zvERZX472ZVTKaVXJXrx/ER0FCNIT9C/GR2ccNcPkcrH0v5zqABWgIcDrXqvb5bZjxp8kK7j7g6mP0b8y53B/rXR6qtTYS3Ozk93vEnl9g4TM5P3/aiHlhfEZ4V4HWH0GsP3ikJlUJ0XD+aPbt+la8MkU/MQbOHU6/ZAZKAcv3xfLmhgiCo68ktBV83XijjSfda3uQJa/y4FfJ+HtyAoQdyD4Gz7JQqorx95QkCP0n+7oepa/8n4ENQq59s1/FvRQHw+Lp1KkTgYGBPDKoFxM+m8G3H73BIy++hSnuErOmvGXUdfOFMld9NoX+k20CfzAolE69HzLafOQRJkyYkHWbYPy/XO7mK/fDD0JyAv273M7Umb+S/NbwK4m21f3K/3Fg9EVSHCTZICIclg6C6FPGtWu+R9x+bGL2P4dCpDXOIiIiIlL0ct3dOlVSDiOPuclptC4lCdz8jCSBLAYqrnVgvpGkndqae9KcQc5tp9TpzplSLUi5pS/418hDu7mwJUHsReOXF/YkzWCMmOeUNAMEbYJT24wR8JxGpq+VU8Kaxsl3M19+NJanll7MkDQDhETG89TSiyw/msNsEydQo0YNBg8ezNq1awkoWxqAoYP6MGvymwzokcMvEnNqs2qVK20GBOSrzf7dO3H+4iU2bNmVrxgcxWQrzBXiki+RkZH4+fkREX4WX1/fzBWcaYpVfupeJ1O1ExMTWbFiJXf26oeLq2uOddOVhGmYV7tOpmqn99Wd3XBx0VTtjHWd5zMiMTGRpUuX0qNrR1ysOazPKyGfEfmqWwI+IzK8n9w1VTvvdYvvMyLLz75s6mbfbhF+RhxaCr89kn17aa4eXYSM7/v4y8Yof8QpuJT6Z+SZK9Oa7U0Y07h4gYe/cfMsbdw8Shv3vcuDmw+4ehv9bfWE80dgyajc231gLlTPes10Ii4sXbaMHj164GJKyf4zIiUZpraAqBCy/bn6VIAn/zL+bduSjD5IiIaYCxB3yei/uCjY8L5RXhg8ShvP61cZSlU1ZgiUqmL8WaaW8TMEY8R53hO5t3f3N8bofgE/I+Li4jlxOpgaNWrgnjbiXIBp0onJKbT5YB2hUVl/zpmAQD93/hzdHov5ml+WFPFU7fRdtdeszr7uNdOkJ0/6mOdHvYAtOat/b3mbqp1Wd/LkyTz//PPZtJlzuzVq1aZ3r158MmXyletZTNWOi4vjxH//UaNyBdzd3a60m/qdIzExkd/n/MR9Dw0lIiIi61yqkGiqtjNx9cr4RS6nenlp0+66nrnXyU9dl2ymsRS4rnvudfJT1+oGuGUuNyWSbHG78oU4p7pZtusKuDq2rsXlyn88hVrXeuULcmHWNVvs/zd8dd20vnL1ApcsXoPZnId281DXZCqauuAkdYvwMyKrfsqurt3tFvNnRIHrOulnxNXvp6vfuyX5MyLXuiX0MyK3z76r6+alXbvr5vK+t3fXanc/CNpsjFiePwoX/4NLQUZynN208auZXcAn8KpbhSt/egcYCbFnWSPBs9r5/kxzOSz3OgDxEdn/7BKv+kVWbp8Rd32Quib82rXZpivXvUrnHMuJjfYlzX0+M5Lh6FCICjYS9mv/TE6A2AvGLWx/1u14+BsJtYud/3a8AwrnMyLFYiSgV68BNpmMXxzZ45q6249fyDZpBqM3giPi2BYUwR21ytjdbl5iyFVeNoVL+7nY85i8bjZnb/2r6vXv35+5c+cy5ZNPMJlM2dc1p/arqye4Zv1/dYrZzv/jCkiJs4iIiIgUvVx3twYwwewHs7mWyr2UkZj5V4NSabeqxppSnwrGiGgBdwzOVnEfbVWvj7GWPctdqSfYtyu1vedTu3gYm4xlx2YzpoJHBUPEmdRfZgTBxdRfalwKMq6n3ezhUdoY1U+IydsvXItBWJR9SwbCokresUrOoFOnTkyaNInDhw9z88035/4AJ6DEWURERESKRkqyMWIcut/YIMmnYu47RWO6Mu23bB1jHbB/anJcqqoxIu0oBT3aKiUZU9CfVLqwGVOQL9Rsl/toXb0+xgZa+T36qrCSfZPpynT2gPpZ14mLTE2iUxPpY2ty3qQx9gJ81R4wgX91o93y9SCgHpSvD6Vr2j8SXcjK+9g3G6G8Tx5mLEm6LVu2UKZMGWrXru3oUOymxFlERERECi7mgrHLb+h+CD1gTOUNO5jzGvA0br7Q5GFodJ+RMOdlWUZxMluM84dzmj7dfULWSW3qecbWyLM0Awj63P7zjM2WKztb51VRnWOdFXdfCLzVuAHc/lTW5zi7+RrHdiXHwbkjxnrsiyeM28HFV+pZ3Y1dmcvXT02m60GFRldOLihCzauXJsDHlbCohOx+agT6udOiRi5T5SVL8+bNo3fv3tccX+XclDiLiIiISN7EXDCO+Dq7O/XPv41RxqxY3Y3jqK4eTSxbFy4cNXZ1zusIqqPlZ/p0tudXBxvlA7+zb9p1fhQk2S8M9oyYR4cbMxLCDlyZnRD2r7FBXvAe43Y138pQ8TbjVqGx8afFu1DDtphNvNSlJi/OO5jdT403etfLvDGY5OrIkSMcOHCACRNyOFbRCSlxFhEREZHsxV6Cs7vsS5L9axijjekjhPWhdI2skzK/HM6pdnZ5mT6dkpzD+dWpU9PTzjMuyuS1oGulCyK3EXPvcuDdHmq2v1KWkmKMQIcduDKDIXS/sWFc5GnjdvXodGALaPY6XPYG/IwTDgo4zbvzzWWY+kBj3l7yL8ERV9YyB/q580bvenSvH2ic9Z2caGyW6OqdcRNZydK8efPw9vama9eujg4lT5Q4i4iIiIghJcVITE5vM84vPrUdwg+SZdLnXwMqpo72VbjNmELrUap443Uke6dP53p+te3Kecb5nY5tj4KulS5uZrMxbb9MLbil95XyuEhjScDVv8g5fxSig40R6svhEJ96LJnVzdjdO+3kGqt7nhPb7rcGcuetFdh24gJhUXGU9zGmZ1viI4xE/urj/swuxs7kN9L7IB/mzZvHXXfdhbt7yVofrsRZRERE5EYVHw1ndsKpbanJ8jZjvem1/KunJsmNb8wkuSDs3dXa3noFUZC10s7C3ReqtzFuaeIi4cw+iHIBVz8wxRtnPSel3mIvGPVMltRjjbyuJNR2/OLAYjZlPHIq9pIxGn6tlMTU8hpO9/4YOXIkI0eOdHibwcHBbN26lREjRhRqLMVBibOIiIjIjSL2onFGctAm+G8jhOwDW0rGOlZ3qNgEqrQwbpVbGFNpJX+K+wirG5G7L1RqCidOQKlK4O4OyUmQeBkSUm+JMWBLNqZWx0ddeazVA9y8jWnWrt65T++22SDidM51Ik4bu79r2nYmCxYswMXFhZ49ezo6lDxT4iwiIiJyvYq5kJokb4KgPyHkHzJNu/arciVBrtICAhsY6zWlcBTnrtZyhcUKFr8rx5fZbJAYmzGZTk4wdn1PijWmeIPxiyNXb0yu3phSUjK3mxCdcXp2VlISjXpuPoX7mq4Dd999Nz169MDX19fRoeSZEmcRERGR64Q16TKmg0vg1F/w35/GhkrXKlPnylTXtKROio6jd7UWg8mUOk3bE7xSZ1Akpya48dHGn0lx6TdTzDn8ANu5UHD1SR2V9jEeYw97691gypUrubNXlDiLiIiIlFTJiXB6Bxxfi+Xoanqc2Ylp3zWjmuXqQrXWUL01VGsDPpoSXOwcvau1ZM3iAh7+xg1SE+nLkBCFLT4aU1IcprR10jGpG45Z3Oxvu4isW7euyNqW7ClxFhERESkpbDY4fwyOrYHja+HERkgw1mua06qUqY2pZgeo3tZImLU+2Tmk7mqddHwDuzeu4La2d2Kt2U4jzc7E4mJs6uVRCltKCpERF/F1M2NKvGysi06KMzYdy4059Wgqua4ocRYRERFxVinJcHQ1HF8H548Y59lGXrMxkUdpqNmBpOrtWPNfCh37DcHFRWuUnZLZgq1aG87sj6RRtTZKmp2czWTB5u6LyTNtRDrBSKBjzhsj09nx8Dc2IjMp1bqeqDdFREREnM2F47DhI9j3a+YRLrMVqt4BtTpBrY4Q2AjMZmyJicSeXeqYeEVuBBZX8Cxj3GIvQcQpSEnKXO9ymLHZmKuXseO3m59xprR22S7RlDiLiIiIOFpSApzcDEdWwuEVxuhydlKSocXjWhcr4kgepYwduxOijbXR5tS0Kj4K4iKMX3glpG46xlkj6Xb3AzdfY6Mxkzmn1sUJKXEWERERcYTYS3BkFRxcbKxZjo+0/7HLX4G6PTXVV8SRTKbMR065+4JfJWM9dFykcUuINqZ5Xw43biaz8Th3P2M0Orezo8UpqJdEREREiktUCBxcYiTLJzZmPA/WsyzU6QalqsD693NoxAaRZyDoL6jRtshDFpF8sLqDtzt4lzdmicRHGb8ci4s03vdxEcYNjI3E3EsZibTV1aFhS/aUOIuIiIgUpfPH4N9FRsJ8ejsZzvEtVxfq9oKbe0DFxmA2w7659rUbHVok4YpIITNb0nfrxmaDxFiIj4DYCEiKvTKlO/I0uHhclUS7a120E1HiLCIiIlKYbDYI3Q8H5sO/iyH834zXKzWDW3pB3d5Qtnbmx3vbec6yvfVExHmYTODqadx8KhhnRMdFQNwlY6fuxFjjFhVsnBnt4Qfu/kZCrSTaoZQ4i4iIiBSGsIOw/3fYPw/OHb5SbrZC9TbGyHLdnuBbMed2qrUy6kQGk2F0Op3JuF6tVWFGLyKOYHUzpnN7lzc2GUubwh0fZWwwFh1m3CyuxjFX7qWURDuIEmcRERGR/Dp3BP5JTZavHlm2uEHtLsbO1zfdaXzhtZfZAt3fhzlDABMZk+fUL8vdJ2hjMJHrjcUFvMoat5TkK0l0XKSxuVh0qHGzuKVO/fbXdO5ipMRZREREJC/OH0sdWZ4Pof9cKTe7GMly/f5w813G7rr5Va8PDPwOlr8MkWevlPtWNJJmHUUlcn0zW8CztHFLSTY2Fou9mJpEx19Joq3uqUdj+YOLu6Ojvq4pcRYRERHJTXQY/PMb7J0NZ/++Um62Qq1OqclyD+MLbGGp18eY2h30l/EF2TvAmJ6tkWaRG4vZYowue/hDSjIdOrSH5CTW/fqFcexVVIhxs3qAZ2o9i3bnLmxKnEVERESyEh9t7IS9bw4cWwu2ZKPcZIGa7aH+ACOx9SxddDGYLTpySkSuMFuMX9iZrRB4qzECHXvRWBOdFAuRscYsFVdv47PJ3Q/MVo4cOcK6det49NFHMRXS1O79+/ezefNmHn30UbsfM3HiRN58803Cw8Nxdy9ZI+RKnEVERETSJCfB8XXGyPLBxZAYc+VapWbQcJAxuuxdzmEhiogARvKcNp07OcnYmTv2grE7d9oRV5g48F8YnQcMISQ0lM2bNzN9+nTMZnOBn37VqlU8//zzeUqc+/fvz+jRo1m1ahW9e/cucAzFSYmziIiI3NhsNgjeDXt+MaZjXw6/cs2/hpEsNxwIZWo5LEQRcaCUZOdfMmGxXtlYLCneGIWOvcDevfvoct9TnL8YAcDMmTNJSohjxszvsFiLPxWsVasWDRo0YN68eUqcRUREREqEy+eMkeW/f4Sw/VfKPcvArXdDg4FQuZl2rBW5kf27CFa8ksUmfe877yZ9VjfwCeTg6Yt0um84geXL8cj9A5jw2Td8+9EbPPLiW5gSLzNr+lfGmuhiXg/dv39/pk6dSnJyMhaLk/0CIgdKnEVEROTGkZwIR1bB7h/h8HJISTLKLW5Qtwc0ut/Y7Mvi4tg4RcThXI4uw7T4KTKdpx4ZbBwXN/A7502egRo1azJ48GDGjh3Ljz/8AMDQoQ9jtbrg4+0BUWeNm5vvlfXQpoJP4c5N//79eeutt9iwYQMdO3Ys8ucrLEqcRURE5PoXdhB2/wB7ZsPlsCvlFRvDbQ9Cg3vydtayiFzfUpLxWPcmmZJmSC0zwfJXjA0CnW3adio3NzcmTZpk3EmbOeNfnYeGv2xM5Y65AImXjaOu4iONjQ89S4NHaXD1LLK4brvtNqpXr868efOUOIuIiIg4XHwU7JsLf38PZ3ZeKfcsC43ug9segID6jotPRJzXyc2Yo4NzqGCDyDPG2ueStvO92XJlPXRinLGhWMwFSEk09ni4HJ56tFUZYyq3ufBTxv79+zN37lymTJlSaLt8FzUlziIiInJ9Obsbds4wkuaEaKPMbIU6d0LjB6FON03FFpGcRYfYWS+0aOMoai7u4FIRfCoYv2yMOQ9xEalHW502fjng4W8k2S6ehbbnQ6dOnZg0aRKHDx/m5ptvLpQ2i5oSZxERESn54qOMHbF3zDB2yE5TpjY0edgYYfYu77DwRKSE8Q60s15A0cZRXEwmcPc1bslJqVO5z0FS6oh07AWwuoNnWWJww9PbN8PDExISsFgsdm/2tWXLFsqUKUPt2rWL4tUUCSXOIiIiUnKd3Q07Z8K+X6+MLltc4ZY+0Ox/UK21dsUWkbyregcp3hUwRYdgynKds8nYXbtaq2IPrchZrMZZ9V5ljbPsL5+D2EuQFMe/OzfQ8d4n+PyjdyEpAYC4uDgGDBhAmTJl+P777+16irTjqLSrtoiIiEhRiY+Gf+YaCfPZv6+Ul6kNTYdCowfAq4yjohOR64HZQmyHN/Bc/BRgIuMmYam/jOs+wWk3BisUJhO4ehk3v0oQc5Fatcy0bHwrA4c9Q7uWjQHo2+su1v+5mblz59rV7JEjRzhw4AATJkwoyugLnRJnERERKRnC/oVtX8PeOZAQZZSZXYzjYJr+D6q30eiyiBSaxNp3Ybt3FqYsz3Ge4NRHURU6szEK7epVlrm/zeP+Bx7kt0XLANiwaTPzv/2Y7m0aQEJMrjtyz5s3D29vb7p27VockRcaJc4iIiLivJKT4PAy2Pol/LfxSnnpWsbo8m0PGNMJRUSKwi294ZZexu7Z0aHGmuZqra7vkeacmEy4ePvzy+8LGfzQQyxYuJCFsz6lS+smxs7cMRfAxcv4XPYoleW50PPmzeOuu+7C3d29+OMvACXOIiIi4nwun4dds2DHtxBxyigzmY0zU5s/BjXaaXRZRIqH2VLyjpy6xsiRIxk5cmShtWe1Wvnxp5+IjY3Fy9MTEi4ba6HjLhlnQ1+6bIzSe5UxjgBMPckgODiYrVu3MmLEiEKLpbgocRYRERHncfZv2PqVsUN2crxR5lnG2Bm72TAoVcWx8YmICABmsxkvLy/jjpu3cUtONHbjvnzOOBc6KgSiQsG9FHiXY8H8+bi4uNCzZ0+Hxp4fSpxFRETEsZIS4MAC2PYlnN5+pbxiY2jxBNTvb5w1KiIizs3iYpwJ7R1g7MR9+ZwxAh13EeIucneHRvTYuw1fb29HR5pnSpxFRETEMaLDYPs3xnTsy2FGmdnFSJRbPgGVmmo6tohISWQyg2dp45YQA5fDIfYi5fw8gBQI/cdYB+1ZFqyujo7WLkqcRUREpHiFHoDNU2HfHEg2zgHFp4IxFbvpUPAu79DwRESc2bp16xwdQt64eoJrNfCtBDHnjancyQnGZmvRYcYmYl7lc92N29GUOIuIiEjRs9ng2GojYT625kp55RZw+1PGzrWpm8eIiMh1yGIFnwDjl6NxEcYodEI0xF40bq7eRgLt7uuUs42UOIuIiEjWUpIxBf1JpQubMQX5Qs12eT+CJTHOGFnePBXCDxplJjPc0gfuGA5VWhR+3CIi4rxMJmOU2aNU6jTuMGM9dEK0cbO4gXc58Cjt4EAzUuIsIiIimR1YCMtfxhp5lmYAQZ+Db0Xo/j7U65P746PDYcc3sO1rY1oegKsPNBlirF/2r1aU0YuISEng6gmu1cEnAWLCjaMIk+Mh4jREBoNLKUhJdnSUgBJnERERudaBhTBnCGDLWB4ZbJQP/C775DnsIGyZCntmXzlOyq8KtHwSmgwGd78iDV1EREogq6uxBto7EGIuGKPQyQnGL14jz8Gq76HZg1ChoeNCdNgzi4iIiPNJSYblL5MpaYbUMhMsfwXq9sw4bTtoM2yaDIeXXymr1BTueMaYlm3RVw4REcmF2WJM0/Yqa6yDvhQM2ODQEtj5BdTsAK2fg5odjSnfKcmUjj5ULKHpfzERERG5IugviDybQwUbRJ4x6lVrDUdWwJ+T4NTW1OsmI6lu9SxUaemUG7yIiIiTS1sHbXKHcwlQ507YPQOOrzNugQ2gelus++dxx7mc/s8qPEqcncjlHTvwad8ekyWPG6+IiIgUluhQ++rt/x2Wvnhlwy+LK9z2ALQaAWVqFV18IiJyY7G6wp3vQvvnYPM02DULQvYZt2JkLtZnc7DY2Fhef/11brrpJtzd3alYsSLDhg3jzJkzeW5r1apV9OzZk3LlyuHi4kKZMmXo1q0b8+bNy3d8p594kqOduxC5cmW+2xARESkQ7wD76u341kia3Xyh9UgYuQ96T1HSLCIiRaNUVbhrAjy3F9x8ACjOOU03TOIcFxdHp06dePvtt4mOjqZv375UqVKFGTNm0LhxY44fP253W5MnT6Zbt24sW7aMm266ibvvvpu6devyxx9/MGDAAF599dV8x5kUGsqZ50YqeRYREceo1srYPTu3ryNe5aHLOHj+H+j6JvgEFkd0IiJyows/CPFRxf60N0zi/M4777BlyxbuuOMODh8+zOzZs9m6dSsfffQR4eHhDBs2zK52wsPDeeWVV3BxcWHt2rVs2rSJX375hU2bNrFu3Trc3NwYP358nhLxDGzGZiyh743HluwcW6+LiMgNxGwxjpwCsk2emw0zRpjbPK9dskVEpHjZu6SokN0QiXNCQgKfffYZAFOnTsXb2zv92qhRo2jYsCHr169n586duba1detW4uPj6dSpE+3bt89wrV27dtx5553YbDZ27NiR/4BtNpJCQojZkXs8IiIihS6gPtRoR6adtT1Kw72zoNckcHF3SGgiInKDs3dJUSG7ITYH27RpExEREdSqVYvGjRtnun7PPfewd+9eFi1aRNOmTXNsy83Nza7nLFOmTL5ivVpSeHiB2xAREbFb2EHY+BH8MxdsKQCkBDTkCNWo1eURrLXaZTyCSkREpLilLSmKTD2qqpjcECPOe/bsAaBJkyZZXk8r37t3b65ttWjRglKlSrFmzRrWr1+f4dqGDRtYsWIFderUoW3btgWMGqzlyhW4DRERkVwF74U5Q2Da7bBvjpE017kTHllF8qNrOFjxXmzV2yhpFhFxAh06dKBDhw6ODsNxrlpSZCvG7cFuiMT55MmTAFSuXDnL62nlQUFBubbl5+fHN998g9lspmPHjrRp04b77ruPNm3a0KFDB5o3b86KFStwdXUtWNBmMxFLl3B56zatdRYRkaJxeif8dB982RYOLABscEtveHw9PDgHqrRwdIQiIlII9u/fz/Tp0wu1zSNHjvD1119js9k/6jtx4kR8fHyIi4sr2JPX6wMDvwOfCgVrJw9uiKna0dHRAHh6emZ53cvLC4CoKPt2ZxswYADLli1j4MCBbNq0Kb3c19eXbt26UalSpRwfHx8fT3x8fPr9yMjIzJVSUoiYPYeI2XOwlCmDV5fOeHfrhkfTpjrn2UESExMz/CnOS31VMqifHMd0cjPmPz/CfGIdADaTGVu9fiS3eh7K32JUuqZ/1E/OT31VMqifikZiYiI2m42UlBRSUlIK3F5aMpjWpjPKS1wrV65k1KhRdm+InJsDBw7QtWtXQkJC+Ouvv/j6668xm3Mfk+3bty+jR49mxYoV9O7dO9f6KSkp2Gw2EhMTsVybA9W5i8SqHdn880fAm/l8Jfa7IRLnwvbRRx/x0ksv0a9fP8aNG0fNmjU5fvw4r7/+Oq+//jpbt25l8eLF2T5+/PjxvPlm1p2b6OdHeM8epLi747N3H97798P580TOnkPk7DkkeXsT1bABUbc1Jq5qFTAV5+llAsYZ3lIyqK9KBvVTMbHZKBt9gJtD5lM2+hAAKZg5Xbo1hwN6cdm1Auw4AZzI8uHqp5JDfVUyqJ8Kl9VqJTAwkOjoaBISEgqt3aioKJJtyew9v5fzcecp416GhmUaYjE5biArKSkJyGbwLRtpI7x5eUx2/vnnH/r378+FCxcAmDVrFrGxsUydOjVzcnuNcuXKUa9ePX799ddMGy1nJSEhgdjYWDZs2JD+uq8V431z3l9EPtwQiXPaLtoxMTFZXr98+TIAPj4+uba1bt06XnzxRZo0acKvv/6a/puVBg0aMHfuXJo1a8aSJUtYtmwZd911V5ZtjBkzhlGjRqXfj4yMpEqVKgROm0pA27bcctU/OFtiIjHbtnF55UqiV6/BGhGB/1+b8f9rM9bKlfHp0QOfnj1xrVnDvh+G5FtiYiKrVq2ia9euuLi4ODocyYH6qmRQPxUf038bMW94H/OpLQDYLK6kNLyflFYjqFCqGjlNdFM/lRzqq5JB/VQ04uLiOHXqFN7e3ri7F3zXf5vNRlRUFNsubuODHR8QGnPlCKQAzwBeav4SXap2KfDz5IfVaqRwvr6+dj8m7WeSl8dk5eDBg/Tr14/AwEAeeeQR3n//faZPn85jjz2Gq6srM2fOzLWNu+++m2nTpuHl5ZVroh0XF4eHhwft2rXLsl8TExNZsGBBfl9OntwQiXPVqlUBOH36dJbX08qrVauWa1vff/89AP379880HcFisTBgwAB2797Nhg0bsk2c3dzcstyd269lS1yv/Qfh4oJrhw6U6tABW2IilzdvJmLxYqL+WE3S6dNc/OorLn71FW71bsGvV298e/bAJcAxW7TfKFxcXPQfXQmhvioZ1E9F6L8/Ye14CPrTuG9xg6ZDMbV+DotfJfIyXqJ+KjnUVyWD+qlwJScnYzKZMJvNdk0Zzk1KSgrrz67nte2vYbtm5+awmDBeXP8iH3f4mC7VHJM8A3l6nabUWaoF/dnUqlWLwYMHM3bsWH788UcAhg0bhqurKz4+Pna1P2DAAN5++23+/PNPOnbsmGNds9mMyWRyivfLDZE4N2rUCIBdu3ZleT2tvGHDhrm2lZZk+/n5ZXk9rfzixYt5jjM3JhcXvNu1w7tdO1JiYohau5bIRYuJ/vNP4g/8S9iBfwn78EM8W7TAt1dPfLt3x2LHKLqIiFxngv6Cte/BfxuN+xZXaPIwtB1lHOEhIiI5Sk5JZsq+KZmSZgAbNkyYeH/b+3Ss0hHLDXTigJubG5MmTcpU/tBDD9ndxm233Ub16tWZN29eromzM7khdtVu3bo1fn5+HDt2jN27d2e6PnfuXAC7FqgHBgYCsGPHjiyvb9++HYDq1avnL1g7mT098evZkypffE6djRsIHPcGHk2bgs1GzNathLz2OkfatOXMCy8S/ecm7cwtInIjOLkFZvWBGXcZSbPZBZo9AiP+hp4TlTSLiNhpV9guwuPCs71uw0ZITAi7wrIemJOc9e/fn/nz5+dpR25HuyESZ1dXV5555hkAhg8fnr6mGeDjjz9m7969tG/fnqZNm6aXf/bZZ9StW5cxY8ZkaKtfv34A/Pjjj5k2AFuwYAE//fQTZrOZ/v37F9Gryczq74//ffdR/ccfqL36D8qNGoVr7VrY4uOJXLKEU48+ytFOnQn76GPijx8vtrhERKSYnNoO3/eHb++EE+vBbIWm/zMS5l4fg1/WxzGKiEjWzsWes6teeEz2ybVkr1OnTpw6dYrDhw87OhS73RBTtQH+7//+jz/++IO//vqLOnXq0LZtW4KCgti6dSvlypXj22+/zVD/3LlzHDp0iODg4Azl/fr149577+XXX3+ld+/eNGvWjBo1anDixIn0Ueh3332Xm28unt3druVSqRJlH3+MMo89Stw/+4mYN4+IJUtICg3l/Ndfc/7rr/Fo1Ai//v3x7XEXlgJuECAiIg50eiesew+O/mHcN1vhtgeg7Yvgn/u+HSIikrWyHmXtqlfOs1wRR1IwMTExmY7kTUhIwGKx5LoxV1HasmULZcqUoXbt2g6LIa9uiBFnMHaSW7t2La+99hqenp7Mnz+foKAghg4dyq5du6hZs6Zd7ZhMJmbPns0333xDu3btOHr0KPPmzeO///6jR48eLFu2jLFjxxbxq7EvTo8GtxL4+mvU2biBSlOm4N2hA1gsxO7ZQ8i4ccZU7lEvGFO5nfR8OhERyULoAfjlQZjeyUiaTRZo/BA8uxP6fKqkWUSkgJqUb0I593KYyProVxMmAj0DaVK+STFHZr9///2XmjVrMm/evPSyuLg4+vXrx9ChQx0XGDBv3jx69+7t0OQ9r26YEWcADw8P3nrrLd56661c644bN45x48Zlec1kMjFs2LBCO0C8qJldXfG9sxu+d3YjKTyciEWLiZg3j/gjR4hcupTIpUtxqVSJUvfcjd+AAdqVW0TEWV04buySve9XwAYmMzS8D9qPhtL2/QJYRERyZzFbeK7Bc7y2/TVMmDJsEpaWTL/c4mWn3hisVq1atGzZkoEDB9KuXTsA+vbty/r169P3eHKEI0eOcODAASZMmOCwGPLjhhlxFoO1XDnKDPsfNRYuoPpvc/F/4AHMPj4knjlD+JRPONqxE6eeepqoNWuxZXPIuIiIFLPIs7BoJHzWHPbNAWxQrx88vQX6f66kWUSkCLSv2J6J7SdS3rN8hvIAzwCHH0VlD1dXV+bOnUvfvn1Zs2YNABs2bGD+/Pn06tXLYXHNmzcPb29vunbt6rAY8uOGGnGWK0wmEx716+NRvz7lXxpN1IoVXPz1V2J37CR67Vqi167FWr48fncPoNTd9+BauZKjQxYRufFcPg9/fgzbvobkeKOsdlfo9H9Q8TaHhiYiciPoUrULnat2NnbZjgmnnGc5mpRv4tQjzVdzcXHhl19+YfDgwSxYsICFCxfSpYtjE/558+Zx11134e7u7tA48kqJs2B2d8evb1/8+vYl/vhxLv06l4j580kKC+P8519w/osv8WrVilIDB+LTuRMmq/7ZiIgUqbgI2DzVuCVEG2VVW0Hn16HaHY6NTUTkBmMxW2ge2NzRYeSb1Wrlxx9/JDY2Fi8vr0Jrd+TIkYwcOTJPjwkODmbr1q2MGDGi0OIoLsqAJAO3mjUJePklyj0/kujVq7k4Zw4xm7dwedMmLm/ahDUggFID76XUvffiUr587g2KiIj9EmJg+9fw5ySIvWiUVWhkJMy1OoMp601qREREcmI2mws1ac6vBQsW4OLiQs+ePR0dSp4pcZYsmV1d8b3rLnzvuouEkye5NPc3Ls2dS1JoKOc+/Yxzn3+BT9cu+N9/P57Nm2PSlzkRkfxLSoC/v4P1H0J0iFFW9mbo9Crc0kcJs4iIXBfuvvtuevTogW8JPBJXibPkyrVqVcqPep6yzwwnasVKLv78M7G7dhG1bDlRy5bjVqc2pe6/H78+fbF4O/43WSIiJUZKCuz/HVa/BZeCjLJSVaHDWGg4EErIGjoRERF7lCvn3Ode50SJs9jN7OqKX+9e+PXuRdy//3Lx51+IWLSI+CNHCX3rbcInfoRfv774338/bnXqODpcERHndmwNrHoDQvYa970Dod2L0ORhsLo6NjYREXFa69atc3QINyQlzpIv7rfcQoW33qT86BeJmDefiz//TMKJE1z86Wcu/vQzns2b4//gA/h06aLNxERErnZ2N/wxDo6vNe67+ULr5+D2p8HV05GRiYiISDaU0UiBWHx8KD1kMP6DHyJm61Yu/vgTUWvWELN9OzHbt2OtUIHSDz1IqXvuweLn5+hwRUQc5+J/sOYd2Percd/sAi0eg7YvglcZh4YmIiIiOVPiLIXCZDLhdfvteN1+O4khIVyaM4eLv8wmKTiYsA8nEv7ZVEr174f/Q4Nxq1nD0eGKiBSfy+dgw0TYPh1SEo2yBgONjb/8qzs0NBEREbGPEmcpdC6BgZQbMYIyTzxB5OIlXPjuO+IPHUqfxu3Vvh2lhwzBq1Ur7cYtItevhMuwZRr8OQUSooyyWp2gyzjjiCkREREpMZQ4S5Exu7lR6u4B+A3oT8zWbVz47jui167l8voNXF6/Abc6tfEfPBi/Pn0wu7s7OlwRkcKRnGQcLbVuAkSHGmUVGkGXN6FWR8fGJiIiIvmixFmKnDGNuyVet7ckISiICz/8SMRvvxF/5Cghr79B+MeTKDVoEP4P3I9LQICjwxURyR+bDQ4uhj/ehPNHjLJS1aDz61B/AJjNjo1PRERE8k3/i0uxcq1WjcBXx1J7/TrKv/IyLpUqkXzpEue//JKjnbtw9pUxxB0+7OgwRUTy5vROmHEXzH7ISJo9y0D39+GZHdDgHiXNIiIiJZxGnMUhLD4+lBk6lNKDBxO1Zg0XZs0idsdOIubPJ2L+fLzat6PMsEfwbNFc66BFxHldOmmMMP8z17hv9YBWz0CrEeDu69jYREREpNAocRaHMlks+Hbtim/XrsTu3cv5b74lauXK9HXQ7g0aUOaRYfh07YrJYnF0uCIihrgI2PgxbPkckuMBE9z2AHT6P/Ct6OjoREREpJApcRan4dGwIZWnTCYhKIjzM2cS8fs84vbt48zI53GpWpUy/xuKX//+2khMRBwnOQl2zoB14yHmvFFWox10e0c7ZYuIiFzHtOhKnI5rtWpUeOMNaq9ZTdmnn8bi50fiyZOEvPkWRzt2InzqVJIuXnR0mCJyI7HZ4NBy+PwOWPqikTSXqQP3/wJDFippFhERuc4pcRanZS1ThnIjnqX22jUE/N//GRuJXbzIuU8/42jHToS89TYJp884OkwRud4F74Xv+sDPg+DcYWPjrx4T4enNcPNdoH0YRERErnuaqi1Oz+zpSemHHsT/vkFErVzJ+enfEHfgABd/+omLc+bg16sXZR5/DLeaNR0dqohcTyLPwpp3YPdPgA0sbnD7k9D2BXD3c3R0IiJyg+rQoQMA69atc2gcNxqNOEuJYbJa8e3Rg+q/zaXqjG/xanUHJCURMX8+x3v24vRzI4k7cMDRYYpISRcfDWvfg0+bwu4fARvcejc8sx26vqWkWURESpz9+/czffp0h7c5ceJEfHx8iIuLK9RYioMSZylxTCYTXnfcQdVvv6X6nNl4d+4MNhtRK1ZwYsDdnHz8cWJ27XJ0mCJS0qQkw67vjIR5/fuQGANVWsKjq+Geb8G/mqMjFBERyZdVq1bx2GOPObzN/v37Ex0dzapVqwo1luKgxFlKNI+GDaky9TNqLFiAb69eYDZzecNGgh54kKDBQ4j+cxM2m83RYYqIs/vvT/iyPSx8FqJDwL863DsLhq2Ays0cHZ2IiDiQLTmZy1u3EbF4CZe3bsOWnOzokEqsWrVq0aBBA+bNm+foUPJMa5zluuB+801Umvgh5Z59hvPTv+HS/PnEbN9OzPbtuN96K2WffALvTp0wmfW7IhG5ysX/YOVr8O9C476bH7R/CVo8BlY3h4YmIiKOF7VqFWHjJ5AUEpJeZg0MJGDsGHy7dXNgZCVX//79mTp1KsnJyVgsFkeHYzdlEXJdca1WjQpvv0XtVSvxHzIYk7s7cf/8w+lnnuVE375ELF6i3xKKiLGOefVb8FkLI2k2maHZIzDib2j1jJJmEREhdu06zo58PkPSDJAUGsqZ50YSuXKlgyIr2fr378/58+fZsGGDo0PJEyXOcl1yCQwkcOxYaq9ZTZknnsDs7U38kaOcffFFjvfuQ8SixUqgRW5EKSnGLtmfNoWNH0FyPNRoB0/+Cb0+Bq8yjo5QREScgC05mchJkyCrJX+pZaHvjdf3yXy47bbbqF69eombrq3EWa5r1tKlKf/8SGqvXUO550Zg9vMj4fhxzo4ezfFevYlYtEgfeCI3ipNbYXpnmP9U6jrmGjDoRxiyEALqOzo6ERFxIrE7d5ISFpZ9BZuNpJAQYnbsLL6griP9+/dn/vz5JWovIiXOckOw+PhQ9qmnqL36D8qNfA6Lnx8JJ05wdvRLHO/Zi4iFC7ElJTk6TBEpChGn4bdH4dtucHYXuPpAlzdh+Fa4pReYTI6OUEREnExSeHih1pOMOnXqxKlTpzh8+LCjQ7GbEme5oVi8vSn75JPUWv0H5UaONBLo//7j7Esvc7xnLy7Nn68EWuR6kRAD6ybAp81g36+ACRoPhmd3QpuRWscsIiLZspYrV6j1HCUmJiZTWUJCAskFmHFZGG1u2bKFMmXKULt27XzHUdyUOMsNyUign6DW6tWUGzUKS6lSJAQFEfzKGI717MmleUqgRUosmw32zYXPmsO68ZAUC1XvgMfXQd/PwCfA0RGKiIiT82jaFHP58tnPSjKZsAYG4tmsafEGlgf//vsvNWvWzLCWOC4ujn79+jF06FCHtjlv3jx69+6tXbVFSgqLtxdlHhlGhQkT8OnRA7OXF4lBJwkeM4ZjPVITaK2BFik5zuyCb++E3x6ByNPgVwXumQH/WwYVb3N0dCIiUkKYLBZ8n38+9c41yXPq/YCxYzA5ceJXq1YtWrZsycCBA1m0aBEAffv2Zc2aNQwaNMhhbR45coQDBw4wYMCAfMXgKEqc5YYWuXIlRzt34fSTTxK1dCkply9j9vY2EuiTRgJ9vHcfIpctw5aS4uhwRSQ70WEwfzh83RFObQUXT+j4f/DMdrh1gNYxi4hInnl07EDFyZOwBmScqWQNCKDSlMlOf46zq6src+fOTU9sATZs2MD8+fPp1auXw9qcN28e3t7edO3aNV8xOIrV0QGIOErkypWceW5kpmMGUi5fBpsN3z69ubx+AwnHj3Pm+VG43nQTXnfcju2uuxwTsIhklpwI2742pmTHRxplDQdBl3HgW9GhoYmISMnn07Urvl26ELNjJ0nh4VjLlcOzWVOnHmm+mouLC7/88guDBw9mwYIFLFy4kC5duji0zXnz5nHXXXfh7u5eoDiKmxJnuSHZkpMJfW989mfzmUzEbNtOzZUruPjDD1yYMZOEw4epdPgwp3ftovzIkXi1aoVJo1giRSslGYL+guhQ8A6Aaq3AnPpl5fh6WPYShB807le4DXp8CFVaOCxcERG5/pgsFrxaltz/W6xWKz/++COxsbF4eXk5tM3g4GC2bt3KiBEjCiWO4qTEWW5IMTt2khQSkn2F1LP54g8eotzw4fg/8ADh07/hwnffEb/vH0498iiezZpRbuRzeDZrVnyBi9xIDiyE5S9D5NkrZb4Vod1oOL4ODiwwyjzLQOfXjR2zzSVjBEBERKQ4mc3mQkuaC9LmggULcHFxoWfPnoUaS3FQ4iw3pLyezWf196fsyOfYWSGQJv8FETlnDjE7dhD00GC82rSh3HMj8GjQoChDFrmxHFgIc4YA18wKiTwLi9M2azFD80eh41jw8C/2EEVERCRv7r77bnr06IGvr6+jQ8kzbQ4mN6T8ns2X7ONDuZdfotaK5ZQaNAisVi7/+Sf/3TuQU8OfIe7QoaIIV+TGkpJsjDRfmzRfzeIKj60zpmYraRYRESkRypUrR9WqVR0dRr4ocZYbkmezplgDA/N9Np9LhQpUeHMctZYtxa9vXzCbiV69mhP9+nNm1AvEHz9RhNGLXOeC/so4PTsryQlXNgMTERG5gaxbt45169Y5OowbjhJnuSGZLBYCxo5JvZP/s/lcq1Sh4vsTqLloIT53dQebjcilSzneuzfBr71OYmhoUYQvcn2LtvN9Y289ERERkQJS4iw3LN9u3ag0ZXKhnM3nVqsWlSdNosb8eXh36ADJyVz69VeOdbuTsI8+IjkiopCjF7lO2WwQesC+ut4BudcRERERKQTaHExuaL7duuHTuXOhnc3nXrcuVb74nJidOwn76GNid+3i/NfTuTjnV8o+9ij+Dz2EuYSdWSdSbIL3wNKX4NSWXCqajN21q7UqlrBERERENOIsN7y0s/n8evXEq2WLQjnQ3rNpU6r9+AOVp03DrU5tUiIiCJv4Ecfu7M6luXOxJSUVQuQi14mYC8ZO2V91MJJmF09ocC9gSr1dLfV+9wk6ekpERESKjRJnkSJiMpnw6dSRGvPnU2H8eKwVK5AUGkrw/73G8b79iFy1Cpsth12DRa53thSqnVuL9YuWsONbsKXArXfDMzvg7ukw8DvwrZDxMb4VjfJ6fRwTs4iIODV9t7q+OFN/aqq2SBEzWSyU6t8P3x53cfHnnzn/+RckHDvGmWdH4NGoEeVeGIVXixaODlOkeJ3ZhWXJC9x2dpdxv3x96PEBVG9zpU69PlC3p7HLdnSosaa5WiuNNIuISCZWq5HWJGlW33UlMTERAEshzAgtKCXOIsXE7OZGmaFDKXX33Zz/5hsuzPqO2D17ODnkYbzat6P8qFG433xzoTyXLTm50NZtixSqmAuw5m3YMQMzNhLN7pg7v4bl9ifBksV/SWYL1Ghb/HGKiEiJYrFYsFgsREZG4uPj4+hwpBDYbDYiIiJwc3PDxcXF0eEocRYpbhYfH8qPHIn/Aw9w7vPPufTrXC6v38CJDRvx7d2L8s89h0ulSvluP3LlSkLfG09SSEh6mTUwkICxY/K0U7hIoUpJgT0/warXIea8UXTrPaymHZ1bPIAlq6RZRETETiaTifLlyxMcHIybmxteXl6Yrj1yNA9SUlJISEggLi4Os1mrW4uTzWYjMTGRiIgIoqOjqVSA78WFSd9URBzEpXx5KrzxBmWGDiV8yhQily4jcuEiopavoPSQwZR5/HEsvr55ajNy5UrOPDfSONLnKkmhoUZ5Ho/ZEikUwXthyQtweptxv1xd6DGR5Mq3E790qWNjExGR64afnx+xsbGcO3eO8PDwArVls9mIjY3Fw8OjQAm45J+bmxuVKlXCN4/fh4uKEmcRB3OtVo1KH39M6WGPEPbhh8Rs3cr56d9wae5vlH36afzvG4TJ1TXXdmzJyYS+Nz5T0mxctIHJROh74/Hp3FnTtqV4xF6Cte/B9q+Njb9cvaHDK9DySbC4QOq6JRERkcJgMpmoUKEC5cuXT18bm1+JiYls2LCBdu3aOcU04RuNxWJxup+7EmcRJ+Fxa32qzpxB9Pr1hH04kYRjxwh97z0u/PgD5Ue9gE+3rjn+xjNmx84M07MzsdlICgkhZsdOvFpqMzIpQjYb7J0NK1+Dy2FGWf0BcOe7xq7YIiIiRShtvXNB20hKSsLd3d3pEjhxDCXOIk7EZDLh06ED3m3acGnub4R/9hmJQSc589xzeDRuTPmXRuPZuHGWj02yc0qSvfVE8iV0Pyx5EU7+ZdwvUwd6fAi1Ojo2LhEREZEC0Ep3ESdkslrxv28QtZYvp+zTT2Hy8CD2778Juv8BTo98noSTJzM9xlqunF1t21tPJE/iImH5WPiirZE0u3hCl3Hw1F9KmkVERKTEU+Is4sQs3l6UGzGCWsuX43fP3WAyEbV8Ocd69iJ0/HiSLl5Mr+vZrCnWwEDIbjq3yYQ1MBDPZk2LKXq5IdhssG8ufNYctkwFWzLc0geGb4M2z4M19/X5IiIiIs5OibNICeASUJ6K77xDjfnz8WrbFhITuTDrO451u5Pz33xDSnw8JouFgLFjjAdcmzyn3g8YO0Ybg0nhCTsIs3rDb49AdAiUrgkP/gaDvodSVRwdnYiIiEihUeIsUoK433wTVb/+iirfTMft5ptJiYoi7MOJHL+rB5FLl+LTtSuVpkzGGhCQ4XHWgAAq6SgqKSzx0cbGX1+0hv82gtUdOv4fPLUZ6nRxdHQiIiIihU6bg4mUQN6tW+P1++1ELFhI+JQpJJ49y5lRL+Dx/Q8EjB1D7dV/GLtsh4djLVcOz2ZNNdIsBWezwYH5xlrmqLNG2c09oPt48K/uyMhEREREipQSZ5ESymSxUGpAf3zv6s75GTM4//V0Yv/+m//uHYhf3z6UGzVKx05J4Tl3FJa+CMfXGvdLVYO7PoCbuzs2LhEREZFioKnaIiWc2cODck8/Ta3ly/Dr2xeAiAULOdb9LsKnTiUlNtbBEUqJlhgHa9+Dz+8wkmaLG7R/BYZvVdIsIiIiNwynHHFet24dGzZsIDg4mPj4+CzrmEwmvvnmmzy1Gxsby/jx4/nll184efIkpUuXpnv37rz99ttUqlQpz3H+999/TJgwgRUrVnD27Fl8fHyoU6cOAwYMYPTo0XluT6QgXAICqPj+BPwfepDQ98YT+/ffnPv0My79OpfyL4zCt2dPTGb9rkzy4OhqY5T5wnHjfu0uxihzmVqOjUtERESkmDlV4hwREUHfvn3ZuHEjNpstx7p5TZzj4uLo1KkTW7ZsoUKFCvTt25f//vuPGTNmsHjxYrZs2ULNmjXtbm/ZsmXcc889xMbG0qRJE26//XbOnz/Pvn37+PLLL5U4i8N4NGhAtZ9+JGr5csI+nEji2bOcHf0SF374gYBXXsGzcWNHhyjOLjIYVoyB/fOM+z4VjHXM9fplf9yZiIiIyHXMqRLnl19+mQ0bNlC7dm2efPJJbrrpJnx8fAql7XfeeYctW7Zwxx13sHLlSry9vQH4+OOPeeGFFxg2bBjr1q2zq62DBw8yYMAAfHx8WLVqFa1atUq/lpKSwq5duwolZpH8MplM+N51F94dO3Jh1nec//JL4vbsJej+B/Dt2ZPyL4zCpWJFR4cpziYlGbZ9DWvegYQoMJmhxRPQcSy4+zo6OhERERGHcarEecGCBQQEBLBlyxZKly5daO0mJCTw2WefATB16tT0pBlg1KhRzJo1i/Xr17Nz506aNm2aa3ujRo0iLi6O3377LUPSDGA2m2nWrFmhxS5SEGZ3d8o+8Th+/fsRPmUKEb/PI3LJEqL++IPSw/5H2Ucfxezl5egwxRmc2QmLn4fgPcb9Sk2h1ySo0MixcYmIiIg4Aada8BgREUHr1q0LNWkG2LRpExEREdSqVYvGWUxTveeeewBYtGhRrm2dOnWKFStWULNmTXr06FGocYoUFZfy5an47rvU+G0uns2bY4uP5/znX3Cs+11c+n0etpQUR4cojhJ7CZa8AF93NpJmdz/o+TE8skpJs4iIiEgqpxpxrlOnDpcvXy70dvfsMUZQmjRpkuX1tPK9e/fm2ta6detISUmhVatWJCUl8fvvv7Np0yaSk5O59dZbGTRoEP7+/oUXvEghcq9Xj6rfzSJq1Spj/fOpUwSPHcvFH38kYOwYPO2YcSHXCZsN9s2FFWPhcphR1nAQdHsHvMs7NjYRERERJ+NUifOzzz7Ls88+y9GjR6ldu3ahtXvy5EkAKleunOX1tPKgoKBc2zpw4AAA3t7etG3bli1btmS4/uqrrzJ37lw6duxYkJBFiozJZMK3Wze8O3Tg4vffc27a58Tt30/Qgw/h26sX5Ue/iEtAgKPDlKJ07igsGQUn1hv3y9SBnh9BzfaOjUtERETESTlV4vzoo49y5MgR2rdvzzvvvEPXrl2zTXbzIjo6GgBPT88sr3ulrvGMiorKta2LFy8CMH36dLy9vfnpp5/o3r074eHhvP322/zwww/079+f/fv3Z3vEVXx8fIZjtiIjIwFITEwkMTHR/hcmxSqtb66bPjKZ8B0yBM+ePbnw6adE/j6PyMWLiVq9Gv/HHqXUkCGY3dwcHWW+XHd9VVgSYzH/NRnz5k8xJSdgs7qT0noUKbcPB6sbFPPPS/1UMqifSg71VcmgfioZ1E8lQ3H2j1MlzhaLBQCbzcajjz6aY12TyURSUlJxhJVBSupa0KSkJL788ksGDhwIgL+/P99//z2HDh1i+/btTJs2jXfffTfLNsaPH8+bb76ZqXzlypXZJvfiPFatWuXoEApfixa4VaxI+YWL8AgK4sInnxLyw4+E9+7F5VtuKbFHEF2XfZVP5SL30vDUd3gnGNOyQ30bsrfyEGIiy8PK1Q6NTf1UMqifSg71VcmgfioZ1E+SxqkS5ypVqmAqgi/oabtox8TEZHk9bV21PUdfpbXl7e3Nvffem+n6//73P7Zv38769euzbWPMmDGMGjUq/X5kZCRVqlShW7du+PrqyBdnlZiYyKpVq+jatSsuLi6ODqdI2B57jOglSzk3aRKuYWFUmvUdHq1aUe7ll3DNwznnjnYj9JXdooKxrPo/zMcWAGDzDiT5zvGUvrkXHRz8CxH1U8mgfio51Fclg/qpZFA/lQyJiYksWLCgWJ7LqRLn//77r0jarVq1KgCnT5/O8npaebVq1XJtK61O1apVs0zyq1evDkBYWFi2bbi5ueGWxRRYFxcXvTFLgOu9n0r370epbl059+VXXJgxg9i//uLk3fdQ+sEHKfvMcCyFdLZ6cbje+ypHyUmwfXrGM5lbPomp41isbs7Vhzd0P5Ug6qeSQ31VMqifSgb1k6RxquOoikqjRsaRKrt27cryelp5w4YNc20r7TirtLXO17pw4QJAhrOiRUoas5cX5Uc9T83Fi/Du1AmSkrgwa5ZxfNVvv+n4Kmd3eid83RGWv2wkzZWaweProPt4cLKkWURERKQkcPrE+eLFi9kmqfZq3bo1fn5+HDt2jN27d2e6PnfuXAB69+6da1utWrWiTJkyhISEcOjQoUzX06ZoZ3VetEhJ41qtGlWmTaXK11/hWqMGyefPE/zq//HfwEHE/P23o8OTq6Ukw6FlMKsvTO8EIXuNM5l7TdKZzCIiIiIF5JSJ89KlS7nzzjvx9vambNmylC1bFm9vb7p3787SpUvz3J6rqyvPPPMMAMOHD89wVvTHH3/M3r17ad++PU2vOsP2s88+o27duowZMyZDW1arlVGjRmGz2Rg+fHj6jtgAf/zxBzNnzsRkMvHEE0/kOU4RZ+Xdti01F8yn/MsvY/byIu6ffwi6/wHOvvwKiTksS5BicmABfFgLfr4PTqwzylw8oNu70GwYmJ3yo15ERESkxHC6b1PPP/88vXv3ZtWqVcTExODr64ufnx8xMTGsXLmS3r17Z9hYy17/93//R8uWLfnrr7+oU6cOgwYN4vbbb+eFF16gXLlyfPvttxnqnzt3jkOHDhEcHJyprdGjR9OlSxdWr17NTTfdRL9+/WjTpg3du3cnMTGRd955hxYtWuT7ZyDijEyurpT531BqrViO390DAIhYsIDj3e/i/PTppCQkODjCG9TWL2HOEIi9ZmZOYhwsfBYOLHRMXCIiIiLXEadKnGfPns2UKVMoV64cn3zySfo07QsXLnDp0iU+/fRTypcvz5QpU5gzZ06e2nZ3d2ft2rW89tpreHp6Mn/+fIKCghg6dCi7du2iZh52DHZxcWHp0qW8//77lC1blhUrVrBv3z7at2/PokWLGDt2bF5fukiJYS1blorvvkv1ObNxb9SQlJgYwiZ+xInefYjOYTd5KWSJsbD6LVj2UjYVbMYfy18xpnGLiIiISL45VeI8bdo03N3d2bBhA8888wx+fn7p13x9fRk+fDjr16/Hzc2NadOm5bl9Dw8P3nrrLY4ePUp8fDzBwcHMmDGDypUrZ6o7btw4bDYbM2fOzLItFxcXXnrpJf755x9iY2OJiIhg9erV9OrVK89xiZREHg0bUv3nn6kwYTyWcmVJCAri1BNPcuqJJ0k4edLR4V3fjvwB026HjR/lUtEGkWcg6K9iCUtERETkeuVUifOePXvo1KkTN910U7Z1brrpJjp16pTlJl8iUrxMZjOl+vWj1rJllH5kGLi4EL1+Pcd79Sb8k09IiY11dIjXl8izMOdh+PFuuPgfuJey73HRoUUZlYiIiMh1z6kS54SEBLy8vHKt5+XlRYLWU4o4DYu3NwGjR1NzwQK8WrfGlpDAuWmfc7xnL6JWr8Zmszk6xJItOQm2fA6fNYcD88FkgduHw4Cv7Hu8d0CRhiciIiJyvXOqxLlWrVqsX78+w67X14qJiWH9+vXUqlWrGCMTEXu41axB5S+/oOyzz2AuVYrEs2c5PfwZTj3xBAlBQY4Or2Q6k3Ym8yuQEA2Vm8MT66H7e1C7C/hWBEzZPNgEvpWgWqvijFhERETkuuNUifPAgQMJCwujX79+HDlyJNP1Y8eOMWDAAMLDwxk0aJADIhSRnESuXMmxLl059+lnpFy6lF5+ecNGjvfqTdiUKZq+ba+4CFjyInzdOeOZzMNWQmADo47ZAt3fT33Atclz6v3uE4x6IiIiIpJvVkcHcLUXX3yRBQsWsHr1aurVq0eTJk2oXr06AEFBQezcuZPk5GSaNWvGCy+84NhgRSSDyJUrOfPcSMhmWrYtMZHzn39B5IKFBIwdg3fnzphM2Y2U3sBsNtj/Oywfc2VtcsNB0O0d8C6fuX69PjDwO1j+srEGOo1vRSNprteneOIWERERuY45VeLs4eHBunXrGDNmDN9++y3bt29n+/btGa4PGzaM8ePH4+Hh4cBIReRqtuRkQt8bn23SjMmExc8PPNyN6dvPPItX27YEvjoW19RfjmXVZsyOnSSFh2MtVw7PZk0xWa7zkdPzx2Dpi3BsjXG/TG3o+THUbJ/z4+r1gbo9jd2zo0ONNc3VWmmkWURERKSQOFXiDODt7c2nn37K+++/z86dOzl71hhBqVixIk2bNsXT09PBEYrItWJ27CQpJCT7CjYbyZcuUeXDr4jZuZML33zL5Y0bOd67D6WHDaPsE49jvuq9HblyJaHvjc/QpjUwkICxY/Dt1q0oX4pjJMXDpk9gw4eQHA8WN2j7ArQZCVY3+9owW6BG2yINU0RERORG5XSJcxpPT0/attWXQJGSICk83K56yRGRlB85klL9+hHy7ntc3riR819+ScSihQS88go+XbsStWpVllO+k0JDjfIpk6+v5PnERlgyCs4dNu7X7GCMMpfRBogiIiIizsJpE2cRKTms5crlqZ5r9epU+epLolevJvS98SSePcuZEc/h2aoV8YcPZz3l22YDk4nQ98bj07lzyZ+2ffkcrPw/2POzcd+rPHQfD7feDVr7LSIiIuJUHJo4v/XWW5hMJoYPH07p0qV566237H6syWTitddeK8LoRMRens2aYg0MJCk0NOuk12TCGhCAZ7OmVxWZ8OnSBa/WrTn/9dec/3o6MX/9lfMT2WwkhYQQs2MnXi1bFPKrKCYpKfD397DqdYi7BJig2TDo/Dp4lHJwcCIiIiKSFYcmzuPGjcNkMjFo0CBKly6dft+W3QZDV1HiLOI8TBYLAWPHGFOpTaaMyXPq6GnA2DFZjhKbPTwoN2IEfn37cnrkSOL/PZjr89k7NdzphB6Axc/DqS3G/cAG0GsyVG7m0LBEREREJGcOTZxnzJgBQIUKFTLcF5GSx7dbN5gyOfOmXgEBdm3q5VqtGuVfeYVTDw/N9bnsnRput5Tkot2ROuEyrH8fNk+FlCRw8YJOr0KLJ8CiFTMiIiIizs6h39gefvjhHO+LSMni260bPp075/sYKa9mzfI85bvADizM5gzk9wvnDORDy2HpaIg4adyv2wvueh/8Khe8bREREREpFk411HHy5Em8vb0pXbp0jvUuXrxIVFQUVatWLabIRMReJosl3+uPc5zyDWCz4devb+FtDHZgIcwZAlzzPJHBRvnA7/KfPEecgWUvwcHFxn2/KtDjQ7j5rgKFLCIiIiLFz+zoAK5Wo0YNRo8enWu9l156iZo1axZDRCJS3Hy7daPSlMlYAwIyXjAbH1fnv/iS08+OIDE4uGBPlJJsjDRfmzTDlbLlrxj18iI5yZiSPbWFkTSbLND6ORi+VUmziIiISAnlVCPONpvNro3B0uqKyPUpqynfbnXrcv7LL7kwaxZRq1YRvWkT5YYPp/SQwZhcXPL+JEF/ZZyenYkNIs8Y9WrYeab86Z2w+DkI2Wfcr9ISek2CgPp5j09EREREnIZTjTjb69y5c3h4eDg6DBEpQmlTvv169cSrZQusfr4EvDSaGr//jkfTpthiYgj78ENODBhAzI4d6Y+zJScTs307Prt3E7N9O7bkbEaMo0PtC8SeerGXYPEomN7ZSJrdS0HvT+B/y5U0i4iIiFwHHD7ivGHDhgz3Q0JCMpWlSUpK4tChQ6xYsYL69fVlVORG5H7zTVT7/jsi5i8g7MMPiT9ylKCHBuPXrx+eLZoT/smnJIWEUAE4+/MvhAUGZr2rt3dAlu1nklM9mw3++Q2Wj4HLYUZZo/uh69vgXcg7f4uIiIiIwzg8ce7QoQOm1HNeAVasWMGKFSuyrW+z2TCZTLzwwgvFEZ6IOCGT2UypAf3x7tiB8EmTuTRnDhHz5xMxf36mukmhocZmY1MmZ0yeq7Uyds+ODCbrdc4m43q1VlkHcf4YLHkBjq817pepA70+hhrtCvbiRERERMTpODxxHjJkSHriPGvWLGrVqkXr1q2zrOvq6krFihXp3bs3TZo0Kc4wRcQJWf39qfDWm/j160vQkIchKSlzJZsNTCZC3xuPT+fOV3bkNluMI6fmDAFMZEyeU3+Z131C5vOck+Lhz8mw8SNIjgeLG7R70dgAzOpW+C9SRERERBzO4YnzzJkz0/8+a9Ys2rRpw7fffuu4gESkxLElJmWdNKdXsJEUEkLMjp0Zj8qq18c4cirLc5wnZD6K6vh6WDIKzh817tfsCD0/gjK1Cu/FiIiIiIjTcXjifLWUlBRHhyAiJVBSeHj+69XrA3V7GrtnR4caa5qrtco40hwdDitfhb2zjfveAdB9PNQfYJw3fbWU5JzbEhEREZESx6kSZxGR/LCWs28jLlt2o9JmS9ZHTqWkwK5Z8McbEBcBmKD5I9DpNfAolbn+gYXZjF6/n3n0WkRERERKDKdMnE+ePMmiRYs4cuQIUVFRWZ7ZbDKZ+OabbxwQnYg4G89mTbEGBpIUGmqsac5G8P/9Hwn/naDsE09gzu1Iu5B/YPHzcHqbcT+wAfSaApWbZl3/wMLU9dLXPH9ksFE+8DslzyIiIiIllNMlzm+99RZvv/12hmnbaYlz2iZiaTtrK3EWETDOfA4YO8bYPdtkypg8p953q1eP+AMHOP/Fl0QuWkzA/72KT8eOmRtLuAzrxsPmaWBLBldv6PgqtHgcLNl8ZKYkGyPNWe7ObQNMsPwVY0q4pm2LiIiIlDhmRwdwtdmzZzNu3DiqVKnCV199RdeuXQHjiKrPP/+c9u3bY7PZGDVqFGvWrHFwtCLiTHy7daPSlMlYAzKeu2wNCKDSJ1Oo8dtcKn36CdbAQBLPnOH0U09z6plnSDx71bTqg0thakv461Mjab6lDwzfBnc8nX3SDMaa5tTp2bYUuBzqSkSQB5dDXbGlANgg8oxRT0RERERKHKcacZ42bRqurq6sXbuWatWq8eeffwKkJ9BPPPEEkyZN4qWXXqJfv34OjFREnJFvt274dO5M5Nat7Fy1iqZdu+LbsmX6EVS+Xbvi3aoV4dOmcWHWd0T/sZpjm/6i3LAHKe2/A9PRpUZDflWh50S46U77njg6FIDIU+6E7vIjKfbKqLLVI5mAJhH4VolLryciIiIiJYtTjTjv3buXVq1aUa1aNSDj1Ow0zz//PDfffDPvvPOOQ2IUEedmsljwbN6cqNtuw7N58yvnNqcye3kRMHo0NX7/DY+mTbDFxhI2dTonPt1OzHkPaD0Shm+xP2kG8A4g8pQ7Zzb5kxSb8WM1KdbMmU3+RJ5yN3bZFhEREZESx6kS5/j4eAIDA9Pvu7u7A3Dp0qUM9Ro1asT27duLMzQRuc64u1+gWusTVGhxEYtrMvERLgSt8id4QwrJMYl5astWuSWhu0un3rvmeKrU+6G7S2Or3LLggYuIiIhIsXOqxLlChQqEhYWl369UqRIA+/fvz1Dv9OnTJCcnF2tsInKduHweFgyHb+/EFLafUvXdqfnpKPwGDADg0q9zOXZXDy7Nn5/ljv5Zidm1m6TL1enM+gABAABJREFUkDlpTmMi6bJRT0RERERKHqdKnBs0aMChQ4fS73fo0AGbzcYbb7zB5cuXAZgzZw4bN26kfv36jgpTRJxZSjKmoD+pdGEzpqA/jR2vwTiTeecs+Kwp/P2DUdZ4MDyzE2v7J6n43rtU++F7XGvXIvniRYJfGcPJh4cSf/x4rk+ZFB5uV2j21hMRERER5+JUm4P17t2bhQsXsmbNGjp16kTr1q3p2LEja9euxd/fH19fXy5evIjJZOK1115zdLgi4mwOLITlL2ONPEszgKDPwbci3D4cDiy4ciZz+frQ62OoenuGh3s2a0bN33/n/MxZnJs2jZht2zjetx9lHn3EOPs5dfnItazlytkVnr31RERERMS5ONWI80MPPcS///7Lbbfdll42b948Hn/8cUqXLk1UVBT16tXj+++/p3v37o4LVEScz4GFMGdI+rFQ6SLPwspXjaTZ1RvufA+e2JApaU5jcnWl7OOPUXPxIrzat4PERM5//gXH+/Ql+s9NWT7Gs1lTrIGBxpnRWTZqwhoYiGezpgV5hSIiIiLiIE6VOLu5uXHzzTdTunTp9DJfX1+++OILQkJCiI+PZ9++fTzwwAMOjFJEnE5KMix/GchhTbLVA57aDHcMz/lM5lSulStT5YsvqDRlCtby5Uk8eZJTjz7KmVGjSLxqLwYwdvIOGDsm9c41yXPq/YCxYzLt8C0iIiIiJYNTJc4iIvkS9FfmkeZrJcXCpaAsL9mSk7m8dRsRi5dwees2bKmbD5pMJnzv7EbNpUvwHzIYzGYily7jeI+eXPjpp/R6YJwhXWnKZKwBGY+csgYEUGnKZHy7dSvYaxQRERERh3GqNc4iIvkSHZrvepErVxL63niSQkLSy6yBgQSMHZOe7Fq8vQkcOxa/vn0JeWMccf/8Q+hbbxMxbz6B497AI3WzQt9u3fDp3JmYHTtJCg/HWq4cns2aaqRZREREpIRzaOLcqVOnfD/WZDKxevXqQoxGREqsiNP21fPOOBocuXIlZ54bCdccO5UUGmqUXzNS7FG/PtVn/8LFX34hfNJk4vbt4797B1J68EOUfXYEFm8vTBYLXi1bFPAFiYiIiIgzcWjivG7dunw/1pTdJjwicuOIOA3Lx8C/C3OpaDJ2167WKr3ElpxM6HvjMyXNxkUbmEyEvjcen86dM4wYmywWSj/4ID5duhL2/gQily7jwqzviFy+goCxY/Hp1lWfTyIiIiLXGYcmzidOnHDk04tISZWcCFs+h3UTIPEymCxQpxscXp5a4epkODWJ7T4BzFcS4JgdOzNMz87EZiMpJISYHTuzHEF2CShPpY8/xq//AELeeovEU6c489xzeLdvT8Brr+FauVLBX6eIiIiIOAWHJs7VqlVz5NOLSEkUtBmWjIKwA8b9Ki2h58cQeGv6Oc4ZNgrzrWgkzfX6ZGgmKTzcrqfLrZ532zbUXLSQc19+yfnp3xC9fj2Xt26l7PCnKTN0KCYXlzy9PBERERFxPtpVW0RKhsvnYP5wmNHdSJo9SkPfqfC/5UbSDEZyPPIfkh6az45qT5H00HwYuS9T0gxgLVfOrqe1p57Z3Z3yzz1HzQXz8WzRAltcHOEffcyJAQOI2bkzL69SRERERJyQdtUWEeeUkmwcMxUVDGd3w98/QHyEca3Jw9BlHHiWzvw4swVbtTac2R9Jo2ptMkzPvppns6ZYAwNJCg3Nep2zyYQ1IADPZk3tDtmtZk2qzppJxIIFhL3/AfFHjhL04EP43XM35V94Aau/v91tiYiIiIjzcKoRZ4vFYvfNalXOL3LdOrAQJt8Ks3rB74/BlqlG0uxXFR5ZBX0+yTppzgOTxULA2DGpd67ZzCv1fsDYMXk+SspkMlGqXz9qLl1CqXvvASBi7m8c79GTS/PmY8sqSRcRERERp+ZUiXOVKlWoWrVqplvlypWxWq3YbDZsNhtVq1alSpUqjg5XRIrCgYUwZ0jGdcppIk5BVA4beuWRb7duVJoyGWtAxmOqrAEBVLrmKKq8svr7U+Htt6n204+41alD8sWLBI8Zw8khDxN/7FhBQxcRERGRYuRUw7b//fdfttdSUlJYs2YNzz33HA0aNODnn38uvsBExH5pU6yjQ41zk6u1yna6dCZJibDwWTLuin2N5a9A3Z72t5kL327d8Onc2dhlOzwca7lyeDZrmueR5ux4NmlCjd9/48KsWYR/NpWY7ds53q8/ZR4ZRtknn8Ts7l4ozyMiIiIiRcepRpxzYjab6dKlC4sXL2bJkiW8//77jg5JRK519RTr3x4x/pycutt1bs7uhi9aQ9ylHCrZIPKMkZgXIpPFglfLFvj16olXyxaFljSnt+/iQplHH6Xm4sV4d+gAiYmc/+JLjvfuQ/TGjYX6XCIiIiJS+EpM4pymRo0a3H777Xz77beODkVErpbdFOvIYKM8u+Q59iIseQG+6gDnDtn3XNGhBQrVUVwrV6Ly59Oo9OknWAMCSDx1ilOPPc7p558nMTTM0eGJiIiISDZKXOIM4OnpyalTpxwdhoikSUk2zk/Ocop1atnyV4x66Y9JgV3fwadNYft0o171dvY9n3dA7nWclMlkwrdrV2ouWULphx8Gs5moZcs53rMnF374EVtycu6NiIiIiEixKnGJc1BQEBs2bCAgoOR+cRa57gT9lfVmXumumWJ99m/4pquxnjnmPJS7BR5eDEPmg29FwJRNOybwrWSsmy7hLN5eBIx5hRpzf8W9YUNSoqMJfecd/hs4iNh/9js6PBERERG5ilNtDvbdd99ley06OprDhw/zww8/EBkZydNPP12MkYlIjuydOn3+GOz/HXbMAGzg6gMdXoGWT4DFxajT/X1jajcmMo5gpybT3ScU2sZgzsC9Xj2q//wTl+bMIezjScTt389/Awfi/+CDlHtuBBZvb0eHKCIiInLDc6rEeejQoZiuPU/1Kmnnnw4ZMoQ333yzuMISkdzYO3V65f9BQpTx9wYDodvb4BOYsU69PjDwO2Pq99Wj2L4VjaS5Xp/CidmJmCwW/O+/H58uXQid8D6RS5Zw8fvviVqxgoCxY/G5s1uOn40iIiIiUrScKnF+/fXXs/1y6OrqSoUKFWjXrh01a9Ys5shEJEfVWhmJbWQwOR4llRAF5etBjw+hepvs69XrYxw5ld9jrUooa7lyVPpoIn4D+hPy1lskBp3kzMiReLVrS+Brr+Gaw/n1tuTkIjtSS0RERORG51SJ87hx4xwdgojkh9mSwxTrVFZ36PwGtHjsyrTs3Nqs0bawIy0RvFu3pubChZz/8ivOf/01lzds5Hiv3pR9+mnK/G8oJlfXDPUjV64k9L3xJIWEpJdZAwMJGDsG327dijt8ERERketOidscTEScVNoUa98Kma9Vaw3P7YE7nrYvaRbMbm6UG/EsNRYswPP227HFxxM+aRLH+w/g8rZt6fUiV67kzHMjMyTNAEmhoZx5biSRK1cWd+giIiIi1x2nGnG+2l9//cXGjRs5e9ZY41ixYkXatGlD69atHRyZiGTLrxJ4BVxZm1yqOvT5FGraecyUZOJWswZVZ3xL5KJFhE54n4Rjxzg55GH8+val3AujCH1vPNiyGOG32cBkIvS98fh07qxp2yIiIiIF4HSJ8759+xg6dCi7d+8GrmwIlrb2uVGjRsycOZOGDRs6KkQRuVZ0GKx+E/7+EbCBmy90HAvNHwOL033MlDgmkwm/Pn3wbt+esEmTuDR7DhELFhD5xx/YLl/O/oE2G0khIcTs2IlXyxbFF7CIiIjIdcapvtEeOnSI9u3bc+nSJSpXrsw999xD9erVAeP85t9++43du3fToUMH/vrrL+rWrevYgEVudMmJsO0rWDcB4iONsob3Qde3wEdnrRc2i58fFcaNo1T//gSPe5P4f/+163FJ4eFFHJmIiIjI9c2p1jiPHTuWS5cu8corr3D8+HE+/vhjRowYwYgRI/joo484duwYY8aM4dKlS7z66qt5bj82NpbXX3+dm266CXd3dypWrMiwYcM4c+ZMgeI+cuQIHh4emEwmunTpUqC2REqMo6vh81awYqyRNFe4DR5ZBQO+VNJcxDwaNaLGr3Pwf+B+u+pby5Ur4ohERERErm9OlTivXbuW+vXr895772G1Zh4Mt1gsvPvuu9SvX5+1a9fmqe24uDg6derE22+/TXR0NH379qVKlSrMmDGDxo0bc/z48XzH/fjjjxMfH5/vx4uUKBeOw8/3ww8D4Nxh8CxrrGN+bC1U0XTg4mKyWgl49VUsOSXFJhPWwEA8mzUtvsBERERErkNOlTgnJibatXa5YcOGJCYm5qntd955hy1btnDHHXdw+PBhZs+ezdatW/noo48IDw9n2LBh+Yr5m2++Yd26dTz22GP5erxIiREfDavfgqkt4dBSMFvh9uHw7E5oMgTMTvVxckMwWSwEvvZ/kLoHRCY2GwFjx2hjMBEREZECcqpvuo0aNeLYsWO51jt27BiNGjWyu92EhAQ+++wzAKZOnYq3t3f6tVGjRtGwYUPWr1/Pzp078xRvaGgoo0ePpmvXrtx/v31TJkVKHJsN9v4KnzWDjR9BcgLU7AhPboLu74FHKUdHeEPz7daNSlMmYw0MzHzRaiXh+AlsCQnFH5iIiIjIdcSpEudXX32V7du38+2332ZbZ8aMGWzfvp2xY8fa3e6mTZuIiIigVq1aNG7cONP1e+65B4BFixblKd7nnnuO2NhYpk2blqfHiZQYwXvg2+7w+6MQFQylqsF9P8HgeVBem/M5C99u3ai9+g+qzppFxYkTqTBhPB4tWkBSEuGTJxtnP2/dlntDIiIiIpIlp9pV28vLi6eeeorHHnuMmTNnMmjQIKpVqwYYu2rPmTOHP//8k6eeegpvb282bPh/9u46Pqvy/+P46173YCyI0akg3UgoKQoIJoKUXYD6NTAB/YkBKAi2gAkoKSmNdIeENKNhY911fn/c7pax7Was7t3b+/l47KG7Tn3OuTjb3vc55zp/ZVq+Q4fs3xW7b98+AJo2bZrt9Iz2/fv357rWpUuXMnv2bMaOHUutWrU4d+5crpcVKfbiwmDNe7DrB8AAZw9o/zK0eR6c3WxdnWTD5OiY6ZVTvn36EL148X/vfh5sfvdz4Kuv4FSunA0rFREREbE/xSo4d+rUCZPJhGEYbNy4kU2bNmWanvFO5y+//JIvv/wyy/JpaWnZrvfMmTMABAcHZzs9oz0kJCRXdcbFxfHss89St25dXnvttVwtI2IX0lJgx/ew7gNIjDK3Nbjf/Hop30q2rU1uislkwrdXL7w6dMj07ueYdesIfPFFyjz4ACY9ly4iIiKSK8UqOA8aNAhTToPc5ENsbCwAHh4e2U739PQEICYmJlfre+uttwgJCWHt2rW4uLjcdD1JSUmZRuGOjja//zYlJeWmBz2TopPRNyW1j0yn/sJx5RuYQv8BwAi6jbRuH2BUaWOewY72u6T31U3x8MD/zTfx6tWL0PffJ+nwP1waPZqIefMIfPstXOvZ7pZ79ZN9UD/ZD/WVfVA/2Qf1k30oyv4pVsF5xowZti7hhnbu3MnkyZMZNGgQnTp1ytM6xo0bx5gxY7K0r1ixIsdwL8XHypUrbV1CgfJICqX++ZlUjNoJQJKjF4cr3k9IuU5wIAIOLLVtgflQ0voq3x59lDJbtlBuxUqS9u/nzIMPEdmuHWHdumK4utqsLPWTfVA/2Q/1lX1QP9kH9ZNkKFbBubBkjKIdHx+f7fS4uDgAvL29ra4nNTWVJ554gjJlyjB+/Pg81zNq1Cheeukly/fR0dFUrlyZbt264ePjk+f1SuFKSUlh5cqVdO3aFWdnZ1uXk39JMThs+hSH/V9hSkvGMDmS3mwYDh1epb57Werbur58KHF9VZB69SJ1xAjCPv6E2BUrKLtxI/5HjxLw+mt4dulSKHf95ET9ZB/UT/ZDfWUf1E/2Qf1kH1JSUli4cGGRbKvYBufk5GT27t3L+fPnAahUqRKNGzfO063RVapUAchxAK+M9oyByHJy7tw59u7dS/ny5XnggQcyTYuMjARg165dlivR69aty3Y9rq6uuGZzdcfZ2Vknph2w+35KT4M9P8Ga9yEu1NxWoxOm7h/gGFSfkvTGX7vvq0LiHBxM5cmTiN2wgUtj3yPl7FkuvfQynh3aU/7tt3GpXLlo61E/2QX1k/1QX9kH9ZN9UD9JhmIXnBMTE3nnnXf4+uuvLc8mZ/Dy8uLpp59mzJgxuLnlfmTfjHc+7969O9vpGe0NGzbM1fouXbrEpUuXsp0WGRnJ+vXrc12bSJE6uR7+fAMuHzB/X64WdPs/qNMdivBKoxQPXu3bU2PRH1z95hvCvv2OuL82cPKeXvg/8zR+w4bhkIcPKkVERERKomI1pGpSUhJdunRhwoQJxMbG0rBhQ3r37k2fPn1o1KgRsbGxjB8/ni5dumQaXOtG2rVrh6+vLydOnGDv3r1Zps+ZMweAXr16WV1PtWrVMAwj26+1a9cC0LlzZ0ubSLFx9QTMfAR+7G0OzW6+0H0cPLMF6vZQaC7FHNzcCBg+nBoLF+LRujVGUhKhn03iVJ97idu6zdbliYiIiBQLxSo4f/rpp2zevJl27dqxd+9e9uzZw/z585k3bx67d+9m3759tG/fni1btvDZZ5/ler0uLi48//zzADz33HOWZ5oBJk6cyP79++nYsSPNmjWztE+ZMoV69eoxatSoAts/kSKXEAl/vglTW8GRJWByhJZPwvC90OZZcNIVRTFzrVGdKtOnUfGTT3AsV47kU6c4M2QI5199ldSwMFuXJyIiImJTxSo4z5w5k4CAAJYsWcJtt92WZXqDBg1YvHgx/v7+/PLLLze17rfeeotWrVqxefNmateuzUMPPUTr1q15+eWXCQgIYNq0aZnmDwsL48iRI1y8eDFf+yRiE2mpsOM7+LwpbJkC6SlQqys8sxl6fgIefrauUIoh87uf76HmsqWUfaQ/mExE/7GIEz3vJmLWbIz0dFuXKCIiImITxSo4Hz9+nE6dOlkd3drLy4tOnTpx4sSJm1q3m5sba9eu5e2338bDw4MFCxYQEhLCkCFD2L17NzVq1Mhv+SLFw/HV8NXtsORliL8K/nVhwBwYOAcCbffOXrEfjj4+lH/nHarNnoXrrbeQHh3NpdGjOd2/P4mHDtm6PBEREZEiV6wGB3NycsrxlVHXio+Px8np5kt3d3dn7NixjB079obzjh49mtGjR+d63Z06ddJzzWJboUdhxVtw7E/z9+5l4Y43odkQcNRokHLz3Bs2pPpvvxHx60xCJ00icd9+Tt3/AGX79ydgxHAc9fo8ERERKSWK1RXn2267jTVr1nDy5Mkc5zl16hRr1qzJ9QjYIiVefDgsex2+bGMOzQ5O0Po5GL4HWj6h0Cz5YnJywm/Qo9RYuhSfnndBejoRv/zCibt6ErlggT4wFBERkVKhWAXnp556ioSEBDp16sT3339PQkKCZVpCQgLTp0+nU6dOJCYm8vTTT9uwUpFiIDUJNn8OkxvDti8hPRXq9oRnt0GPD8xXnEUKiHNQIJUmTqTK9Gm41KhB2tWrXHx9FCEDHyXxyFFblyciIiJSqIrVrdqPPvooGzdu5Ntvv+XJJ5/kySefxN/fHzAP1gVgGAZPPfUUAwYMsGWpIrZjGHBgLqweA5FnzG1BDaDb+1DzDvP36WkQshliL4NXEFRtCw6OtqtZSgzPNm2osWA+V3/4gbAvviRh1y5O9euH38AB+L/wAo5eXrYuUURERKTAFasrzgBff/01v//+O7fffjvOzs6EhoYSGhqKs7Mz7du35/fff+fLL7+0dZkitnF6E3x7J8x9zByavStAny/gqb/+C82H/oDPGsAP95jn++Ee8/eH/rBt7VJimFxc8H/iCWouXYJ3t26Qlkb4Dz9y4q67iFq0WLdvi4iISIlTrK44Z7jvvvu47777SE1N5erVqwCUK1cuTwOCiZQIYcdg1Wj4Z7H5excvaDcS2jwHLh7/zXfoD/htEHBdcIm+aG5/8Ee4tXcRFS0lnXOFCgRPnkTsho1cfv99kkNCuPDKK0T+/jvl33kb11q1bF2iiIiISIEoFkl06dKlLFiwgLNnz+Lq6kqjRo0YOnQo1apVIygoyNblidhOXBis+xB2TgMjDUyO0GwwdBoFXoGZ501Pg+WvkSU0w79tJlj+OtS7W7dtS4Hyan87Hov+IHzaNMK++pr47ds5eW9f/AYNwv/ZZ3H08rR1iSIiIiL5YvPgPGDAAGbNmgVgub1v0aJFfPLJJ8yaNYvevXV1TEqhlATY+gVs+BSSY8xtde6CrmMgoG72y4RshugLVlZqQPR583zV2xd4yVK6Obi44P/00/jc04vL48YRu3o14dOmEb1kCUGvv4Z3jx6YTCZblykiIiKSJzYNzt9//z0zZ87EycmJRx99lCZNmhATE8PixYvZsmULgwYNIiQkBF9fX1uWKVJ00tNh/2xY85455AJUaGwe+OtGYTf2cu62kdv5RPLAJbgSladOIWbdOi7/3weknD3L+RdfwvP33wl6621ca1S3dYkiIiIiN82mg4P98MMPODg4sGzZMr7//nuef/55Ro0axaZNmxg8eDAxMTHMmzfPliWKFJ0Ta+GbDrDgaXNo9q0M/b6DJ9bm7gqxVy4fa8jtfCL54N2pEzUW/YH/889jcnEhbvMWTvbpw5WJn5IeH2/r8kRERERuik2D899//03r1q3p3LlzlmlvvPEGhmHw999/26AykSJ0YQ/82Ad+uhcu/Q2uvtB1LDy/Exo+AA65PE2rtgWfikBOt8OawKeSeT6RIuDg5kbA889RY/EiPDt2gJQUrn7zDSfuuYfolSs1+raIiIjYDZveqh0dHU3NmjWznZbRHh0dXZQliRSdqyfMt2QfnG/+3sEZWjwOHV4Bz3I3vz4HR+jx0b+japvIPEjYv2G6x4caGEyKnEuVKlT+6iti16wx37594QLnXxiOR7t2OLdtY+vyRERERG7IpsHZMAwcHbP/I97h36ts6enpRVmSSOGLuQTrP4LdP0J6KmCChg/BHW9A2ar5W/etvc2vnFr+WuaBwnwqmkOzXkUlNmIymfDu3BnPtm0J++Ybwr/7nvhNm6i6dStXo2MIfOZpHNzcbF2miIiISLZsPqq2SKmRGAWbJsHWLyHl32c8a3eDzu9C+QYFt51be5tfORWy2TwQmFeQ+fZsXWmWYsDB3Z3AESPw7d2bi++9T8LmzUR8/TWxixcT+PpreHfpotG3RUREpNix6TPOYB4gzNHRMdsvk8mU43QnJ2V+sRMpibB5CkxqBBsmmENzcAsYshQG/F6woTmDg6N5QLHb7jf/V6FZihnX6tWp+NWXXHh0IE7ly5Ny/jznXxjO2cceJ+nkSct8Rloacdu2E7V4CXHbtmOkpdmwahERESmtbJ4+8zo4jAaVkWIvPQ32zYK1H0D0OXObf13o/I75irCuqkkpZzKZiG3QgCrPP0/U9OmEfz+NuM2bOdm7D36PPorrLfUInfgpqZcuWZZxKl+eoDdG4dOtmw0rFxERkdLGpsFZzy9LiWQYcGQZrB4LoYfNbd4Vzc8wN+oPjjb/vEqkWHHw8CBw5EjK9OvH5XEfErt2LeHTp2c7b+rly5wfMRImfabwLCIiIkVGf8GLpKcV3PPApzaYR8o+u838vVsZaP8StHwSnN0LrGSRksilShUqf/kFMevWce655yG727INA0wmLn8wDu/OnTHlMMCkiIiISEFScJbS7dAfOYxA/dHNjUB9doc5MJ9ab/7eyR1aPw3tRoJ7mYKsWKTEc3D3yD40ZzAMUi9dIn7nLjxbtSy6wkRERKTUUnCW0uvQH/++8/i65+WjL5rbH/zxxuH54j5Y839w7E/z9w7O0GwItH8ZfCoURtUiJV5qaGiBziciIiKSXwrOUjqlp5mvNF8fmuHfNhMsf908iFd2t21f+QfWfQCHFpq/NzlC4/7Q4dX8v4tZpJRzCgjI1Xxp4VcLuRIRERERMwVnKZ1CNme+PTsLA6LPm+er3t7S6pF0GceFz8CBOVgC9m33Q8fXwb9WYVctUip4NG+GU/nypF6+bH6mOQeXPxhHwr79BL76Cs5BQUVYoYiIiJQ2Nn+Ps4hNxF6+ufkiz+K45EU6H3oNhwO/AwbUuwee2QT3fafQLFKATI6OBL0x6t9vrntt27/fe7RtCyYT0UuWcOKunoR98y3pyclFXKmIiIiUFgrOUjp55fLqlKMLLH0VPm+Kw96fcCCd9Jpd4Im18PAvEFS/cOsUKaV8unWj0qTPcLruSrJTUBCVJk+i6rTvqfb777g3bowRH0/oxImc6tWb2PXrbVSxiIiIlGS6VVtKp6ptzaNnR18k++ecTeDiCXOfgLREANKrtGWT6x20fvBFHJydi7RckdLIp1s3vDt3Jn7nLlJDQ3EKCMCjeTPLK6jcG9Sn6q+/EPXHH1wZP4HkkBDOPvU0nh3aE/T667jWqGHjPRAREZGSQlecpXRycDS/cgoAUzYzGJAcaw7NlZrDoIWkDVxIuFfdoqxSpNQzOTri2aolvvfcjWerllne22xycKDMvfdSc/ky/IYOBWdn4v7awMnefbj0wQekRUXZqHIREREpSRScpfS6tbf5lVM5vTaqQiPoPxseXwU1OmV91lJEig1HLy+CXnuVGn8sxKtTJ0hNJeLHnzjRvQcRM2dipKbaukQRERGxYwrOUrpVaQP1+4Kj639tFRqbA/OT66FuDwVmETviWr06lb/6ksrffYdLrZqkRUZyacxYTvXtR9yWLbYuT0REROyUnnGW0inmMmyeDDu+h9QEc1vFptDpdajdTWFZxM553d4OzwULiJg1m9DPPyfp2DHODB2GV5fOBL36Ki5Vqti6RBEREbEjCs5SusRchk2TYOe0/wJzpWbQaRTU6qLALFKCmJyc8Bs4AJ+7exI2ZSoRs2YRu2o1cev/wm/wIMo9/TSOXl62LlNERETsgG7VltIh+iIsHwWTGsLWqebQXKk5DJgLj6+G2l0VmkVKKKeyZSn/9lvUWDAfz3btMFJSuPrd95zocReRc+ZgpKXZukQREREp5hScpWSLOA2LRv4bmL+A1EQIbgED55oH/aqtq8wipYVr7dpU/u5bgr/8ApeqVUkLC+PiW29z+oEHid+509bliYiISDGmW7WlZLryD2z8FP7+HYx/ryZVaQMdXoGadyosi5RSJpMJ7zvuwKtdO8J/+ZWwqVNJPHSIkIGP4n1XD4L+9z+cK1WydZkiIiJSzOiKs5QsF/bA7IHwRSvYP8scmmt2hqHLYNhyqNVZoVlEMLm4UG7oEGr+uZwyDz4IJhMxy5ZzoufdXPn0M9Ji42xdooiIiBQjCs5SMpzeBD/1g286weFF5rZbesETa+HReVC1rU3LE5HiyalcOSqMHUP1+fPwaNkSIymJq19/zYnu3YmYNVvvfxYRERFAwVnsmWHAsVUwrQfM6AknVoPJERo+DM9ug4d+hkpNbV2liNgBt3r1qPLDDIKnfG5+/vnqVS6NHs2pvn2J3bDB1uWJiIiIjekZZ7E/6Wlw+A/zM8wX95nbHF2g8QBoNwL8qtu2PhGxSyaTCe8uXfDq0IGIWbMJmzqVpGPHOfvEk3i2a0fgq6/gVreurcsUERERG1BwFvuRHA97f4EtU8yjZQM4e0DzYdDmefCpYNPyRKRkMLm44DfoUXz79Cbsq6+J+Pln4jZt4lTfLfj260vA8OE4BwbaukwREREpQgrOUvzFXYUd38L2byD+qrnN3Q9aPgEtnwLPcratT0RKJEdfX4Jee5Wyj/TnyoSJxCxfTtScuUQvXUa5x4ZRbuhQHDw8bF2miIiIFAEFZym+wk/Blqmw52dITTC3lalqvrrcZAC4eNq2PhEpFVwqVyb4s0+J3z2IKx99RMK+fYR9PoXI2b8R8OKL+PbpjclBQ4aIiIiUZArOUvyc3w2bJ8OhhWCkm9sqNIZ2w+GWPuCof7YiUvQ8mjah6qyZxCxbxpUJE0k5f56Lo0YR/tOPBL36Gp6tW9m6RBERESkkSiBSPBgGHF8FmybB6WtGsK3VBdoOh+od9P5lEbE5k8mET8+eeHXuTMTPPxP21dckHTrMmSFD8LrjDgJf+R+uNWrYukwREREpYArOYlspCbD/N9j6JYQeNrc5OEGD+6HtC1C+gW3rExHJhoOrK+Ueewzffv0ImzKViFmziF27lti//qLMgw8Q8NxzOPn727pMERERKSAKzmIb0Rdhx3ewcxokhJvbXLyg6WBo/QyUqWzb+kREcsGpbFnKv/0WZQcM4Mr48cSuWUPkzFlELfyDckOG4DdsGI5eGo9BRETE3ik4S9E6v9t8dfngPEhPNbf5VoFWT0GTgeBexqbliYjkhWuN6lT+Yipx27dzZfwEEvfvJ+yLL4iYPRv/Z5+h7AMPYHJxsXWZIiIikkcKzlL40lLhn8XmwHx263/tVdqary7X7akBv0SkRPBs2ZJqs2cRs2IloRMnkhwSwuX33if8hx8JfHEk3j16YNJ4DSIiInZHaUUKT0Ik7P7R/P7lqLPmNgdnaHAftH4aKjaxaXkiIoXBZDLh070b3nfeQeTcuYROmUrKmTOcf/El3L6fRuD//qcRuEVEROyMgrMUvCuHzc8v750JKXHmNo9y0PwxaPEYeJe3bX0iIkXA5OxM2YcfxrdXL67+8APh331P4oEDnBkyBM/27Qn838u41a1r6zJFREQkFxScpWCkpZhvx97+HYRs/K89sL75duzbHgBnN9vVJyJiIw6engQ8+yxlH3qIsC+/ImLWLOI2bODUxo349u5FwPDhOFeqZOsyRURExAoFZ8mf6Auwawbs+gFiL5nbTI5Qrye0eELvXxYR+ZdTuXKUf+tN/AY9Suhnk4heupSohX8QvXQZZQcMoNxTT+JUtqytyxQREZFsKDjLzTMMOPWX+Xbsf5aAkWZu9wyEZkPMX766eiIikh2XKlWoNHECfkOHcmXCBOK3biV8xgwi58zBb9hQ/AYN1iusREREihkFZ8m9xCjYN8scmMOO/tdetZ352eV6vcBJr1sREckN99saUGX6NOI2bebKhAkkHT5M2OTPifjpZ8o99SRl+/fHwdXV1mWKiIgICs6SGxf2mG/H3v/7f4N9uXhBw4egxeMQdKtNyxMRsVcmkwmv29vh2bYNMX/+SeikySSfPs2VDz8ifPoM/J99ljL9+mJydrZ1qSIiIqWagrNkLzEK9v9mfp3Upf3/tQfUM4flhg+Bm4/t6hMRKUFMDg743HUX3l27ErVwIaFTppJ68SKX3n2Xq99/T8ALL+Bzd09MDg62LlVERKRUUnCW/xgGnN1mHujr4HxITTC3O7rALb3Nzy5Xu12DfYmIFBKTkxNl7rsPn169iJw1m7CvvyblzBkuvPIKV7/9loCRI/C64w5M+jksIiJSpBScBeLDYd9M89Xl0H/+aw+oB00HQ6OHwcPPdvWJiJQyDi4u+A16lDL39SP851+4+v33JB09yrlnn8OtYUMCXxyJZ5s2ti5TRESk1FBwLq3S0+DUetjzMxxeBGnJ5nYnd2jQzxyYK7fU1WURERty8PTE/6knKfvwQ1ydNp3wH38kcf9+zgwdhkfr1gSOHIF748a2LlNERKTEU3AubcKOwd5fzaNjx1z4r718Q2g2GG57ANx8bVefiIhk4ejrS+CLI/F7dCBhX39D5KxZxG/dyumHt+LVqRP+LzyPe/36ti5TRESkxCpVo4wkJCTwzjvvUKdOHdzc3KhYsSLDhg3j/PnzuV5HZGQkv/76K/3796d69eq4uLjg7e1Nq1atmDRpEikpKYW4B3mUEAk7p8N3XWFKc9g40Rya3cpAiyfgyXXw9AbzoF8KzSIixZaTvz/l33yDmn8ux/f++8DBgdh16zh93/2cfe55Eg8ftnWJIiIiJVKpueKcmJjInXfeydatW6lQoQJ9+vTh9OnTTJ8+ncWLF7N161Zq1Khxw/WMHz+e//u//8NkMtG4cWNatWpFaGgomzZtYvv27cyZM4c///wTDw+PItgrK9LT4ORa89Xlw4shLcncbnKEWl2g8SNQ9y5w0jtCRUTsjXPFilR8/33KPfYYYV98SfTixcSuXk3s6tV4d+2K//PP4Va3rq3LFBERKTFKTXB+//332bp1K23atGHFihV4eXkBMHHiRF5++WWGDRvGunXrbrgeT09PXn31VZ577jmqVKliaT927BhdunRh48aNvP/++3zwwQeFtSs5Mwzzq6P+/h3+npv5VuyAW6DJALjtQfAOKvraRESkwLlWr06lTz7G/5mnCZv6BdFLlxKzciUxK1fi3b07/s89i1udOrYuU0RExO6Vilu1k5OTmTJlCgBTp061hGaAl156iYYNG7J+/Xp27dp1w3WNGjWKjz76KFNoBqhduzYffvghADNnzizA6nMh/BT89QlMbQVfd4DNn5tDs3tZaPmk+VbsZ7dA2xcUmkVESiDXGjWoNGE8NRb9gfddPcBkIubPPznV517OvfgiSceP27pEERERu1Yqrjhv2rSJqKgoatasSZMmTbJMv//++9m/fz+LFi2iWbNmed5Oo0aNALhw4cIN5iwAcWHmdy3v/w3Obf+v3dEV6vYwD/JVu5tuxRYRKUVca9Ui+NNPSXzmKGFTvyDmzz+JWbacmOV/4nPXXfg/9yyuNWvaukwRERG7UyqC8759+wBo2rRpttMz2vfv35+v7Zw8eRKA8uXL52s9OUqKgSPLzGH5xBow0sztJgeo3sF8G/Yt92iALxGRUs6tTh2CJ31G4pEjhE2ZSszKlUQvXUr0smX43H03/s88rQAtIiJyE0pFcD5z5gwAwcHB2U7PaA8JCcnXdiZNmgRAnz598rWeTJJi4MhyOLQAjq38b5AvgIpNzGG5QT/wLqSwLiIidsutbl2CP59M4uHDhE6dSuyq1UQvXkz0kiV4d+uG/zNP41avnq3LFBERKfZKRXCOjY0FyHGka09PTwBiYmLyvI2vvvqKVatWUaZMGV5//XWr8yYlJZGU9F8Ajo6OBiD1xF+k3NoFUuIxHVuOw+E/MJ1Yg+masGz41SS9fj/S698H5Wr9t9Li+BqsEibjVWPF8pVjkon6yj6on4qOY61alP/0UxIPHSLim2+JW73afBv3n3/i0akjfk8+idttt2W7rPrJfqiv7IP6yT6on+xDUfZPqQjOhW3Dhg2MGDECk8nEtGnTqFixotX5x40bx5gxY7K0O/32CA5uLmCk40iapT3GtQIXyrTgQtmWRLtVhlgTbDsKHC3oXZFcWLlypa1LkFxSX9kH9VMR69YVl4a34bdmLd779xO/bj3x69YTV7s24Z3vJKF69WwXUz/ZD/WVfVA/2Qf1k2QoFcE5YxTt+Pj4bKfHxcUB4O3tfdPrPnDgAH369CE5OZnJkyfTt2/fGy4zatQoXnrpJcv30dHRVK5cGQBHw/ypieFVnvRGA0i/tQ9uAbdQw2Tixm+ZlsKUkpLCypUr6dq1K87OzrYuR6xQX9kH9ZONDRtG8qlTRHw/jZjFi/E8dgzPY8dwa94cvyefxL11K0wmk/rJjqiv7IP6yT6on+xDcmIiKz7/vEi2VSqCc8aro86dO5ft9Iz2qlWr3tR6T506Rbdu3YiIiGD06NG88MILuVrO1dUVV1fro12bHBxx7Pwmjg6ON1WTFD5nZ2f9ALUT6iv7oH6yHec6dfD86EOSX3ieq99+R9S8eSTu3MmFnTtxb9SIcs88jWvbtuZ51U92Q31lH9RP9kH9VHxFr1jBpf/7gOCieKMRpeQ9zhmvidq9e3e20zPaGzZsmOt1Xrx4ka5du3Lx4kVGjBjBu+++m/9CrxV9HkI2F+w6RUREsuESHEyFMaOpuXIFZQc9isnVlYR9+zj39DOce+ghvPbvx0hLu/GKREREikD0ihWcHzGStMuXi2ybpSI4t2vXDl9fX06cOMHevXuzTJ8zZw4AvXr1ytX6IiIi6N69OydOnGDo0KF8+umnBVnuf2KL7h+CiIiIc/nylH/jDWqtXkW5xx8Dd3eSDv9DxV9+5XS37oT/OpP0awa3FBERKWpGWhqXPxgHhlGk2y0VwdnFxYXnn38egOeee87yTDPAxIkT2b9/Px07dqRZs2aW9ilTplCvXj1GjRqVaV3x8fHcfffd/P333zz44IN8++23mEymwincK6hw1isiImKFk78/bg0b4nTN2B9pV65weexYjt3enrBvviUtH2+iEBERyav4nbtIvXSpyLdbKp5xBnjrrbdYtWoVmzdvpnbt2rRv356QkBC2bdtGQEAA06ZNyzR/WFgYR44c4eLFi5na33zzTbZs2YKjoyNOTk489thj2W5vxowZ+ajWBD4VoWrbfKxDREQkbzJugcvu0/z0mBhCJ07k6tdfU7b/w5QdNAjnwMCiL1JEREql1NBQm2y31ARnNzc31q5dy7hx4/j1119ZsGABfn5+DBkyhPfee4/g4OBcrSciIgKAtLQ0fv311xzny3tw/vfqdY8PQQODiYhIEcvVLXBOTqTHxXH1u+8J/+FHfO/tg9+wYbjm8CorERGRAuNQSHf73mizNtmqjbi7uzN27FiOHz9OUlISFy9eZPr06dmG5tGjR2MYRpYAPGPGDAzDuOFXnvlUhAd/hFt7530dIiIieZSrW+BSU/F/cSTuzZphpKQQ+fscTva8m3PDR5Dw999FU6iIiJQqiUeOcP7VV7nwv1dssv1Sc8XZLvT/DRp01ZVmERGxmdzeAudSKZiAX34mfvdurn77HbFr1xKzYgUxK1bg0aIFfkOH4NWpEyaHUvUZvYiIFCDDMIjbuInwGTOI27TJ0u5Spw7JR49CYY01lQ0F5+KkWluFZhERsSmngICbms+jaVM8vvyCpGPHuPr9NKIWLyZ+xw7id+zApWpV/IYMxrdPHxw8PAqzbBERKUHSk5KIXrSI8B9+IOnYcXOjgwM+PbrjN+wx3BvUt7zHGb3HWURERIqaR/NmOJUvn/On+CYTTuXL49G8WaZm19q1qfjhOGqtWkm5Jx7HwceH5JAQLo0Zy/E77uTKp5+RcvlKEeyBiIjYq9SrVwmdMpXjd9zJxbfeJunYcRw8PfEbPJiaK1ZQaeJE3BvUB8CnWzeq/bmcc0OHFEltuuIsIiIiFiZHR4LeGGUeVdtkyjxI2L9hOuiNUZgcs79Dyrl8eQJffhn/p58mcv4Cwn/8kZQzZ7j69ddcnTYN35498RsyGLdbbimCvREREXuQdPw44T/8QNTCPzCSkwFwqlABv0cfpcwD9+N4zesRr2VydCSxiAamVHAWERGRTHy6dYNJn3H5g3GZBgpzCgoi6I1R5uk34ODpid/AAZTt/zCxa9dydcYMEnbuImrhQqIWLsSjdWv8hgzGq0MHPQctIlIKGYZB3ObNhM/4gbgNGyztbrfdRrmhQ/Du1g2TU/GJq8WnEhERESk2fLp1w7tzZ6K3bWPXypU069oVn1atcrzSnBOToyPeXbrg3aULCX//TfiMH4hevpz4rVuJ37oVl+rV8Rv0KL69e+Pg6VlIeyMiIsVFekIC0UuWEP7jTyQdPWpuNJnw7tIFv6FDcG/SBFMRDvqVWwrOIiIiki2ToyMeLVoQExqKR4sWNx2ar+d+221UmjCewJdfIvyXX4j87XeST53i0pixXJkwEd9+ffF75BFcqlUrmB0QEZFiI/ncOSJmziRqzlzSoqIAMHl4UOa++/B7dCAuVarYuELrFJxFRESkSDlXrEjQK6/g/8yzRM2bR8Qvv5AcEkLEjz8R8eNPeLZvj9/AAXi2b6/buEVE7JhhGMRv2UL4z78Qu3atZdwM50qVKPvII+bnl318bFxl7ig4i4iIiE04enniN+hRyg4cQNymzUT88gux69cTt2EDcRs24FylCmUf6U+Zfv3s5g8rERGBtNg4ohYuIOKXX0k+edLS7tm2LWUHDsSrY4d838VU1BScRURExKZMDg54tb8dr/a3k3zmDBEzZxE5dy4pZ85w5cOPCJ00Gd/evSn7yCO41a1j63JFRCQHSSdPEfHrr0TNn096XBwADh4e+PbtS9kBj+Bao4aNK8w7BWcREREpNlyqVCHotVcJeOF5ohYvJuLnX0g6epTI2bOJnD0bjxYtKDvgEbzvvBOTi4utyxURKfWM1FRi//qLiF9+JW7TJku7S/XqlB0wAN97++Do5WXDCguGgrOIiIgUOw4eHpR98EHKPPAACTt3Ev7zL8SsWkX8jh3E79iBo78/Zfr2pcyDD+BSubKtyxURKXVSLl4k8vc5RM6dS+rly+ZGkwmvTp0oO3AAnm3alKhxKhScRUREpNgymUx4tGiBR4sWpFy6RMQs823caaFhXP32W65++y2ebdtS5qGH8L7zDkzOzrYuWUSkxDJfXd5A5G+/EfvXX5CeDoBj2bLm27Ef6Y9LcLCNqywcCs4iIiJiF5zLlydw5EgCnnuOmLVrifztd+I2bSJu82biNm82X4Xu148yD9yvq9AiIgUo5dKl/64uX7pkafdo1YoyDz6Ad9euOJTwx2cUnEVERMSumJyd8enWDZ9u3Ug+d878x9y8f69Cf/MNV7/5Bs927Sjz4IO6Ci0ikkdGWhqxf/1F5G+/E7t+/X9Xl8uUwfffR2Vcq1e3cZVFR8FZRERE7JZLcDCBL44k4Pl/r0LP/s18FfrfL8cAf8r07UeZfn1xqVbN1uWKiBR7yefOE7Vggfnq8sWLlnaPFi3Mj8V07YKDq6sNK7QNBWcRERGxe5muQp89++9V6HmZrkK7N2tGmX598e7eA0cvT1uXLCJSbKQnJBCzciWR8+YTv3Wrpd3R1/ffq8sP4lqj9Fxdzo6Cs4iIiJQoLpUrE/jSi+ar0GvWEjlvLnEbN5GwaxcJu3Zx6f3/w6d7d3z79cWjRQtMJpOtSxYRKXKGYZCwdy9R8+YTvWwZ6bGxlmkebVpTpl8/vLt1K5VXl7Oj4CwiIiIlksnFBZ8e3fHp0Z2Uy5eJWvgHUfPmkXz6NFELFhC1YAHOlSvj2/deytx7L84VK9q6ZBGRQpdy+QpRfywkav4Ckk+etLQ7V6qEb9+++N57Ly7BlWxYYfGk4CwiIiIlnnNQEP5PPkG5Jx4nYc9eoubPI3rJUlLOniVs8ueEfT4Fzzat8endG+8uXXUrt4iUKOkJCcSsWUPUH38Qt2GjZaAvk5sbPt274du3Hx4tW5So9y4XNAVnERERKTVMJhMeTZvg0bQJQaNGEb1iBVHz5hO/fTtxm7cQt3kLl9zG4N25M769e+HZtq1G5RYRu2SkpRG3dSvRfywiZuVK0uPjLdPcmzTBt19ffO66C0cvLxtWaT8UnEVERKRUcvDwoMy95tu0k8+eJeqPP4j+YxHJISFEL1lC9JIlOJYti0/Pnvj27oVbw4Z6HlpEijXDMEg6fJioPxYRvWQJqaGhlmnOlSrh07sXvr16l/qBvvJCwVlERERKPZfKlQl47jn8n32WxAMHLH90poWHE/HLL0T88gvOVavge08vfHvdo1dbiUixknL+PFGLlxC16A+Sj5+wtDv6+uLd8y58e/XCvUkTffiXDwrOIiIiIv8ymUy433Yb7rfdRtCrrxC3ZQtRixYTs2oVKSFnCJs6lbCpU3GrXx+fnnfh3b2HBtEREZtIuXSJmD//JHrZchL27rW0m1xc8LrzTnx798Lr9tsxubjYrsgSRMFZREREJBsmZ2e8OnTAq0MH0uPiiFm9mqhFi4nbtInEgwdJPHiQK5+Mx61hQ3zuugufHt1xrlChQGsw0tKI37mL1NBQnAIC8GjeDJOjY4FuQ0TsR8qVK8T8uYLo5ctJ2LXrvwkmEx4tW+Lbuzfe3bri6O1tuyJLKAVnERERkRtw8PTEt3dvfHv3JvXqVWJWriR62XLid+wgcf9+Evfv58pHH+HepAk+d/XAu3t3nIOC8rXN6BUruPzBOFIvXbK0OZUvT9Abo/Dp1i2/uyQidiI1LIzoFSuIWbac+J07wTAs09ybNcPnrrvw7tYV58BAG1ZZ8ik4i4iIiNwEp3LlKPvww5R9+GFSQ0P/+4N21y4S9uwhYc8eLo/7EPdmTfHp2hXvLl1wrnRzt3NHr1jB+REjM/2BDJB6+bK5fdJnCs8iJVjK5cvErFpFzMpVxG/fbnl9FIB748b/fUBXvrwNqyxdFJxFRERE8sgpIAC/AQPwGzDA/Iduxi2Uu3eTsHMXCTt3cXnch7jeegveXbrg3aULrrVrWx2gx0hL4/IH47KEZvNEA0wmLn8wDu/OnXXbtkgJknTylDksr1pF4v79maa53Xab+ZGQ7t1u+oM4KRgKziIiIiIFwDkoCL9Bj+I36FFSLl4kZsUK89Wi3btJOnSYpEOHCZv8Oc5Vq1hCtHujRpgcHDKtJ37nrky3Z2dhGKReukT8zl14tmpZyHslIoXFMAwSDxy0hOXkEycyTXdv3Bjvrl3w7t4dl+BgG1UpGRScRURERAqYc4UK+A0ejN/gwaSGhxO7Zg0xq1YTt3kzKSFnCP9+GuHfT8MpIACvznfifeedeLRsiYObW6b3rlqT2/lEpPhIT04mfscOYteuI2b1alIvXvxvopMTnq1a4d21C1533qlnlosZBWcRERGRQuTk50eZ+++nzP33kxYbR9zGDcSsXEXs+vWkhoYSOWs2kbNmY3Jzw7N1a5yrVs3degMCCrlyESkIKVeuEPfXX8SuX0/cps2kx8dbppk8PPBq3x7vLl3w6tgBRx8fG1Yq1ig4i4iIiBQRRy9PfHr0wKdHD4zkZOK2bSdm1b8h+tIlYtetu/FKTCacgoLwaN6s0OsVkZtnpKeT+PffxK5fT+y69SQeOpRpumOAP14dOuDduQuebdvg4OZmo0rlZig4i4iIiNiAycUFr/a349X+dgzDIOnIEWLXrSd2/XoS9u7NfnAwAMMg6I1RGhhMpBhJjYggbvNm4jZsJHbDBtKuXs003e222/Dq2BGvTp1wu/WWLGMbSPGn4CwiIiJiYyaTCbd69XCrVw//p58iNSKCsC++JPL33zESE7PMHzb1CxL27sOzbVs8mjfTFSuRIpaenEz8zp1EzZ1Lwt59pJw/n2m6g6cnnu3a4dWpE14d2uPk72+jSqWgKDiLiIiIFDNOZctS/s03CHr9NeK2bSdh506SQ06TePIkyYf/IenIEZKOHCF82jRMLi54NG+GW6tWuKanY1zzvlcRKRiGYZB07Bhxmzabryxv3QopKZlncnLCq317/AYPwqNpU0wuLrYpVgqFgrOIiIhIMWVydMSrbRu82raxtKVevUrclq3mP943bSL18mXiNm8hbvMWqgKnf/oJz9Zt8GjZEo+WLXCpXt3qe6NFJCvDMHAOCyNq7lySdu8mfsvWG49kn5ZG7Lp1+Pa9V6G5BFJwFhEREbEjTuXK4XvP3fjeczeGYZB88iRxmzYTs3EjsVu3QngE0UuXEr10KQCO/v54tGiOR4sWeLZsiUvNmgrSNmKkpRG/Ywfee/cSHxCAT6tWela9mDAMg+TTp4nfvoP4HTuI276d6leucG1UNrm64t68OYn795MeE5PdSsBk4vIH4/Du3Fl9W8IoOIuIiIjYKZPJhGvNmrjWrIl3/4dZ+scf3FGxIkm7dhO/fTsJe/eSFhZGzLLlxCxbDoCjnx8eLVr8+9Uc11q19Ad+EYhesYLLH4wj9dIlKgAXZs7iSvnyBL0xCp9u3WxdXqljpKeTfPIk8Tt3Er99B3E7tpMWGpZ5HkdH3Bs1xLNVKzxbtsS9aVMS9u7jzODBVlZskHrpEvE7d+HZqmUh74UUJQVnERERkZLCyQn35s3xadMGeI705GQS9+8nbvt24nfsIGHPXtLCw4n5809i/vwTMA9i5N6oEe6NG+PepAnujRrqXbIFLHrFCs6PGJllpPTUy5fN7ZM+U3guZGmxsSTu30/8nj0k7N1Hwr59pEdHZ5rH5OKCe6NGeLRogUuzpqy/eJG7+vTB2dnZMs8Nb9e+yfnEfig4i4iIiJRQDi4ueDRvjkfz5gAYyckkHDhgvh313yvS6XFx5uelN282L2Qy4VKzBh5NmljCtEu1anp9Th4ZaWlc/mBc9q8X0629hcIwDFJCQojfu5eEPXtJ2LuXpKNHs/SByd0d94YN8WhpvgPDvVEjHFxdAUhJScH493GHazkFBOSqhtzOJ/ZDwVlERESklDC5uODRtCkeTZvC009hpKWRdOwYCXv2kLB3L/F79pJy5gzJx0+QfPwEkb/PAcDBxwe3+rfi3qABbvXr41a/Ps7BwXpWOhfid+4i9dKlnGfQrb35YhgGqRcukHDwIIkHD5F44ACJBw+SFhmZZV7nSpXMd1U0box7k8a41amD6Zqrybnh0bwZTuXLk3r5cvYfhphMOAUF4dG8WR73SIorBWcRERGRUsrk6Gh5f3TZ/v0B86jdCXvNV+kS9uwl4e+/SY+OJn7LVuK3bLUs6+Dri3v9Wy1BWmE6e7q1t+BkCckHD5pDckRElnlNLi64NWhgDsmNzY8iOAcG5rsGk6MjQW+MMt9ibzJlDs///tsPemOU7h4ogRScRURERMTCqVw5vDt3xrtzZwCMlBQSjx79N6SYr+glHT1KelSU5TVYGRx8fXGrUwdXy1dtXGvXwdHL01a7Y3PF6dZeIy3NfAU8NBSngAA8mjcrtgEvPS6OpOPHSTx6lKSjx0g6epSkI0eyvZKMkxOudWrjXr8+bvXNd0W41q2DQyG9EsqnWzeY9JllsDdLGUFBGuytBFNwFhEREZEcmZydca9fH/f69S1tRnIyiceOmcP0AfNVv4wwHb/D/DqfazlXqpQpTLvVqYNL1ap2/a7b3IbQ4nJr77WjemdwKgajehspKSSfPWsOxkePWoJyytmz2R8vJydca9fGvcF/dzq41qljeTa5qPh064Z3585280GE5J+Cs4iIiIjcFJOLy39h+kFzm5GcnPUK4dGjpF65Qsr586ScP0/s2rXXrMSEo58fzsGVcG/YCNeaNXGpVg2X6tVxCgwo1rd830wILQ639tp6VO/01FRi16wh8fBh0hMTId08eFfyqVMknzsHaWnZLucY4I9b7WvvYKiDa+1aRR6Sc2JydNRz6aWIgrOIiIiI5JvJxQW3W2/F7dZbM7WnRkSQdOyYJUzHb99OckgIGAZpV6+SdvUqifv2Z1rGwcPDEqKdq1TGJTgY50qVcA4Oxjko6KYHdCpIeQmhtry1t6hG9TZSU0m5dNn8Icm5c6ScP0fy2XMk7NuX89Xjf5k8PHCtVctyN0JGSHby88tzPSIFTcFZRERERAqNU9myOLVsiWfLlkSvWEHk77/nGKIcA/xJC48gPT6exEOHSDx0KOtMDg44lQ/CpdI1YbpSJZwrlMcpMBCnwEAcPD0L5Yp1fkJoxq290du2sWvlSpp17YpPq1a5Cqv5eTa5oEb1To+LI+XKFVKvhJJ66SLJ58+Tcu78f0H50qUcrxznxP+55yjzwP04BQUV6zsMREDBWURERESKgNXQCWAyYXJ0ou7OHaRcuEDy6dOWW3ktAe38eYzkZFIvXCT1wkW47llqy6o8PHAOCLAEactXQABO/v44li2LY5kyOJYtc1MDSOU3hJocHfFo0YKY0FA8WrTIVfjN77PJuR2tO27LFtLCr5pvrc8IyFeuWL7S4+JuuA6TszPOFSviXKkSThUrErN8OemxsTnMbCJy7lz8n31GoVnsgoKziIiIiBS63IbOhP1/49mqJa41a2adJT2d1LCwf69yZoTpcySfO0fq5X8DXmwsRnw8ySEh5lvCb8DBw+OaIF32v//39cXB0xMHT49//+tJ4v79N1wfFNyrpW50W7gx/hM8W7cmPS4uy1dabCzpcfEkHjmSq21d/eqrG87j4Olp/gAiKAjnShUz30JfqRJOAQGYHBwAiNu2nag5c3Jemd5fLXZGwVlERERECl1BvM/Y5OCAc2Cg+X28TZpkO096XBypoaH/XTUNvebK6eXLpIaHkxYRQVpUFKSnkx4fT3p8PCnnz+dpv7Jz+aOPCPviC0wuLpicnc3/dXICBxNGukGlq1c5P28+JodsrrQaYKSmkJ6UTNLhwznfFg5cePl/BVOwyYRztWo4lfPDOTDov6vzlqv1ATgFBN7Ua8X0/mopaRScRURERKTQFdX7jB08PXHx9MSlWrUs0659VtjRvxxudeqQHh1NWmQkqRERpEVGmUN1ZCRpUVGZr+LGx5MWG0vKmTOQnm61hrTQUNKsBEJPIOH48Xzt57VMHh44eHrg6OFpuTp+7Vfq1avErl6dw8ImKhXCqNrF6f3VIgVBwVlERERECp2t32dcUO8xttw+DdnuR8Ar/8OjaVOMlBSM5BSM5GTz/6ekAJCWlsrevXtp3Lgxjo7Z/ylucnYmYe9ewqdPv2E9FT/+GN/evXJVd1G+x9nW/S1S0BScRURERKTQ2fJ9xgX5HuMcXy2VyxCakpJCDODdsyfOVl6r5VimTK6Cs1NQUK7r9u7cOc+jc9+s4vD+apGCpOAsIiIiIkXCFu8zLoz3GBdFCC2MK7YmR8ciHYjLlu+vFiloCs4iIiIiUmSK+spnQb3H+HqFHUJLyhXbou5vkcKi4CwiIiIiRaoor3za8+jOJeWKbVFf6RYpDArOIiIiIlJi2fvozrpiK1I8ONi6gKKUkJDAO++8Q506dXBzc6NixYoMGzaM83l4b19ERAQjRoygatWquLq6UrVqVUaOHElkZGTBFy4iIiIieZLxrHDG7c1ZmEw4lS9frEd3zrhi63vP3Xi2aqnQLGIDpSY4JyYmcuedd/Lee+8RGxtLnz59qFy5MtOnT6dJkyacPHky1+sKCwujZcuWTJ48GScnJ+699168vb2ZNGkSrVq1Ijw8vBD3RERERERyK+NZYfM314VnO3pWWERsq9QE5/fff5+tW7fSpk0bjh49yuzZs9m2bRsTJkwgNDSUYcOG5XpdI0eO5Pjx4/Tr148jR44we/ZsDhw4wAsvvMDRo0d56aWXCnFPRERERORm+HTrRqVJn2V5dZNTUBCVbuJVVCJSepWKZ5yTk5OZMmUKAFOnTsXLy8sy7aWXXuKHH35g/fr17Nq1i2bNrN+mc/HiRWbOnImLiwtffPEFTk7/HcJPPvmEWbNm8fPPP/Pxxx8TGBh4U3X+smctj7W7GxenUtEtxUZyaiq/7lvHmehLVPEpzyONOtl9H9hynwpq23ldT3JqKj/vXcOGq/u5uteNgU0633C5gjxetjr2JfHfMZTc/bIXeTmfpGCV1HPAFvvl060brh07svi3aURfPINPhSrc8+AwXF1d87S+vOxDXs8pW/9uzS/9O5bCkpyayuaokCLZlskwsnsxXMmydu1a7rzzTmrWrMnx48ezTH/vvfd45513ePfddxk9erTVdU2fPp1hw4bRuXNnVq1alWX6Y489xrRp05g+fTpDhgzJVX3R0dH4+vpyy5e34ORSjkdrD+eV9g/kalnJn082/M5PxyZjOEZa2kxpZbLtg5SUFJYuXUrPnj1xdnYu4kpz72b2qbhuO6/ryctyBXm8bHXsbdnn+XGjc8pe96uk0PG3vZvtA/2eKrrtFuXvG1v/bs0ve/1Zot9Rxd8nG37np6OTSE0J5/Azh4mKisLHx6fQtlcqPhLZt28fAE2bNs12ekb7/v37C2Rd06ZNy9W6spPuEMkPJ8YC6KQrZJ9s+N18rB3g2iee7LkPbLlPBbXtvK4nL8sV5PGy1bEvif+OoeTul73Q8be9ktoHJeFnZVH+vrH179b80r9jKSyWPnAEUopmm6UiOJ85cwaA4ODgbKdntIeE3Pgyf0GuKzsZ77f/6egkBjbugotjqeiiIpeclsqPxyaZf+BlM05Idn2QkpJCTGoyV+NjiuUn+XnZp+K27byuJy/LFeTxstWxt2WfF4Sczil73y97p+Nve3ntA/2eKvztFuXvG1v/bs0ve/9Zot9RxZe1PihMpeJW7SeffJJvv/2WN998k/fffz/L9OPHj1O7dm1q167N0aNHra6rW7durFy5km+//ZbHH388y/RVq1bRtWtXunbtyooVK7JdR1JSEklJSZbvo6OjqVy5Mrd8eQuO7hrRUUREREREJDfSEtKK5FbtUjOqdnEybtw4fH19LV+VK1e2dUkiIiIiIiKSg1Jx/0DGKNrx8fHZTo+LiwPA29u7SNY1atSoTK+syrjifL0nar3Pgw3a37AmuXm/HdjAt8ffuuF81/ZBSkoq69evp2PHjjg7F79TJy/7VNy2ndf15GW5gjxetjr2tuzzgpDTOWXv+2XvdPxtL699oN9Thb/dovx9Y+vfrfll7z9L9Duq+MptHxS04vdTtRBUqVIFgHPnzmU7PaO9atWqRbIuV1dXq68+MAxwSCvD0630aqrC8nSru/nuyHjSHSKzfTYiuz5ISUnB28mF8r5li+WzY3nZp+K27byuJy/LFeTxstWxt2WfF4Sczil73y97p+Nve3ntA/2eKvztFuXvG1v/bs0ve/9Zot9RxdeN+qCwlIpbtRs1agTA7t27s52e0d6wYcMiXVd2Mp44f7TOcJ1shcjFyYlHaw8H/jvmGey1D2y5TwW17byuJy/LFeTxstWxL4n/jqHk7pe90PG3vZLaByXhZ2VR/r6x9e/W/NK/Y/vaL3tirQ8KU6no0Xbt2uHr68uJEyfYu3cvjRs3zjR9zpw5APTq1euG6+rRowcODg5s2LCBK1euEBgYaJmWlJTEokWLcHR0pGfPnnmq1SGtDI/W0fvfikLGMb7+HXz23Ae23KeC2nZe15OX5QryeNnq2JfEf8dQcvfLXuj4215J7YOS8LOyKH/f2Pp3a37p37EUFksfHJ1EOuFFss1SMao2wFtvvcX//d//0bZtW1asWIGnpycAEydO5OWXX6Zjx46sW7fOMv+UKVOYMmUKffv2Zdy4cZnWNXDgQH755Rfuu+8+Zs2ahdO/nyiNGDGCyZMnM3jwYGbMmJHr2qKjo/H19eWLdQt4rJ1u6yhqyamp/LpvHWeiL1HFpzyPNOqUbR+kpKSwdOlSevbsWSxvgbtWbvepOG87r+tJTk3l5z2r2fD3dtrf1pKBTTrfcLmCPF62Ova27PO8ys05ZY/7VZLk5XySgnUz54B+TxXtdvOyrryeU7b+3Zpf9vizXL+j7ENcQgKv/zCJKc+MKvRRtUtNcE5MTKRTp05s27aNChUq0L59e0JCQti2bRsBAQFs3bqVGjVqWOYfPXo0Y8aMyTYEh4WF0bp1a06cOEHNmjVp3rw5Bw8e5MCBA9SuXZutW7fi5+eX69oygnNhd7bkjz39QVLaqa/sg/rJPqif7If6yj6on+yD+sk+pKSkMGfOHB555BG9jqqguLm5sXbtWt5++208PDxYsGABISEhDBkyhN27d2cKzTfi7+/P9u3beeGFF0hOTmb+/PlERUUxfPhwtm/fflOhWURERERERIq3UnUvgbu7O2PHjmXs2LE3nHf06NGMHj06x+l+fn5MnjyZyZMnF2CFIiIiIiIiUtyUmivOIiIiIiIiInmh4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlY42boAAcMwAIiOjrZxJWJNSkoK8fHxREdH4+zsbOtyxAr1lX1QP9kH9ZP9UF/ZB/WTfVA/2YeMfoL/MlVhUXAuBmJiYgCoXLmyjSsRERERERGxPzExMfj6+hba+k1GYUdzuaH09HQuXLiAt7c3JpPJ1uVIDqKjo6lcuTJnz57Fx8fH1uWIFeor+6B+sg/qJ/uhvrIP6if7oH6yDxn9dOjQIerWrYuDQ+E9iawrzsWAg4MDwcHBti5DcsnHx0c/QO2E+so+qJ/sg/rJfqiv7IP6yT6on+xDpUqVCjU0gwYHExEREREREbFKwVlERERERETECgVnkVxydXXl3XffxdXV1dalyA2or+yD+sk+qJ/sh/rKPqif7IP6yT4UZT9pcDARERERERERK3TFWURERERERMQKBWcRERERERERKxScRURERERERKxQcJZSKyEhgXfeeYc6derg5uZGxYoVGTZsGOfPn7+p9VSrVg2TyZTj1z///FNIe1Dy7dq1iw8//JB+/foRHBxsOaZ5FRERwYgRI6hatSqurq5UrVqVkSNHEhkZWXBFl1IF2Vc6pwpHfHw8CxYs4LHHHqNu3bq4ubnh6elJo0aNGDt2LLGxsTe9Tp1TBa+g+0nnU+GaOHEi/fr1o3bt2vj6+lrOg0GDBvH333/f9Pp0ThWOguwnnVNF5+rVqwQGBmIymahVq1ae1lGQ55QGB5NSKTExkTvuuIOtW7dSoUIF2rdvz+nTp9m+fTsBAQFs3bqVGjVq5Gpd1apVIyQkhMGDB2c7fdy4cVSoUKEgyy817r33XhYuXJilPS8/tsLCwmjTpg3Hjx+nRo0aNG/enIMHD3Lw4EHq1KnDli1b8PPzK4iyS6WC7CudU4Xju+++44knngDglltuoUGDBkRHR7N582ZiYmKoV68e69evJzAwMFfr0zlVOAq6n3Q+FS5/f3/i4uJo2LAhlSpVAuDgwYMcPXoUZ2dn5s2bxz333JOrdemcKjwF2U86p4rOkCFD+PHHHzEMg5o1a3L8+PGbWr7AzylDpBR68803DcBo06aNERMTY2mfMGGCARgdO3bM9bqqVq1q6FQqHB9++KHx9ttvG3/88Ydx8eJFw9XVNc/HesCAAQZg9OvXz0hJSbG0v/DCCwZgDB48uICqLp0Ksq90ThWOGTNmGE8++aRx6NChTO0XLlwwmjRpYgBG//79c70+nVOFo6D7SedT4dq4caORkJCQpX3q1KkGYAQFBWU6P6zROVV4CrKfdE4VjVWrVhmA8eSTTxqAUbNmzZteR0GfU7riLKVOcnIygYGBREVFsXv3bpo0aZJpeqNGjdi/fz87d+6kWbNmN1xfxiePOpUKn5ubG0lJSTd9rC9evEhwcDBOTk6cOXOGoKAgy7SkpCQqV65MeHg4Fy5cyPVVHLEur30FOqdsYcuWLbRt2xZXV1eio6NxcXGxOr/OKdu42X4CnU+2VKtWLU6cOMG+ffto2LCh1Xl1TtnOzfQT6JwqCgkJCdx22224urqyYMEC6tSpc9NXnAvjnNIzzlLqbNq0iaioKGrWrJklNAPcf//9ACxatKioS5NCsnz5ctLT02nfvn2mH5wArq6u9OrVi7S0NJYuXWqjCkVsq1GjRoD5j4mrV6/ecH6dU7Zxs/0ktuXs7AyQqw84dE7Zzs30kxSNMWPGcPLkSb766itL/9yswjinnPJUiYgd27dvHwBNmzbNdnpG+/79+29qvZ988gknTpzA1dWV+vXr07dvXwICAvJXrBSI3PT5tGnTbrrPpXDpnCo6J0+eBMx/QObmeS+dU7Zxs/10LZ1PReunn37iyJEj1K5dm9q1a99wfp1TtnGz/XQtnVOFY//+/UyYMIGhQ4daxiDKi8I4pxScpdQ5c+YMAMHBwdlOz2gPCQm5qfW++uqrmb5/8cUX+fzzzxk2bFgeqpSCVFh9LoVL51TRmTRpEgA9evTA1dX1hvPrnLKNm+2na+l8KlyffPIJBw8eJC4ujsOHD3Pw4EEqVqzIzJkzcXR0vOHyOqeKRn776Vo6pwpeeno6jz/+OGXKlOHjjz/O17oK45zSrdpS6mS8ysPDwyPb6Z6engDExMTkan29e/dm3rx5hISEEB8fz4EDB3jppZdISkri8ccfz3akYSlaBd3nUrh0ThWtpUuX8v333+Ps7Mx7772Xq2V0ThW9vPQT6HwqKn/++Sc//PADc+bM4eDBg1StWpWZM2fmaqwU0DlVVPLbT6BzqjB9/vnn7Nixg08++YRy5crla12FcU4pOIvk0+TJk+nbty9VqlTB3d2d+vXrM2HCBL788ksMw+C1116zdYkidkXnVNH5559/GDhwIIZh8Mknn1ieoZXiJT/9pPOpaKxatQrDMIiIiOCvv/6idu3adOzYkf/7v/+zdWlyjYLoJ51ThePMmTO89dZbdOzYkSFDhti6nGwpOEup4+XlBUB8fHy20+Pi4gDw9vbO13Yee+wxAgMDOXLkSJ6fz5CCUVR9LoVL51TBOn/+PD169CAiIoKXXnqJESNG5HpZnVNFJz/9ZI3Op8JRpkwZ2rdvz9KlS2nWrBlvv/02O3bsuOFyOqeKVl77yRqdU/nz3HPPkZyczFdffVUg6yuMc0rBWUqdKlWqAHDu3Llsp2e0V61aNV/bcXBwoGbNmoB5SHyxnaLqcylcOqcKTnh4ON26dSMkJIShQ4cyfvz4m1pe51TRyG8/WaPzqXA5Ozvz0EMPYRhGrt7SoXPKNm62n6zROZU/ixcvxsPDg6effppOnTpZvh5++GHA/CFiRtulS5duuL7COKc0OJiUOhm3uO3evTvb6RntuXmX341EREQA/z1HIbZRlH0uhUvnVP7FxsZy1113cejQIfr168e3336LyWS6qXXonCp8BdFPN6LzqXD5+/sDEBoaesN5dU7Zzs30043onMqfyMhI1q9fn+20xMREy7TExMQbrqtQzilDpJRJSkoyfH19DcDYs2dPlukNGzY0AGPnzp352s6BAwcMk8lkeHh4GElJSflal5i5uroaefmxdeHCBcPBwcFwcXExLl++nGlaYmKiERAQYDg6OmaZJnmX176yRudU/iUmJhp33nmnARjdu3fP83HUOVW4CqqfrNH5VPgGDx5sAMYnn3xyw3l1TtnOzfSTNTqnCsepU6cMwKhZs+ZNLVcY55SCs5RKb775pgEYbdu2NWJjYy3tEyZMMACjY8eOmeb//PPPjbp16xqvv/56pvYlS5YYq1evzrL+ffv2GbfccosBGMOHDy+UfSiNbhTGcuonwzCMAQMGGIBx3333GSkpKZb24cOHG4AxePDgwii51MprX+mcKjypqalG3759DcBo3769ERcXd8NldE4VvYLsJ51PhWvjxo3GsmXLjLS0tEztycnJxuTJkw0HBwfD3d3dOHPmjGWazqmiV5D9pHOq6N0oOBflOaVbtaVUeuutt1i1ahWbN2+mdu3atG/fnpCQELZt20ZAQADTpk3LNH9YWBhHjhzJ8szK9u3bGTNmDFWrVqVRo0Z4eHhw8uRJdu/eTWpqKp06deLDDz8syl0rUZYsWZLptSvJyckAtG7d2tL29ttvc/fddwM59xPAZ599xtatW5k7dy716tWjefPmHDx4kAMHDlC7dm0mTpxYyHtTshVUX+mcKjxTpkxh/vz5gPnWxGeffTbb+caPH2+5dVHnVNEryH7S+VS4jh07xtChQ/H396dZs2aUK1eOsLAw/v77by5evIibmxszZsygcuXKlmV0ThW9guwnnVPFT1GeUwrOUiq5ubmxdu1axo0bx6+//sqCBQvw8/NjyJAhvPfeezm+LP163bt35+zZs+zYsYNNmzYRFRWFj48Pt99+OwMGDGDo0KE4OjoW8t6UXKGhoWzbti1L+7VtuX0myd/fn+3btzN69GgWLFjA/PnzCQoKYvjw4YwZM4YyZcoUVNmlUkH1lc6pwpPx7B1gCWbZGT16tCWQWaNzqnAUZD/pfCpcHTt25I033mD9+vXs37+fsLAwXFxcqFatGvfffz/Dhw+nVq1auV6fzqnCUZD9pHPKvhT0OWUyDMMonFJFRERERERE7J9eRyUiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiJ2wWQyZfpycHDA19eX1q1b89lnn5GSklIg2xk9ejQmk4kZM2bkepnTp09jMpno1KlTgdRQ0OuzhU6dOmEymTh9+rStS8lWamoqdevWpWXLlrYuxWLPnj2YTCY+/vhjW5ciIiLXcbJ1ASIiIjdj8ODBAKSlpXH69Gk2b97Mtm3bWLx4McuXL8fJSb/aioLJZKJq1arFNhjfyNdff83Ro0dZsmSJrUuxaNKkCb1792bcuHE8/vjj+Pn52bokERH5l/66EBERu3L9leBt27bRqVMnVq9ezaxZsxg4cGCR11SpUiUOHz6Mh4dHkW+7uPrxxx+Jj4+nUqVKti4li6SkJMaOHUvDhg3p2bOnrcvJZNSoUfzxxx989NFHfPTRR7YuR0RE/qVbtUVExK61atWKIUOGAPDnn3/apAZnZ2fq1atHlSpVbLL94qhKlSrUq1cPZ2dnW5eSxZw5c7hy5QqDBg2ydSlZtG7dmlq1ajFt2jSSk5NtXY6IiPxLwVlEROxe/fr1Abhy5UqmdsMwmDlzJg8//DB16tTB09MTb29vWrZsyRdffEF6errV9W7bto3u3btTpkwZfHx86Nq1K1u3bs0yX16fST579iyPPvooAQEBeHh40KxZM37++ecc5x8yZAgmk4l169ZlO91kMlGtWrVMbTNmzMBkMjF69GiOHj3Kww8/TFBQEA4ODixYsACA48ePM3r0aNq0aUP58uVxcXEhODiYQYMGcfTo0WzXBxASEpLpufNr99/aM86HDh1iwIABVKhQARcXFypVqsSgQYM4cuRIlnnXrVuHyWRiyJAhhIeH88wzz1ChQgVcXV1p0KAB06ZNy/F45eS7777DZDLx8MMPZ5l27fE6ceIEDz74IP7+/vj4+HDXXXdx6NAhwPyM9AcffECdOnVwc3OjVq1aTJ06NdvtHThwgIEDB1KjRg3c3NwICAigcePGjBw5kosXL2aZv3///oSFhTF//vyb3jcRESkcCs4iImL3YmJiAAgMDMzUnpSUxCOPPMKqVasoX748vXr1onXr1hw8eJDnnnuOYcOG5bjOzZs306FDB86dO8ddd91F3bp1WbVqFR07dmTFihX5rvnUqVO0bNmSn3/+GR8fH/r06YOnpyeDBg1iwoQJ+V7/9Y4cOUKLFi3Yvn07d9xxB127drVcDf7uu+8YO3YscXFxtGjRgt69e+Pj48NPP/1EixYt2L9/v2U9tWrVsjxn7unpyeDBgy1fPXr0uGEdq1evpnnz5vz6669UqFCB++67j8DAQH766SeaN2/Ohg0bsl0uMjKSNm3a8Mcff9C+fXvatWvHP//8w2OPPcZ3332X6+MQHR3Nhg0bqFWrltXbyDP658CBA3Tp0oVq1aqxfPlyOnXqxKVLl7j//vv5+OOPqV+/Pp06deLs2bM8//zzfPvtt5nWs2vXLlq0aMEvv/yCt7c3ffr0oXXr1qSkpDBp0qRsPyzI+ACiOD1/LSJS6hkiIiJ2ADBy+rXVoUMHAzB+/vnnTO0pKSnG/PnzjeTk5EztV65cMZo3b24Axvr16zNNe/fddy3bevPNN4309HTLtC+++MIAjAoVKhjx8fGW9lOnThmA0bFjx1zvT48ePQzAGDZsmJGSkmJp/+OPPwxHR8ds1zd48GADMNauXZvtOgGjatWqmdqmT59u2Z/nn3/eSE1NzbLcli1bjJMnT2ZpnzZtmgEYd9xxR662da2OHTsagHHq1ClLW2xsrBEUFGQAxpQpUzLNP3HiRAMwgoODjYSEBEv72rVrLfU//PDDRmJiomXa/PnzDcCoUqVKjnVcb9myZQZgPPLII9lOv/Z4vf7665b+T09PN4YMGWIAxq233mo0aNDAuHLlimW5VatWZXtMBg0aZADG+PHjs2zr8OHDxoULF7K0R0VFGQ4ODje1XyIiUrh0xVlEROxSeno6J06c4JlnnuGvv/6iT58+PPTQQ5nmcXJy4t57783ynG1AQADjxo0DYOHChdmuv2rVqpZXU2V45plnaNWqFRcvXmTu3Ll5rv3kyZMsX74cHx8fJk6cmGkk8F69enH//ffned05CQgI4KOPPsLR0THLtNatW1O9evUs7UOHDqVdu3asW7eOqKiofNfw22+/cfnyZdq0acNzzz2XadqLL75Is2bNOHfuXLbH1sfHhylTpuDq6mppu/fee2nQoAFnzpzJ9ejeGVfP69ata3W+GjVqMHbsWEv/m0wmXnzxRcB8q/lnn31GQECAZf7OnTvTpEkTQkJCMtUSGhoKQJcuXbJso169elSoUCHbfa1QoQJnzpwhIiIiV/slIiKFS6Nqi4iIXbk2yGZ44okn+Prrr7OdBrB3715WrFhBSEgI8fHxGIZhub372LFj2S5z3333Zftqq/79+7Nt2zY2bNiQ5xG8N27cCECPHj3w9fXNdhuzZ8/O07pz0qVLF6ujfsfGxrJo0SL27t1LeHi45b3YFy9exDAMTpw4QdOmTfNVQ8Zt2AMGDMh2+sCBA9m1axcbNmzIMk+zZs0oV65clmXq1KnDgQMHuHjxYpbnu7OT8Rx82bJlrc7XqVOnLB+41KhRAzAPBpfd8+w1atRgz549mWpp1qwZy5Yt47nnnuP999/n9ttvz9Ur0/z8/Dh//jyhoaE3rFVERAqfgrOIiNiVjOdrExMT2bdvH//88w/ffvstbdu2tYyunSE5OZkhQ4Ywc+bMHNeXEaCvV7Vq1WzbMwLRhQsXbr74f2Use6NtFCRrI36vWbOGhx9+2HJ1NDs5HaebkbHfOe1fRvv58+ezTAsODs52GW9vb8D8PHtuZFw5z1guJ9k9/+zl5QVA+fLls71ynzH92lpeeeUVNm7cyLp167jjjjvw8vKiTZs23H333QwZMiTbD07AfNUZzM92i4iI7elWbRERsSszZsxgxowZzJo1i8OHD/Pxxx8D8NxzzxESEpJp3okTJzJz5kxuu+02li1bxuXLl0lOTsYwDMugTIZhFPk+FIYbjRDu5uaWbXtsbCwPPvggYWFhvPPOOxw6dIi4uDjS09MxDIP+/fsDRXOccrpjAMDBoWD+ZMkIqjf6IMDa9m6mFh8fH9asWcOGDRt49dVXufXWW1mzZg0jR46kbt26Od7xkBHwy5Qpk+ttiYhI4VFwFhERu/bKK6/QrVs34uPjGTNmTKZpGa/zmTlzJj169CAwMNBy++3Jkyetrvf6EH59e8WKFfNcc8ZzrTfaxvVcXFwAc9i93tmzZ/NUy4YNG7h69Sr33XcfY8aM4ZZbbsHDw8MSYm90nG5GxjHLaf8yng22Ntp1fmWMvB4eHl5o27ieyWTi9ttv56OPPmLbtm1cuHCB/v37c/nyZd58881sl8l4tvna56hFRMR2FJxFRMTuffjhhwD89NNPmUJZRvjI7jbf3377zeo6582bR1paWpb2WbNmAXD77bfnud6MZZcvX050dHSO27heRuC+/t3KACtXrsxTLdaO0fHjx9m9e3e2yzk7O5OamnpT22rfvj1AjrfOZ7zDOmO+wtCoUSOAbF8DVVQCAwMZPXo0YH7H8/Wio6O5cOECVapU0fPNIiLFhIKziIjYvSZNmnDvvfeSmppquXUbzANHAXz11VeZ5p8zZw4//vij1XWePn06yxXsb775hi1bthAUFMR9992X53pr1qxJt27diI6O5uWXX84U0JcuXcrvv/+e7XIdO3YE4Msvv+Tq1auW9r179/LOO+/kqZaMYzRv3rxMzzhHRkby2GOPWQYJu17FihW5fPnyTT2D++CDDxIUFMTGjRv55ptvMk2bPHkyO3fupFKlSvk6tjfStm1bHB0d2bFjR6Ft41pfffUVp06dytK+dOlSACpXrpxl2o4dOzAMw9LfIiJiewrOIiJSImS8OmratGlcunQJgFdffRVHR0def/11mjdvziOPPEKLFi144IEHLK8WyskTTzzBhx9+SIMGDXjkkUdo2bIlTz31FM7OzsyYMcPqCNW58eWXXxIUFMR3331H3bp16d+/Px07duSee+7hqaeeynaZO+64g44dO3L8+HFuvfVW+vXrR4cOHWjVqlWOI1XfSPPmzenatStnzpyhTp069O3bl759+1K9enUuXLhAnz59sl2ud+/epKam0rRpUwYOHMjjjz/OJ598YnVbnp6e/PLLL7i7u/PUU09Z+qRp06aMGDECLy8vZs6cmePz2AXB29ub9u3bc/z4cc6dO1do28nw1VdfUaNGDerXr8/999/Pww8/TOPGjXnxxRdxc3PL9gOPdevWAXD33XcXen0iIpI7Cs4iIlIiNGrUiL59+5KYmMjEiRMB6NChAxs3buTOO+/k5MmTLF68GBcXF+bOnZvlPcLXa9u2LevXr6d8+fIsXryYw4cP07lzZ9atW0ePHj3yXW+NGjXYtm0bjzzyCJGRkSxYsIDo6GimT5/O//73v2yXMZlMLFy4kKeffhqTycTSpUsJDw9n0qRJNwyt1ixcuJA333yTgIAAli1bxq5du3j44YfZunVrjoNTjRs3jueff57U1FRmz57N999/z5IlS264rc6dO7Njxw769+/PuXPnmDNnDpcuXWLgwIHs3LmzUG/TzvDEE08AOd8yXpDee+89hg0bhslkYvXq1SxatIiEhAQef/xx9u7dS7t27bIsM3PmTPz9/enbt2+h1yciIrljMkrKcKIiIiIiuZCUlETVqlUJDAxk//79ti4nky1bttC2bVteffVVPvroI1uXIyIi/9IVZxERESlVXF1deeedd/j7779ZvHixrcvJ5MMPP6RMmTK89tprti5FRESuoSvOIiIiUuqkpqZSv359fHx8imygsBvZs2cPTZs25aOPPuLVV1+1dTkiInINBWcRERERERERK3SrtoiIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVTrYuQCA9PZ0LFy7g7e2NyWSydTkiIiIiIiJ2wTAMYmJiqFixIg4OhXddWMG5GLhw4QKVK1e2dRkiIiIiIiJ26ezZswQHBxfa+hWciwFvb2/A3Nk+Pj42rkZERERERMQ+REdHU7lyZUumKiwKzsVAxu3ZPj4+Cs4iIiIiIiI3qbAfedXgYCIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoddRiYiIiIjcgGEYpKSkkJ6ebutSREosJycnnJyKZ0QtnlWJiIiIiBQDaWlphIWFERMTQ0pKiq3LESnxPD098ff3x8PDw9alZKLgLCIiIiKSjbS0NM6ePUtSUhK+vr54eXnh6OiIyWSydWkiJY5hGCQlJREeHs7Zs2epXr06Li4uti7LQsFZRERERCQbYWFhJCUlUaVKFdzd3W1djkiJ5+7ujre3N6dOneLKlSsEBwfbuiQLDQ4mIiIiInIdwzCIiYnB19dXoVmkCDk6OuLr60t8fDyGYdi6HAsFZxERERGR66SkpJCSkoKXl5etSxEpddzd3UlLSytW4wooOIuIiIiIXCdj9GxHR0cbVyJS+mScd8VpFHsFZxERERGRHGggMJGiVxzPOwVnERERERERESsUnEVERERERESs0OuoRERKqU9XHrXZtl/sWsdm2xYRERG5WQrOIiIiIiJ5ZMsPIXNLH1aKLfz555/06NGDJ598kq+//trW5eSbbtUWERERERGRArVt2zYAWrZsaeNKCoaCs4iIiIiIiBSojODcokULG1dSMBScRUREREQk106fPo3JZKJjx45ERkbyyiuvUL16ddzd3WnUqBFLliyxzDtr1ixuv/12vL29qVy5Mq+//jopKSnZrvfQoUM8+eSTVK9eHVdXVwIDAxk0aBDnzp3Ldv7169czcuRImjVrRmBgIB4eHtSrV49XXnmF8PDwLPMfPnyYxx57jBo1auDq6oqPjw9169ZlyJAhHDhwwDLfzz//jMlkYtSoUdlu97777sNkMrFz584cj0t4eDgvvfQS1apVw8nJiREjRuRpP4vDsT5+/Dgmk4muXbsSFxfHmDFjuOWWW/Dw8KBmzZp8/PHHGIZhmf/jjz/GZDKxdOlSABo1aoTJZMJkMtG5c+ds67EHCs4iIiIiIpJre/fuBaBMmTI0adKEuXPn0qpVK+rVq8f+/fvp168f//zzD0OGDOHxxx+nbNmy3HHHHVy+fJmPPvqIiRMnZlnn119/TePGjfn++++pUKECvXv3pkyZMvz000+0bt2ay5cvZ1lm4MCBTJs2DRcXFzp16kSXLl0IDQ1l/PjxdOrUKVNoXL58OU2aNGH69OkEBARw77330r59e9LT0/nhhx8yrX/Pnj0ANG3aNNv937NnD46OjjRo0CDb4+Lp6UnDhg359ddfadmyJV27drVcdb3Z/SwOx3rfvn0A+Pn50ahRI7777jtuu+02br/9dk6fPs1rr73G9OnTLfMHBgbywAMPWP5/8ODBlq9BgwZle0ztgQYHExERERGRXMsIUn/88Qfvvvsub7/9No6OjhiGQZ8+fVi0aBH33HMP5cqV49ixY1SoUAGAhQsXcu+99zJv3jxee+01y/oWLlzIM888Q+3atfn9999p2LAhAGlpaQwfPpwvvviCcePG8dlnn1mWSU1NZdKkSfTs2RM3NzdLe3x8PN27d2fjxo2sWrWKu+66i7S0NB5//HE8PDzYsmULTZo0ybQ/27Zt49Zbb7V8nxGcr58PIDIyklOnTnHrrbdm2u61x2XZsmU88cQTTJ48OdM8ednP4nCsM2r47bffeOONNxgzZgxOTuYYOWPGDIYOHcq8efMYNmwYAEOGDKFGjRr8/vvvdO/enRkzZmQ5jvZIV5xFRERERCTXMq6CPvDAA4wePRpHR0cATCYTd911FwDh4eHMnTvXEuQAy7SLFy9a2mJjY3nyySfx8vJi+fLlliAH4OjoyMcff4yDgwOrVq3KVIOTkxP9+vXLEl49PDwsVzvPnj0LwLFjxzh//jzt27fPNgy3atUKb2/vTPvn4+NDzZo1s8ybEaobNWqU43Fp27YtX331Vaba8rqfxeFYZ9Rw33338X//93+W0AzQo0cPAK5cuZLtMo0bN85ynOyVrjiLiIiIiEiuZYSid999N8u06OhowHzVMTg4ONO0qKgoAPz9/S1t33zzDVeuXOHVV1+levXqWdbn6elJuXLluHr1arbbWrp0Kfv27ePq1askJydnqs/Pzw8AX19fTCYTf/75J19++SWDBg3C09Mz230LCQkhIiKCDh06YDKZskzfvXs3kH0gzNjuJ598goND5uuTed3P4nCsM644v/POO1mWCQsLA6BixYrZ1l2SgrOuOIuIiIiISK5ERUVx+vRpatasSf369bNMzwhZvXv3zjLt77//Bsh0W/SCBQuA/waUyu4rNDSUsmXLZlrX5MmTqVSpEv379+fDDz/k22+/5YcffuCHH36w1FCnjvn91RUqVGDs2LGkpKTw7LPPEhAQQK9evZgxYwaJiYmZ1mvtNu1rp19/xTnjuJQvX542bdpkWS4v+1kcjnVkZCRnzpyhevXqma5QZ9i/fz9Alue9M2rL7sq8vdIVZxERERERyZWMQJTTK4b27t2LyWSiWbNm2U6DzKF03759uLq68vDDD1vd7m233Wb5/0mTJjFy5Ejq1KnDG2+8Qfv27alYsSJubm6kpaVRvnx5YmJiMoXGt956i/79+zN79myWLVvG0qVLWbx4MWPGjGHz5s2W25xvFJy3b98OZA2EGcclpyvVednP4nCsM9aT07uYM47XtVeWU1NTOXjwIMHBwZQrV87qtuyJgrOIiIiIiOSKtVtwExISOHr0KDVr1sz0zHCGjCCYEeaSk5OJjo6mevXquR5AKi0tjf/7v//D3d2dv/76i6CgoEzT16xZQ1hYGM2aNcv0LC5AzZo1eeONN3jjjTc4efIkDz/8MDt27OD777/nrbfeAv67Unv9FdSMaceOHSMoKIjy5ctnmpZdUM2Ql/28dp22OtbZred62QXnw4cPk5SUVKKuNoNu1RYRERERkVyyFqT2799PWlpajiHr+iDo4uKCu7s7Z8+eJTY2Nlfbv3TpEqGhoVSvXj1LaE5JSeHtt9/Osb5r1ahRg0ceeQSA9PR0S3vGe4yvfTY4Q0a4zi4QWjsuednPG62zKI71jWoAc3D29vbONJDa4cOHgew/fLBnCs4iIiIiIpIr1q6s3uiq6+HDh6lSpYpl0C6Arl27kpqaylNPPUV8fHymZQzDYM2aNWzYsMHS5u/vj5OTE8ePH+fo0aOW9piYGAYPHsy2bdsy1fDnn3+ybNky0tLSMq377NmzfPnll5hMJnr16mVpDwgIAGDx4sWWtsTEREaOHMmiRYsA6yNq5xQwb3Y/b7TOojjWN9rOmTNnCA8Pp2HDhpluT894f/b127B3ulVbRERERERuKOPZ1UqVKlkC5rWsPR988OBBUlJSskz76KOP2LhxI7/++isrV66kefPm+Pn5cfXqVfbt28fFixczhVhXV1ceffRRpk+fTrNmzejSpQsA69evp3bt2nTq1Il169ZZtjN79mymT5+On58fLVq0oFy5cly6dIkNGzaQkpLCuHHjMtXUt29fli5dyvPPP88vv/xC2bJl2b59O35+fgwYMICff/45y63TGcelYsWKBAYGZnvsbnY/i8OxTk1N5dChQzes4frj0apVK5ydnfniiy84fvw4gYGBuLm58dVXX2V7bOyFgrOIiIiIiNzQP//8Q1JSUo6vGLL2TG7GLb/XT6tXrx579uzhgw8+YMWKFaxevRoXFxcCAwNp2bIld999N3fccUemZaZOnUq5cuWYPXs2f/75J7Vq1eKNN95g+PDh1K9fHwcHB8sI0P3798fV1ZXNmzezY8cOoqOj8ff3p1evXowYMYIOHTpkWvdjjz1GWFgY33zzDXv27KFq1ao8++yzvPrqq5b3Q19/xTnjuFi7Pfxm97M4HOsb1ZBTcK5VqxazZ8/m/fff56+//iIuLi7bAczsjckwDMPWRZR20dHR+Pr6EhUVhY+Pj63LEZFS4tOVR288UyF5sWsdm21bRCQ3EhMTOXXqFNWrV8fNzc3W5YiUKjdz/hVVltIzziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIhIDgzDsHUJIqVOcTzvFJxFRERERK7j5OQEQFJSko0rESl9UlJSAHB0dLRxJf9RcBYRERERuY6TkxOenp6Eh4eTlpZm63JESg3DMIiKisLV1RVnZ2dbl2PhZOsCRERERESKI39/f86ePcupU6fw9fXF3d0dR0dHTCaTrUsTKXEMwyAlJYWoqChiY2OpVKmSrUvKRMFZRERERCQbHh4eVK9enStXrhAREUFYWJitSxIp8VxdXalUqRI+Pj62LiUTBWcRERERkRy4uLgQHBxsuRqWnp5u65JESixHR8didXv2tRScRURERERuwGQy4eLiYusyRMRGNDiYiIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWFEsg3N8fDwLFizgscceo27duri5ueHp6UmjRo0YO3YssbGxOS47Y8YMWrZsiZeXF35+fvTs2ZPNmzdb3d6mTZvo2bMnfn5+eHl50bJlS3788Uery5w7d46hQ4dSsWJF3NzcqFOnDu+++y6JiYl52mcREREREREpnkyGYRi2LuJ63333HU888QQAt9xyCw0aNCA6OprNmzcTExNDvXr1WL9+PYGBgZmWGzlyJJMmTcLd3Z1u3bqRmJjI6tWrMQyDOXPmcO+992bZ1ty5c3nooYdIT0+nQ4cO+Pv7s3r1aiIjI3n55ZcZP358lmWOHz9OmzZtCAsLo0GDBtx6663s3LmTkydP0q5dO1avXo2rq2uu9zc6OhpfX1+ioqKK3bDrIlJyfbryqM22/WLXOjbbtoiIiJQcRZWliuUVZ2dnZ5588kkOHTrEoUOH+O2331i+fDlHjhyhSZMm/PPPP4wcOTLTMqtWrWLSpEmUK1eOffv2sWDBApYvX85ff/2Fo6MjQ4cOJTIyMtMy4eHhDBs2jLS0NObMmcO6deuYM2cO//zzD7Vq1WLChAmsW7cuS31DhgwhLCyM4cOH8/fffzN79myOHDlC37592bRpE+PGjSu8gyMiIiIiIiJFqlgG58GDB/P1119zyy23ZGqvUKECU6dOBWDevHkkJydbpk2cOBGAt956i9q1a1va27Rpw9NPP01kZCTff/99pvV99913REdH06dPH/r162dpDwoK4uOPPwZgwoQJmZbZvn07mzZtIjAw0DIPgJOTE19++SXOzs5MnjyZ1NTU/BwCERERERERKSaKZXC2plGjRgAkJSVx9epVABISElizZg0A999/f5ZlMtoWLVqUqX3JkiU5LnP33Xfj5ubGqlWrMj23nLFMr169styOHRQURPv27YmIiGDjxo152j8REREREREpXuwuOJ88eRIw387t5+cHwJEjR0hKSiIgIIDg4OAsyzRt2hSA/fv3Z2rft29fpunXcnFxoUGDBiQmJnL06NFcLWNtWyIiIiIiImKf7C44T5o0CYAePXpYrvieOXMGINvQDODp6UmZMmWIiIggJiYGMD9EHhUVZXW5jPaQkBBL2422ld0yIiIiIiIiYr+cbF3AzVi6dCnff/89zs7OvPfee5b2jNdTeXh45Lisp6cnkZGRxMTE4O3tnemVVjkt5+npCWAJ27nZVnbLXC8pKYmkpCTL99HR0TnOKyIiIiIiIrZlN1ec//nnHwYOHIhhGHzyySeWZ53t0bhx4/D19bV8Va5c2dYliYiIiIiISA7sIjifP3+eHj16EBERwUsvvcSIESMyTffy8gIgPj4+x3XExcUB4O3tnWkZa8tdv0xutpXdMtcbNWoUUVFRlq+zZ8/mOK+IiIiIiIjYVrEPzuHh4XTr1o2QkBCGDh3K+PHjs8xTpUoVAM6dO5ftOuLi4oiMjKRs2bKWQOvj44Ovr6/V5TLaq1atmuttZbfM9VxdXfHx8cn0JSIiIiIiIsVTsQ7OsbGx3HXXXRw6dIh+/frx7bffYjKZssxXt25dXF1dCQ0N5fz581mm7969G4CGDRtmas+43Ttj+rVSUlI4cOAAbm5u1KlTJ1fLWNuWiIiIiIiI2KdiG5yTkpLo06cP27dvp3v37sycORNHR8ds53V3d+fOO+8E4Pfff88yfc6cOYD53cvXuvvuuzNNv9bixYtJTEykS5cuuLm5ZVlm0aJFmQb4Arh8+TIbNmygbNmytGvXLre7KiIiIiIiIsVYsQzOaWlp9O/fnzVr1tC+fXvmzZuHi4uL1WVeeuklAN5//32OHTtmad+yZQtff/01ZcqU4bHHHsu0zOOPP46Pjw8LFy5k3rx5lvYrV67w6quvAvDyyy9nWqZly5a0a9eOK1eu8Nprr1naU1NTefbZZ0lJSWH48OE4OzvnbedFRERERESkWDEZhmHYuojrTZo0iZEjRwLQt2/fHJ8BHj9+PP7+/pbvR44cyaRJk/Dw8KBr164kJyezcuVKDMNgzpw53HvvvVnWMXfuXB588EEMw6BTp06UK1eOVatWERkZyUsvvcSECROyLHPs2DHatGnD1atXue2227j11lvZsWMHJ0+epG3btqxZs8byjunciI6OxtfXl6ioKD3vLCJF5tOVR2227Re71rnxTCIiIiI3UFRZqlgG59GjRzNmzJgbznfq1CmqVauWqW3GjBlMmTKFw4cP4+LiQuvWrXn77bdp27ZtjuvZtGkT77//Plu3biU5OZlbb72V559/nsGDB+e4zNmzZ3nnnXdYvnw54eHhVKlShf79+/PGG29kurU7NxScRUovW4ZXW1JwFhERkYJQqoNzaaPgLFJ6KTiLiIiI5F1RZali+YyziIiIiIiISHGh4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlY42boAEREpfT5dedRm236xax2bbVtERETsk644i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYoWCs4iIiIiIiIgVCs4iIiIiIiIiVig4i4iIiIiIiFih4CwiIiIiIiJihYKziIiIiIiIiBUKziIiIiIiIiJWKDiLiIiIiIiIWKHgLCIiIiIiImKFgrOIiIiIiIiIFQrOIiIiIiIiIlYoOIuIiIiIiIhYoeAsIiIiIiIiYkWxDc67du3iww8/pF+/fgQHB2MymTCZTDnOP3r0aMs82X29/vrrOS67adMmevbsiZ+fH15eXrRs2ZIff/zRan3nzp1j6NChVKxYETc3N+rUqcO7775LYmJinvdZREREREREih8nWxeQk/fee4+FCxfe9HLt2rWjVq1aWdqbNWuW7fxz587loYceIj09nQ4dOuDv78/q1asZPHgw+/fvZ/z48VmWOX78OG3atCEsLIwGDRrQvn17du7cydixY1m9ejWrV6/G1dX1pmsXERERERGR4qfYBuc2bdrQsGFDWrRoQYsWLahWrRpJSUk3XO7xxx9nyJAhudpGeHg4w4YNIy0tjblz59KvXz8ALl++zO23386ECRO455576NSpU6blhgwZQlhYGMOHD2fSpEkApKam8uCDDzJ//nzGjRvH6NGjb2Z3RURERArMpyuP2mzbL3atY7Nti4gUlmJ7q/Zrr73G2LFj6dWrF+XLly+UbXz33XdER0fTp08fS2gGCAoK4uOPPwZgwoQJmZbZvn07mzZtIjAw0DIPgJOTE19++SXOzs5MnjyZ1NTUQqlZREREREREilaxDc5FYcmSJQDcf//9WabdfffduLm5sWrVqkzPLWcs06tXryy3YwcFBdG+fXsiIiLYuHFjIVYuIiIiIiIiRaXEBec1a9YwcuRInn76ad5//3127dqV47z79u0DoGnTplmmubi40KBBAxITEzl69Giulrm2ff/+/XneBxERERERESk+iu0zznn1008/Zfr+7bff5r777mPGjBl4eXlZ2qOjo4mKigIgODg423UFBwezc+dOQkJCaNiwIQBnzpy54TIAISEh+dsRERERERERKRZKzBXnWrVqMX78eA4ePEhsbCxnz57ll19+oVKlSsydO5dHH3000/yxsbGW//fw8Mh2nZ6engDExMRkWe5mlrleUlIS0dHRmb5ERERERESkeCoxV5wHDhyY6XtPT08eeeQR7rjjDm677TYWLFjA1q1bad26tY0q/M+4ceMYM2aMrcsQERERERGRXCgxV5xzUqFCBYYOHQrA8uXLLe3X3rYdHx+f7bJxcXEAeHt7Z1nuZpa53qhRo4iKirJ8nT17Nje7IiIiIiIiIjZQ4oMzQO3atQG4ePGipc3HxwdfX18Azp07l+1yGe1Vq1a1tFWpUuWml7meq6srPj4+mb5ERERERESkeCoVwTkiIgL47/njDI0aNQJg9+7dWZZJSUnhwIEDuLm5UadOnVwtc217xmBiIiIiIiIiYt9KfHA2DIP58+cDWV8hdffddwMwZ86cLMstXryYxMREunTpgpubW5ZlFi1aRFJSUqZlLl++zIYNGyhbtizt2rUr0P0QERERERER2ygRwTk0NJSpU6dmGck6NjaWZ555hm3btlG+fHn69euXafrjjz+Oj48PCxcuZN68eZb2K1eu8OqrrwLw8ssvZ1qmZcuWtGvXjitXrvDaa69Z2lNTU3n22WdJSUlh+PDhODs7F/RuioiIiIiIiA0U6qjaYWFhlClTBienm9/MkiVLeO+99yzfJycnA2QaFfvtt9/m7rvvJi4ujueff57XX3+dFi1aUKFCBUJDQ9m9ezdXr16lTJkyzJkzJ8srpPz8/Jg2bRoPPvgg999/P506daJcuXKsWrWKyMhIXnrpJTp16pSltunTp9OmTRsmTZrEmjVruPXWW9mxYwcnT56kbdu2jBo16qb3V0RERERERIqnfF1x3rlzJ2PHjuXQoUOZ2ufPn0/58uUJCgqiXLlyTJo06abXHRoayrZt2yxfhmEAZGoLDQ0FoFy5crz22ms0a9aMo0ePMnfuXDZt2kT58uV5+eWXOXDgQI63Tt9333389ddfdO/enT179rB06VJq1arFjBkzmDBhQrbL1K5dmz179jBkyBBCQ0OZP38+Dg4OvP3226xevRpXV9eb3l8REREREREpnkxGRiLNg8GDBzN79mzOnz9PuXLlADh16hT16tUjJSWFChUqcPnyZQzDYPXq1dlevRWIjo7G19eXqKgojbAtUsp8uvKorUsodV7sWufGM4nYOVv+bNE5JiJFqaiyVL6uOG/dupUmTZpYQjPAtGnTSElJYfz48Zw/f55t27bh4OCQp6vOIiIiIiIiIraWr2ecL1++TOPGjTO1rVy5Ek9PT55//nkAmjVrRvv27dm3b19+NiUiIiIidkBXu0WkJMrXFee0tDRSU1Mt38fGxrJ7927atWuHi4uLpb1ixYpcunQpP5sSERERERERsYl8BecqVaqwa9cuy/dLliwhNTWVLl26ZJov475zEREREREREXuTr+Dcq1cvzpw5Q79+/fj888/53//+h4ODA3369Mk03549e6hatWq+ChURERERERGxhXwF5//9739Uq1aNBQsWMGLECM6fP8/IkSOpXbu2ZZ5t27Zx/vx5OnTokO9iRURERERERIpavgYH8/f3Z//+/cyZM4fQ0FCaNWvGnXfemWmeS5cuMWLECAYOHJivQkVERERERERsIV/BGcDLy4shQ4bkOL1Pnz5Zbt0WERERERERsRf5ulX7eklJSVy8eJHw8PCCXK2IiIiIiIiIzRRIcP7mm29o0qQJnp6eBAcH87///c8ybd68efTr14/jx48XxKZEREREREREilS+3+Pct29fnnnmGQ4fPswtt9yCYRiZ5mnUqBELFixg9uzZ+SpURERERERExBbyFZynTJnCwoULueuuuwgJCeHvv//OMk/NmjWpVasWy5Yty8+mRERERERERGwiX8F5xowZBAUFMXv2bIKCgnKc79ZbbyUkJCQ/mxIRERERERGxiXwF5yNHjtCqVSs8PT2tzufp6UloaGh+NiUiIiIiIiJiE/kKzs7OziQmJt5wvjNnzuDt7Z2fTYmIiIiIiIjYRL6Cc/369dm1axcxMTE5znPlyhX27t1L48aN87MpEREREREREZvIV3B+9NFHuXr1Kk8//TTJyclZpqelpfHcc88RHx/P4MGD87MpEREREREREZtwys/CTz75JL///jszZ85k8+bNdO/eHYB9+/YxYsQIFi9ezKlTp+jWrRsDBgwokIJFREREREREilK+rjg7OjqydOlSnnnmGS5cuMA333wDwJ49e/j88885c+YMTzzxBAsWLMBkMhVIwSIiIiIiIiJFKV9XnAHc3NyYOnUqo0ePZt26dZw+fZr09HSCg4O54447qFixYkHUKSIiIiIiImIT+Q7OGQICAnjggQcKanUiIiIiIiIixUK+btVOT08nOjqalJSUHOdJSUkhOjqa9PT0/GxKRERERERExCbyFZw//fRTypYty/r163OcZ/369ZQtW5bPP/88P5sSERERERERsYl8Bef58+dTuXJlunTpkuM8/8/efYdHVeZ/H/8MCQQIJVQRKSoIiqxUUVQEWXARVCzgqquLoKu4dmzr/sSC7mNFZO0FwYqsqCCoKEjvvffeexohPef5I0yYcubMmT5J3q/r4mJy5pR7Tr2/5249evRQo0aN9P3334eyKQAAAAAAYiKkNs5btmxRx44d/c7XunVrLV++PJRNAQAAlCojpm6OdRIAAGESUolzenq6atas6Xe+mjVrKjU1NZRNAQAAAAAQEyEFzmeeeaZWr17td77Vq1erfv36oWwKAAAAAICYCClw7t69uzZs2KBx48b5nOd///uf1q9fr6uuuiqUTQEAAAAAEBMhBc5PPvmkKlWqpL///e968MEHtXr1amVlZSkrK0urV6/Wgw8+qDvvvFOVKlXSk08+Ga40AwAAAAAQNSF1Dnb++efriy++0IABA/TBBx/ogw8+cPveMAxVrlxZo0ePVuvWrUNKKAAAAAAAsRBSibMk9e/fX6tXr9Z9992n5s2bKykpSUlJSWrevLnuv/9+rVq1Sn/961/DkVYAAAAAAKIupBJnp+bNm+v9998Px6oAAAAAAIgrIZc4AwAAAABQloWlxFmSCgoKdOzYMeXm5vqcp0mTJuHaHAAAAAAAURFy4Dxt2jS9/PLLWrhwofLz833O53A4VFBQEOrmAAAAAACIqpAC58mTJ+vGG29UYWGhatWqpXPOOUfVq1cPV9oAAAAAAIi5kALnF198UUVFRRoxYoQefPBBJSQkhCtdAAAAAADEhZAC53Xr1qlz58565JFHwpUeAAAAAADiSki9alerVo0OvwAAAAAAZVpIgXOPHj20dOnScKUFAAAAAIC4E1Lg/NprrykjI0NPP/00PWYDAAAAAMqkkNo4jx49Wtdcc43efPNNff/99+rWrZsaNWqkChW843GHw6GhQ4eGsjkAAAAAAKLOYRiGEezCFSpUkMPhkJ1VOBwOFRYWBrupMi0jI0M1a9ZUenq6atSoEevkAIiiEVM3xzoJiKLHeraIdRIQRVzf0cc1BpQ/0YqlQi5xBgAAAACgLAspcB4wYEC40gEAAAAAQFwKqXMwAAAAAADKupBKnAEAAIB4Eat25bStBsq+kANnwzD09ddfa+LEidqyZYsyMzNNOwtzOBzatm1bqJsDAAAAACCqQgqc8/Ly1KdPH02fPt1nz9p2e90GAAAAACAehdTGefjw4frjjz907bXXasuWLbrzzjvlcDiUm5urDRs26IUXXlBycrKefPJJFRUVhSvNAAAAAABETUglzuPGjVPt2rX1zTffKDk5WRUqFMfhFStWVMuWLfXcc8/pqquu0lVXXaWWLVtq0KBBYUk0AAAAAADRElKJ89atW9WpUyclJycXr+xU4FxYWFgyT5cuXXT55Zfr/fffD2VTAAAAAADEREiBc0JCgmrWrFnytzOAPnLkiNt8Z511ljZt2hTKpgAAAAAAiImQqmqfddZZ2rt3b8nfzZs3lyQtXLhQN9xwQ8n01atXq1q1aqFsCgAiJlbDlwAAAKB0CKnE+dJLL9XatWuVm5srSerdu7ck6dFHH9WUKVO0Zs0aPfTQQ9qwYYMuueSS0FMLAAAAAECUhRQ433zzzapcubJ+//13ScUlzo8++qh2796tPn36qG3btnrvvfdUtWpVvf7662FJMAAAAAAA0RRSVe0+ffrowIEDbtOGDx+uiy++WBMmTFBqaqpatGihhx9+WOedd15ICQUAAAAAIBZCCpx9ufXWW3XrrbdGYtUAAAAAAERVSFW1hw0bpp9++snvfJMmTdKwYcNC2RQAAAAAADERUuD8wgsvaMKECX7n++mnn/Tiiy+GsikAAAAAAGIipMDZrsLCQlWoEJVNAQAAAAAQVlGJZtetW6datWpFY1MAAAAAAIRVwJ2DDRo0yO3vuXPnek1zKigo0KZNm7R06VLdcMMNQSUQAAAAAIBYCjhwHjNmTMlnh8OhrVu3auvWrZbLXHTRRXrjjTcCThwAAAAAALEWcOA8Y8YMSZJhGOrevbt69eqlp59+2nTeSpUqqWHDhmratGloqQQAAAAAIEYCDpy7du1a8nnAgAHq0qWL2zQAAAAAAMqSkDoHGz16tM/2zaFatmyZXn31Vd10001q1KiRHA6HHA6H3+XGjBmjTp06qVq1aqpdu7Z69+6t+fPnWy4zb9489e7dW7Vr11a1atXUqVMnffHFF5bL7N27VwMHDlTDhg1VuXJltWjRQs8//7xycnIC+p0AAAAAgPgWUuB86NAhzZ49W4cOHXKbvm3bNt16661q3bq1evfurYULFwa87pdeeknPPPOMfvzxR+3bt8/WMo8++qgGDhyotWvXqkePHurUqZOmTp2qK6+80ud4099//726du2qKVOm6KKLLlKvXr20ZcsWDRgwQE888YTpMlu3blW7du00ZswY1alTR3379lVhYaGGDRumHj16KDc3N+DfCwAAAACITyEFzq+++qquuuoqpaenl0zLyMjQFVdcoe+++07r16/XlClT9Oc//1lbtmwJaN2dO3fW0KFD9dNPP+nAgQNKSkqynH/atGkaOXKk6tSpo1WrVmnChAmaMmWKZs+erYSEBA0cOFBpaWluyxw/flyDBg1SYWGhxo8fr5kzZ2r8+PHauHGjmjdvruHDh2vmzJle27rrrrt09OhRPfzww1qzZo3GjRunTZs26cYbb9S8efP0yiuvBPRbAQAAAADxK6TAeebMmWrVqpVatGhRMm3MmDE6dOiQbrvtNm3atElvvfWWsrOzNXz48IDW/fTTT2vYsGG67rrr1KBBA7/zv/XWW5KkZ599Vuedd17J9M6dO2vw4MFKS0vTqFGj3Jb59NNPlZGRob59++qmm24qmX7GGWfo9ddflySvdC9evFjz5s1T/fr1S+aRpMTERH3wwQeqWLGi/vvf/6qgoCCg3wsAAAAAiE8hBc779u3Tueee6zbt559/VmJiot5++22dd955evTRR9WmTRvNmjUrpIRayc7O1vTp0yVJ/fr18/reOW3SpEleafW1TJ8+fVS5cmVNmzbNrd2yc5nrrrvOqxT8jDPOUJcuXZSamqq5c+eG8IsAAAAAAPEi4F61XWVmZqpq1aolfxcWFmrBggXq0KGD6tatWzL9/PPP1+TJk0PZlKVNmzYpNzdX9erVU6NGjby+b9++vSRp9erVbtNXrVrl9r2rSpUqqXXr1lq6dKk2b96siy66yO8yzunTp0/X6tWr1a1bt6B/EwAACN2IqZtjnQQAQBkQUolzw4YNtXHjxpK/586dqxMnTngFjAUFBapUqVIom7K0e/duSTINmiUpOTlZKSkpSk1NVWZmpqTittjOttm+lnNO37Vrl+1tmS0DAAAAACi9QgqcO3furNWrV+vtt9/WmjVr9Oyzz8rhcOi6665zm2/Dhg0666yzQkqolRMnTkiSW+m3p+TkZEkqCZydy1gt57mMnW2ZLeMpNzdXGRkZbv8AAAAAAPEppMD5mWeeUVJSkh5//HG1bdtW8+bNU7du3XTZZZeVzLNz506tX79el1xySciJLSteeeUV1axZs+Rf48aNY50kAAAAAIAPIQXOF154oebOnas77rhDvXr10rPPPus1XvJvv/2mNm3a6IYbbghlU5aqVasmSTp58qTPebKysiRJ1atXd1vGajnPZexsy2wZT88884zS09NL/u3Zs8fnvAAAAACA2AqpczCpuDOszz//3Of39913n+67775QN2OpSZMmkqS9e/eafp+VlaW0tDTVqlWrJKCtUaOGatasqfT0dO3du1etWrXyWs65vqZNm7pta8WKFT63ZbaMp6SkJL/jUgMAAAAA4kNIJc7xomXLlkpKStKRI0e0b98+r++XL18uSSU9Yzu1adPG7XtX+fn5Wrt2rSpXruw2TrXVMlbbAgAAAACUTmELnHfv3q1FixZp9uzZPv9FSpUqVdS9e3dJ0nfffef1/fjx4yXJq9OyPn36uH3vavLkycrJyVGPHj1UuXJlr2UmTZqk3Nxct2UOHTqkOXPmqFatWrr88stD+EUAAAAAgHgRcuD82Wef6ZxzztE555yjyy67TFdddZXPf5E0ZMgQSdLLL7+sLVu2lExfsGCBPvroI6WkpOjuu+92W+aee+5RjRo1NHHiRP3www8l0w8fPqynnnpKkvT444+7LdOpUyddfvnlOnz4sJ5++umS6QUFBfrnP/+p/Px8Pfzww6pYsWLYfyMAAAAAIPpCauM8evRo3XPPPZKk1q1bq0WLFpadYgXi559/1ksvvVTyd15eniTp0ksvLZk2dOjQkhLgHj166JFHHtHIkSPVtm1b9ezZU3l5eZo6daoMw9Do0aOVkpLito3atWvrs88+0y233KJ+/fqpW7duqlOnjqZNm6a0tDQNGTLEa0xq5+/u3LmzRo4cqenTp6tVq1ZasmSJtm/frssuu0zPPPNMWPYBAAAAACD2Qgqc33rrLSUmJmr8+PG6/vrrw5UmSdKRI0e0aNEir+mu044cOeL23dtvv622bdvq3Xff1dSpU1WpUiX16NFDQ4cOdRsiy9XNN9+s2bNn6+WXX9bChQuVl5enVq1a6cEHH9SAAQNMlznvvPO0YsUKPffcc5oyZYp+/PFHNWnSREOHDtW///1vOv4CAAAAgDLEYRiGEezClStX1hVXXKFp06aFM03lTkZGRkkP3zVq1Ih1coByZ8TUzbFOAsqJx3q28D8TworrG9HAtQ3ETrRiqZDaONeuXVt169YNV1oAAAAAAIg7IQXOffv21bx585Sfnx+u9AAAAAAAEFdCCpz/3//7f0pOTtbAgQOVmpoarjQBAAAAABA3Quoc7PHHH1erVq00duxY/fzzz+rQoYMaNWqkChW843GHw6FRo0aFsjkAAAAAAKIupMB5zJgxJZ/T09M1ffp0n/MSOAMAAAAASqOQAucZM2aEKx0AAAAAAMSlkALnrl27hisdAAAAAADEpZA6BwMAAAAAoKwjcAYAAAAAwEJAVbXPPfdcORwOTZs2Teecc47OPfdc28s6HA5t27Yt4AQCAAAAABBLAQXOO3fulCTl5+e7/Q0AAAAAQFkVUOBcVFRk+TcAAAAAAGUNbZwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALBM4AAAAAAFggcAYAAAAAwAKBMwAAAAAAFgicAQAAAACwQOAMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAAC4mxTgAAAABQmo2Yujlm236sZ4uYbRsoTyhxBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggeGoAABARMVyqB4AAMKBEmcAAAAAACwQOAMAAAAAYIHAGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWEmOdAAAAyosRUzfHbNuP9WwRs20DAFDaUeIMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWEiMdQIAAEDkjZi6OdZJAACg1KLEGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsFCmAudu3brJ4XD4/DdlyhTT5caMGaNOnTqpWrVqql27tnr37q358+dbbmvevHnq3bu3ateurWrVqqlTp0764osvIvGzAAAAAAAxVCaHo7r55ptVrVo1r+lnnXWW17RHH31UI0eOVJUqVXT11VcrJydHU6dO1e+//67x48frhhtu8Frm+++/11//+lcVFRXpyiuvVN26dfXHH39owIABWr16td58881I/CwAAAAAQAyUycD5zTff1Nlnn+13vmnTpmnkyJGqU6eOFixYoPPOO0+StGDBAnXr1k0DBw5Ut27dlJKSUrLM8ePHNWjQIBUWFur777/XTTfdJEk6dOiQrrjiCg0fPlzXXnutunXrFoFfBgAAAACItjJVVTtQb731liTp2WefLQmaJalz584aPHiw0tLSNGrUKLdlPv30U2VkZKhv374lQbMknXHGGXr99dclScOHD49C6gEAAAAA0VBuA+fs7GxNnz5dktSvXz+v753TJk2a5Db9559/9rlMnz59VLlyZU2bNk05OTnhTjIAAAAAIAbKZFXtUaNG6dixY6pQoYJatGihG264QU2aNHGbZ9OmTcrNzVW9evXUqFEjr3W0b99ekrR69Wq36atWrXL73lWlSpXUunVrLV26VJs3b9ZFF10Urp8EAAAAAIiRMhk4v/zyy25/P/HEExo6dKiGDh1aMm337t2SZBo0S1JycrJSUlKUmpqqzMxMVa9eXRkZGUpPT7dcrlGjRlq6dKl27dpF4AwAAAAAZUCZqqp95ZVX6ssvv9S2bdt08uRJbdq0Sf/5z3+UmJio5557TiNHjiyZ98SJE5KkqlWr+lxfcnKyJCkzM9NtGavlPJcxk5ubq4yMDLd/AAAAAID4VKYC52HDhumOO+7QueeeqypVqqhFixb697//rQkTJkiSXnjhBWVnZ8c2kZJeeeUV1axZs+Rf48aNY50kAAAAAIAPZSpw9uXqq69Wx44dlZaWpkWLFklSyTjPJ0+e9LlcVlaWJKl69epuy1gt57mMmWeeeUbp6ekl//bs2RPArwEAAAAARFO5CJwllQw3deDAAUkq6Sxs7969pvNnZWUpLS1NtWrVKgmCa9SooZo1a1ou55zetGlTn2lJSkpSjRo13P4BAAAAAOJTuQmcU1NTJZ1ug9yyZUslJSXpyJEj2rdvn9f8y5cvlySvDr7atGnj9r2r/Px8rV27VpUrV1aLFi3Cmn4AAAAAQGyUi8D5yJEjmjNnjqTTw0hVqVJF3bt3lyR99913XsuMHz9eknTddde5Te/Tp4/b964mT56snJwc9ejRQ5UrVw7fDwAAAAAAxEyZCZznz5+vCRMmqLCw0G36zp07deONNyorK0vXX3+92zBSQ4YMkVQ8fNWWLVtKpi9YsEAfffSRUlJSdPfdd7ut75577lGNGjU0ceJE/fDDDyXTDx8+rKeeekqS9Pjjj4f99wEAAAAAYqPMjOO8efNmDRw4UA0aNFD79u2VkpKiXbt2admyZcrJydGFF16oTz75xG2ZHj166JFHHtHIkSPVtm1b9ezZU3l5eZo6daoMw9Do0aOVkpLitkzt2rX12Wef6ZZbblG/fv3UrVs31alTR9OmTVNaWpqGDBmibt26Re+HAwAAAAAiqswEzpdcconuv/9+LVq0SEuWLFFqaqqSk5PVtm1b9e/fX/fff7+qVKnitdzbb7+ttm3b6t1339XUqVNVqVIl9ejRQ0OHDtVll11muq2bb75Zs2fP1ssvv6yFCxcqLy9PrVq10oMPPqgBAwZE+qcCAAAAAKLIYRiGEetElHcZGRmqWbOm0tPT6WEbiIERUzfHOgkAAATlsZ50SIvyLVqxVJlp4wwAAAAAQCQQOAMAAAAAYKHMtHEGAAAAyptYNjeimjjKE0qcAQAAAACwQOAMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHOwQDEDcZTBgAAQDyixBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALBM4AAAAAAFggcAYAAAAAwAKBMwAAAAAAFgicAQAAAACwQOAMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALibFOAAAAAIDSZ8TUzTHb9mM9W8Rs2yifKHEGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALibFOAID4MmLq5lgnAQAAAIgrlDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWKCNMwAAAIBSJZZ9sjzWs0XMto3YocQZAAAAAAALBM4AAAAAAFggcAYAAAAAwAKBMwAAAAAAFgicAQAAAACwQOAcguzsbD333HNq0aKFKleurIYNG2rQoEHat29frJMGAAAAAAgTAucg5eTkqHv37nrppZd04sQJ9e3bV40bN9bo0aPVrl07bd++PdZJBAAAAACEAYFzkF5++WUtXLhQnTt31ubNmzVu3DgtWrRIw4cP15EjRzRo0KBYJxEAAAAAEAYOwzCMWCeitMnLy1P9+vWVnp6u5cuXq127dm7ft2nTRqtXr9bSpUvVoUMHv+vLyMhQzZo1lZ6erho1akQq2YAtI6ZujnUSAAAAYOKxni1inYS4E61YihLnIMybN0/p6elq1qyZV9AsSf369ZMkTZo0KdpJAwAAAACEGYFzEFatWiVJat++ven3zumrV6+OWpoAAAAAAJGRGOsElEa7d++WJDVq1Mj0e+f0Xbt2RS1NKFuoLg0AAABPscwjlvdq4gTOQThx4oQkqWrVqqbfJycnS5IyMzNNv8/NzVVubm7J3+np6ZKK6+cDkpSTdSLWSQAAAABKxGus4kxXpLvuInCOgVdeeUUvvvii1/TGjRvHIDUAAAAAYO3fsU6AH5mZmapZs2bE1k/gHIRq1apJkk6ePGn6fVZWliSpevXqpt8/88wzGjJkSMnfRUVFOn78uOrUqSOHwxHm1IYmIyNDjRs31p49e+jxuxzgeJcfHOvyheNdvnC8yw+OdfnC8TZnGIYyMzPVsGHDiG6HwDkITZo0kSTt3bvX9Hvn9KZNm5p+n5SUpKSkJLdpKSkp4UtgBNSoUYMLtBzheJcfHOvyheNdvnC8yw+OdfnC8fYWyZJmJ3rVDkKbNm0kScuXLzf93jn9oosuilqaAAAAAACRQeAchMsvv1w1a9bUtm3btHLlSq/vx48fL0m67rrropwyAAAAAEC4ETgHoVKlSnrwwQclSQ888EBJm2ZJeuutt7R69Wp17dpVHTp0iFUSwyYpKUnPP/+8V9VylE0c7/KDY12+cLzLF453+cGxLl843rHlMCLdb3cZlZOTo27dumnRokU688wz1aVLF+3atUuLFi1SvXr1tHDhQp177rmxTiYAAAAAIEQEziHIzs7WK6+8om+++UZ79uxR7dq11atXL7300ktq1KhRrJMHAAAAAAgDAmcAAAAAACzQxhkAAAAAAAsEzuXQyZMnNWHCBN19991q2bKlKleurOTkZLVp00bDhg3TiRMnfC47ZswYderUSdWqVVPt2rXVu3dvzZ8/P4qpR6iOHTum+vXry+FwqHnz5pbzcrxLryNHjuiJJ55Qy5YtVaVKFdWuXVvt27fXk08+aTr/pEmT1LVr15KxIbt166aff/45yqlGMJYsWaJbbrlFDRs2VMWKFZWSkqIuXbpo9OjRMqtUVlhYqBEjRuhPf/qTqlSponr16umWW27Rhg0bYpB6eFq2bJleffVV3XTTTWrUqJEcDoccDoff5YK5X8+bN0+9e/dW7dq1Va1aNXXq1ElffPFFuH4KbAjkeBcVFWnOnDl66qmn1KFDB1WvXl1JSUlq1qyZBg8erB07dlhui+MdW8Fe26569OhRstzevXt9zsexjhAD5c4nn3xiSDIkGRdccIHRv39/4y9/+YtRvXp1Q5Jx/vnnG4cOHfJa7pFHHjEkGVWqVDH69u1r/OUvfzESExONhIQE48cff4z+D0FQBgwYYDgcDkOS0axZM5/zcbxLr6VLlxp16tQxJBkXXnih8de//tW45pprjKZNmxoJCQle848YMcKQZCQmJhq9evUy+vbta1SpUsWQZLzzzjsx+AWwa/z48UZCQoIhyWjfvr1xyy23GFdddZWRmJhoSDJuv/12t/kLCwuNG2+80ZBkpKSkGDfffLPRtWtXw+FwGFWrVjUWLVoUo18Cp759+5Y8o13/WQnmfu08dxwOh9G1a1fj5ptvNlJSUgxJxuOPPx6BXwYzgRzvLVu2lHzfoEED4/rrrzduvPFG46yzzjIkGdWrVzfmzJljuizHO/aCubZdjR492pBUkofbs2eP6Xwc68ghcC6HxowZY9x7773G+vXr3abv37/faNeunSHJuO2229y+mzp1qiHJqFOnjrF58+aS6fPnzzcqVapkpKSkGKmpqdFIPkIwbdo0Q5Jx7733WgbOHO/S6/Dhw0bdunWNqlWrGhMnTvT63jMw2rhxo5GQkGAkJSUZ8+fPL5m+adMmo06dOkZiYqKxZcuWiKcbgcvPzzfq169vSDK+/vprt+/Wr19v1K5d25BkTJ8+vWS688XpeeedZxw8eLBk+vjx4w1JRvPmzY38/Pyo/QZ4e/XVV42hQ4caP/30k3HgwAEjKSnJMnMdzP362LFjRo0aNQxJxvfff18y/eDBg0bz5s0NScaMGTPC/dNgIpDjvXXrVqNnz57GH3/8YRQVFZVMz8nJMe666y5DktGkSRMjLy/PbTmOd3wI9Np2dfjwYaN27drG1VdfbTRt2tRn4MyxjiwCZ7iZP3++IclISkoycnNzS6Zfc801hiRjxIgRXss8/PDDhiTjzTffjGJKEaiTJ08azZo1M1q1amVs3rzZMnDmeJde999/vyHJeO+99wKa/5FHHvH67q233jIkGQ8++GCYU4lwWLNmjSHJaNmypen3zmv1tddeK5l2wQUXGJJMSyGvv/56Q5Ixfvz4SCUZQfCXuQ7mfv3aa68Zkoy+fft6LfPDDz8Ykoxrr7021KQjCIEEU65Onjxp1KxZ05BkzJw50+07jnd8CuRY33777UblypWNrVu3WgbOHOvIoo0z3LRp00aSlJubq2PHjkkqHnZr+vTpkqR+/fp5LeOcNmnSpCilEsF48cUXtX37dn344YeqWLGiz/k43qVXdna2vvrqKyUnJ2vgwIG2lnG2Y+ZYlz5JSUm25qtTp44kaceOHdqwYYOqVKmiPn36eM3H8S59gr1fW133ffr0UeXKlTVt2jTl5OSEO8mIkCpVqqhFixaSpP3797t9x/Eu3aZMmaJvvvlG//d//6dmzZpZzsuxjiwCZ7jZvn27JKlixYqqXbu2JGnTpk3Kzc1VvXr1TMenbt++vSRp9erV0UsoArJ69WoNHz5cAwcOVJcuXSzn5XiXXkuXLlVmZqbatWunKlWq6Ndff9WQIUP0z3/+U2+//bZXZiotLU27d++WJLVr185rfY0bN1bdunW1a9cuZWRkROU3wL5zzz1XzZo106ZNm/TNN9+4fbdhwwZ99dVXqlWrlm688UZJ0qpVqyRJrVu3Nn15xrVd+gR7v3aeC87vXVWqVEmtW7dWTk6ONm/eHIFUIxKKioq0a9cuSVKDBg3cvuN4l15ZWVm6//77df755+upp57yOz/HOrIInOFm5MiRkqRevXqVlGY4M9ZmD2VJSk5OVkpKilJTU5WZmRmdhMK2oqIi3XPPPUpJSdHrr7/ud36Od+m1fv16SVL9+vV1ww03qHfv3hoxYoQ++OADPfbYY2revLnGjh1bMr/zWNeqVUvJycmm63SeB84MGeJHQkKCPv/8c6WkpOhvf/ubOnTooFtvvVXdu3fXRRddpEaNGumPP/4oeQnq79rmWJc+wdyvMzIylJ6ebrkc50LpM3bsWB0+fFj16tXTZZddVjKd4126Pffcc9q5c6c+/PBDVapUyXJejnXkETijxC+//KJRo0apYsWKeumll0qmO4enqlq1qs9lnZluAqn4884772jJkiV64403SqpsWuF4l16pqamSpJ9++klTpkzRe++9p8OHD2vnzp164oknlJ2drQEDBmjlypWSONZlweWXX65Zs2bp3HPP1fLlyzVu3DjNmDFDFSpUUM+ePXXuueeWzOvveHOsS59grmHXISc5F8qGPXv26NFHH5UkDRs2zK0ZB8e79Fq+fLlGjhypAQMGqGvXrn7n51hHHoEzJEkbN27UHXfcIcMw9MYbb5S0dUbptnv3bj377LPq2rWr7rrrrlgnBxFWVFQkSSooKNCwYcP0z3/+U/Xq1VPTpk31xhtvqH///srPz9cbb7wR45QiXMaOHatOnTqpcePGWrRokU6cOKHNmzfrrrvu0vDhw9W9e3fl5ubGOpkAIiQrK0s33XSTjh49qhtuuEGDBw+OdZIQBoWFhSW1Bd98881YJwenEDhD+/btU69evZSamqohQ4bokUcecfu+WrVqkqSTJ0/6XEdWVpYkqXr16pFLKAL2wAMPKC8vTx9++KHtZTjepZfz2Eky7RzMOW3WrFlu83OsS6ctW7ZowIABqlu3riZPnqxOnTopOTlZ5513nj766CNde+21Wr58uT777DNJ/o83x7r0CeYadr1PcC6Ubvn5+erfv7+WLl2qK664wquvA4njXVq9/fbbWrFihV5//XXVrVvX1jIc68gjcC7njh8/rquvvlq7du3SwIEDTd9qNWnSRJK0d+9e03VkZWUpLS1NtWrV4kKMM5MnT1bVqlU1ePBgdevWreTfrbfeKqn4pYlz2sGDByVxvEuzpk2bSiquolWvXj2v788++2xJ0uHDhyWdPtapqaklD1NPzvPAuW7Ej2+//Vb5+fnq1auXW4bJ6ZZbbpEkzZ49W5L/a5tjXfoEc7+uUaOGatasabkc50L8Kyoq0oABA/Trr7+qbdu2mjRpkqpUqeI1H8e7dJo0aZIcDoc+//xzt/yba36tf//+6tatm6ZMmSKJYx0NibFOAGLnxIkTuuaaa7R+/XrddNNN+uSTT+RwOLzma9mypZKSknTkyBHt27dPZ511ltv3y5cvlyRddNFFUUk3ApOWllZSwugpJyen5Dvn0AQc79LL2TN2dna2cnNzvYYrOn78uKTTb6VTUlLUpEkT7d69WytWrNAVV1zhNv+ePXt09OhRNW3aVDVq1IjCL0AgnBkgZ0bJk3O6s+27swnO2rVrlZ+f79WzNtd26RPs/bpNmzaaPXu2li9frlatWrl9l5+fr7Vr16py5colwxsh/jz00EMaO3asWrRood9++00pKSk+5+V4l06GYZS8+DSzcOFCSXJrisexjixKnMup3Nxc9e3bV4sXL9Zf/vIXjR07VgkJCabzVqlSRd27d5ckfffdd17fjx8/XpJ03XXXRS7BCIphGKb/duzYIUlq1qxZyTRnaSTHu/Rq0qSJ2rRpI8MwTF+WOKe5Dj3lHM/XeVxdcazjm3PImaVLl5p+v2TJEkmnaxqcc845uuCCC5SdnV0y1qcrjnfpE+z92uq6nzx5snJyctSjRw9Vrlw53ElGGDz77LN6//331aRJE02dOlX169e3nJ/jXfrMnDnTZx7OWVq8Z88eGYbhFjhzrCPMQLlTUFBg3HjjjYYko0uXLkZWVpbfZaZOnWpIMurUqWNs3ry5ZPr8+fONpKQkIyUlxUhNTY1gqhFOO3bsMCQZzZo1M/2e4116ff3114Yk409/+pOxf//+kukrVqwwateubUgy/ve//5VM37hxo5GQkGAkJSUZCxYsKJm+efNmo06dOkZiYqKxZcuWqP4G2LNs2TJDkiHJeP/9992+W7BggZGcnGxIMqZOnVoy/ZNPPjEkGeedd55x6NChkunff/+9Iclo3ry5kZ+fH7XfAP+SkpIMq+xaMPfrY8eOGTVq1DAkGd9//33J9EOHDhnNmzc3JBkzZswI90+BDf6O91tvvWVIMho0aOB2vK1wvOOTv2PtS9OmTQ1Jxp49e7y+41hHFoFzOfT222+XZLZuvPFGY8CAAab/jhw54rbcI488YkgyqlatavTt29e45pprjMTERCMhIcH48ccfY/NjEBR/gbNhcLxLswEDBhiSjJSUFKN3797GVVddVfKA/sc//uE1vzMjlpiYaFxzzTVG3759jSpVqhiSjP/+978x+AWw64knnii5n1944YVG//79jcsvv9yoUKGCIcm499573eYvLCwseXFaq1Yto1+/fka3bt0Mh8NhVKlSxVi4cGGMfgmcJk+ebFxyySUl/xwOhyHJbdrkyZPdlgnmfj1+/HijQoUKhsPhMK666iqjX79+RkpKiiHJGDJkSBR+KQwjsOO9YsWKku87d+7sM/82Z84cr+1wvGMvmGvbjFXgbBgc60gicC6Hnn/++ZKMltW/HTt2eC07evRoo0OHDkbVqlWNlJQUo1evXsa8efOi/yMQEjuBs2FwvEuroqIi4+OPPy45dsnJyUbnzp2NMWPG+Fzmp59+Mrp06WJUq1bNqFatmtGlSxdj0qRJUUw1gvXDDz8YV199dUkNgVq1ahlXXXWV8c0335jOX1BQYAwfPty48MILjcqVKxt16tQx+vXrZ6xbty7KKYeZ0aNH+30+jx492nS5QO/Xc+fONXr16mWkpKQYVatWNTp27Gh5n0D4BXK8Z8yYYSv/ZnZ+GAbHO9aCvbY9+QucDYNjHSkOwzAM2/W6AQAAAAAoZ+gcDAAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAIOLuuusuORwOzZw5M9ZJAQAgYATOAAAAAABYIHAGAAAAAMACgTMAAKXAnj179PDDD6tFixaqUqWKateurY4dO+rFF19URkZGyXwnT57USy+9pNatW6tKlSqqWbOmrrzySn377bem63U4HDr77LNNvxszZowcDodeeOEFt+ndunWTw+HQzp07NWHCBF166aVKTk5W7dq1ddttt2nv3r1e2/j8888lSVdddZUcDkfJv507d0qSDMPQ119/rSuuuEJnnHGGKleurMaNG6tHjx567733gttpAACESWKsEwAAAKzNmTNH119/vdLS0nT22WfruuuuU3Z2tjZu3KgXXnhBffv2Vdu2bZWZmamrrrpKy5YtU7169XTttdcqKytL06dP15w5c7RgwQKNHDkybOl6//339dZbb6lLly7q3bu3Fi1apG+//VbLli3TqlWrVKVKFUnSgAEDNHfuXG3btk1/+ctf1KBBg5J1VKtWTZL01FNP6c0331RSUpKuvPJK1a1bVwcPHtTq1au1detWPfDAA2FLNwAAgSJwBgAgjh0/flw333yz0tLS9MYbb2jIkCGqUOF0hbEFCxaoYcOGkqR///vfWrZsma666ipNnDhR1atXlyRt3LhRXbt21X//+1/17NlT1157bVjS9t5772nOnDnq3LmzpOLS7p49e2r+/PkaO3asBg0aJKm45Pquu+7Stm3b9K9//UvdunVzW09OTo7eeecdVa9eXatWrdI555xT8l1BQYEWLFgQlvQCABAsqmoDABDHPv30Ux05ckS9evXSE0884RY0S1Lnzp1Vv359ZWVladSoUapQoYLef//9kqBZks4//3w9++yzkhTWEufHHnusJGiWpKpVq2rIkCGSpNmzZ9teT0ZGhnJzc9WsWTO3oFmSEhMT1aVLl/AkGACAIBE4AwAQx6ZNmyZJuu+++yznW7ZsmbKzs9W+fXudf/75Xt/feeedkqR58+apqKgoLGm7+uqrvaa1aNFCknTgwAHb66lfv74aNWqklStX6l//+pe2b98elvQBABAuBM4AAMSxPXv2SJKaNWtmOd/+/fslyWdHXykpKapZs6ays7OVmpoalrQ1atTIa5qzpDs3NzegdX3++eeqV6+eXnvtNTVr1kxnn322BgwYoF9//TUsaQUAIBQEzgAAlBMOhyOg+f2VTHtWGw9F9+7dtXXrVn399de68847VVRUpC+++EK9e/dWv379wrYdAACCQeAMAEAca9y4sSRp27ZtlvM5OwjbtWuX6ffp6elKS0tTlSpVVKtWrZLpFStW1IkTJ0yXcZZ2R0uNGjV0++2364svvtDu3bu1YMECNWrUSN9//71++eWXqKYFAABXBM4AAMSxHj16SJI+/vhjy/k6dOigKlWqaNmyZdqyZYvX91999ZUk6fLLL3crKT7zzDN17NgxHTt2zGsZZ/vqcKhUqZKk4l6y7br00ktL2mavXbs2bGkBACBQBM4AAMSxe+65R3Xr1tWvv/6qt99+W4ZhuH2/cOFCHT58WMnJyRo0aJCKior0wAMPKCsrq2SezZs36+WXX5YkPfzww27Ld+3aVZJKvnd6/fXXNXfu3LD9DmeJ+KZNm7y+2717t8aMGaOTJ0+6Tc/JydGMGTMknS55BwAgFhyG5xMYAADElZkzZ+r6669XZmamzjnnHF188cXKzs7Whg0btHXrVq1YsUJt27ZVZmamrrrqKi1btkz169dX165dlZWVpenTpysnJ0cPP/yw13BU69atK1lf27Zt1axZM61Zs0Z79uzRwIED9f777+v555/XCy+8ULJMt27dNGvWLO3YscOrM7KdO3fqnHPOUdeuXTVz5syS6cuWLdPFF1+spKQk/eUvf1HdunUlSa+99pr27Nmjdu3aqWrVqurYsaMaNWqkrKwszZ8/X0eOHFHHjh01d+5cJSUlRWoXAwBgiRJnAADiXLdu3bRq1SoNHjxYhmFowoQJmjdvnmrWrKlhw4aV9LhdvXp1zZo1Sy+++KLq1q2rn376SXPmzFHHjh31zTffmI7hfOGFF2r69Onq1q2bNm/erKlTp6pZs2ZasGCBLr744rD9hg4dOuirr75Sq1at9Pvvv2vUqFEaNWqUMjMz1axZMw0fPlzdunXT7t279cMPP2ju3Llq2rSpRowYoVmzZhE0AwBiihJnAAAAAAAsUOIMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALBM4AAAAAAFggcAYAAAAAwAKBMwAAAAAAFgicAQAAAACwQOAMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAACwTOAAAAAABYIHAGAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgAUCZwAAAAAALBA4AwAAAABggcAZAAAAAAALBM4AAAAAAFggcAYAAAAAwAKBMwAAAAAAFgicAQAAAACwQOAMAAAAAIAFAmcAAAAAACwQOAMAAAAAYIHAGQAAAAAAC4mxTgCkoqIi7d+/X9WrV5fD4Yh1cgAAAACgVDAMQ5mZmWrYsKEqVIhcuTCBcxzYv3+/GjduHOtkAAAAAECptGfPHjVq1Chi6ydwjgPVq1eXVHywa9SoEePUAAAAAEDpkJGRocaNG5fEVJFC4BwHnNWza9SoQeAMAAAAAAGKdJNXOgcDAAAAAMACgTMAAAAAABYInAEAAAAAsEDgDAAAAACABQJnAAAAAAAsEDgDAAAAAGCBwBkAAAAAAAsEzgAAAAAAWCBwBgAAAADAAoEzAAAAAAAWCJwBAAAAALBA4AwAAAAAgIVyHzgvW7ZMr776qm666SY1atRIDodDDofDdN6ioiLNmTNHTz31lDp06KDq1asrKSlJzZo10+DBg7Vjx44opx4AAAAAEGkOwzCMWCcilm644QZNnDjRa7rZbtm6davOO+88SVKDBg3UqVMnJSQkaPHixdq3b5+qV6+uX375RVdccUVAacjIyFDNmjWVnp6uGjVqBPdDAAC2GIahE7kFql65YqyTAgAAQhStWKrclzh37txZQ4cO1U8//aQDBw4oKSnJ57wOh0M9e/bUH3/8of3792vixIn64YcftG3bNt11113KzMzU3/72N+Xn50fxFwAAAvHk+NX60wu/a9WetFgnBQAAlBLlvsTZU+XKlZWbm2ta4mwlOztbZ555ptLT0zVz5kx17drV9rKUOANA9Jz9r58lSVe3OkMf/71jjFMDAABCQYlzKVOlShW1aNFCkrR///4YpwYA4E9eYVGskwAAAEoJAucwKSoq0q5duyQVt38GAMS3fAJnAABgE4FzmIwdO1aHDx9WvXr1dNlll8U6OQAAAACAMEmMdQLKgj179ujRRx+VJA0bNsyygzFJys3NVW5ubsnfGRkZkUweAMAEPXwAAAC7KHEOUVZWlm666SYdPXpUN9xwgwYPHux3mVdeeUU1a9Ys+de4ceMopBQAAAAAEAwC5xDk5+erf//+Wrp0qa644gp98803tpZ75plnlJ6eXvJvz549EU4pAMATJc4AAMAuqmoHqaioSAMGDNCvv/6qtm3batKkSapSpYqtZZOSkvxW5wYAAAAAxAdKnIP00EMPaezYsWrRooV+++03paSkxDpJAIAAGKLIGQAA2EPgHIRnn31W77//vpo0aaKpU6eqfv36sU4SAAAAACBCCJwDNGLECP3nP/9RgwYNNG3aNDVp0iTWSQIABOHYibxYJwEAAJQS5b6N888//6yXXnqp5O+8vOKM1KWXXloybejQoerTp49Wrlypxx9/XJJ0zjnn6D//+Y/pOu+55x5dccUVEUw1ACBUDWpWjnUSAABAKVHuA+cjR45o0aJFXtNdpx05ckSSlJaWJuNUN6wLFizQggULTNfZrVs3AmcAiHPVksr9IxAAANhU7nMNd911l+666y5b83br1q0kcAYAAAAAlA+0cQYAAAAAwAKBMwAAAAAAFgicAQDlEi1vAACAXQTOAAAAAABYIHAGAJRLhihyBgAA9hA4AwAAAABggcAZAFAu0cYZAADYReAMACg3DKJlAAAQBAJnAEC5UUTcDAAAgkDgDAAoN4oocQYAAEEgcAYAlBuugXO8hNCZOfmxTgIAAPCDwBkAUG7EW4Hz5NX79acXftfIaVtinRQAAGCBwBkAUG64lThbBNH5hUXan5Yd8fT8+4c1kqQR0zZHfFsAACB4BM4AgHLjUEaurflu+3ihLnt1uhZuPxbhFAEAgNKAwBkAUG6s3JNqa76lu4rn+3bx7kgmBwAAlBIEzgCAciOhAo89AAAQOHIQAIBy44zqSS5/xVlPYQAAIG4ROAMAyo2aVSuWfLbTwzahNQAAkAicAQBlQNrJPM3YdFiFRdahrmuwXBhvY1MBAIC4ReAMACj1bnp/vgaOXqLR83ZYzucaK/uJsQEAAEoQOAMASr3tR7MkSZNXH7Ccz3CpfF1kI3KmUBoAAEgEzgCAcsS9xJmoGAAA2EPgDAAol/y1h5boHAwAABQjcAYAlBn+Al3XQmYKnAEAgF0EzgCAcsO1jTO9agMAALsInAEA5UagbZwNP/Nk5RYov7AoqLSkn8xXRk5BUMsCAIDoInAGAJQdfgJd12/t9KptJTuvUB1fnqY/D58V8LJFRYbaDPs9pO0DAIDoSYx1AgAAiBbXUuZQx3HedChT2fmF2n38pPILi1Qxwd676PX7M7TlcGZoGwcAAFFV7kucly1bpldffVU33XSTGjVqJIfDIYfD4Xe5MWPGqFOnTqpWrZpq166t3r17a/78+VFIMQDAF3/3b9cC6VB71a7kEigXFNqPwnv/d44e+Xal7fkBAEDslfsS55deekkTJ04MaJlHH31UI0eOVJUqVXT11VcrJydHU6dO1e+//67x48frhhtuiExiAQCWKvh97+la4hxakbNrR2Np2XmqUqlKSOsDAADxq9yXOHfu3FlDhw7VTz/9pAMHDigpKcly/mnTpmnkyJGqU6eOVq1apQkTJmjKlCmaPXu2EhISNHDgQKWlpUUn8QBQDvjroMtVhQBKnEMNnF11fmV62NYFAADiT7kPnJ9++mkNGzZM1113nRo0aOB3/rfeekuS9Oyzz+q8884rmd65c2cNHjxYaWlpGjVqVMTSCwDlyd7Uk7rs1en6cNY2W/P7DZxdPttq42wxD6NZAQBQfpT7wDkQ2dnZmj69uFShX79+Xt87p02aNCmq6QKAsuq1KZt0ID1Hr/660d4Cfqpqu5U4h9o7GAAAKDcInAOwadMm5ebmql69emrUqJHX9+3bt5ckrV69OtpJA4AyKdDg1l8TZ/detQmcAQCAPQTOAdi9e7ckmQbNkpScnKyUlBSlpqYqM5OhRgAgVDYGOXDjr6r2ybyCks+FNgJnw6KuNnE3AADlR7nvVTsQJ06ckCRVrVrV5zzJyclKS0tTZmamqlevbjpPbm6ucnNzS/7OyMgIb0IBoIywMzygqwp+Xgfn5heVfC4qspjRBqugGgAAlC2UOMfAK6+8opo1a5b8a9y4cayTBABxyTVszsot8Dmfk78S56Iw9qpNiTMAAOUHgXMAqlWrJkk6efKkz3mysrIkyWdpsyQ988wzSk9PL/m3Z8+e8CYUAMoI1zj4hvfmhbw+I8BxnEMJjlOz8nT0RK7/GQEAQNyjqnYAmjRpIknau3ev6fdZWVlKS0tTrVq1LAPnpKQkv+NFAwDcS5y3HD7hd/5ASpwLQ66q7VthkaF2L02VJG18qZcqV0wIbWMAACCmKHEOQMuWLZWUlKQjR45o3759Xt8vX75cknTRRRdFO2kAUCYF3MbZ73BUhuln3/PbW5en3ILCks+UOgMAUPoROAegSpUq6t69uyTpu+++8/p+/PjxkqTrrrsuqukCgLIqwE61/ZY4u7LTqzYAAIBE4BywIUOGSJJefvllbdmypWT6ggUL9NFHHyklJUV33313rJIHAGVKoCXO/mZ3G8fZxhjRGTn5Pr8j7AYAoPyIqzbOeXl5mj9/vmbNmqWVK1fqyJEjSktLU0pKiurVq6e2bduqa9euuuyyy1SpUqWwbPPnn3/WSy+95JYGSbr00ktLpg0dOlR9+vSRJPXo0UOPPPKIRo4cqbZt26pnz57Ky8vT1KlTZRiGRo8erZSUlLCkDQDKu0DHcfZXRm249artf23ztx2ztS673+1Py/a/UQAAEHfiInDeuHGjPvzwQ3311VdKTU312W5s4sSJcjgcSklJ0d///nfde++9uuCCC0La9pEjR7Ro0SKv6a7Tjhw54vbd22+/rbZt2+rdd9/V1KlTValSJfXo0UNDhw7VZZddFlJ6ACCeTd94SC9N3qDht7RR+ya1Ir69wKtqW39vZziqT+dsD3CrZttxbUvt/N/QZa9OD3ndAAAg+mIaOO/du1fPPvusvvrqKxUVFalJkybq06ePOnXqpPPPP1+1a9dWjRo1lJ6ertTUVG3YsEGLFy/WrFmzNHLkSL3zzju688479dJLL6lRo0ZBpeGuu+7SXXfdFbXlAKA0GzRmqSTprs8Wa/ULf4n49gItcfbXxtn1xWyhR5Gz8++Xf95gc2u+i5zNvsnJD7EbbwAAEDMxDZxbtGghSfrHP/6hO+64Q5dffrnl/H/+859LPs+dO1dffvmlvvzyS3333Xc6ccL/MCUAgPA4mVfof6YwcARY5lzBT88drgGta4GzYRi6ZuRsFdipv22yvHMdzjbZhkmMPHXDIdvrBgAA8SWmgfN9992np59+Wg0aNAh42SuuuEJXXHGFXnjhBb3++usRSB0AwJfA2x5HZzsJfiJntxJnl88Z2QXafCiwF7CeIXaRISWcSu/j360sme78Df/6fnVA6wcAAPEjpoHziBEjQl7HmWeeGZb1AAB8O5SRo3rVkqK+3UAD50Q/jZwNX22cg3gRYFbifOxEnqpVTtS0DYd9zgcAAEqfuOgcDAAQv2ZuOqy7Ri9RrwtP1w6KXjDoP6J1LUX2FzgXuVXPPl29OpgSdM/OxfamZqvbmzPVpHZVr3nX7kuPWvV2AAAQfnE1jvOgQYN07733auvWrZbzjRo1SoMGDYpSqgCgfPt4dnEv01PWHYz6tjOyfY+j7OQakCYm+Kmq7VHB2hlIB1Pz3PPlwR8bi0uZdx8/6bENQ9e+MzeILQAAgHgRV4HzmDFjNGrUKF166aX6448/fM43d+5cff7551FMGQAgFvbaGPf4E5fhowKpqi1596wdCM+hE30NpRhIh2MAACA+xVXgLEnnnnuusrKydM011+jdd9+NdXIQgBmbDqvnW7O0ak9arJMCIIw2H8qM2bYLCv0P4bR6b3rJ58QE+8NRSaerWwcT29pdZEuAnY4BAID4E3eBc5cuXTRz5kzVqVNHjzzyiO677z4VFBTEOlkwUVhk6JYPF+ixcSslSQNHL9GWwyc0cMyS2CYMQNikn8zX0RN5Mdl2YZGh5KTAuuJI8DeOs8ffJe2UgwmcbS6z7QiBMwAApV3cBc6SdMkll2jJkiVq27atPv30U/Xo0UPHjh2LdbLgYd3+dC3eeVw/rtjnNj0zx3+bRAClw7Gs3Jhst6jI0NUjZmnxjuN+5w2kKnSRx7zOqtqebZ9trctm5PzeDOt+OwAAQPyLy8BZkho1aqR58+apX79+mj17tjp16qS1a9fGOllw4SuvytArZcOJ3AJNWLGPFyHlXOWKCabT7QaNwTp6IlfbjmTZmtc1GPaXKs/v//n18uLpEayqHUo7agAAEB/iNnCWpMqVK2vcuHF68cUXtXPnTl1++eWaMGFCrJMFP8gilg1Dxq3Uo+NW6tFvV8Y6KYghX9dzJGPBrYdP6Pmf1tme3zUw9RcAe6Z7zpajxcv5mN9saKnT27K3EyL9kgEAyoMD6dn6dM52XugjZkrFOM5Dhw5V69at9fe//139+vXTGWecEeskQe7Dt3hWf0Tp9/v6Q5JOD7GD8slucBhO1787N6AxjwMp0fX1e3wFt5Ur+n6/7LmIr12VX8j9EQBCdfP787U/PUfr9mdoxF/bxjo5KIfiusTZ1Y033qi5c+eqUaNGOnDgQKyTA0muffC4ZjpjkdEGEBnRvpzTT+YHFDRLUrP61Uo++2ur7KujruCqanOvA4Bo2Z+eI0mas+VIjFOC8iquSpxnzJihBg0a+Py+TZs2Wrp0qT788EMVFgaWsUJkuRb4kJUEyo5oB84vTLJfRdvpwoY1Sj77S+/YxXtMpwfVOZj/kbIAAGHm8DN6AhApcRU4d+3a1e88devW1bPPPhuF1MAfh0tlbfcS51ikBkAkRLtUNZhx4F1TmBFs27cgOjv0/IoSaHe5BYVaszddbRunKDGh1FRwAxDn/A07CEQKTzIEzVdVbZROhzNzNGfLEaraw020T4fCIDboes7+sHyf8gsDLwoO5mdyrVh7evxq9ftwgV7/bVOskwKgDKlA3IwYiWmJ86BBg4Je1uFwaNSoUWFMDUJB32Cl3xWvzVBeQZE+vKO9erU+0+27/h/O1xNXt9Ql59aJUeoQK9G+tIMZusmzc8L07HzVrZYU2DqCCIK571mbsHK/JOnj2dv1794XxDg1AMoKqmojVmIaOI8ZM8bnd86LwtcbfQLn+EKJc+mXV1BcSjd04jqvwHnJzlT99eOF2vlqn1gkDTEU7VLVYDYXjgA2uJ/pvhC3QQCIvArUl0WMxDRwHj16tOl0wzA0aNAgXXHFFbr77rujnCrY5VZVm6KXMuNIZm6sk4A4Eu0rO5iXcF5tjYPqITtw3PYAIPpo44xYiWngPGDAAJ/fDRo0SM2bN7ecB7Hl3jlYDBMCIGKiXeJ8MCMn4GU80xhMJ13B/E5KmIHSq6jI0NQNh9S2cYrOqFE51slBACoQOCNGqOyAoH27ZHfJZ6pql0+GYejbxbuD6gkZpUO0L+3gqmp7LBRMibOvXrUtlknPdu/Bm7sgUHp8t2yP7vtyma58fUask4IAETcjVgicEZSiIkNfLNjl9jfKn5mbjuhfP6xR3/fmxTopiJDScGV73n6C6yE78GX+/eMat7+D6djM1eEgStsBBGfW5iOSpNwCBmQvbfanca9EbBA4IyBfL9qlV37d4DVkDHFz+bTxYGask4AIi7fKJGZVqsORxnCMwRxqtfb96WQGgWiJt3sb7MvOL4x1ElBOxbSNM0qf//txrSSpe8v6btOpqg2UTeEIKMPJMLyr6Xnef4LqHCxmPXMDiAWu19Jv+5ET2nXspK46v77/mYEwoMQZQUnzaNvnWUWxoJCqT+VBvAVVCD+rzGV+DK5zs+SEpXOwINPjyrMmDgAgcroPn6WBY5Zo6c7jsU4KygkCZwRl6vpDbn975hevfWduFFODWCFOKPusapOcyCmIYkqKmVWH9mrjHFSJc+gnM01WACDyrmxRz+3vNfvSY5QSlDcxDZwTEhJ8/nM4HPr88899fp+YSC3zWBq/bK/b356Z640HM+kwrAyi86LyxyqejEUTDfMSZ//zOLU+q4bpdF+3q0AC6lDveXQUCwD+OSRtPXy6jxWGp0K0xDRwNgwj6H9FRbGvCrxkyRLdcsstatiwoSpWrKiUlBR16dJFo0ePjvrYp7FmloH2HKoFpd+D36wo+ey8Fp1en7JRGTkc8/IkmKrJ24+c0PXvztWUtQeD2qbZJr3bOPtO15k1q3hNK75XmS8TyE+krweg9KCpUek2a/PRks8ViJsRJTENnIuKikL6F0vff/+9OnfurO+++05nnnmmbrrpJrVv314LFy7UoEGDdMcdd8Q0fdFmlmHkkVT2rDw1XvOvaw7o4v/8ocU7U0u+e3/mNr00aX2MUoZIsYoFg4kTH/9ulVbvTdfgr5YFnyivdNjvHMzsu6ET1vpcJpCXA6FWsqlSKSG0FQCwjfdcpVuhaxxAiTOihDbOQSgoKNA///lPFRYW6uuvv9ayZcs0btw4TZ8+XatXr1bt2rX1zTffaMaMGbFOatRQK7vsadckxXviqWfT/V8v19ETuZp9ahxMJ9oZlT1WpTLBjFvsWhPlvRlbNWfLEYu57aXHMxmBlvzO2HTY56+0+o21kyt5zBv7mlAAUB64Vs8OpMT593UHNfz3TeWuZijCI64C55kzZ9qe98UXX4xcQvzYuHGjDh8+rJYtW+r22293++6CCy4oKW1esmRJLJIXE4FmVLlhAaVDJNs4v/HbJt05anHI6fEMpq3jee8vM3MKfP4Wq3bLVzSv6/Z3QYhvEKnqDQD2JFRwDZztR873frlM70zfqmkbDkciWSjj4ipw7tu3r5YvX+53vqFDh2rYsGFRSJG5pKQkW/PVqVMnwimJrvSTvtuv5hV4l7T4Co6/X7ZXHV6epuW7U02/R2y1OKOaz+/MjjPKNqtgzm4B64eztmns4t1hSpFJOgIocfb1VTBVtT2/CaYE3hUF1kD08Jqq9HI4gi9xdjqcSWenCFxcBc6S1Lt3b23ZssXn9//3f/+n//znP2rRokUUU+Xu3HPPVbNmzbRp0yZ98803bt9t2LBBX331lWrVqqUbb7wxRimMjLs/912Cfv2787ym+cpDPv7dKh3PytPgL8PXxhHh44wTghvSJ7xpQexZHVI7JaS7jmXp1V836pkf1kgKvedoe52DhWe9kmQ1VLXny8H8QkqcgdKCy610cw2WHUE8WTj+CEZcBc4TJ05URkaGrr76au3bt8/r+3/961965ZVXdP7558e0/XBCQoI+//xzpaSk6G9/+5s6dOigW2+9Vd27d9dFF12kRo0a6Y8//lDt2rVjlsZIWLorsBJif5lA2kUD8c/qMrbTcVaaR02VUC97szbOXsNRWaTrj43m1fN8teVOqVrRIi3uQm3jTEYOAOxxONwiZyAq4ipw7tatm8aOHau9e/fq6quv1rFjx0q+e/LJJ/X666+rVatWmjFjhho0aBDDlEqXX365Zs2apXPPPVfLly/XuHHjNGPGDFWoUEE9e/bUueee63PZ3NxcZWRkuP0ri8pq6UlZb5/tfBaV7V8J+6yqPfs/S1zb/Ybj2jFt4+wxMZiXcr6SVr1you11FFDijAgr688fwC73qtpBlDiHMzEoN+IqcJaK2zl/8skn2rhxo6655hqdOHFCQ4YM0fDhw9W6dWtNnz5dZ5xxRqyTqbFjx6pTp05q3LixFi1apBMnTmjz5s266667NHz4cHXv3l25ubmmy77yyiuqWbNmyb/GjRtHOfXREWp7v3j07vQt6vjyNO0+djLWSQGi4vd1h3x+Z1WN2ck1GIzULSHUXrUl34GzZZo9vqNzMETSsxPW6IrXZigjx3d/I1a2HTmhR75doS2HMsOcstLnmR9Wa9oG3/c2xL8KFDgjBuIucJaku+66S2+88YaWLl2qli1b6u2331br1q31xx9/qH79+rFOnrZs2aIBAwaobt26mjx5sjp16qTk5GSdd955+uijj3Tttddq+fLl+uyzz0yXf+aZZ5Senl7yb8+ePVH+BdFx9ESenzlKXybxzd8361hWnl6bsjHWSYlLVkMXoXT6eM52n9/ZeTnmWgpbWGRo+5GskNJjtkXPgDOowNnHuXswPdv2Mvl23iRYKIPvGhFGXy3crX1p2Rq/dG9Qy9/56SJNXLlft3y0IMwpK33GLi6b+a7ypIJL5MytE9ESl4GzJA0ZMkT//ve/deDAAbVp00YzZsxQvXr1Yp0sSdK3336r/Px89erVS9WqefdAfMstt0iSZs+ebbp8UlKSatSo4favLHryu1WxTgKCUBLYUPoFhT4clWtwvXTXcfP1BBAx3vDePB3wCGa92zjbXp3fZQ5lmNccMlvm17UHA9+w2/q45uBfsGfJ/vTiXoRTLUbIAEoL11LmUO+dmw5m6vJXp+t/S3mhAmv2G29FQPfu3f3OU7Ficccs/fv3d5vucDj0xx9/RCRd/uzdW/y2t2bNmqbfO6enppbv4Za2HD4R6yRETFkuWQ2luin5/vLFVuDsMs+4JeaZkkLDUAWble22Hj6hF39arw/v7FAyzTPTFFTgHPgiIVfN9lSWS5wzc/JVvbLvjtZgHy9YAHcFRYa2HMpU8/rV3DsNs+mp8au0Ly1bT41frVs6Bt988vP5OzV28W59cXcn1a9eOej1IH7FNHCeOXOmrflWrfIuuQzmwggXZ8dkS5cuNf1+yZLiYZvOPvvsaCUJUUa+BbAX6Ln2NO3rrh1o1erMXPcSs/C0cQ58manrw9tGsiy3cT6cmUvgDCAinMMdvnLTn3RbpyYBL58XYseOTs//tE6S9NqvmzT8ljZhWSfiS0wD5x07dsRy80Hr27evhg0bptmzZ+uDDz7Q/fffX/LdwoULNWLECElSv379YpXEUiErtzDWSQAQAjttnF2b/foa5zjQUZw8x+wMRxtnq59SVGS4tafzJblSgrLygr+vleXAuQz/tKiL9r585dcN2p+Wo//e2jamhRaAq2pJ3iHMp3O22w+cXS6kcJ/VqSf99fGD0iqmgXPTpk1jufmgtW/fXk888YTefPNN/fOf/9R7772nVq1aaf/+/VqwYIGKiop07733qkePHrFOalzLzi+9gXN5yASWg5+IENkppd1w4PRwez+vOWA6j53xoN22K89A2fP7YPheym5V8jNqVg6p87OyfF8pyy8F4lFuQaESHA4lJoTelc1Hs4o7CLy3y7n6UyPzJmpANPm6Gwd7l+F9EOyK287B4t0bb7yhH374QVdffbUOHjyoH3/8UevXr1fXrl31zTff6KOPPop1EhFGXm0oXW7P2XmF+m3dQWXlFkQ7WREVrXaiKL3slDi/NXWz33lCD6o82ziHbzgqyfx3ppt0sBTqEHxlObgsy78t2vz1sZFXUKT2w6aq6xszw7rdvMLS+7Lb1fr9Gf5nQtwzvQq4zSDCYho4Hz16NK7WE6gbb7xRv/32m44ePar8/HwdP35c06dP12233RaT9CAyJq3ar4v/M01Ldpr3CPzvH9fovi+X6bFxK6ObsAjzldFdvtt3p3dbD5/QuCW7I5UkxJlwdWYVSK/a5st7/B3mlz43vDdP707f4jZt21Hvzg8LQmwnV5Y7ByNuDh9/+3LnsSxl5RVqX1o2HYmZOJ5FNdqyIJynNiXOp+UVFHHfsBDTwPmcc87RM888E3Tge/jwYT311FM655xzwpwy4LSHxq7Q0RN5GjRmScm0zYdO6IGvl2vLoUz9uGKfJOn3MHcUFGu+MvE3vT/fcrmnv18TgdQgHoWrFDHQktp5W49p7b50n+kIJhC3+ikbD2bqzd/dS87fn7HVa75Q90dZLpWN9G8zDEOfzd2hRduPRXQ78cDfnnQfpifEbZXBc5IgqWwwq3kRyNnqOq9nvxnBcK2FVFqvm8ycfLV58Xfd/smiWCclbsU0cO7fv7/eeOMNNWrUSDfccIPGjRungwetx8E8cOCAxo4dq+uuu06NGzfWW2+9VTJuMhBJrpnxHUez9POaA7r144UxTFFkldYbP6InXMGQ89IK5Jy79p25Xsv7+tteGuwvVFRkaNqGw17TQ62qXdquuV3HsvS/pXtUUOi/d7dwZEyt/LHhsIZNXq+/luF7slMgp0nIjSBK1ymJciTeSpx/XWveh0dpMnPTEWXnF2pBOXgBGayYdg722Wef6bHHHtOzzz6ryZMna9KkSZKkhg0bqmXLlqpVq5aqV6+uzMxMHT9+XJs2bdKBA8UnZoUKFXTdddfppZde0oUXXhjLnwELD41doaHXXqD61Svr6IlcpZXingbN7tHHynCVr1CCIsMw6H21HAg1UHRynmvBnnKeJQ/BjLMeyLZ9XRue069sUU+zNx+xv94AexePNWcb2pO5Bbrr8tjW/Fq7P93/TGWE1fmdnVeofh8uOD2vYSiUPoPLci0IlG5mZ2YsXz66dnhL/qfsimngLEl/+tOfNHHiRO3du1ejRo3S5MmTtXLlSu3bt89r3sTERHXs2FF9+vTRoEGD1KhRoxikGIGYtGq/cvIL9cnfO6rjy9NinZyQnAxhmJnS5tkJa7T5kHcbTrsKiwwlJvDgKOusMtVT1h7UwfTsgNYTbCbdczGr1VRwmJdIBxJs++oFvMBjxRc0qB5Y4FxKg5QF24/FPHB+Z7p31fmy5IRL55NWp8m4JbuVnn26ymio77bKYrt7nkxlQ6hBsuvi4TgnwvUiOZZK/y+IvJgHzk6NGjXS888/r+eff15ZWVlav369Dh8+rPT0dNWsWVP169fXhRdeqKpVq8Y6qQjQnuMnY50E+LDzaJaa1K7qNUbtVwtD6+CroMhQYkJIq0ApYFVCOvirZbbX48xwBJvvCGQcZ1/bCKRNV06++Q9P8+xpO8DcWGnNdy3eYd5xoqtACmDGzNuhWsmV1LftWbaXKQuZViubD2Xamm+xRyeWwdS+cOV6LZXS9zooR4I+RcNQQlxaX3wiMHETOLtKTk7WxRdfHOtkAGXaVwt36dkJa9W/QyO90b9NWNftWfJWmq3dl66Hxq7Q073OV6/WDWKdnLjiWvJ67ESuUqpWUkKFwDMgzgA80IzHv39cI4e8e8n1dfqFqxrfCBtDbElShQAzY/vT7JXQx5tUk6G5PNnd9TuPZumFSeslKaDA+aZ2Z+mHFd411coK13Np0Y7jeuAq6csFOzV++T6Nvuti1U6uJEn6ZY17PzGhdw4W+DK5BYX6aeV+XXFeXZ1Zs0poCQjQpoOZGj1vhx7+83lqmBLdbSP2Yhm72ujqAWUA4zgj4ngJF5/enlY8vM53y/aGfd2FIQ7LE0/u+3KZdhzNCqgEtbxwXtsbDmSow8vTdOvHC6wX8CHYNs7fLNqtrxft1pwt7iMz+G6DHFTyvPy2zroTS6dAXyEMm7xeo+ftCDxBpYDdks+0bP9BuJkqlcp2FRfX91HO6v9DJ67Tqj1pesdjqLRwCqYU7b3pW/Xk+NXq9facCKTI2rXvzNG3S/bogW+W+5ynalJclhkhQMHkLdfsNe8LIRxVtQO5VnLyCzVnyxHlFpSfJoBlBYEzIm7ToUyfYyAjdhKDKBm0K7+09XJkISuvwP9M5VZxRmHckj2SpCU7fY/xbaUwxDbOPpLl5VBGTlhWb7da8J7UwEuQXzxV2lrW8AI1NFa9kmdb9L8R6n53vSbtvniaeSqwTw/yJUgo8k+9tF2/P8PnPMll/CVLeWE+HJX1Sbp8t/kzKhx9eQVSo+mp8at156jFem7CutA3HEalbWSHWCBwRlT8fdTiWCcBHiLZeVdZb2+IYqv2pisjJz/gKsmeiorCGzj7Ws9//zhdMvf9/Z11wZk1Alrvy5PX6/mJa3U4M9fW/L5GEfjXNecHtN3yikycPUdP+B7dIdRrynVpu8cjHg6bVRLiIHkIguf5F9bhqMKwjkCqav+0ar8kadzSPbb6iED8IHBGVLh20++qz3/n6N8/rolyaiBJlRIid/nHaxvnwiJDt3+yUM/8sNr2MuW5B1Z/GeUPZm5T75FzQn5b7zxdwnXa+FqP63nZskENvXxD64DW++ncHfp8wS7b8/sakuTWixsHtN3SIMfHPd7JbgDnusfi9DYSE1YladM2HFK6j3bmvpb67x9bNGOj91jkXsuX1mNQWtMNnzzPxXlbvcca3nPcupaP6zMt3C/mfI224M8tHwXXxAmxQeCMmFq3P0PfLNqtk1SHjTrXEucJYe5UpyBOe8lYvz9D87cd09jFe0pKOeGbnXzA3tTskF8uOGsohCsjYydIq+AIT/U8K75WXxbH+Bz5h3U722AOrd1g2zAMFZShfhWCsclHr9u+rqm3pm7WwDFLLKt5ey5fmvZwXmGRxi42Hx1i7T7vdq7+Xvwg9jzPv++XB94/i69zONRaU5LIU5QTBM6IC1tCGDNYkt6fuVU935ql1CzfVdZKq9SsPO06lhX29SZWOH35PzpuZVjXHa8lzkkVT//mk2SU/LJdShhyiXNow1F58pVs12RWcFi1Gg0PX/ulDMbNmuvRQZsnu4fWdd/YPf9u+2Shxi3dY3MLpVNVP+1yHQ7zINnfHtyTaj1cpOsqS9twO8/8YF6bbcj/VnlNK6092pcn4Xix6usZE4578mcuHTsGmtZP52zXtiOh5YMRHQTOiAvJSaF11vH6lE3acviEPpq9PUwpih/tXpqqrm/M1L4wP9grRrCNc7yW/rgOlVSWev6OFPvBTmjnkjOPEa6Mua9Mi2syHY7Il/wm+Fh/OEo34pWvfR9MptfuIgu3x6aNoGEYWrknLSqdYLmeq86hpzyZBQWGn8o//s5Ew+cfVsuEdh2PmbdDfd+b57P6eSSUxVogZU04Xqz6fDaE4TXqSZfaG4Gm9eWfN+jPw2eFnAZEXlwFzp9//rlycsLT6ylKl3C9yM6P0yrC4bBqT1pY1xfJjEJBnPaq7RrIlKWevyMlM8deE4pQT6W3p23W0RO5YQucc3wM8eEasDrCklWy5mu/RLBD+6jx7ADQ4SiuAtvx5WmmVWSLDEP/9+OagIbbivcSzhmbDuuG9+ap51uRz/C67orOzep4fe+QrxJn633o79qNRVXtFyat16o9aXp/5tYobbFsXJNlXagvZCT36yiS53N837kQirgKnAcOHKiGDRvqoYce0qpV3lVpEDuR7t00Tmv2IkjxWuLsmip6/vZv5LTNtubzDEEDvV/8sfGwHvpmRdheoL35m/90+8so//Tg5SGn48KGNU2nBxqy5xUUqe+7c+OmI8VvFu1W6+d/85r+xHerdCwrz7SK7JKdqfp60W6/w2257pt4v0SnrC0ez9tuL+uh8Z/jNy1x9rMP/b1AdV3cfptzW7P55atT0Ugoy7VAyopwnFc+g+8wH35GBCi74ipwvueee1RQUKD33ntP7du316WXXqpRo0YpKyv87TsRmEhnYOK9ZCEelKZdFK9tnF3Ps7JcOyFc9qfbqwHkmufMLywK6lxdsP2YNhzwPfZqIHw1a3BNVwWHw7K07ey6ySGnI6VqRVWvnOg1PdA8+tytR7Rqb7q+WWTe2VG0/fvHNV5BzcaDmZYvo1w7obKbqeS5cJp7SZmhw5nu16bDYb6/Vp6qqWQYhupVT/L63m9VbZdVRvtlo+fPOZSRo1d+2aDdx6zbZQO++GzjHN1koBSLq8D5448/1oEDB/Txxx/r4osv1uLFi3XvvfeqYcOGGjx4sJYuXRrrJJZbkX5ghq9tY1hWE1O/rzto2utnOKopRUu89qrteh7bLRUvz23f7P5y1/lenLQu6DP1uYnrglwycA5HeNq1WWlUq6ppe9RAS7dKw30tr6DI8ne5v1zx/YPcOgeL0xdwseC6JzJzCtTpP394zOEwPU+qVErQJ7O365L/94eOmJSM+y9xPr3SaL/I8NzevV8u00ezt+uuMYvDvq0K1NWOe66nQ7DPZV/ncGl+zOcXFmnGpsPKzAm9TwB6l/cvrgJnSUpOTtY999yjhQsXavXq1XrggQeUmJiojz/+WJdcconatWunDz/8UBkZ4SmZgD2RfmCWhoxhNGw8mKF7v1yma9+ZG+ukhCTeqkHP2XJEd45apFs/XlgyLV5LxeOJ3cyE63xfLdwd9P0i0pkX1yDA4afEORxJufTc2qbrCfR3lpZMvdXvcg2C7Z4f8X6JRr6V/Gmuu2xvqo8aFSavrAxD+s8vG3xWJw+kxHnTwej2+uv5a5z9fGw/EnwtxLyC+HypC//C3sbZx+rCUc06mnnakdO2aODoJfr7Z6G9UNp8KFNPfx8fzYHiWdwFzq5at26t//73v9q/f7+++uorXXnllVq1apUeeOABNWzYUHfffbeWLVsW62SWC5EOnMNW4lyKSmXN7LDIEJSmlwvxFpTeOWqx5mw5quMuw5XFawdmkZSTX6hvF+/Wmr3pYe0J2LOkMdhzdVeEq2AGkq5w1DQoDs691xNIifOB9GwNHL2k5O94LYWtW62S5T5zTbbd+wNVtU+zsy9M2ziH3DnY6c+vTdnoNw3hFInD76vdNG1S4184DpHr/dP1faTrS7A3f98U8naimRf936mh+FbsTgtpPR/O2haG1JR9cR04O+Xn5yszM1OZmZmSim9w+fn5Gj16tDp16qR+/fopLS0ttoks4yLfxjmy6y8tykq14NIQlMZrB2aR8OuaA7rlwwV6avxq/euHNbru3blq8+LvfpezW6LmOVesX2Dd0rGR6XTPVEW6xNlXQXEgBciPfLvS7e8iw9COo1matGp/XGX2C4oMy99V6JJWuzVSgg2c42m/hIvrT3J9AejKbH/57RzMz5kezLUcz7vf14uneE4zioXjELm+tPNVk+e9GaEHkNHsbC5Sp25ZvI+Gg3evJXFk4cKF+uSTT/S///1PJ0+eVEJCgm666SYNHjxYXbp00Q8//KDXXntNP/74o6pWraovvvgi1kkus2jjHB1WGc/S9NNKw3GwW+pVFl5l3P/18shuIEwlzuHSoGYV0+lbDmW6/W0VNIQj32M25NV59asF9IJs00H3NBuSrnpzpqTiccl7/+nM0BIZgD3HT5oONSUVj4vumVlMqVpRaafG4nUNWKyeJ67PgmDPoyJDiuAw9SWi+Z7TNYA1qy3icJjvL/+9avvZbkyvZfsbNwzD1nWVF6f9b8C/cOQTXe894b58G9WqUtKMolGtqmFeu2+RukYNo3S3/Y6UuAucU1NT9eWXX+qTTz7R+vXrZRiGGjdurKefflr33HOPGjRoUDLvbbfdpv79+6tdu3b65ZdfYpjqss/qzVMFR+glxrzZKmb1lrI07aPSUIOgNO3PWLHdxjmyybCtz5/O1M9rDvg8tqv2une6Z/X7wnF6OBxy2zkf/K29Lj3XewxeK55BkmvGb9mu1KgGzrd8tEAHfPS0nlNQ6FWC47oPC2wGzuHoxbnIMJQQhbMymrcQ/yXH5ve0UIMN16V7XHBGSOsKeNs2k/7D8r0a8r9V+ujODn7n9fXClGYB8S8chyjUZmSLth/TOXWTVb9GZa/v3NMXzfMpMtsqMgxViJune/yIq6rad9xxh8466yw99thj2rBhg6655hr99NNP2rFjh5599lm3oNkpMTFRF198sVJTU2OQ4vLDKgMTjiop4Qq0cgtKd4+AFaJ0RRa/nY/c+ktDJqQ0BPelhefQXrE6/M7hdsJx/hmSPv17x5DX43qZXfOnM1XLpJftQExdf6jkc7T3s6+gWSruKTvRK3A+ncBRc3eUfLZb4jxr8xG/aTqZV2C5jvLEvI2zNX+7yvUYXnx2LVvpWB+mYeXsHsYh/1slSbrvS/993hT6aKJTTk+Z0iUMx6jQpRlZoKubv+2o/vrxQnX6f5492p9aXxhqywQjYiXOkVltqRdXJc7ffPONGjRooEGDBunee+9VkyZNbC134403qmnTphFOXflmFWSEIwAL14W/2qNEqbRxrWpmt+pZoFbvTdOdoxaHtXMoT6WhNDde03goI0cPj12hv3c+W30uCq008dkJa3Q007w9ZDi9P9O9TVisAhfnSzy7L0Use4E2DCWEWN/XzuX74R3tteXQCQ2futnWOt3GQ46jrE3t5EpK8Kyy72PeQovzw/XYbTvsvxfnKWsPek2L1ukX1arafqtcO5SVa/ISwc/F4O8cch8/OroicX776n+jvL5sKU3CcT64NuMJtKPF+VuPWX7vurponk+Ra+McoRWXcnEVOH/33Xfq27evEhMDS9Z1112n6667LkKpgmQdZBQHd6FdYeG6yZSlUkTP9iXhuok9Nm5lRINmqXQch3hN40uT12vRjuNatOO4+lzUJ6R1fbXQvD1qpMVq1zoLPO1miKzaOFetGHpl3wo+etX2SkeQG4qnjE2LM6p5708f6fNV6ue5UIKNXtTM9kFZDILsBA0/LN/nNe13lxoKpusNYFdFY7+6vhjKyg1/DTLP2g41KicqI6dAJ/MK9fj/Vqn3nxroz1Gukg57wnH6nZVyOnB2rs4wDC3Ybh0USzb6A3C5RqNb4hy5qtrwFldVtW+++eaAg2ZEh1U+1DVv07BmZVVLCvwYbrFRsmBHvJYiBqM0/5LScMONt7GmnSL9UsOVv+sl2MMYq+vQmbGxu3VfGaGlz/ZQYkLoj0ePJs4W6bAfOcdTKbOrIsNeoCtZ97rvelnaqdZutjfi9NIOib/f5JB3kwlJPjtzO71e6xWHo7O2QLhWvc8yqYbvlFdQpI0HMwK+13i2cXW2y39/5lZ9v3yv7v58aZnKR5Qlrkdl+sbDIa/FeSostzmMk7+7m+vlZ6i4M8VonEvh2kI0x6UvzeIqcF6+fLmGDBmiJUuW+Jxn8eLFGjJkiFauXBm9hMEys+baxvnaNg31wR3tA17/0Alrg0qXp7L0vCsyDG07cvqFQrgyzNEY8qo0ZFzjNXMUrmEs7Pw+f7MEPRxQUEuFzrnv7B5bX3u6brWksKTHzrUWaM+lrj/ty4W7gkhVZBQVGV6Bs6+jYHVeuX7VrnGK3+1GokMsu6JbVdv6Nzkcwf3uUNtAh5vdzd0xapF6vT1Hw3+318TByfOFqfMQ7ks73X7/5zUHAlonoiMcz2zXw+9cX46Psb29WFzw45bs1tETuSV/j1+2V11en6GPZm8PKp2BiGSv2vAWV4Hzu+++q/fff19nn322z3nOOeccvf/++3rvvfeilzBYt3F2/eyQVoY4CHsoSkNJp12G4T58Tmn6afEalLoKRzvYSLBbauePnd/nN9Mc5LZjdvidVbXj5Ng6bG4jkDf9hzNPZ87iqdbE0l2pbhlHyfd9wKpnW7cSThvbNZvHKIMjDkWyHeP8rUf1xYKdfrcb7fu61eYW7zguSXp3xtaA1rn9aJbb3yUvKl029u70wNaJ6AjH2efWZv/UZ7vPXKu5nv5+jen0V3/daDNl9q3em6YXJ60rqZ0WqesyXms3xVpcBc5z5sxR+/btVa9ePZ/z1KtXT+3bt9esWbOimDLfjhw5oieeeEItW7ZUlSpVVLt2bbVv315PPvlkrJMWVtbDUTncPqdFsaqp5J62shQ4FxmGEiLQzXY04sDScBziNY3RLXGO36raVzSvG/AyzgDU/uYjezU4HPaC4kAO+Vs2OxGLhXX73XtT9nUY7A5HZesa9dPGubDI0E6PYKk0srMrgnuPYuj2TxfpuYnrtGCbdztP9+drMOsPXjg259nfwZPfrXL726x5x0aPsdMRH+ze1zNyfOdB3V/MFX+uaLMTSM/7dEFhkU6YdMgXCduPnNDrUzYqNStP1787T6Pn7dT/+3mDpPC9qPYc4zyO3svGlbgKnPft22dZ2uzUtGlT7d+/P/IJ8mPZsmW64IILNHz4cFWsWFF9+/bVpZdequPHj2vEiBGxTl5YWV2YrjeTWLSQiGWvn+G2wqO03nN4l3CIRgmqSVO7uBOvgXMYmtZKCk+Js+sc39xzic5vUN3WtsOxa9+5rZ16tgqsk54KJZng0wnYc/yk/vPzeh1Iz/aa39+1YKeq9Wd3+R6yyuFw2OhQpjiNkTJ/21Hd+vECt2Yf0eLrPLAOnAO7oZuVirhe2w+NXa5ub87U+GV7/a8srvmpqi1HUPcO1929J9X7PDR8zBsN4XgB5xlE5RZ4PpwCfdnmX35hkf7YcCiq/VWUB3bPh32p3vf6knW4fHbehiravHA8X4L2/u8ctX7+Nx3PivzIFdeMnKP3Z27T09+vLpm26VSNxMwwBe9ztrgP/1caag7GQlwFzklJSUpLS/M7X0ZGhhISEiKfIAtHjhxRr169lJ2drYkTJ2rt2rX69ttv9csvv2jnzp2aP39+TNMXbr6un56tzijpXEMKX2lZIFyT5jkcSmnz3z+2lHwuLnF2uPwdixQF54nvVpl2VBNP4qVU0ntrYSpxthMWW8ySfjJf0zac7oDlsuZ1dV2bhqbznlM32WPboauVXEkDLzvb9vyXnltbJrUudevHC/XJnB0abGOM10C93u8idT8/9B54f1oVuRfBt3+ySAu3H7c1xm20WI/jfPqzrarapiXOpz//sqZ4uKqPZm3znjFk0bs32LlftWtsb5xlt/W6fDb7Ne7vMUrRQ+iUD2dZtzE1e9kWqnf+2KK7P1+qv3+2OGzrhP3nSp7XyxGXdZh0dpdos2afZ/Zy86Hil5Hztx21mbLgOV/4LN+dGtByD3yzXH/9aIGtkSY878ul72qPjrgKnC+88ELNnTtXx48f9znP8ePHNXv2bLVq1SqKKfP2/PPP6+jRo3rjjTd0/fXXe33fqVOnGKQqcnyVzl3WrI7bw7aCI/qlzq43Ql8Z+9LIc5fHawmpL89NXBfrJFiyuz9L67sYe1U7fc/0z2+8Ay1fb6A9q1WH+qa6aqXiF6OBdGT37u3tTTsH25dWXPqwymSM91APbThODcOITlvl/Wm+S2EixVcwYlniHOCQLmaril5JSfTuyf625HAElxr/uyqw4xFvXNvdT17t/YLKeYvJzQ/fi94fVhQPC7ZqT1rY1onwnH9mTUESbVbVdq0A6FqDJ5oFRq7p97fZnPxC/bz6gBbtOK7dNmo1ee7f0ni9R0NcBc533HGHTpw4oX79+mnvXu9qVfv27dMtt9yikydP6m9/+1sMUlgsOztbX331lZKTkzVw4MCYpSOarK4f15tGNHps9uSacUpKjKtTOiRFhuFRTa503cUiWf00HEpTCb4d+9OytcLlbXSop8u8rWbtHc3n9eyFNtRde0vHxpIC6yitbrWkkkDWfudgkb9f+dtGUmIFt86y6lUPT4/enk7mFdoe3zpcfJ0v1p2DuSxvqzGB9zyFJhsurS/AnOyVGAUe/LnuP4fDoYXbj+kvI2aXdL4V7aZQ4X7MFbjUfHrwmxVe3zvzL+EaElOKTBMr2K8VYBXIhlIA4Xovd60dGMjR3nwoU4czc/zP6IO/1I+Zt6Nk/VkuVbgr2cgbt2/qXmOltOU5oyWuBk2+5557NHbsWM2cOVMtWrRQr1691KxZM0nStm3b9Ntvvyk7O1uXX365Bg8eHLN0Ll26VJmZmbriiitUpUoV/frrr5o6dapycnLUokUL3XLLLWrYsOyUfEq+bzbFQ6m4Bs7BZVA6eFywgZiy7mDQy8Yzzz0erjxvJMbq+3fv8/X/fnHvPTJeMqrr9nuXNEoBlDiHMzF2thfkBi97dbokacqjXXR+gxq2fl+gz0Vfs3u28Qq1doRzHwS7L75cuEvD+l7oN2j1t/pwHHtf6xjSs4XW7EvXVefXd8ugRPJ8m7hqn25s1yiCW3Dn6yywCgLNqlL68si3KzRxpXcp4u/rDmlAANX8gxXNfKWdTQXTOsatBEvFTRsk6ZaPFmjnq33cnjvZFuMqu+rbtqHpcQklbcHK9/PgjMT1Fo4x4OHN7vlgddt3K3E+dW4Ec54FU2C0+9hJXT1iduAbc+Hv/vjCpPX6atFuTRvS1b0Zho0kXnJObc3efLqdM3Gzubi6uhMTE/Xrr79q0KBBys/P14QJEzR8+HANHz5cEyZMUF5engYOHKhff/1ViYmxi/nXr18vSapfv75uuOEG9e7dWyNGjNAHH3ygxx57TM2bN9fYsWNjlr5IcL1YVz1/9enpcr8gKzgcQZXiLNuVan8sPQ8Pj/V+i1wWFBUZbp1chKs6ZyQCWrNgPBa1D8wcTKNU2scAAQAASURBVDd/u2v3barrfg9XiV36yfyIdda0cneacvIL7QXOAZYh3dzBZtBlsdozavgvUXVmSgLd3wczTh/rk3n+7yexPEUf/vN5+uTvHZVQweEWnEQyTav2mL9EipggSpwDKeH0FZz955cNXtMi8cIwms1n/G3KMMxL2kNer8tR+GTODlvrDNeQeuFod1zg521CJJ5TFDhHht2zwW6Js1Hyf2jNtuyeQqv2ptmb0YKdlG49VXvCbOgtK577jbjZXFyVOEtS1apV9emnn+qll17SzJkztWfPHklS48aN1a1bN5155pkxTqGUmlpcHfKnn35SQkKC3nvvPfXv318nT57Uu+++qzfffFMDBgzQBRdcoLZt23otn5ubq9zc0+1uMjIyvOaJN86LrnZyJdWsUtHtuwoO98/BPjTTs/NVuWJonb6VpQv9uYnrNG/r6U4n/AV6uQWFemPKJnW/oL4uaxb4UD6hMHtwxEvmwddus9XrtGHomEtp6om8AtWoXNFiCXvavvS7DEOaNqSrmtev5rHN0Na941iWzh86RV1b+B7WL9htnZVSReuH/UUv/7xB3yza7Xu9PqZXT0rUR3d21A3vzbPcjvPcCeU9RTjGTw5HntrOOlyvbWfmZfnuVNVNjky17Wjx2cbZ4sRzy9gGeTGYdQ4UjmN57ESuaidXKgm20k6e7jU5J78w5OeXFX+Z+4yc/KBe7PlbbyxLncKx7YJCPyXOcfKcgn/hqDrsuoZAX3y5Pi9+PNWOXbLfxjkcl1IgSXbdX/6uA8l7RA+qapuLqxJnV2eeeaZuu+02PfXUU3rqqad02223xUXQLElFp9oRFRQUaNiwYfrnP/+pevXqqWnTpnrjjTfUv39/5efn64033jBd/pVXXlHNmjVL/jVu3DiayQ+K83nseXuoXz3Jq8rKrRcH93ui0UFOMC5rVicm2/1p1X7ToRN8+WzuTn06d4du/2RRRNNV2visLmrjoZCRHZkxGp2bdrYjDKfP5++UJM3afMR6Rtl/kPe4oH7J56qVEv32Xu9r1xqyVzWypJOvALMagZYq+svvNEyp4mf58IzR7PorD6TnaOfRLN30/nxd+cYM/8sahr5YsFOvTdnod954UWiRiYt2m9qCwiL9/bPFeuv3TZbzzd1yVB1enqZHvl1ZMs11aKMMG0MPGYahPcdPBpch9bPI4cwcy5J832k6/dnzXF2267jb95c3t/csDNvIAGE4AfyVwkcicCbeiAy7+9Xq2W42Lrnn7L7GdfZ1rkSzkCCQYN91zvHL9vidf/rGw25/x2mWPObiNnCOZ9WqnS4hMusczDlt1qxZpss/88wzSk9PL/nnLFWPZ84MrDOj+Px1rdS1Rb3i4ag82jgHWyJnN3DOyS9U2snIj5vnFMsOx1z3rb/9s/Nolq11RqJ6odkb13jpIMXX77V1vjks/wxZJDJtgTzs7GbgXYelkvyn2yrgtfWbTYaVssN1VJFwnObN6lXzP1MYeKb1B5fSDDvLPjdxnT6YuU0bDljXXgqlBOGYS+/Edvkcx9lmiXM0IudpGw5p9uYj+u/0rZbzvTujuDMg16HDXNNqp5r0iGlb1OX1GRrp0rGQXf7WXrNKxbD1LeB08wcL3NZZOTHyw4C63jtST+bpH18s1e8h9GNyTesGlt9HokfkeC0EKC+shvdzu0QM8zbOPVuZDzHo60yJaq0Fm6eWYRhuv2uvjVEVFm53f5FfGoefi4a4q6otSTNnztTs2bN14MABtyrNrhwOh0aNGhXllBVr2rSppOJq5fXqeVeHPPvssyVJhw8f9vpOKh6vOimpdFXBc3bW6bxBDLz8HA28/Byv+So4HEE/iOw+bDq+PE0ncgu06rmrVbNq6NVm/YnlreOsWlVKhtPwlymyc5M7npVXMvZgOJnFyNHshMiKz9JPO3GzZ+Ac5iek69r2pp7UU+NXBzxOo5cATlhfl9zMTeb3Lid/17iv9RqGYas0avexk6fWE+jVd3rd8TJ8WzClb+OX2n+ZutKl3ZxrL6pmjmYF/8IxIyfw2he+joD1cFSnhfMY+rp2cy3GfPUn0ADJ2RPv29O26NEeLQJa1t+uKCoK7sWIv0Xem3H6hYKv4/Hlgp1yOBy649LivFFIt0mXTWw8mKmNBzM1df0h7Xy1T8l0h8P+izF/1ecjEfNst/kSG4Gxez/4Y8Mh/bv3BT7W4f3ZM+/k657tu42z77PIs2ljqOxe4e9M36p+dvskCXVj5UxcBc7p6enq27ev5syZ4/cBEMvAuV27dpKKh6XKzc31CoKd41C7lkyXds4bi1mA5FrKU8EhOYIsoLXbscmJU5nDtfvTdXnzyLflnbnJf5XXSOnXoVHJWJB2Oofx5/tl3sO8hcPa/d4lXc7jmZNfqBO5BapbLVYvi8x3TDwEVpsOZZZ8fuaHNZq/zXsIqEAF9JbYZNa1+9J11+gloaXBqqqcjfT9ura4hCnQghvX/IudRUN9D2Jn8aM2Smpv6dhI/1t6+trc76NDOzO3neoFWfL/e35efUDv3e5/nQWFRcrKK3TL9AVTgcTXeWDdOZhrG+fAtxkpZpnpYKpGB8v/i9Pgqla6DUdl8hud16JzG57ST+Zr6MR1kqQb252l5KREt7UYhhHQC0db163N+ST/+y2aY/AiNHbvB7bHiT/12bWvAiu+Amqrcyjc7YTtru+tqZvtd+bpa1shLV12xVXg/PTTT2v27Nlq3ry5Bg8erBYtWqh69eqxTpaXJk2aqE2bNlq1apVmzZqlq6++2u17ZxVtZ4BdFjivVbMbh+dNI9gHUaAdm0QjU7X1cKb/mSLINbNqJ+PklH4yX9UqJ3p11Fa5UmSq2m036SH64bEr9KezauqqN2dKkhY8011n1rRuMxoJe46bV1Gy1zmY59/hPelGz9up56+7UJK04UDw59pJl2Fi8m10AuJkFsT6q+4r+b/GfWVcDAV23QbeeYvLtmwUJEaj5/cDNoLgWzs1cQucXVWvnKhMi9Je1xLTv360UB/d2UF/vsC8qqFd1787T+sPZLhds8GUnPs6elb3+rxC14xtfHM9zyP9PPK3+iLDCOplYGDXo/e0nILTvdebvUgoHrIyvOlxBFDk7Hc2k7RVrxxXWWO/DMNQfqFha6ze0szuqWp3nHjnZ7tNJ0Jt4xyO/EMkmmL53la834FjI67uDhMnTtQZZ5yhhQsXqnbt2rFOjqWnnnpKf/vb3/TEE0/ot99+K+m4bOXKlRo+fLgkxXSs6XBzXkCmJc4ud5ONBzKD7ijBebP7fd1BrdiTpievbqkKFiszy/SH+zrPyQ++Gl+4+bthuv72NsN+V6dzaut/93V2m6dihNod++pJ3Rk0S8Wd6/TvGP2O8MyGppFsPhQ8A+cwpMcXOyWTvrw9LfA2k5L59WLnxZe/WXwFeoahgIadC/TB75quE3kFfptyxEtZk9XPbNekltvYmlYKigzd/flSt2qtwVh/6uXJ1PWH9PfOZ0sKbzs+XxnbrYdPuA0vGO+9uroOdRTplBYWWT+LDMMIqsT5lzUHbM9rdjzMDpHD44VvBZtX2uZDmfp/Pu7Xbuu3tbZi/k4hs3WdWzc5gC3E3u2fLNLqvWla+O8/q3oYRn2IV8EMIWmyEq+PJzyfVz5OMF/9tli9VAz3fSGQGmWh3j7j/PYbM3H1eio9PV2XX3553AfNknT77bdrwIABWrNmjVq1aqU+ffqoe/fuuvTSS3X8+HH94x//UP/+/WOdzLApKXE2yT25Tim02YbRjPNmd++Xy/TBzG36ff2hoNYTTrGuxuV64/IX83reUF17bM7OK9Tv6w6qWoTepMd6PwXDzkPYM7j2XOTLhbt0z+dLgx6DPFxWBzk+pNkesHMo/Z2L//XxBr9u9UrKDmBfBdo+zPXe88tq/wFBuE7bxwJsr+rJ6lysHMNSJNeS4WD2lc+h4HxkbN+ettl9+cA36dOGAxlaszc9jGsMrHZHqPIKrLdVZAQ3zvxHs7eXfPbb6Z/J6t2qepssH0iSbn5/vq2mUYE8b/xt3nRdpex5tmD7MWXlFWrOlqP+Zy7Fwl3i7Lzves3tY/EEz/GaTonm6RJI34krTjXzC3pbIS1ddsVV4HzeeecpK6v0dKowevRoffzxx2rWrJlmzpypxYsXq3379hozZow+/vjjWCcvrJwBhL9nTKDVslx5viU8kmm/nV+kxPr56bpHmtSuan9mD89OWKt7v1ymIeNWhSVdnuyM3R1vN2E7GTqvUmmPP4dOWKtpGw7pfwF05hQJiRWCu5WbBWx2MqX+5vF86TWkZwt1bFpLn/794oBeMrRvUkv/7NZMr/e7yPYyTnmF/muLhDpsjnM33Nf1XLfpH9/ZQRMeuNz2eqzOxUi97LLjeAidiVnxlbH1PK/CXeJx3btztW5/cMGz2SnvXlU7sne4fD/ns2FEvmql2frdhrMyWSaQErJMP53bWW7IB//95XhPWxViwFFapJ/M19jFu5Vus41vrNk9vY9k5vp8zrj1oWAyzYqvGnuWiwdwSSZWcPh9+RXIJf7FqaEpJal6UuDPkXiv8RMrcRU4P/TQQ5o5c6a2brUeFiJeOBwO/eMf/9DSpUuVlZWlEydOaP78+RowYECskxZ2zsvHLMPsWgpt+JjHjkAf+tG4pmNdkup6E/X3c62+/355cftJO8FEoLqfX99W4BxvkbOd881zDl+ZQKs2qNFg1aTBitkzOpDhouy6rFkdjb//MrVsUF1/apRiezmHw6Gnep2vWzo21nVtGga0zWCHhPk/H72xWvHcZ1df2EBtG6fYXt6yM7UYXjeuQzQFmg6rQM9XR5Dep3FoP9617b/Tou3hGzu9wE/16XDyt62iIKtqB8LuOeD6QirWee+gSpzDuf0o7oBAf8kD3yzXMz+s0YNjl0ckPeFnf1/e/MF8HT2RqwGfLdaUtadrH010HU7OZ18c5tN95XOsXg45v8nJL/Tbg39BkaFrRs6xvHcGkodzfUF5ZQvvEYD8ifW1G6/iKnC+55579PDDD6tr164aPXq09u6NTA/ACJxhVeLs8rnIMMI3GHwQD7RwjzsXzbj5oEknQq7Bnf9etaN/l5v79FX65O8dbWU+7PaaHi3BlDjH2U8oEeyxNwss7HSYFWhm03X2s1KC6yDuv7e2VbN61m0PXa//ij6q1RV/5/CaXyouOf7Hle6lxx/e0cFv2kItubbKUMXLW/9Ak7HRorM7X1X5w13ivHC7dy/1wZbKep7yhUWGth05XUMu0ofJX4xeZBgRP1f87Tuze2okSsEdkhrVin5Hk8GIk8vX1NytxVW7S0sV70D25br9GXrt142atfmIBn91+sXAit1pp9fnY72BHjN/eYmCwiJd9MLvemr8ar/r2nQoU2v22asV4++pE+q1F8/nbizFVeCckJCgN998UwcOHNA999yjpk2bKiEhwfRfYmJc9WtW5jkvIL8ZZiP4N7ie12gwawn3hR6hvrRMmb1ldC01C6RX7WhpVKuqEio4bO2njGz36mAH03O0ZGfopT8fzNymCSv2+fy+ckXz25ytTKZ1Te24Eex5/51JT852jmUs6mE4HA5VC6C62QVn+h6R4S8XNjCdXskk2O7V2nxe97TZTpapsyyCgHg55wLu4dxinxzJNO8Iz/OlTbC/vdapTuGSEr1HEZhls6M1f8YtCb15xmtTNuofXyy1Na+d/R9qRtnfSzOztbtOyz3Vw7Zn861wq+BwKMlu238/2490z/r0TBw+gdaoSD1p3dTEeWw8X6D6OmS+Nm91jA3D0PGsvIBKinPy7DVn8rc7XNMV6lB1OC2uos/GjRtHZXgQBM550dl5wxXsIQz0+WL6EA/TQyonv/BU9dv46RzsxxX79PHs7frwzg6mpXZWP72CI7gbp112qmp7bv/SV/6QJH1/f2d1aBpch4Dr92fotSkbJUk3tDvLdJ77uzbXCI9OhyR7Hel4zuLr/Ir1bSvYB5zZA99O6WngL8fi677ua28Fm8pQf12zetU0euDFGmgyfnawVc7DLdAAwFbzDa9l3P8O9naeejJfRUWG6b4LV+na5kPuJepFhqH7vlyqJrWr6v/6tPK7fE5+oT6YuU1S8UgSV/t4meNkJ5Mc6qni74iZ96p9etpPK/frni7navvR0yXxESlxdth/qeLv3hjoaXo8K0+1qla0nVeN9NUbLzVSoiHQ55y/59T6/Rm64rXp2pvqPmSlz+34mhxM1+0WwlU7L9dlVJhgzpNydGoFJK4C5507d8Y6CfDBsqq2x9AT4Xr5Ec5eXAPV/c2Z2p+eoy8GdQrPCoPkegOdvvGwJOmFn9bpk7939JrX6qcHMu6lL0uf7aHMnAK3Iaac7ARS59WvZjp98Y5Uy8B56+FMDRyzRA9ddZ5uudh9OKvJq/f7WOq05CTzsavtdIjr9Sba/yIxEc7gys51F+sXBZ4a1KgsybvHUZ+ZBR+TrXpjjbSrWtY3nR4vmZdAd00w6fa8j4QSdBUZRlSbh6zck6bf1hV3imcncHa9ZpftTvUfOPurJl0Ujc7BrL8/eaqkzHVEB7sp8lULwYxD9s8vvzFNAPeyaesP6Z4vlur2S5ro/934J1vLRPqYuN6zgu3rorQItJaHv5d3i3aY13hzHrKc/EKdyC1Q3WpJxdN9nM1WzSiy8grV5bUZ/hPrItSmP05bDp8o+RzMWUhtCXNxVVUb8ct5bzbtHCxspUnmF2leQZH+8cVSjZm3w+8azB7s/nojNbP/VHtju+OnSoEPnePJ7AFu1jOkWbtUyTpjFY4jVLdaks7xMb6lndKlSav3B1VN8pkf1mjP8Ww99b13+6D3T5XYWPH1IsfecFSey5z+fCgjuF7fgxkyxu86g1yl2a6x86yMVc2gdk1q2Z/ZCHy/TFl3MLAFTonk/oiXEudASyyCyXSFq6q2VPzSMdBrrcDiWeH5nPM85AUBDk3l+iw9ZNK/hSd/P8WQlBbh3pH9jeNs9uLJcNmlwyat10gfY86n+alWayctpvP5+T6Q2jNv/r5JkvTNot22l4l07OF63iXE2xvNMBs9b2dA87se28MBPK+de/SyV6er48vTdOxE8UsdX9egv0Psr1OwUGw66LsvCVdBlTgHvET5ENeBc25urg4cOKDjx8PXCyaCY1Xi7CqUPJ7nde3MqExYsU9T1x/SC5PWm6bJVbpHO9oh41aqzYu/B/Q22y0NATyHgn1mbT6UqcM+ht562yST4ZmBm7Bin277eGHEho6xo29b/z0eT1y5XwM+W+w13V/1q1AfOr4Oi602gx4ntGtaj504vb8DeXmUH4GeeMNZXc9OdbhACzYCvTZ8nU9P/qWl7XUYMq+q6/zOzPYjgQ2H6PxdkSzoiWUpuKtAk7EvLdv/TB4892Mo57Vh2Hvp4LqJHyz6SvDkec0HOiKc6zk4YaX/mjP+O+Yy9LHLmMzBCKbzL1dmL6md69x5NEufzduhEdM2mwfg9pOpCg6H7fPxmR/W6M3fNvn8vrSHmq69rQfTPCJeZeTk6+fVB5Rts72vGdcS+E7/7w/l2cxLOE9PZ55q9anOunxdHuEumbX7vNx5NEt/eXu2rXkHf7Xc71CQN3g8dylwNheXgfPHH3+sdu3aKTk5WY0aNdITTzxR8t0PP/ygm266qdQMWVVWOK8f8+GoXOYLJaPjY72+xnY029JnHqXSP6zYp5N5hUGPsztzU3g6kvFlz/GTunrEbHX6zx+2l/E8BI+OW6kF249p/jbvHmR9LRNuFzasaXtez3Nk7GL7b+8DWa+Tr98eVDziskxGTr7LZPsrC7Rkyo6gS5yDzDYGulyViubV5SXpuWu9q7X28lFtNdlH52DO/e+6GwzDd4am5Rk1fKYnGA6HQ5MfukJ1q1UKasxpK/FSXS7QdNz35bKAt5EYxox/YZERcJqtXrB63kc8k+p6Tew5ftLvtuxm4p38ljiH4Tx55NuV1tsw3e7pz2YvKpxTXF+AmiU1oGPlCOye++4Mi/xiAA/HYHaxazvTSCgso1W1B3+5TA98s1wv/LQuqOUrJVRQXoF7oLjVpeqyNffaKkdP3Rd8nXPhbmfuehSt1u1ZUOTPAos8omQ2EkV8PHviTVwFzoWFhbrxxht1//33a8OGDbrgggu8Tpo2bdpowoQJGjduXIxSWT7ZfahFIo8XaAbDyfXcyfXzps2XLbZvtMFZtTet5HMkq3vmRyBYcxXIA9szLXuO+y6ZmrX5iFbvtTc0Q6DBYzC91Lr+9dJk9xoQ4dxuoIJ9cAdbVTvQ/Nn5DXz3cN2/YyOvaeG4FKwC59s6NTadHorWZ9XU0md76paO4V13/FTVjvw2Kldyf8FitU1/53yhYchXzWvXZ0qw55rncq5/2yltd1b7tctvG+coHB+zNLheY2YvBZ3fu71gN123/XQ45H94LrsCuZe5Bk4vTrIX0LUZ9rtGTPXunDJcXJ+nZSdsVklBwPfLgxuWNq+wyKsa868u4zlbMQz3/mWePDWMlO8S56CS6NdT41ep25szfTbPC1RigvcZ8tu6g3p3+hYZhvdrgTh59MSduAqc3333XU2cOFHXXHONdu3apTVr1njN06xZMzVv3ly//vprDFJYflkNR+VaRTiU68zzpuR8oDl7TS6ex2UmPxtzvej322hDFgv+2lfderF3Jjwee54PJEUFRUW2gwGzqt2++AqSfPYPZSMJnvO4/r1uf4bNlLnz9dOrBzDUkqdg34s4j9vmQ5nacMD+7wn0FLQ6ZxMDreNqkyHf+zrx1Jv1UINBO2lvfVZopdvxEjhHo+Q7uZL7NWBVqugvOUaR795pcwrMX6SGcmt13ZSddrNfLQysps3Oo9al2NF4sWF2DrhOKTCJZp3LuAaopusJJHB2OIJqCmC6riCXs2pve3nzOm5/j/Qxbnk4uN4f4uNOEZrcgkJ9tXBXyd/hrH5+MoBhnszP0cDyGKH639K92nXspKasDa7vDU9mz6v7vlymN3/frAXbj4U8nnV5EVeB85gxY3TGGWdo3LhxOuOMM3zO16pVK+3atcvn9wg/s7fGToddqreFkrnyvCmZVQd1ncVzOBBP7mPYRf4OEFwvsn6+N5khFmFzm0bWVbED6WBlf1qOpm04FGqSJEmdzz2dQfHdntWcveGoPEucQ39wRqJzsFCqihUUFunqEbN1zcg5SrXZTt4sEK5brVJQ2zc/dYI7y93eqxm+2ziH6xryNR60qw6BdGhmIl4C51wfwWY4eY65blni7Gdd24+esHWtuW7ji/n28hWHM3N0MMO9WrfruMK+evIPhWczJE+xesYt2n66+qdpzaaSSaevOrO0pmXb76Mj0CqqVgJ5dmXl2rsGzqzpe1z2cHNtV14WhqZ6b/pWPTthbcnf3tWH7fPqbNDm7jEMw7RGg8+8RLj3ewwyeYczck1GESn951MkxFXgvGnTJl1yySVKTjbvudcpOTlZR45Etu0p3DkvH38Pmeb1zIccCmQbdud55deNPueTPG5mMXob74/rjd1sz5o9CKNd4Dzl0S768Z+XW84TaJqKx8gO3ZUt6pV89l3ibD7dTjziOcuWQ6er7gd7HCKRwQ12ne2a1nLrfOqLBbtsXSpmv/2h7ueZzms2dJq/dQV6wT5zzQWmawi03XulRPuPxGF9L7Q1f6g1RBZst26XFi23frww6tsMpar2+GV73fohsOOgzZ53O/3nD01a5d6hl+u5kBTAeRQu0QiazO6Z//rhdM3ArYe9X2Y7l3HvC8V7Pfd9Yd0mPtDf95cLfRe+uLJ7eWbk5IetlDuc3Eqcy0CcM3er+zjrZtWLg2X3WBsyr60Stc7BIhQ5W9bgkeH12I1AP6ZlQlwFzhUrVlROjv8H1+7du1W9uu82cwg/u71qP9i9ua31mWU4DcPj4Wja/tL/DWrn0ayS9Tmdf2Z1/W/JHl3/7tyghxFyuqn9WXq8Zwt1P9993NXgSpytd6jZOqP9MvL8BjX8tmEOLE2BDxPji3v1v8CWtfOw8zzfHhq7ouRzsEN/+ExnCAd27b7gqo17Vo1tadEW2ZXZeetZWihJj/dsoZ6trDOwZutaZbNdu9MN7c7ymmbVq7KvjEkgtQLtnsOxbFkR6r3OVaT7SZBMhn+zmNdfagwF3z+Gq/TsfB094X9UBte0x6KSQDQ26e/5u2Rnqvcyp1Lm3uGR97K+OgF18rVPfXU86Hlfcb13u7IbpCzb5f3b4sHPa0632w22hLBaCM2EIi2cQ6zZfTHmq3+MaFXVfuXXDX57wA6G9YtI73sIJc7m4ipwvvDCC7Vs2TJlZvqugnv48GGtXLlSbdu2jV7CUHLB+Ss9qVLJXhW1lc/1NJ3u+hAwLYG1sW5nKYPrja9J7WQ99f1qrd6brld+2WArjb4kVnDooT+fp/ZNUtymB/PW0XXc0G1HvDsiMw2cI5gT73JeXb/zdGjqXfU0kDQV+XgohcpnVW0fm7LzEsZzFtcqgsH2YOqzFDSotYXGMNx7Hk4yCX7NmP10s3PAzj4ym8NqPF37DJ+Zbcepn1nDY+z1QKpt2o0jI1V6YMf4ZcF1rONUKYRqkv58Ome77vtyqVtVU89r2Ooa9dvGOUz3mTYv/q6OL09Tlt/A7vS2YlG9PhJNQDzZ2Z1v/OZeE8yZLNdrK5jj4iuQ8PWyz/Na9qwh4BSHXYYEZLhLJ3OUELrzPLQ/r7bZOZjMr6cPZm3zMX94r73Ve9P1/kzzbYXCb+Ds2TSNuNlUXAXOd955p44dO6bBgwcrL8+7vUthYaEeeOABnTx5UgMGDIhBCsuv0w+/8KyvaiXvN5yGDC01eWPtNo9XVRLf1Wlcv3J9OE5YuT+kjLkzI9zOo+1iMDeZ+79eXvL5zlHeHWGZZTAi9Zx3OPxXq/W1/UAyH0WG4bPTnkC5Zo58jWPt66FmJ59pNY9riXMg1QjjpMmqJGd1ZvcJdn6LWTBodgrYCUTN5gn+9HCvtujvJYVnSUsggbPdYx7LjPnMTYdDWv6CMyNTs+vKFvX08s8b9Nu6Q/rFtcTMRsbt/Zlb9eWCnX4zq4Zh+H5pFsTtf8dR6zG+XbcViyHEonFfsfO73pvhnuF3PqNdr4Ng9k+X12eYTh/ctZlanlE9oGYWriJxfUbz8Lse9zh6tJR6ZtfToQzzmieReGFhVpASKqvrrsikV22Yi6vA+d5771W3bt00duxYtWzZUoMHD5YkrVq1So888ohatGih77//Xj179tTf/va3GKe2fCnpHMzPfEmJ9jtFaV6/uD10raqnSn08Mrp2SjEnrNznNc0ZlFndJMYuCW5c5+J0Ff9/efO66v2n050DFRYZYW9nZra2SGXEr7uooSpbjLdrtf1AklRcEhTAAjbT8vFsH2+DQ2iXZDWP65izv6+339nZgXTzdnKx6i3dPW4OPhis4HDogauaeUwLbl3hOD0M+e5V2XVft3OpORLIEbBbqhjLAq1QS7sjlZFyvU+69nTr+T7T83zcn5at16ds0tCJ6/z2k1Bk+K5xEEwW0Ub9lNOfYpADjUrnYCEs63ouBrMeXy9GaydX0m+PXanBXU/fe5rVS7Z94dl9WRbQMy5GIUiw+Y8TfmpTlFpB3v6sOpY0U2QYOiuluEM4z2dgsCLRZ4HVGr1eois2LwBLg7gKnBMSEvTLL7/o/vvv1/79+/Xxxx9LklasWKF33nlHu3fv1j/+8Q9NmDAhLofkKcuc108gJTL+THmki9a9+BfVq55UvA35D6g8H0iLdxz3nudUYl1LFTxTvWyn93LB6HHB6fabeYVFtoc7sCua9y27mzLLjAdyXnw6Z0fYqhW67p+8Ah9VtX0sa6fU22oW12rIq/ema7vFG+LUrDx9vWiX0rPzNWjMEtN5YnFLsyqVtWJ2/3U4vNt92/lNZusKx3lv9YLGdYtu525ANSfszReL41pSrTXEbds5Dn86q6bfeVKqVvQ7jySNmOY+3q3nPna9vz7zg/dwla6K2+wFX9skUD+5VAWOl57Qwy2YjLTpOM5hLKFzrtf1VP/9sa62T/2IXJ8xOvyhnHa+XkxEW7xcOYGc667P0T9fYK9TOr/bj3a1e8O7TwziZnNxFThLUuXKlfXee+9p7969GjdunF577TW98sor+vLLL7Vr1y599NFHqly5cqyTWe7Y7RwsEIkJFZSclOjxJtqlxNk0He5/bznsu12w640vnC9arFYV7jd05kFNhHpctJt20/at9rfz/fK9YctYuu5vX6Wbvts4B7Z+T57bs+px9b4vl+n/flyrf/+4Rqlh7OwkHFwPxZ7j9nqNNdvXFRwOr+ss2BdtViU2n1o0J3A9XIZFJ3Su6XJNYSCptXutxyKGmrW5eNQJu79n1Z40fTRrm9d1aefwvdj3Qr/z/PJwF7e//fQBWcLqPjHVTy2PIiO02iaSRw0oP/P+sub0WKvO9RuGoRd+WqfP5++0tT1JmrL2oAZ8tthWh2SuolI6FMQmzHrVjkRaXdefUMH7XuTLvK3evdafXaeqyfoDaMZhe85wC37LJ8I00kU8CTanFGj/CK61WxLD1J4xErUWrPJ40zce1iKPgijiZnNx25VevXr11L9//1gnA6c4L6BIlPQ7V2l4ZHRMq3B6XMlmqXFmtqxufKH9DpdMt8dqwp1JNltduNqZe20r+Lg54CqhuWHo7VZy3z8VA2zjFmqpd0IAB2LxqRoOru05nQ6m56hBzcqx6RzMYwiK539ap5G3tvW7nK927p7XQ7DXmdW52KPVGbrz0qb6cuEuv+vxdQ9wTZbruZgcQO+ydl80NUyJ3piuTs6k2X1B1fe9eZKkapUT9bdLmga0rQsb1vA7T51qlbTxpV6asvagHh230namMJTqilYvTjxfsPgSbPtR53LLdqVqzKmgecBlZ9tadvBXxcMy/b+fN+itv7YNeJuR5LyeDqbn6FBGjto0TvG7zOmX7qF1DuaLo+R/h+n0WIhVFdfSUNEhK7cgoPtsLBgKrKq2odM12ALJF1gJS/+YAZiy7qDXtLIwLngkxPfZi7hht41zKAyLXnCdPKvXmuXLnetwy/R4LhdMAi22eXpDIazYhGnnYAEk3jAM28GL3cysWdtCR4B1V8LTa7J7BrjT2bXN5wmhuqbVw9OzNNXOM8Yh71PkcGZx4BwrwbzZNust2+FweO2TYPMQ/h7YdsbJNQx7geMZNSprzb50SYEFznZP4TsubaKjJ3J1Vcv6/mcOm+LfvTTAIXQ2H8wMqJR12pCutvq1cMihpIoV3F6S2hFMCbhTraqVfJ7Zzt+4Ly1bb0zZ5GOu4DOOzvt2KOPVHwuw6mw08rjOw3HpK39Ikn579Erby7geunAm1dfzLZR342adlwYiVvFGKNuNRpOSYZPW67N5O3Re/WqaOqRrQMsGU0stlBe3gVSVLigydCSzuIZIuJozTttgv9+USCkNL2JiIa4C52HDhtme1+FwaOjQoRFMDVxFoo2z2TbcOwfznqfQYwyYAtNetQ23/yWTG0CYfobnW+5wV68JtUpzkSEl2Pytdh+66w94jxkc6O7MD1cbZ5f97WsotECrazpLqSpUcFi2gw6mSpbZ2mL5UvfH5ft0ztXJbtNsvQAwuTi3HznhtU987aHECg7Ta7ckDX6276sHXfeSRIs2zi4Jq5R4+o9z6yabzG3ObqlSUmKCnu51vu31hkMo55TbPvNzv69o8+ZS0g711AfbgbNxukZGoDqfW0dr96ebfvfN4t16tEcL3fnpIu1P9z22q2syA9mnzntIKM+DQB+1RYahmlUqug2Z53R/t2b6IAzD23j+npV77LyY8d4HYS1xPrWfwlkTKznJXieng79cprf+2sYr0I5ViXO894v82bwdksyb2FlZtSctqF7To1VV+6BLh5/+hq0rXeL7fIqVuAqcX3jhBTkcDt9DiJQ8dA0C5ygz6+AjXEqOq+SWiTGr/lvg8Rpwxe40r3nMSpw9bwCh9DbrumTj2u7VMIsM6enxq3Uit0Dv3t4u5Krtk03GHZy/zbtNli9FhqEEm7/Vc2ieQAT6OyNR4hxo6ZDZ/IZh6Ib356mg0NDkh66wrM6d6DHGrZ1dYJZE56RYdHj43bK9XuOgLrNRSmmW0i2HT6jVme7Vdn2N4zz98W668o0Zqlutkun3KVWsO5Oyc6Q9x6h2S5dbG+fTn/9z459srLlYPPc4GkrKAilxtnsfdc7lPB3sZvBfmrxeL01er9s6NdYrN10UYM/Gvl+cvD1tix7t0ULbAxhiKhCxKKkpHn7LfMPhGo87mA6LnPvCNWWLth/XdW0ahiVNTl7NRMK6dnNT1h1Ui1nVNaRnC7fpwZw3gdQO86XIKB7XuUntqurfsXFI64onR0/kqnFt73bnkWIosCEzXbMz4aqqHQ/i+BEXU3EVOI8ePdp0elFRkfbs2aOpU6dq3rx5euCBB9Sxo//xZhF+wZQ4v3t7O63cnabtR7M0faP3uKKua5x9qlMbX6xKqZzW7k/XFefVdbvxhfMGkJ1/umfXDk1r6z83ttb//bhWkpRXUKRxS4uHunr6+PlqYtLJSKgycwpsl8LYydx3aFpLDklP9moZdJoCfVbYOY6B+tcPa9Sr9Zle031lJs2SkHYyX6v3FpdSHcvK048rvIc7c+rZ6gyNmrvDZTunv8vJL9SSncfV6ZzaEa2lEQ6e54idtsNmv8mzR23J98uAJnWqascrvX1+f/cV51pu3+717LONs48/nD38h7LueOcvgx7IpWn31HZuzxloB3r5j128R6/cdFFAyxRZBJKStPvYSVvrCMbpzsHcp1dwRC6oLu5F3Nv3918mwzA08o8tYd+mnRcnZvtw/rZjYQ+ca1Z1fwkXShAayDE6YNIpZKAlvwWFRbrh/XlqlFJVH97ZIaBlXa3YnarR83ZKUqkNnH1dctF8jnrWfvQ/v2tNyfh+3geidD7hIi+uAucBAwZYfv/cc8/p9ddf17Bhw3TvvfdGKVWQgi9x7ti0lq69qKGuvaih7vYzDI9XO2STbY1fttfvNl/9daMGd23mVlro+SC0+h1mN8xz6yVr+5H/z955x1dRpX38N/em94QkhJIASQi9914EBESaNCsg9i6WxQ7qriuuqK/rqrsqiAVUiiIqKgiIgAICAiKd0HsPhECSef8IczMz98zMmblz752bPN/9sObOnPLMzJkz5znnOc9Ttjoxf8MhTBnR3HPuxna1PIrzD5u9Pav6g0NnCrkUZx4RrmtZEze0y/L8zk6NNVyJUWN2BX+HSVMtLeTP+LSGt2qrnnVFiJi9Vru9RYVrr+I8PmsD5v1xENe3zUTd9HjNdGXyXXm3dFNZp0pshO5+SSvWF6z3J8zlbS2kN6GiN8CIMTCV5BmY6u1xFhQrztZw8v4vvaZdXCrqmlibGfRrWRSo8Thwkv6weO9MeTY2MLfs+spia0JwoKU4C4JgeRbXqF8uFeF1X1tmJaFVrWSIooiIMBdiI9w+efW38k2Tsij7BvtfnhGta2L59uPompcGwLf+lDUG0CpPmgQ+dKYQD81cj7Gd6phemd9w4Aw2HTiLTQe8t0EZ0aVuKpZtPw6gLOyhVZw8DygI1rZGWdVhRZhzzhUq4ecW/XUU3U342rArbGhFw3HhqIx4/PHHUbNmTTz55JPBFqVSIX0IzM6myZMnaphfehRnjvJe+V7bkYsaPc+puv69GILIO229mchnv/qTVzyf4O3OeAY6aiuAbx/sgnpVy5S96HC+vV5mP1DqOnccPYdfrnz8zcBlsqtxnOebYEaplBcnxXSdsWofdujEd5bn89dEdd/GGfjnUH4TZB5YYxiWiZrVLRGW96bJnsJDn63Hbg1FQ16+1ZUMJ6846ym/RmIbRTaQwzuWlRRsKflfh8sVBDO339yTMnY2aVhCgEy1W9dKZh4/U3gZT3+5Eb/vOYmc9DjdMkpE76fuWekXBGx9oS9+f7q3OcFU+BLHWY4/Xp3IMDfeublV+SSwD/0pSzytdir1H898+Sd+230Sd338u+n75Iv34iybTJidvj+a1wR64rUNAQCpcZHWt+TpTLqykFvQOdkTNY81mRznXklwCTnFGQCaNGmCX375JdhiVCqkF8jspJ+845rQvz7a1knRDnejekt96X8KiopxTBYH03vmXzsv66PndjnrVbHqlZbFTpViFxXuxow72mNMx9qYc09HW+Vh0a5OCnpN+Rk3vf8b1u4t31+7Ov+kTi7+erXS8Hzg7FBmjWZtA/Gd1bsOK9fIGpCEuQWmaSoP/1WZJxops5eL2TetbZ0qit/jP/+DmU5evJk+TRGL1sGz8XptytDSQq44G9RjdtJBSi73Nl14qUQjNR81kqIxtGUNr+NlnnF9e0ZWlYly52ByeURtZ3kaFgD//G4LPv51L657e6VhP/F/i7Z7PVt5qYIgcFsIaGFt7653Xjv7vFgND9i++DExI5/Upk+el403LNdsHqOQaWcKL6Pnq0vw8oItuuU4WN+DAO9vi5xc2aRSzeSyiYQaydbDAJZFeOG/IfM3HLRcl5NxcpsIJs7SBjjZuXMniosrkuc652NHOKr0+Ch8fmcHDGquHORofeB8mQFt/Nz3uO7tFZ7f3gMKPVNFb3i9xyrqMMjiU4xSzrw848aiy94D15TYCEwc2AgNqhnHaAW0B348yEUc+p8VWL6jbOV5+DsrOfJymOxqpOHZZ91aI8QVC607YDR5MeLdldh38oJOCb4hwrdBJAtW2y4u8b5OXsWqT6MMw/LlfLORPVAZ2kLZt1zSiBcut5wZ0aZsL2DTmolGYioGEla8vAYKEWX7HVn8uFk/zIn8fTGyMDI/6eKdYeLXmz1/d8yp4nXeCK2+p9TAVJsHvZCG+vlEbD9yDidUk7da74NWF7FTtqWFp34zE8RWsBKdgu0QUXnw3EXr5uNq54Z2YKbVsOYizDuqNJVcsy5WO5q5ai92HTtvi1f1oCHov8spsv3t5dtBRDSuYdynszDrVft4QbmJfEXSNZ1uhRAsnPvlZ3Dq1Ck88sgjWL9+Pdq2bRtscSoXV94ffzhokIocq9oDbcWDpx2wOkxL+2sMRhU+fSx503FUcskGD9fhbhe+uKsDPr29nem8ahlfXrAFG/af5sxrPY2WUiWnr0qhKytPxMe/7vHyPm3VJLykVMRjs9gro3YgivoD6OpJ5mfmWf1ANUY5VrsLI4WtcXX2gMjlEtDtyh5HXjrmpOLnx3pg1l181hXPDGiIpjUTcUeXHFP1BBJRFHHLB6uY51jhiuSYcg52pY+7qX2WQcor6TUe664rVi9G3xfW6T0nLihevvKtP6KpfYpqft11Ajf871fPbzPd9e7j59H7tZ8xYc5Gz7FSUdRUNLmUYo56/T/QVZuFGefgMdU2apNW4Ol7YjRCGJr5OEttdq0swkcgV+qUK/nlP/KPn0dJKb93aKeoSCw59hw/rzvRLTfjlp57qQg0qGZtUkVE6Oxb9ie04szGUc7BsrO1PakWFBTgxIkTEEUR0dHReOmllwIoGfHnlXiYe04aeyI1i9b3zc53Vv3x1rO8Zu9xdumet0MmU3k5O3WeZNUSzSlOOWmx2HnMe+9oGxOrs3LUt2HD/jMY+O/lfHl9SHNRttJeeKkELy/YgrZ1lNfAGoj+vP04nv5yE5d8AN+g+NR5+weOcvQU0X0W3mlWcQK877W/PIzqrfZaqdKM9/txnetgXOc65isJICWlosIcWo7U78z+fT82HjiD567sCZQwE45KGq/WrsIX/1qrvJ6vLsX0W9uaMiPXlqks/rrZVSOJklIRbpeAUf/91TixBmvyvVf7S0XtyTpWP132PslXE82vONuNFV2ClcXbWacfJuQNzk8Z0Qzts6ug4z9/8jpn5jJZovvybTcblkpel7za7v9aggFNq3Gvujp5b+7Erzcr/ADERLhxQbbF46FedfH7B6cwukMtz2SeCNHy+1AWytBa3rQ4/sgMTsfBTSKoOGrFOT8/X/PfmTNnkJmZiZtvvhmrV6921IrziRMnkJ6eDkEQkJubG2xxbOe9Zbvw4coypwJmvSHzfED+uBL+xyuvjTN+3mLomGozqrUSm8/o22cmTqAa3pw8939Cv/qW5bADn8wpfVhyls9gv71kB6atyMc9n6xVyeadb7eBsy+v6rnSiH5zDhYdrh/Jm6fevKpKx0SslUHmADIIs/b+uI2NqpdtWejVoKofSi8nPkp7LvvFwY25y9FbLZHOPfLFH5i2Ih9LtipDAJp5ZOb3OGunn712P4ene446rvy3VDS3T1Gi2aQfcOgMI8SQiaJY9U7W2WNql1LsbUlt79uglvPfi3eYzgMAnXKVJvn+eGeNmubQljVRPSma+f1jmpdr3n/visx2e/LkZpusIq+qBczfcAj7T5VPjCoijaiEdIyOpHED1qgsvOTUSYvF5klX46lrGsqitFgX4cKlEkW7rWvgmE9OIONN+xsy1WbjKMW5tLRU819RURHy8/Px4YcfomHDhsaFBZBHHnkEx4+b9wgcKrz4zV+W8/oSr9fOWL++Dnrk++jsUnCMZIqL1B5E8w4IedKlxEYYpvEnvjxmX1qIfIZ9NyOm67FzRcyHZHYShXdvop/0ZiRGhxuEXzMu4+PblCb4rPKGtqjpdWzFTv/0i3/rqz3Z44/Vq2lj22LitQ3x6vBmtpctJzlG+12slhiFr+/rzFUOj+IsceqCKoQNy+xZo5GYVpwNzhv1BVpyJGv0YVbMLQuKivHaj9t8KovV774ni/nund5amWrU92cVw8HiSB/i+6pF2MthrSLJJM+r922zC95JA9YK4cYDZzBtufJ5rZOZYsth7nHWqGvviQvMyUSFubWWoGC3f3m72MWwBGtaI4lZtro9BWt1ccfRc/h8zT5Tk6xqWUURCHOXqTNS/y+K1hW/LYfPYeXOE57fba5YolXnCAFakaAVZzaOUpxDkUWLFuHDDz/E7bffHmxRHIkvq4nFNm5y9nIOpqdIMI65FeGo+Or8bfdJFBUrHW8VFBXj8zX7cOr8JcN7oztQ45RBFI0VNztXjazgm/JrvXwjxz/X/N8vzAHtuSK2Cewjn/+BRX95O17iGQ/48/sU5hZ0n7HR43znppZIj1cOGFhtwIy5s6/UTo31hB0Z1Ly64pw/JiDS4iMxplMdJMawQ+rZhd5ATxDAFbsd0LdmUfc7apNu+XlJ+Vi37zRbJo4RhHz7g68OE7XO3tal3HRemnAtM9U2lo8FK58ZCyHTK442jVB5irm+Hd9+dBa+xHFWlqP87Q9rG1/LnPj1ZjR6dgGeubIt57WF3pMpAPv7yXqeH63MR9dXFuMJ2b53Flpt4Y2F29HhpZ9w+MxFVfryv7ccPueVTy6evGzv9hwcLanXlJ/x+KwNmLPuAABr+93liywe32Awr/jFyva8T5FNngVbgfSHDwAenBxyMZiQ4uwDhYWFuPPOO9GwYUM8+uijwRbHkbA87fJy2Ye8arxN2HTSslYZZV8f3pXwR7/4w/PRlXhyzkY8PmsDbv1wNZfTKF8pFUX8bBAf2V8mwrz45F2cx6u2RhJR4285rA9Hgcbe0eMFRRj34RrdevSw+hwkL+RahLsF/XBUBqomS0nWyqFejfdnGLfRHWtj4fiumDKiueJ4sNuzvxAgcIfO0us71Kf+9f1WxW9WzosMz/tlMnHAWMHWTKrzsvz35524V7WVQiKWsYIpQrS8VYBtqmtmRcxcvZp9lOw4zyCW59tkxdFluUDmr00SSd5Xq8uw26TcLs5fKjGMfctq06xb9OoVReyzNft0y9N6hK8t3IbDZy/iLZV5vJnJJsXfDtOJpCgA+QzrLzXpCUorAfl7LreSMXuJfx/SROOMKPv/wDNJFnkgkDisiTgGRzkHmz59uk/5b7nlFpsk4WPSpEnYtWsXli5divBw/65EhCq+KH/FNnh7lvD6UOt8p1kiHz9/yfsgB5+v2Y/Jw8rNO+f9URZGZ93e04YDITu8XZeUijitNsVU4WNoT5/ZoLHHnQdvky1vxypan09Fm9B4FOq20DUvzfTedN6BptXB4+aDZ3XPh/movLJWVLRWsNXHfRqkGyAIAnLT7Q9F41QEgX+bgF6/qw79o7agULTXK9VptSHpefNaoei18RMFl3T3Mf7jW+09wqz2WFpqfcWk8LL35JiZiVyznz2WnGolmHUpaidJPFjx1yFRasFpEuvaAuH6IFATaKzJCtY161n9yN85I3VP/fyM7qXW5BHL3DmYSNULgrEs793SGr1f+9nzu0Sx4mzeOlAiIZqtEknGj0blyR2Y8dCsZqKmjx9HQJozE0cpzmPGjLFkBioNlgOpOG/YsAGvvvoqxo4diy5duiA/Pz9gdQeSH/487FP+QDjA4ipLbRqmp6QwKv5Dw1TRF8QAhNsSRWD6Sv0Zc//trvU/XsZmoveAiWc1h0u5vvI7J5XfUYia1LgIRcxHebnqeLQRbj6F16jLDA9z6farRvlZp7XG3mqR3T7E97aOdp3VEqPw3YNdAiiLOfS6S0EQuL+PYTpt563FO5ESq+35lTXO1opj71GcdWRpL4/NrJPwFwPLCT1Yk1MirH9/Ll72rXM2q7Cz7vmy7cfRSjYQV5eZlRKDnx7phtynvjNVly+TWSJDDsM8ovK/sFCGNQLT93z62178Q7VSybq+k5yT7x+t3IPbumhHmFG/i/KaqsRG4ISqHuWWJG0Z/f1EXtcwdffUzylARkIU6lZVTpjKHXKVh6MSbXNuJZVj9AzNqi9ZVWIdrTiTczA2jlKcn332WezatQsff/wxYmJi0KdPH9SuXRsAsGfPHvzwww84f/48brrpJuTkBC+GZmlpKW677TYkJSVh8uTJQZMjEOg5NOGhTipfmBIWdn5ceWe4C4qKselgYDoyrevr1SAdC/86ylXGTgMPz6Wi6BVvWI3ZcZTdw5HIMBeKOGIqs9D7+JeUinj0iz80J3/kedfuOc1MIyVxuwSUlJZ56a2WZM5BCI95nIiyWNhy1Iq0FkYrSCkxEbofQCOrEGboKU3FWXkN4UEwZ9AbvPRrXA1JOg64nMzekxfQMiuJK62RE5sX5mub/inaypU/tbx9S/d6aMsaeF6jTHnoLn+0hjEdazNbtyiKmLP2gKUyff32mM3OY5Wifk1LRVF3ggQAajBiq/u+4uy7xY0ZnyNW4S2T92oGN6+OL9cf5CvToNDV+ScV4RvlyV/85i9dxdnlElBaKmLstNXISIhS3MuqCVEMxZm9ms2adPYXoiji9YXbjVJxycFqvoo4zp46zV+T1oKKVI6RFaBZSwqnL1nY6GaoQuEoxfnGG29E27ZtcdNNN+H1119HSooypuqpU6fw0EMP4euvv8avv/6KvLy8oMj55ptvYvXq1Zg6dSqqVKlinEFFUVERioqKPL/PntU3tQxFvry3Ez75dQ8e61vPchl2duRPzlU65ND6qA588xfsOu7tmdIfaA1AOuemGirOUs6R7640qMNYDn/F2uUlPioMRYxVWC4Yg0kp+NLCv45g7jrtgbP8/h8+e1E3TdgVxXn5jhO4rbP2oIYpoq5DFikRYyWBs/3LBw3PDGjopRT1bZyBBZu0LUeMBtLs0FPsPGpd3xUMxVnvnNNHKjocOOUdIkkLq6awC/86int6lIdUFA0Gs1LbSIqJwON962Hygq1eaeSWE/7oa8LdAhKiyrdKSRNxvnw+fPUvYX7F2fx2Ep4qWJYCvinOFiYFVP8FAmMmrHeVs+/uaLq8LJ1Y5Z+vVu5bNrqe4e+sRP4/r+FO/+uucg/PEIHNh85i6bayMHLXNKnmOcXalqUwAxfZx8uK9Z/mzPM+SeIYmWpLfYhL0OjnpD3OJmWU51Wz4M/DSIkznnA1WqTwqi7I36NNB/QXiWi9mY2jnIM98cQTSE5OxtSpU72UZgBITk7G+++/j6SkJDzxxBNBkBDYu3cvnn76aXTr1g1jxoyxVMZLL72ExMREz7/MTOvhIZxK88wkvDK8mZc3XjPY5WmUhVZ/FSilGdAe3IaH8b+WLLNfZR3G9zDYe5ztDEclv9zzGt6vWWm1kGSTmzeOnbaaUzpGeRoXe/BModeKMy9yZaRL3VSv826XvnMwo3qZK84aadWDcjNmodWurJJ+MKY1dx4W+o7QQhveV8XqiumB04X461D5RK5UjNY7Kr/Xbo0bLz/sj/svKe9z7umIfw5tgk65Ze+AL98PX1ec7TDVNiqzuknLFwlfFGeI5icV2Huc/a+0afUDEW6XwgSeG51n+vjsDcqkNl+Pd6x1kfn3wTPeE8BajyuQK848WyZ46z9wumwCceLARszzUn9g5f3XejPOXSzGu0t3mS7Pan2BYpmB41h/jsFDGUcpzkuWLEH79u3hdrs104SFhaF9+/ZYunRpACUr595778WlS5fwzjvvWC7jiSeewJkzZzz/9u3T97IYVIL43vjTgYjdKx8PXFXXdB6twZVVBYoFT8cXbI+mvqzuqK9PEUrHBw++6vJ8GWwqZ/nZaS5eLvUKOcE7+JKLplZUY66E19AT32iQz3pXNJ2D+eBV+7sHu2D23R3Qs35V7jwsgt2efUF/pQWKlVX9cqy/U7Nl5s3lq4Xs8uTtgM+6xbJYOmWWFdoyKxmj2mZ52rovYz5fokEA1pyD7TpWgBHvKC2IRMbft3Wug94Nq3p5k+cl0KbaHq/aCmVPmcYf4/MjZ4uYx622QTPP1NexyymVubViSKCS3+h5aFszWJHMGqy61H1U4RXP/bxyafWFHlNtRh1GBNr6LtD1dc1LM5We1GY2jjLVLiwsxKFDhwzTHT58GBcvsk0r/c38+fORlJSEu+66S3FckufAgQPo3r07AGDmzJnIyMjwKiMyMhKRkdrOWYgyQimGXPPMRNN5tK4vKtyN2Ag3zut4Sy0VRdz03m8cdRjLEWxzIashYwB9kz8jBYpHMV28pcxk/qxGCCoetFYH1OxXmeLyNn+58hIZrpx09N755Y1RPaycWm0mRbV/2MyKc1JMBFrV8rY0MovuinPo6tS6rblJjUS8N7o17vlkLX7fc8qngbs8lqmEtqm2LA3H+8Q7qTG2U21MXZ7Plda7iV1ZceLKzcZXU20r4agemLkOmw5ob9uS+o62dVLw9ICGlmXzaRIQ9sSoDoRjqh83H2Ee1/NurYVo0tHUNkY8Zd3yVWW3eOFHhSm3OkST/D0y9KqtNXHL+HYeOF2I+z9di7Gd6uDaZtW5ZOeB532SzJx5vGrr4VFGLWwrCPTnIdD1pcQoJxuM2jStOLNxlOLctGlTLFu2DAsXLkSvXr2YaRYtWoSff/4Zbdu2DbB05Zw+fVpzxfvixYuec8FS7isK/nxn2bEXrVdoZYVC61viEowHN3tPXuDyQsvzwbJbmUiMDvdaPdXDlwkSL3Mz2RGj6+IZAOqFx7GC3mSIGt67Ei1TlqvEsvdh6Ydf06/JTDiqPo2UE4U+mYVaJKSVY4tK/+WSUlRNiPLsaVU/0xcGN/aKKa9F1YRyE2CpT5SKS4+PxNFz5St58hWTeK0VIIH9tx7VDJybyVG3RblXXav4Eg0CAHYdM7flp1QUcfyccpUxzCUwrVWsKH7Kcq1bNJWKouUY1QqdTa04B3CArnf7/niuD5pN+sHruCiamzBQh3jzlW82aC8oGcZx1nAOxjKXv/eTtVi/7zTW7l1nr+LMkHHnsQJFOEG72oBMbzY9IRPwb0eA6zN7P0hvZuMoU+0nnngCpaWlGDBgAG699VZ8//332LJlC7Zs2YLvv/8e48aNwzXXXANRFDFhwoSgyChe+XCo/+3evRsAkJOT4zkmeQSv6Dx4xUz5sautOwJj8caiMi+MESb2/PLCWvnwZZHhr0PmZpgB7ZVWAYKhU6XLDC/U8+7rhJ8f66Gsg2uPs7299+y7O2Bk60xuj+o+hSxTZS2UKaZGZlCBsmjwd8xS6Tq61E1lKBFlv/XuhLHi7H1My+O3L3uc7UJvVdPutm43uqbaOtclPUPp+tRtLo3DsY1Eo+oJ5fKoyte7fSNa18TVjap6rVgLXHYP1lH3ldJPXyxZfDXVVns2NoL1DlZXecQWNZ7BJI29nlpo7UXnQRSN/Wqo4YnjHMgBuroPkCtsWhN9ZRMGvtWr6xHfh7KNJse1tgp5TTqLwHo/hN0E2O/iip0nlGnMrg5rNGOPqbaFZxbobT5W6kuM5tuuw8K0tYjlmio2jlKcBw4ciP/85z9wuVyYNm0a+vfvj0aNGqFRo0bo378/pk6dCkEQ8Oabb2LgwIHBFrdSsCr/pGGah3rVxS9/64F7utsfIqy0VPSL8ypWp+uLIlXFxMBUQs/80eiSWR1gVLgbWVViFMd4LsnsOMoofW56PF4e1lQRW1EP35yDKTP/e/EOz99GlxWowZrlmXTObNLAye0SNCeZpP1jLAzvP+NGxkVqGyuNbF3u7DAoeqpenc7Wm31GyzGOmb108pzr9p5WDKb1BnqRYW68e3NrXN82S1W3NTl4UX8fJBl9Mbcu9vdslwpW2BevePRX/qtW/BpUS4AZfI2t3muKOf8y7P2t7LTR4W5MGdEM/725Faom+Gc7m14T1JpUKC41v9LuXa/OdhkT5Uxbno9PV+31/DZqq1ohqAK5ys96F3/eprSYM/Lgz0u5xYl5J212dU8sJ5121cfylM+Lt6WHfvpQ2i4ZSBylOAPAXXfdhS1btuCZZ55B9+7dkZeXh7y8PHTr1g3PPPMM/vrrL9xzzz3BFpOQIQgCaibH+GVQtPCvI1yKlR0f2UB3Elr1CYKAUxf0TZ15PR0HY8W5vFy+dJcsxnAGvDv+dXtPy+o32OPs8BVn3o++dBmsQZ90ZMP+M5r5rbR7PQd28oF5MEKdOX1V2Sp6lyU9QimN+p0yc0/U78Xgt5Z7lW8GeRZ/PBotU+1/fLfFcpnFBvFa7UYURa97o75VnvdUdcKsL0lfVpytIMktb1ZeZsIeM3RgaMua6NMow2+WOpd1nq3WrRn81nJPb3xHV3PhCK3yzJebsGq398JFUXEpZsgUZ6P+e00+e7sRa8XZX7Ce5cK/lHvQ7atf8nFgZcXZHj4c2xYLHupimK5ZZpLpsn35ppKptj04ao+zRFZWFiZNmhRsMQgHcPrCZa63vUmNJBw5y3YGwoLV9fjSSVjJqvXB41E4WUlY/enyHSe8D3KUZQerGR99f3PxstxUWz+tv74JasXD6soXb3uUTN2ZH9Qrh/TalLFzMO/MenuXP/1tr+a5QKA3Ix/KHrd5kJTIx2ZtUB3nL4O5QgilKbge7bKr4L1fdnt+y9slrxhmntNFlTWF1uSBGQK+4syoTv0+a+1x1htIs8JBBtrvAOvatKIhyK/FX4P2i5e124VW+95y+Bw6XwlzFqi799Gve/DRr3sUTsJYGH1fTsliO2vFdPY3PJOzZl+5ZjWTmMc9e5ytXJ9ND9flEpCi4W9EYkiLGripXRayU2NRNSEScZHhaP/SIuOyfZCRd7FAipFNejMbx604hyq1a9eGKIrYsWOHcWKCGxF84S/Mdias76OvnlTNou0czPhi2Hm98321/gAjnSqXn1YgzDjCsor6Q1Agc8pi1Cb8ZWGgfjZW6+HNVW6qrZ1G7xkb7QVlZQ3G3mVewn1wfuRkeJqR1mNxCQLaZ/N5LGdVY6Zr7NUgHcNa1fT8Vq4487UbM13SV+sPqvL63jb1ViX9gWbIIEWasv+qnzFrBfndm1uhS91UvDqimde5wL+7ouq/jD3OV/4rl+x4ATucFC9WYjXrTjBKf/jh9vnyLWKZ+cuRj2sUptqqN90fsbRZMmhh9h7UTo3Ftw90wW9PXqU4Xr7H2YJzMBsfrlFZr41sDkEQ0Ck3Fbnp8cjgdIjoi4y8t1hyIEhetdk4coSxefNmPPzww+jUqRPq1auHxx9/3HNuxYoV+L//+z+cPBn41SwCSI6x7pjAKjyvrvl9ut4ZtMJXcGGhg9HqlHjG/ayPjHRJd8pMyXj2GZu+dw5atVPfBWVIJyNTbdvFuVKusuCtJkOTWK1PWkm6sV35HlOeJ2VlvsjIeV0wCQ/Tls3BYhvCM7DVmnQTBP73ljXI1XJMxa5LQK8G6Yq6JXgHx768m3Y84n6NvcNI+hPW5aqvw/MMVGdYz7xr3TR8NK4ds/8P9LvL6l/eXbpT8dvTZ9oomhWfK3qT1lLbtWrqrpfLl0n704X6ztouySaBtByFsX7bxfmiYqzm8JUj3d9OuVW4y25YPUERBQBQPUOTF2XnGoK/XjPfVpyVvws0vL9LY1DSm9k4TnGeMmUKmjdvjjfeeAMrV67Ejh07cPy40onAww8/jC+++CJIElZu3r6pVUDrE0W+WS879jXOXWe8OgsAmSnRxok4uKzhuZVnxYT1oZXuwRP9G3iOXdOkmmFZZu9dpysma/E6DqL8xdFzyhBvegNxo1UjM2OVtrX54wury5WH7zED72yv1BakdvP3IU280ug9YSNlhpWXd/AYjO3GerKF8vbnQc1rGKbR6jtcgsA1IQd47z0EZCuC3PePvc89EH4kWPfAbF/1u4kwdDWTff8eaL3rRTIzdMn03MsZGmuHhs3tvH5GvHEiDaRLk1/iwTPKfpy14uwrVqJx6E0qaJnK24Ev78W2IwW65xWxwfUUZ8sS6HPje7/hwZnrDdNJ8vBG49Ci3FTb/Bq6nU/Wb75jfNCc1e3s7SU7mek8TibJWJuJoxTnb775Bo8++igyMzMxZ84cHD161OuD0rFjR6SlpeGrr74KkpSVGz1vuv6CR8Ex25mwzFP1Pl739iifve7byJ7ViM/X7GMe54kBytwTJ/tb8urI0/GZ7YYfu7oeJg1shG8fNHZ+YTdPz1XGotUbb7y3bJduWWbMkBKi+du9XR+bUhE4fMY4Frw0/6KrzOrucdaXt06a90BGvU/yHwxlHQCaW3B+4ivBcEgWCJJ0rH2kJ6hnqs07kDvJCKUktRHeMjSTcb4aZh6hOi3rHqijDRixVuZk0Ag79gxreQCWP4vzl66sDqmqY72+Rvdvyohmmu/m9Fvbeh2bd19n/QIBzLyjPfM4j1JY7nzOvnc3wqzXtCtU0dibWh7yzZo8epf2zYbD1grloLqGCbC6vd0/Y61f6ucNcSXdX193zEkWGSLMr5ja2f70irq+bab2SR/KNYL3dkhjCVpxZuMoxXnKlCmIjY3Fjz/+iMGDByM1le3SvXnz5ti6dWuApSMA53qsPVuo74VaDdthiXb6JjUSdcuz0r/8puE86/QF4ziZ6/Z6r4jIH430nHi26pl9ptERbozuWNvQDLx/E/vNHfNPnIcoiigqLluJ0brvpy9cwp8Hz2qcLcPMR8GcV2L+co14+LP1HPUpTbXlSAMBfRNE/fKNHAytmNATN8jMw9vVKV+d75jDF5YjUDhpmwELX/eUaT3n/BPGk3HlMngfk+Ia8+6P1ZLDH24k1FWxHHv50yGWHV6qmcqloA4fVPZf9b0tYeQ1audDW9bE7V3Y3qFZ94onBI5WitIr/bX+o+ffCsCL1ZU5LRnkDsxiVLHKedB7tWev3a957q3FvvnNkd8HRWgqlTz7ThbCCqIoYtOBM15O+swXxJbLLOXhqES/haNqzbF/Xk8JT44xH75UwpcxOO+9lSJjkOLMxlGK8++//4727dsjO1vf3X9qaioOH/bfDB2hTaD1ZtaggMWy7ceNE8lgDVT0ZsaNQxuZql4XnlnPHxj7seWDJWmAW2LkOaQso19IjbM/DqdLEDDuwzVoMvEHnDp/iXnf567bj+bP/2joGbdUFPH56n34aGW+Yb1mBt5GKyxmxnPbj+qb4QFyU23vc9IxrVUUgP8dkyO/H2qTyL8PaQJBUFppBBKHzu0FBC1z7M2HznIPuFiDTamNhXHae2svONs/ElMriazJB39aIdixZ7i0VMSRs95bOhR7Uq/8V10bq4/n24vOPs5qJzz3TyvN0m3HUO/pBejz2s+aef2wxdn2z5o8JNtH47xX5f3FK9/bt0jEak9yosPNTwh88ft+DHjzF4x8d6WpfLd0qKX4Xf7dtKePKNvmZy4PT5sRBGB0x9pc6bTwZU87Tz++8omeaFTdO74778SsNBlIcZzZOCoc1aVLlxAfb7yX5ujRowgLc5TolYaAK84B9HSt10nIFQXWAMHKStHhM+wZXrcgIDbCbdortWLF2aM4G+cLJYdJgiDgpy1HAQDfbToM1kf2mS//5CrrUkkpHp+9wTghzA2OjZqC2yWgVGN/uxUkxVcvjrOe4q+1z0kPlnWDRG56HLa/2A9hFk0lfeWMjvVJKLV1L3SajChbDdOCt+9mzbVJfZGvK7f+6M699vwyhr/+9CRtx4qzxoIzoFghvGIqrLqWwksMxZmjTq00Vp+x1m2Ys9bYd0j5HvrAmMpaodyruYBWtfh9XvhLHiv1ypsZa7tarSox2GLSmeV/rqyI/7H/jKl8EW6XYtwkicMz16+HdL1WfIvwPCOXIHCn08KXcS1PM0qNi8RLQ5tg4L+XK47z1iq9h6Q2s3HUinOdOnXwxx9/6Ka5dOkSNmzYgLy8vABJRcgJtKl2MaeCEWnSEQh7xVk7vXywkpseZ6ouLU5dYA/w3S6gc13zJq7yRyMN5nhWE/21GuOPUuVlloqipf19EpdNxHk1Mzg2mqU199Hk3x8YrG0UrEmjYCnNgHdcXwUOX472daCi1QZGtcnkbh+s9rvrWJnlQxiHyS6gfZt5Jxj7czg1lPCOa8xKw12caezZ48xeJZcflixo1LW1ru1tNsrzrLVXnA2z2poPkPdh1svwhtfCgi9fcQnbOZsd9G5Y1f5CryA3wTZ6/6yMBfJPXDCdR0IujvQO+GqVIp8428FhsWUWl8C35UevnVix8vLAY00Cdh/AveIsfb5Jc2biKMV54MCByM/Px5QpUzTTTJ48GceOHcPQoUMDKBkh4Y/xR9e8NM1zvErGnd3MmYWyBip6nYpbEPDlvZ3wZP/6uK5lTa/zdvYvVhVZeT5pMHeGY7+0vwaV/lDI1bPn2is1xpj5eJkZHBuVakZv5kkrvSOsVfG0ePvN5QHlwMFp31Z3BY3jnMgRClCrmSZEhXO/F6zXQnpXfHUOxvPK3dk1G7E+OKFk9Tv+3Ntul3MwFvLDau/5ElEM81q+x8ROZN303Pp9KP8eO3fF+Yvf918p11rB6me8bPsxXPf2Cuw4eo5rz6zdBNsK1+0WFO1bitbhs3Mw2eM5wXB0aJCbIwXfirNen8Na7edlr8ZExR2yMKRaq+K81XriODvu6+4MHDXCePzxx1GjRg089thjGDlyJGbOnAkAOHLkCObOnYtbbrkFzz33HOrUqYP77rsvyNJWTvyhDF1VP13znNE+VYkIzpUQCVaxelW5XQKaZybhjq45fnU0A1zp9CwMIATGj3/9sI0jn7NX4eQoFGcN5x+8Az8eM3ZPmWacg8nKtRL3XB6Hm+XhWI00oJYv8n5yWzu0q5OC/9xYFj7O7ics91jrTzNYK+it+DtLUn5u61wHkWFlCpKeoybdOM4+rDibXRHU6lN49sy5XIK5yTwvU23jNHZiR/NnrjhDOZmr58vAK68PK86W1WZVxpZZSdx55fuH1bTPNm8WDdh3HWqsWvaonX7e/P4q/L7nFO75ZG3A1BMRZWHNSkvNO86yG7cgKNq3FIbKLudggHmTaJ5He6mklOud1yuLd1zLm/e9W1qjQ055/GvNiUvOOqS55wDulAwpHKU4JycnY+HChWjUqBG++OIL3HjjjQCABQsWYNiwYfj444/RoEEDLFiwgGsvNGE//rB01HeiwKfdmFXoWavLTnEOZke4i2ITWqHDrVcVyAfkpaVsU21uJ0imVpy5kyoGJFYmmnji9SrqY6wGdspNxWd3dvBsK7D7+5cYE457e+Tg7u45SPLBQ6g/0HuuWQae4IONVmtJljl303PQpa0M8SujLEsMnj3UqgqZ8LxyLsF+L/b+7OLscA6mdQ3ygeuFK/vM7dqSoVWK1clxuVzJMeGm+gWpz5TXPPOO9ujdsCpeHdHckjxa1+Hr7bP6uAs1fJYcL2A7ufQHF4pK0PKFHzHk7RVBX3EOcylXnD3xvn38WsnboRnnVjERbhP9hPWJKQAIt3krk/qTIGgsvsi/jXpbmigclT6O87CVl5eH9evX4+uvv8YPP/yA/Px8lJaWombNmujduzeuu+46uN3mPf8R1kiNi8DxgvKZUn8MQI7rOHHwZWZOD1aHoNdJGK0y922cgefnb/ZRqjJ4nU+okXeUZlaR/aU4J0abX201Qv4YymKf6qfRw1+m2r42WbOWxmbNaO3isavrB7Q+XrQe64NX1cXgFuYmJQJNlbhIHDSI3c1qi9Il668488mgF6rPVx2Ra8VZ4I85DXhPlOh5l/cHdjgHu8SY6BQE9v2yy8DDbusxeWllz5A/L2vFuX12FbTPruKVNibC7ZlE0EOrfvXhLlf8ifBGgbDaz2q91yfPXwrY6u+q/JMoKCrGH/tOB90INyLMpfLyfWWPs4FgWvHHJeRPx/yKs29bUeRotZPYCDdulIVvtAPWeI9VvRR9pqRURNOJP2iWJ8kebKsEp+IoxXn8+PFITk7GM888g0GDBmHQoEHBFqnSo375/TE4n7/hkOY5X/aC6GF6j7OBMlM9KdpXkTyYGeSq80mwlK/kmHCmQzJ/KVy3d83G+n2n0S0vzbZJBfmHTWuPM+/0TokJz9Zm7pG8bVnxtm7W9Fkac+sp9yFkVOAzWrf84d7Odyj5xqjmmDB7I1bls2O8A/rPWa/p+GKJUb4DlXNgqVU2T16TE4dcZTp8j7MWrO+fXdeiueJstTxB/rdgqSSeaytTqFPwj2+3+FwWAFRLjMaap3sh7sq+eqNcoWShpUbenlhjoBMF5j1RWyUxJoIZV9rom1k/Q9/aVJ77z4NnueUx02J50mn1t+uf62P7ijMEb5n02un5S8XMyToJqU+jFWc2jjLV/ve//40NG/jCwxCBQf3yBdpU218rzmb3OAdyNc+OcCCsQUO7Ot6z92Vp/UNcZBg+vLUthrf2dqZmhn/f0MLzN88e5+OcH38zbesUh5O1crnK/z5fpL0y0riGd5xFgD9Wbnl90oqzqWwVllCOPZmdFofP7+qgm2bKiGYAylbe1Oj1U/wrztp7nHk7C62VG56JJJfJicMxPsZU9RV/Kc4CBGZIQruuRdOs30L5tarEKL45Zeb2/Pn19jhbRV7Wo33yZMe9K0mNi2Q6WWPhj7FAoLos5aSu93krIZz00H3fVVExvt14CCcKiny22PLJYbWP/ZsijcZxPaW5c675aCp6dWnBEzKzLF3ofkv9iaMU55o1a6LU1yBuhK2oFTB/fDT0OiFeUxsz4UsArXBU1vc420nTmokWnYMpNGfv8xpF+vvafDUJ7Ne4/NnKSyoVRa4YoVqYMePSU4DVyD82erO6D/fKw/IJPb2Oq0P+GMmp51W7MlLRHZpc1aAqtrzQF5smXu11Lv/EeWYeUeR/D5nbWGBuckYrGc+zMWuq3adRBlfd/sJfivPWI+x4un5XnC3cwZeGNlGtOJvcp87Y46yFFBrNDFc1KA/35OuKcih3s8r3z/8dpd77rj519FwRBv9nuaFURs/HqnmxIAi4eJnTpw5XeeZl+M9NLfF/17dA/yYZmmlaMTywm4kkkH+c/Y2QU26qTbBwlOI8ePBgLF26FOfOmQvATviPQOiLelUU6sVkvULrWskej4zcMHqEyzpKTiDNs5pneneMPChXnNnn7++ZC0C5SuPva/O1ePlAZd+p8piUl0zEYWZRbGKSzoz5NO/HRhCAJMY+8AjVjPSTczbqliMNTvT2WkoTS20YMV8rGhVxllx9TVHhbuVEyZXTpzViw4sQTcRx1j7Gq1RlJEax5fCDczD1qxkf5f1O+bOPC7RvAfucg7HLsVJ8VLhbuVVIEHCB49stUb7ibFw5b9xgeVHyVT6jKo6c1V91NTNBObQln0+FQPVZRivOdmMUFUItw76ThRz3Qv/+81xXuFswNPnWlYCjCVhZMEiICsfAZtURHa69i5YVqk9rvMei+7+WGA5SJKO3CvgptQVHKc6TJk1CVlYW+vfvj3Xr1gVbHIJBoE21p6/cY5j/2mbVTdfJWl1uUiNJMz3PgPHT29uZloOFy9r2MEUWrRnIR/rUw6ZJV6OXfAbe4Zu25PIdk5mSFfmoOJtZmTTnHIyvYEEQmOa2YSrF+bM1+7jq0xtQpydEYfPzV+OzO/TNgCsCFfFbX9tgYlB69FpNTxT5uxSmXwmpjXGOGPKqxuOfQ5tg6tg2yrI53o1LxXzhXiTU7Z5lChmqe5xZ2DaYtVFsdQjFQ2cu4udtx7jzS+2C32k7j6lseRp7TcD5C7u1Ux2udEEx1fZzXTuOnkObvy/UPC+K7NVho3thuOLMeWFqxfnJ/g1MmGrzpbOKma1hQNmzVLdLPRGN+mG3FMeZNGcmjnIONmjQIERGRmL58uVo3bo1qlWrhqysLERFec9eC4KARYsWBUHKyoX65fPH7LqvZd7UvpbpPKyxYfOsJMxeu5+ZnkdEO0LdlDkGszbMk3ecejOQcZFhATU389dHRi+cgt34w6u2gLJnViMpGgdOl6+kR4SZm8/kNdWOiXBUd+83QnmPsxZXN9I23QP4+lDeJszc43zlv2Z6plFtvT3Hds1LM8w3d/0BjO9Tj7setUQB96odYMXZaOsG70SynVK7TTp0UyO1OR5v2byoV8A9x328cjOPW73tRotA9VjySTG7uklBYJf1xRr2WKq8frYAvnpy5s0vbxN3dM3GDe2ysEbHKaMcrTYk98cCAA2qJeCvQ/wOyiR+2nJUp25vRFH0OLfjwSiiiNRsK/q2J6s4aiS1ZMkSz9+iKOLgwYM4ePAgM63TV8kqKk677bWqxFgauDAH1z5+Sexok75MIshvA6sYxeAhgA/SjtWe+MgwnCsqVhz737LdPpfLi5k2xjtLKz0Dddlm75bUlnnD4rx8XRO8sXC7YdijUKUifuyN+gXDOPPgf+e3H/XeQ2qX86bE6HA0z0zC+n2nNdPsO1moeY4Fz3Xd2yMXy7YfR2yEm+lwyxfMesH3FaNB7wNXtuMYYWecY7N7mtV88uteANqmvd3y0rDUxAq2JJPnb8UJk8KpMHOdvG0jUJN98r7RrjpdguBpk6fOX/LEnDc004b5sKCA8ePj3qcse44ZCVFXjnFl1UwXrXIw979bWuGtxTswY5W+1ZgZWHWLYtne5xvaZaFOlVhdGQGeFeeyzBXwU2oLjjLV3r17N/e/Xbt2BVvcSoH64xpo52CGeS3mY3UIegNurj0tFmWRIyk/Vu6JMo4z47xiBt508Zaxo8mMapupez43Pc73SnQwMzjefoTPeY1Uoq/3p9xUmy/9yDZZWPHEVb5V6mAqonmZ0aPl23Nnvf4Sjymt7y+z3Su0apFYpbfProL1z/bGlJHNba0b8N5a4W+M7h7vHlzl1h75cfPPx+0SvCcATRQzZ52+k8c3RjU3XbaWqbavvjGky5x4bUNjGThvQrGJ0Ii+IA8LaTbGsRbyciZ/v9Xz9xe/6684A1rjMH25jG5p05qJhvUCWgsNnO+OhhDqvqBmcgxeGtoUsYwtWXYiQoQgCPjHkCa4vWu2JKVmeiP3Lh7nYKKI9ftO45YPVmGbhrPCykhQV5ynT5+O3NxcdOzYEQBQq5Z5k1sisPhD32J953PSYrHzmLH3P6sDOWasUp0Om2cwwTOp0LN+ur4Zji83WPEh8C5ILh+vCZlTMLq3/l71MTNhVKBaGTcqk8fUVA/yqq2kAurNHJ5+pRUCDfNHUfTJ8kMy8bSjiW06cEb3/OTrmpoqT/1uat2rpJgIU+Xycu4i2yGbv2hUnR3GToK3r1KbMhutZBvVGanaYiLAvhUrK89Oy1TbV6Rv62EDJ2Jm6r0chGgy/ljlPnqW34pJFNljLqNJBKN+LNztQqtayfh9zynddHLHs2Zbh1b6cI0O0t+fJLPNx8gxqnwSbPBbywEAtxw+h1+frLgT7mYI6orzmDFj8N5773l+Z2dn429/+1sQJSLUePX7fhibs74tLM+ozLyyv1Pj+D+urH5Dr3Oza0XHcLVAQ5niQdM0jXG+eWYy2tROxtAWfF4/fcGOMYvRBIm/JwLMTNAUcpqCSkV6O/XwrktvUsfjVdsPinPLrCTby/Q3dq2khBJ10vSdh4nwTemV7mkgpma0PHJrob4uvXfVH/LLTVLN7DO0itEKN3/IsPKE6tW3AU3NhXd0CfBWnHWeQyIjmoDd+GseUfpG8xgauASgWWaSYbpgTPb5o5uU++owQgR7zGWHWKyJ9G8f6KL4PX/DIc/fUlv11VRb69208/myqmZNguhdS+eXF+vWIY0l5OUeNjEpUtEJquLscrlQXFy+OpOfn49jx8ztYyECi+Ocg8myzri9PUa0rsmVjR3H2boYKlFM1StH6rA2HdRflTGqnzVoiZcN6twuAV/c1dEvpovecvneZoyaiJvX3a9FLhbz74v89+IdXOnOXSzr+3hWnKVmc6KgyMvrsZ2rgWo+vb29/YX6mZW7Tngdq5kcHQRJ7ENLCZl1Vwdc17Imnh/YSDd/amykT/2snabaRnKYrULdv+w/xReuyC7k98QJ2wR4+1v5fb6vR13F8Tevb2GqTpdLYGzr0k7P9NxuO7KJARs7R6mooS2NxxoC59MIxmSfP+rccticOS/rdTGSi6d/YE2kN9Sx1PC1z9Gr125YfTDrjvkiSbmptvc5X7c6VASCqjinp6dj40b9GKVEcOHZP+ZzHT7klQ/C6laNx+RhzbjysSOu6JhqcwjJ8xkyjFB4pZ5dHGbq3nkF2d/e52/uUNt0mU7BaNzjb1PtlTu9lTEt9p7kG7hfuHRl0pBD9FJRxNq9p9DqxYW4bfoaxbkSzx5n++9BVLh/92YFilBXnLVoXTsFr45ohipxkZppPrujPRJjwn3zemzj5IyRHNKg9PM7O2B87zzj8lSjmHV7T1uUzBqMcNpBhdersPwx1JOF5hHgrQQb4WJ41dZTGX0xCweATrmpZXXoiGlkgWUVqZ+N5YhSIAh8Y4dgKM7Bjj4glsWj8sJwjzNH2WYn0vdwxgb3yKAhRLifJ/C1YG499KE8Ledgj8/6A3lPf4d9nGOcikpQ9zj36tULH3/8MXJycjz7mxcsWICePXsa5qVwVIFB/fHzx+Cc1QvxvvTWpTHXrfB8JIs5Pn5GSWzzqs04z4oZHAjsaDLB3uPcvV4avtt4WLEvylfMmFZvPHAG05bnAygLVbFu7yk0rJ6AyDC3X021Kwr+jOPrdNplVwHg22pxuaIjYOYd7fG32Rvw4uDGlsri7ePa1klBckw4pvy4TTedujS/fKN0qF0l1qOsO2DBmd9ySnabjCIyGOEWGG+YTjk830o9GlRLwPcPdUV6vPaEkdICy6fqlOVeKYulIzXLTMIfMo/xgsC3uh4MJTZQDsn0YE3yGE0i8DQds+OBxVuP4pkBDbm/ElrpwsPsaWgRbhculbBXdtmm2t7H9p8yF51AjtSHqtvl51dCjL3/y25MNLByqsgEVXGeMmUKTp8+je+++w67d++GIAg4fPgwDh8+bJiXwlEFBq9ZZD/cdrvH+13qpmLZ9uO6aVgdDevjNfHahjh7sRiZHDGaSzg+RKyZwZvaZ+HjK+E4WMrPtLFtMGbqasOynaoc2CFVsPc4R4a5seiRbuj4z59sK1O6pmPnilTHvdMO+c8KxX7jIf9ZgV4NquK90a1lq4HOfP6BhhW6jCd+cEXHl+Yh9U8uocxD9dLHeliXw0xaDqF5nYP5i5ZZSZh7xSu0rzFo7YA7HJ5ij7NvN61sZVX1HHTS+2KqLcWplq+Ss2Wy7/pY5bLC/13XsoZCcQaAP/Ybb7sKxorzifPGzs38SZlzMO/jRveCx1rB7CTy3d1yTKXXatxhNq04t6qVzNxypAVr7JrigzNEaau21q2u7EONoJpqp6amYt68ebhw4QLy8/MhiiKGDRtG4agchPrF8ceEhS8fNVYH+e7NrdC2TopuPlZHw+okxnSqgweuqut9ggHPbKO8Dkn29ldWhAD2JEKNpGgMal42WIiP0p7rUpimMcoJVmdnR5sxKsGuD5Yedq/oSsVJe50ltCZA1PvHFv51BAB51VYjf41XTOiJKSOa4bYudYImj1MIhJm1HWUY9WNq1N8Pvfe0epL9Jvv+9q/Aw/WycH3cC87yFWfZJUiHW9VKBgBUTdBe1VWUp1O+Gl9MtWtxTGKr5bHXVLvsv6x2ZnUsE4wV5/s+XRfwOtVYCUfFg+E+aQh4qn8Dz2+9cZVWfhbhGhP4dk6osZoY63Jz0vUdRupB1mv6BHXFWSI8PBxZWVnIyspC7dq1KSyVg1C/8E6baWK94DERYeiSm4pVu09q5mPucfZRlmqJ0XigZy5iI8Ow79QFzyqNso7yWkoYK4UsJVMQgH8ObYr+Tco8nd750e+GsrA69mCtSNuz4qx/XuuDZRe8e9XMoDXI0qpHa0BhNo6zxBP96uOl77aYyxQCyO9T9aRoLic+FQW9Macdq252lGFmIo2nPnUSvTyNayTi5eua4G+z7fOtIjcLZd1/QVAeH9OxNqatyLetfgCYOLARZqzapykDC6Ups/wbVPbft29sifeX78aNbY3HY6WiaGqPsyiWrTpbmezjtS5STsDY13lL7Yslu3o8wvssKlIkAFEUue63CJFpHWF0L3juKY9PEuWYwVz70Gq2dnnVblkrSWfFmbP9+zDykiYDneDs0IkEf6pURn5+PiZPnhxsMQy5cOECvvzyS4wbNw716tVDVFQUYmNj0axZMzz//PMoKCgItoi24bXi7Ic6zIYfkaM1M2b0QWZ1CHbMdI7vUw93dsvBHV3KTH/U+4pZYbDkAz2W+RcAREe4cXWjDN291mZXagIFryx6+5KMPgKBmCG12xRaS2StWi5eZu95ktqtVtvRIjYAoXOCQUUahDoNe/wVGNSh8bdmelWiHIPQXCPbZHGUyo+872GG11EdfKJ/fVvrB5ROiWIj+XxZaJsyl/2dnhCFJ/o1QFYV9gqvOlSduo82aitWV53DeeJAqeQx026719Pf1uFZcWYUarYPltDYzhqSGHW/UqgzDd9gMNrxxqPMGe1xFiEq3lvTXrU1MmjFcTbL/T3rYkK/+lg4vqvqeC5u7uA9kcW6J7701dKcgtazdOq2wEDhKMU5VPj0008xZMgQfPDBB3C73Rg4cCC6dOmC3bt347nnnkObNm1w9OjRYItpC+r30R/7KMd2rG05r1WFidX32hprT2CXyTLZkV8D+3L4OnieletgoDf73E229zQizLs7+u7BLlfK0K9DL7ZpVLgLc+/paCClPgLsnzSy612yaqpt1NxHts40SOFMaJKczVfrD/pchj2h5fjL4EmqlinQnuDl/Xd3jr30EZyKnxlcLgGThzXFswMaolqieXN0KwrfDe3KB/CiCK8O0qhEqxNcVqyLzPS1b17fAtV0JvOl9ssq0+p2mYq0slfMWh2QERlW/n6yLlva/969XhqGtfK2FuJZ4HCr2sjd3b33MMuflfQXb9+klcwoxjovUeFu3NUtB7npyn389TLi0S0vDcse72FYhi/DC71wVAQpzpYIDw/HHXfcgc2bN2Pz5s34/PPPsWDBAmzduhUtWrTAli1b8NBDDwVbTFtQd+hOWskEtGcWjT6U7D3O9u9DUdfDqkLp0VRf4dUNv6GRh3U+0FzTtBraZyv3nQ9tUQNTx7TRzJP/z2vQoFpZ7EUj2fVmmHs1qIoWWclIignnlleNIPhjxVnLVNtcPdL406x8Rg56/j7EmtfkYNM+p8xngN7g1+moV/Ps4EzhZZ/LsGWPs4m0PG1a/eqb6cWjwn0fAslFzE2P40jvn554ROtM3Nq5jqW8VrxqyxVY1lYWK99gHqz0w2ZyxEeF4+pGGYb1s7a2q/UmblPtCqShLN16DDuOsq0u/zm0iedvEfpetYe0qIFoxiQYz61STwTJ/ciw0ph9J7VS+9tJ6YVLZVE9eJzV+jLJKU0qOMHZoRMhxdkCo0ePxrvvvosGDRoojlerVg1vvfUWAGDOnDm4dOlSMMTzK/5YcW6emYTWVxyRmEXTVNtATF7nYFaROmJ1kUzFWSas0Qq6Xmeo2KdmLGJAeeuGlph5RweFglu/Wjz3DL1R+BLWB1ZCqrNPw6pcdbEQIPjc9tVm+1rFma3FY6ptsje/bGAfaNfseaB5bUQzPHhVXXx+Z4dgi2KZWXd1xKN9jGMYBxpbHP0ZFcFRhdz5o9d7aaIfv6q+9T6BVX+oDjOtfDvCXC6MbJ2Jfo0zkJUSY8o5GGB9xZm3H/bX1iU9U22r34iKtL1k1/Hz6DVlKfNcVkqM0hpPJ7qJSxCY4yGeW+U9ieOdhrXizIvWY06Isj45z8OsK+GgeJDLmG2wfUWNNGYir9psQnNk5GCaNWsGACgqKsKJE/zu5J1KILrzMLcLs+7uiLpXZuuNVgE656Z6/tZWnPXf7HMXi7F02zGF8mDntbo8HwfVijOjFrmsrMsxWknmTueAzu4FWexX1jPqmOM9MwwYWwPE6O799t3sSBAAwcfekndbgdmPknygYYaKNFiTUyUuEg/3zuOalXcqLpcQcJNjHuzZwsdfCGuCM8LtQpxsf75PgzgbrkehOIfQKyXvU42snpj5IeLlYU3x9k2tIAiCVz6jSVHrijNfOvmz8IdzMFZ/rj4Ww7nf3Ff/Kte3tXffvhnqq8KCbT54VjuxUP7KiRrrmVK70LLy4rEM9A6N5v1bueLsEY+T8pT1qpZd/ye3tdNMbVe3oBXbmW3JWC5jb5OLBp4V51Dq0AIIKc42I4XJCg8PR0qKfkikUCBZFQvOnzNN741ujcHNq+PLezvppvtY1kGp5ZMwkvPPg2cx+oNV+L9F2z3H7AwJIXXUXnucGVW4FYozy1Sbb2ZUMcPuBC2Zgdyxy67j5xXnBGgPPI1M2fQmW6TO39en6+uKM2+8Wa1BXm2Gkx5RFJne2Xkw+zElCDt6FTPNlPnaq/L7ohTZ42nc5yKCglxvlZuY8l6OkeNQo3trWXG2cMPtHLd4lCwO52CpcXyhvAy2BRuiVl4DiXpLjN5TFSAongUzjvOVg2VWXt7neVqNOh/r+StCsF05z7syK58gee7ahtg06Wp0ki3oeOHD4EP+bLXaMWsKQp400qT1mNSOSW1mQ4qzzbzxxhsAgL59+yIykq/TdDKBjOdWq0osXh/VAvUzEgzTvjGqOdrUTsYzAxoyz8s/arER2rO+n63e5/nbL87BVMdZVcg7cKNBgd4g0WgA6QRlWi5BgSp+MaC9p8bI6yiPWbGZiRH1rexRP5377mntpfb1XWJJf6ygyDPoMlt+dprxfkwitDAyv/cVW8JRGZ73zfTZzL48uycCQmlPoLw/DLMQi1rdn/KYx8qxuq+Xtw3Kn4WdW8z0yrLqHMzXPc689cb5IZKC93Y07WtRK82s96XUMxFcZhloBa9JaoM0Up8THxWObx/o4jkurSZ755WVLQh+ua8S/76hRXldJvLJbwHL8SpPXq37H/yRZHAhxdlGvv32W7z//vsIDw/HCy+8oJmuqKgIZ8+eVfxzKuq9pVbDLdjNoOY18MVdHVE1ge0ASN6x8X5U7BzyaDsH85+pNqt+o2OBRi6DmTGGkcmQXruUcppZ4VB/eBtkJHAPvrTq8VlxZhT7+KwNWJVfFq/cH/4HiNCiUfVE/1Zgs2mzEVZMBc1kscOBnKDUnEMGeT/FE5pn9t36kQm8J2b1n7PVVVZfnYNd3cjY0kavCnn9U8cqnVtaHR8ZOWo0grdWf2zPMfO+CVBNjDH3OF9JKwCfrdnnnYADrzbCuEGFVxxtqc9XiSu3YtQaOypCiPp5ccktm9SS9zVG7Vie1mw3On/DIQCwPd58RSGogTz37t3rU/6srODt61CzZcsW3HTTTRBFEa+88opnrzOLl156CZMmTQqgdNYpufJ1u6VDLXSvlxYyDoMUXZlOpyE/ZatXbS1TbUZaece77Yi3N0oryibT5Jsva8AwY2Jp9MHnaZbFRgEiZbgEQPZZZXqN1UJrEKQeVJm1ADh4utDr2JKtxzx/h6rJKGEfkwY1wuWSUvyw+YhfyrcnHBV/Wq3X3te++n+3tMa8Pw7ivp65ePfnXT6VFarOweSTuvknyrfNaN3aVioHnl77SE2uOBuFLdLCyh5nO/ehy+vvUS9dcc6qEuXrijMr90O96uL1hdsVx/xhkaKuW1K6WJTthdfOC8j3OFvva7z1Zu+yjpwtkp0H82+tx2l1AcAKWuW/eX1L5D39HQDf2/QzAxrihfmbPb9Pnq94jo3tJKiKc+3atS2/HIIgoLjYmhmH3Rw4cAB9+/bFqVOnMH78eDz44IO66Z944gmMHz/e8/vs2bPIzHRmzFRpxfmaJtXQjuHS31/42hfJZwrPFfG1E3+YapeVK+o6qGJ1jBkJUTh89iKrZGZ9PLFBnbAiqdyH7Y3WMzCaKOexKjAzaCi7V+WVCmDfvxpJ0TigUmi1BkG+zkwbeRYP5LYKwpmkxkXiv7e0Ru0J3/ilfDuamFE/pO47zcKTpXfDqp49/rd1roNdx8/jpy1HucqfNrYN1u45hf/7aQeA0J2wkivO2anl2zZ477jRZRuGo7Kow5k1OwXU5vS+oTdmtWqq7Q8/jQ/1yvNSnI2+IXLk4xajdLyo321WXqld+vJaGfkTESEqJtsVUijMsMv2Pe86pvTHorAS5HjmvmzhUJqUl2PlPdCieWaSqfQOGEoGlaAqzl27dvV6MYuKivDrr78CAJKTk1GrVi0AZavTJ0+ehCAIaNeunWP2D588eRJ9+vTBnj17MHbsWPzrX/8yzBMZGekY+Y2QZv/8HZ/Obrg9g8r6M6lzG96qJg6cLsS1zapbrl89w/3l+v14+LM/uGVVmM7Jukuty/r1yatUZbLq0ZM48Kg/4uFhLs3Py9FzrEmEcvQGadIz7t+kGn7YfAQpsRGGM6osUy/eMZHWgNDfiq3VQRtB8BKIPkReBa9zJTlmB6lPD2iIMxcuo9nzPximrZMai+710lFUXP6SK/v60Flzls8j1kiK9vzNew1eXaTJFWf5BGOXuqlYtv24bvp7uudg1e6TuKZpNS75/IXedQXLVDuO03u3GUSR73032+Q9/l9EDedgfllx9kbreykfb7kEAVFh3vdWub3Ov52iYrFBoyqjZ2D0iMyOTZywCBNMgmp3u2TJEixevNjzb968ebh8+TIaN26M7777DidOnMDatWuxdu1aHD9+HAsWLECTJk1w+fJlzJs3L5iiAwAKCgrQr18/bN68GUOHDsX//vc/W8MeOAE7OrFgwCtt4aVirM4/idJS0TPrGx8Vjk9vb+9TiAd5/SKgqTQD7E5Ia8+Z1nWlxCq9iw9uXoMhU/CfobyDn/fHQcU5LUccgL75F8D3vAc1r47P7+yAbx7obJi28HKJ4neZN1BlLVNGsLdjaJkgen2cbH4clf1jRpTTo14agDJLITuxxTmYiSKSY72jJhhlt6S7cso0onWZZZhiFUiW18++2WxFsf2F8/pzZF6HWSF+FL8NHrRcQW9Yvcwh6LjOdTTTP963Pmbd3RGRDEWGWb7sb1tNtXWUDKtzl77uPc5Ns9+rNq9E5p3xlf3/xgNnmGmOniszoda6lzwTO9UToxW/1c9MHY5KkZZjvCVPc/iM/qS+r9gx4W77fF4lH2o4asPqM888g507d+Knn37C1Vdf7XW+T58+WLhwIXbu3ImnnnoqCBKWU1RUhEGDBmHVqlW4+uqrMWPGDLjdzou76SvSCxfoQbmv7zmvvOcvlWD4Oyvx4cp8z7XacalmViHkHXjqFccUWp0l7wRGs8wkZKgdpzmgs2Pditl3d8CAptXw+qjmlh88z20RBAFt66QgKZodwsyofPUjSY9nOxbSGgP5e0HYKY77iODzf9e3wBujmmPysKa2lmtHE7PDQZFeCV3z0jx/D2lRA/+9uZVhebzXJb3DCueTssxanvsjbTSrtAu5rLzXP+uucgdhhivOBpes9C9S9t/AbDfxn1WAP71qX9eypuY59b2feC072ogZeC0PTDkHE4AlW8u2RPyw+QjOFF7WTHuiwPo+W3V4SuaKs4b9Ps8YS54mnMMa0xfFVS5nbjp7gsSo+EVb9H1emA3F6oRFmGDiqN58zpw56NmzJ1JTteOhpaamomfPnpg7d24AJVNSUlKC66+/Hj/99BO6dOmCOXPmICLC/GA8FJA6TydYgQ5uXh2vDtd2uibHrLyfrd7nmTm15VplZRjvzy3/u3lmsq4MZmSrkayadXXAM2Tdila1UvDvG1qimmqW2AzhOnu81XVaGfwL8P6ghrsFxEfx7XaJDncHwFTbr8UTIUR8VDgGNa+BWFmYlL8Paaybp3+TDMNy7bA8OmSwQhMZbjwBrTfOu75tFt66oSVWPtETr41sjj6NOK7LMEUZ0iBWy1TT7AA0mMgnMHgdnCVEl4faM7pnRoNr+a0q9Vi2GRRqAl8mEuV7Wm9TrYKv23tKu06re5wNBgk1kqKRkci/bWFMJ+2Ve154W7K5Ni8o3v9dx89rpvTFYZr6ObCagraptrnym9TwbyQDuezjOtdWnOvbKAPJMeGGHrY37Gev7kuYvdVOGEsGk6DucVZz7NgxLodfxcXFOH5cfz+MP/n3v//tUdxTU1Nxzz33MNP961//0p0ECAU8oQECPMOkrm3KiGYYqjPj6pVfVkBEmAuXivVt6ARBkK0422uOaGTKxFod1zIFNPMcft+j/MCHgrm9VScaI1pnYtLXm5nn6mcoZ2l9uQ0uofydCA9z4f+ub4E+r/1smG9U20ys3HnCesVcsjn/+RLBo8WVSTktYiKMhwOBaGHNavo2EHW7BNP7YOV9Y1ZKDPaevMBMt+3IuSvp5XnL/9ZaTXeiOi2XVbG1SEdYeTrvPc2C6reRBOUVHSuQzHPta2GZKdHo1SAdcZFhCkdKPErC0m3l0QrmrDugOKc3lvCnV229b78/un6F/xdRxP8t2oG6VePQX7X9w+yKsxy9CQOtcs2uCF854pWGx2M2VE5CWXm5HKgZptBG/k6otym8fVNLlJSKNkS7KZewf5MMfLvxsG7qyj7UcJTiXLt2bSxatAj79u3T9DK9b98+LFq0CLVr1w6scDJOnSpXSPRWvidOnBjyirOkyAT7RWlm2utfucDts6vgZ9mHkJke5avrdlyqYhXCYN8baz9zj/rp2H5UPzSVWZzQ18nNv1gztff2yMWYqasx0KRjNvnKmkR2aiwGNa+B27ooZ98txQG9kkcQBM8X3S0IyKsaj7T4SBw7V6SXvWxPldc+K3uxMmhLignH6Qva5nJExcGo2fMMgP39HYhwuwwHov6QQV6kniNMaV+memIzPioM5y4Wo1teGj75jRFmU3VvW9dKxhrVxGagyaoS4/lb2SdqNwTlvVfeJ+9VPv4V56/Wl/m72H5lYsIOBEHAe6PbGCc0Kkf1OyctjpkOsG6Sz+McTO92ChDQrk4Kftt9UnF8VJtMzFxtLRZy6RWv1xv2n8Hhsxfx2sJtAID8f16jSGduvVmJ3rYNrUl0IydygLdSLN27bnlpWLrtGG5ol4UwWSJ5XUrnYOzr43HYahd64xVBEGxx3Ct/F9+6oSXqPPGtbnoy1XYQ48aNw/nz59GtWzdMnz4dFy+Wm3QUFRXho48+Qrdu3XDhwgWMGzcuaHJOnDjR40pf718wlXu7sHPfry+YVXbk6cM4FAqZPmTLyqy8TqPZZJZ4cmXP6Nofu7oel0xOWJGU34mrGqR7ne9eLx1rnu6FN0Y1N1320JZKh2hNaybiwV51vWZprdwFKY/8Q28m1uHlklK/m2pb8cw6zgaTPqLy4K8+JNwt4K5uOfj2wS5+Kd8IHgeMgLap9vIJPfHjw13RWMNsU60EOMEDfsusZEwe1hRf3NWB+/suCPwKg9ElsnorteLnD8z2kurr7JirvRgSx5jA5YFvxVnnnMB+N4e08HYSaoaftx/HoLeW486PftdMYy4clVJGvesWReBWxvfpeIH+JDXACEd15b//ubElpo5pgyf619d+Bzn6AnnxPH2iL297ILoK+VPgW9H3nyyhgKMU5/Hjx2PkyJHIz8/H2LFjERsbi4yMDGRkZCAmJgZjxoxBfn4+hg0bpoiDTPiPYJlqqzFbuzw9j8Ky7+SF8mu14VLlnamRMxx52h83lzlxkMdl1jLbBoDvHuyCe3vkcsnkiM5Odiu0PKimxkXqdt6z7urAPB6rMjXVKsPK4J+VR9rbxTNuWLz1qFc7bFenLC56Auc+aSOs+FwyE9eTqNiolbvRHWp5pfFXF5IcE4EJ/eojN117NU+O/U5iy69Mz1+C9A6rnYMlRIWjbtV4TbnUfYQD9GYAZVtc2tROUcXX5ctrdAlmVpwDCY+iJ/cerr5SPWdQVsdJRlZpB04XYomO1Zwg2P99F0Xghz/1TXaldLyoRdR7FoIAVImz5jvIO45z2e/YyDD0qJ+OyDC35jjArFLM8y77MmkeiEm2WjLrEwB47opzOfnYRO5wLRS2/fkTRynOLpcLM2bMwIwZM9C5c2eEh4fj6NGjOHr0KMLDw9G5c2d8+umn+Oyzz+AiTzgBwhmm2maVncuyuCAtspIM05+9WFxulm6qJjbyFWejlUCjTlXe1NX3oUG1BPPCOQS9AaoeeRlsz5JeXl418vM0JfUjCQ/zzlQ9id+ZWUmp6CVPdETZSrjv+5PKsOKYyA4Px0Ro88KgRvjpkW5ex1vXTvE65q8Bk5li/TGJK6//4OlCzLmnI6bf2tYrndRXy++DvK9IiWEP9NVvmbzP59nTnVeVb0LBKn65pwbnx05dhX98+5fyoEO6I71QlGE6Y0+rrwdP3y0Pe/RI7zzc0K5cRgECBjUv296UrVD6rSNC5Lqe/BNsfwAs1OX5K3yb9/577zRy53Hy26+2JikxmNXgeXesjnXU8lhtX8Nb6fsHSo2NxPcPdcWyx3sAKPPzAAB1ZNsS0uLLndNVbrXZYYqzxMiRI7F06VIUFBTg0KFDOHToEAoKCrB06VKMGjUq2OJVKoIVjkqN2eqLZA48WOY+LOy8VvksodGqHqs+LY+nPu1xdkBvJ1/VsnqftXJ5ladlicVRb70M5YQEa7BUJ1UaoBgPekpFUfN6Wcf1Qo9oYUUJvmy01EFUeG7uUBvZaXFezZjVXP3VhwTboknO2YvFaJmVjI45VbzOhTFWnOX9SXSEG13qKk1546PCvFbWlH168K9d6cySN4++3G3qeE+8yDl45iL++/MuxbFAeCXnqUE+8a2+TL2tX6xbkhwT7n1QBc91y5WvxjUTFZM0ggAMb5WJz+/sgC/v7eQ5znOtbRkTZID2SvKKHcdRe8I3+PjXPQD4TKc9cqrecyNTbauvhrc/Ee+CuLxqC8C2I96+ZhRJOLQoudJpFjt6h1a1kvXrEIB6GfHIvKIwS/dd3m8Fcl+303Gk4iwRFhaGqlWromrVqggLc5Qfs0qD1KEH+0UxW7/8hed12CHafK3SB9boo8jqwLX2aPsywAz25Aegntm1VgbvdVi5V8sn9MQ3D3RGDdVqMk+sRj1KSrXbAWsg9nhfvn3rcqwMOUtKHLLEQwQdr7BtTE+0we9DAHP7KnmIYoTAYimGHlNt2Turfn3VZo+sO6a1Yq2FvycWFOGoOG+tkdxP9m9gWo5ATCLwXJ9L8c3VPienXZ0UxX2UlKWayTHM9HKkSc92OpMN8m+QSxAUk56XikvhcgloWycFCVHlijrPtf5jaBPmcRHsdnfDe78BAJ7+cpNx4SrUivJpAz8hVkOKeZtqM8rWGBby+Dvg9Ykg8e7NrdAsMwlTx5p3VmfHK2FUhvq9k37L/biYNWGvyDhSGy0uLsY333yDVatW4fjx42jXrh1uvfVWAMDBgwdx/PhxNGzYkJTpACB1c8Hek2X2RTXr7ECex65LdV2JXWS84ux9LDk2Aje3rwVBAJJUM8tWcUJXZ+W5AMD1bTMxY1WZd1CevUms3zzUSIr2UprLyvJNiThfVMwcnAO+7X+SY2W1hvY4E1owV5wDWJfEP4c2wYQ5G/1UszeSgsI079TY4yxHrWyIYE1KyP52wCDUigRGYsdFhiEjIQqHzyrjdoe7BVzWmLDztwNFXmoml38DrD6e925pDQCKUFhaSN1wp9xUhLkFLN/hHbpQPVlTcLE8dOv5IuMwrlpo3fP9py7g242HLJfLQi3nqz9u002frLH1wYiMxCjDNFqTRTwrq2bf2byq8fhKZglgFavzhWbzSVe3/1QhswxnvKXBw3Erzr/88gtyc3MxdOhQvPTSS3jvvffwyy+/eM6vXLkSLVq0wLx584IoZeWh/GUJ7qvi69hC7thACzu9agPls6WGe5w16nthcGM8P6ix4pgvM31OGKBZXXGeOLARXh3eDKuevEqzLWh50rSCvChp349WGp6PUuHlErwwuDHzHCuchBXZrazC0R5n5xJo50nq9sNeKfVP3XrFjmqbhVVPXuWfihlI4Yb0VpyVK8bGN0X9LKvElisEPMqB1fj2vMgvgVd5tboKrpcvEIozz53sUS8dj/ethw9vbYvBzfk8U6snSGpXKdvKw2OtJPXDAgC3xh7qXcfOe/52CYIqnJJ1lJNA5X/3fX0ZTpiIHMHDZRObmkUA15oMSylxb49cxESUT1SzXtGGGv5h1Cur3fLSFOerJijNrv3dTUeHu9EptwpaZiUxJ/X9Aavvk0/MO2AoGVQcpThv3rwZffv2xaFDh3D//ffj888/9/qYX3vttYiJicHs2bODJGXlwimm2maxMui0+1oLL5cAAPYYOM8w4zUxwicnE5az+gUzinxkmBvXtaqJ9IQozedTrPoo2/UcpX0/aswWrxX/kzlxYkH2qgnGs+xqaMWZMIO/TPSM+oJ0C23bKnrvRPmKs/aqFM8tekJmxpzEsQfW3wiCgDEda2NQ8+oqj9L+qEv5e6nMW3QgTEB5JhgFQcA93XPRLS8NgzgVZ0A5SS7pv9k6cZ8l5BOYP+t4zy6Xj2/cwDPhIr/n/v4c1M8w58w0OoJtpWVEXGQYXhvZ3PP70OmLXml4PPi7BMHrDv59cJOALiMJgoCPx7XD7Ls7Wvawbfa1YlWjME8PNYXAZhxl6/zCCy/g4sWL+Pbbb9GnTx9mmoiICLRs2RLr1q0LsHSVFIc4BzOrCFvp/8tNte291gdm6rdV+b01isnMY/alhRM6OztWTrSezyWV+Z8/2qwg2GOZ0Cm33PGQXassjaobe+dVY+QxlAgedjtKMmquXubEzCVnu6Sxjp5zJjvQsxCS6taTgEc6ubMgHo+7gbA+mDiwkbkMHBfK6u/V7eqdJTs9fwdicldrAlMLM/2z/DlJDiUf7VMPn/62Vzef2Ul7lyDw7f/laDeBHBbwmFB78LHRyy/rwpVFDMV5uam24rjyb/VES1JMOFJkFiPRGluw7CTQYzejMbADhpJBxVErzosXL0bbtm01lWaJGjVq4ODBgwGSqnJj975fqwTC2+baPacA2P/xPmnCAUZCtP7qA8usN5Sw4zFqPR879jh78nIcl/62cklypZ41MAuUl+Ficg7mWOy2BjB697zPe7dB/60486f1d1xTnhXnSzLrloOnCxVpzA5yIzj6dKvxbP2J1aeg14b8OUE/++6OGNOxNh7pk2cqH3dzE5XjFGnFOTbSWLEqV5zZlVVXKZxul2DbhKsv5Ww9fM6nuvVCZ/na+ykUYxMDD+UeZ8a3WRAQFe7G8gk9sfKJnj6FmnIqhs7Egq4RBBdHrTifPn0amZmZhunOnz+Py5cvB0Aiwimm2qZXnL326xlfwJYrH4FAXytPOANPWhPlju+dhykGzjcCzZGz3iZTZlEPrnrUK9uDZM9OYX7saidMxTlAbfCyhpKQEUDTWIKNPKReMHCKczA1/l5x1tv3L+1HtjOcFE8c9+vbZiErJQZdVfstgwnPdbNupbr/lq9K+3OPc6tayYZheVhYnaiRrjNctWc5LT4Sx84pQzgZbf1tWSsZBzeUO+pyCWrFkJ2PZ9jky2TF1a//bDkvADSpkYhdx84jwu1STEbZgfWJnfK/WQq3dLsCtd84GLD7fu3tKZUNR02VpKenY8eOHYbp/vrrLy4Fm/Adp8RxDqSjnECbxZi5t2ZkUzuxcALf/3nY5zK8TP1ubsU87o/HKDAGzFaUTHk5/lYE9MhgtJE7u2Vj8aPdAy8MoWBA02oAgPoZ8baUZ/Q+qFfGBEaeYH4HhrQo22t6f89cv34P9KybHulTtpVG7zawzukNsnmsiMJcLkwe1gwDmlpzluQPeFoCy9uzOp9cuXaKV205vG1ehIhaVWLQvV4aBjWv7lmJVCvezWp6b6kxWqD4Y/9pNKpevj/YzjFKMO/45oNnAYCpNDfQcN7Fy8kL5ZZ+xtY2csdX5Xdk2fbjXg7KnNdCtZEmino1qGoqH2uhae/JC7LzlRtHKc49e/bE+vXrsXjxYs00c+fOxY4dO9C7d+8ASlZ5CYSJNItAKEG8dfsb+XfVqGozojnRhOjWTnV8LkM9aIgMY5vC+fIYzbSBt25saXolSP7MgzlYvP+qul7HMpNjLDtlIewjr2o8fnvyKsy7r3NA6nu4t9KEVRAEPHNNQ8Wx85esh73Rg+cz86/hzbBwfFeM7ljb716mtUhlmEzzWDd9eGsbXFU/HfPu8w5Jw7NXNVjXqwdPH8kye1fnOyqzQnKm4syfVhAETBvbFm+MasE8f02Takylt9yrNruyfScLFRO0LkGwb5wS4FsuN8/efrRAM12b2uyY1o/31fcDIyF3Oiffk2yE+nYMa1kTD/TMLT8fQsutX9zZAX893xdV4iJ1Y4SrMbrEYC+kBRtHjawnTJiAiIgIDB48GG+//TYOHy5fnTp16hQ++OADjBs3DrGxsRg/fnwQJa08eOI4B/mDJo+raAUz73mg92+Y6YTMpHXiIKRKXOBWwf2y4sw4Vic1FtNvbWvKdMulWHH27oYDNV+VEBV8j76ENlUTonxyCGiG9PgoXNeypue3AKB3Q+VKxdTl+X6pm6dfc7sE5KbH+33gKn/3Vj15FRaO74rs1Fi0qpXsqdusBLnp8Xh/TBs0rZnkdY6nnw7S/LUuPN/J925pjaSYcDzRr77nmHoskS+LOuHE8bitSoLAbjuSQzpeSwaXRjlqeNqN3nNM9cP3evqtbX3Kz/udHdG63Cq1dW1+E30vKxuXgHbZ5c48HdhENXG5BM8k+LSx/PfdqM078T0NJI5SnOvXr48ZM2agtLQU9913H2rUqAFBEPDhhx8iNTUVt99+O4qKivDJJ5+gTh3fV64IDiQPwgGudt3e04rfZgdLvgw0At0pyAdOhnWHeIflT11e/cwDPQFidVabFY4mmCtMDarZYxpMOIuq8cZbChSDc1fg+kKnOj1MT4hCbno8fhzfDbPu6sBMUydV6eDI7D1TK86sfsSBerOuYyeJrnlpWPdMb1zdKMNzTO/2OHEly85FA9b2BwAokUy1NfKVmdyWn1WvOLfISmLm4/mO6N1yf0RdqJnMDu/IS3wUn2smeax0o3al9KrtnVZ+xIltlIfoCDfXOwuQYmyEoxRnABg8eDA2bdqE+++/H/Xr10dUVBQiIiKQnZ2NO++8Exs2bMDAgQODLWalwSnOwczii+IR6IVac3uc/ShIAAjkR8c/e5y1z208cMZSOawwMMFYYRrTsTbeuakVWtXiN+kiQofk2Ah8eW8n3RVs5bYRIaTMEu2C9e1wu7TvRW214myyPrWpdoecKl5pzHgF9jdLHu2OL+/thOqcK3/q+6b3DeAKsRRgeMcDXKu7gsCc0C0xWHEWRVEhh0tVTpRGSCS+FWdt/B3XmYf7e+aiWc1EPNm/Pka0ronueekBl0Hp2yTg1dsG7/jLKFWoTh7YhaO8ap89exYJCQmoVasWXn/9dd2069evR/PmzQMiV2XGY6od4BclOtyNwiux9+zwXmhG+kCvVJqTzURaB3ZuiYzVVX/h0x5njdxlx9mjiRvaZRnG65SXJFE9KRrz7uuEwW8tx709yvZSBWOcXC8jHn0bZxgnJEKW5plJaFgtAev3nWaeV7R7TnPQYBBsPVLet6q/jV7droGsNTi2ITmpL1dPFPCgjI2rfS3B3hLGgleZ52mSWivORu25VPSfNYje8wiWjxs5j/Sp53HKZxVf75WDXj+f4H29jPobuaOwyoijVpwHDBiAixeNw9X8/vvv6NWrVwAkIkQDEyJ/0aN+ubOlz+5sbzq/ur830/0HPByVmf3XZlanLcjib1pkJuG2znXwwuDGtpetfsbZaXGWy7LSBp7s30DznDoOp/oD1rRmEv56oa9ngOBEZ0BExUe5j1Lwy4Rps8wk74MOau5mdQX1u8zbR793S2vc2S0bg5rXUOY3V33IoXd7HOjP0l4P1hpFeSz7tCZs1ftuBfum93VXnAO05NwiK8kTRcAu1H2ZHkbvvDx3KCvR3CvOBsmmrcj3XZgQxlHd1C+//ILhw4ejpKREM82aNWvQu3dvnD17NoCSVV48/WbAlcnyCn3dEwPAs8eKx9FOoGf3FWZABjfa3IqzRYH8iCAIeHpAQ9zcvpbtZcs/fs0yk3BDuyzb69B7AHGR2gY8b93YUlkMoxy5d/BgmMg5YHGBCDLKvqhsZctukqIrlkM69feCt9vt1bAqnujXwJFOHK3yaJ8yz+x3ds3WTKN3uU40AbXz8Qhg97PFBqbaCVHhirEBt/m4SfnUlNj4UdD75gsActOtT3QbYXy79K9TbgkRaItEf1HXj/e7ouMoxfmBBx7AN998gzFjxjDPr1q1Cr1790ZBQQE+/fTTwApXyQn0B83X+tTd4AuDG+PFwY0x43bj1Wu7rtRsiCIenOwd3EmM753nUziui5fZk3eXiq05S1FvNzB6NtEae9b8SQyFoKr0KE1qgUSHKrn+nOThNbm1drJic2+PXCx5tDsmyLxoA1ApfTqm2g5UnO2c2OCZlJdHEHlhcGNkpcTg+UGNVJNYAtdggEcZ1bvndvoG07My09r77Qu8bY6vrHL8MZkYKOT3oSJN2AUaRzWB119/HTfddBM+/fRTPPDAA4pzv/76K3r37o3z589j5syZGDZsWJCkrDwogsIHuO5ODAcpZlAPrOIiw3BT+1pIjzcOr2BXf5Lrg6mwFmY+Lg4cg4QMi7ceM05kBrWpnUHPmxIbgb8Psd+cncWAptVQq0qMwvMtUTlROyBihUrzC9RXeTDy6utkBEFA7dRYr2uQbz3Rm3x04mCe1wLt9z2nDNOEG3iPFwQByTHl3qBvbl8LPz/eA7WqxCq+/QKn/4EaV/xn/PxYD800eq8474qz2cc2pmNtxW+tvd++oJ4E1MPQVFvh+sF5bZQX+bN24rsWKjjKORgATJ06FadOncJbb72FlJQUTJw4EcuXL0e/fv1QVFSEzz//HIMHDw62mJUCublooGeCR7TORHSEGy2zki3lr6IRGojnMuwy1fZHv0TKsB7lDVbr+QcL9ceW5+N7Y7taeGruJq7y7+6eY0kuAHjz+hYQRWc65iHsRzdWrHxwjgD2YSZXkIuKtbdz+QqPrpCqMwGrfrf/eV1TU/XrbfmoCJw4f0nznK8T5v7Ajnfgyf718elvezG+dz1MnPenZjoBOg65VJNavGMBVuxwOfKxXU5aLHYeO+/5zescLMzlwqUS/uVpdchD3okAMyjDfPq44lwBvWqT4mwdx/XQbrcbX3zxBfr06YMXXngBx48fx0cffeRRmgcNGhRsESsNihXnAL9jLpfg5TTFDENa1sCaPSfRMSdVcZynA7XrWq0oIkZ1mzPVrrw0rpEYbBEUeD03zofTPDNJ0wOynEd98DoqmBiEERUbRZclePeX1RKNY0Gbra9UBFqYnCA9XXjZVjnMUiMpGm/d0JJpyi6/Za+PbI5rTDo9Gt87DzNW8XrnDw14V+nu7p7rZ0nMY4eCcUfXHNzRtWxyU3fiSigPTaWHAPtWPl2CtoLJuyXC7RIAnbmssjjU2pSFviv//djVvnnRBsos/rrUTWXGRTeLoPF3qKEXDYDgx3GKMwBERUVh/vz56NatG95++21ERERg9uzZGDBgQLBFq1QogsKHWHcR7nZh8rBmXsd5rsKuFWe/xBEmU21NDp8x9sjvFHg/Wu3qpHApzjR7TNgBy1GhIJQPoH0NC6Pmx/HdMG/9QYzrUsdUvmGtamLygq1on+2PmON82oKWQix/E2MtrB6nxkVgeKua+OL3/eVlVpLXOyrcUbsHAQRWwTh1/pKmsupSrXraN8Ff/neNpGjsOFpgugy9788zAxpiUPPqimNe4xjVJJ0dVhcul4CPxrXTTVM9MQoHz1xE2zrsfiT7Sug19b0PVeSih9GYwTJBVZynT5+ue/6GG27An3/+icGDB+PkyZNe6W+55RZ/ilfpkZvpCM77nlmC5yNoV3fij8kGc512eeJPbtP/gFQEbN+XbIE7u2Xj3aW7vI6rHxuvZ+HUOOM9+fEV3LSTsBe9VSRlCJcrx1CuShrt0TRLTlocHu6dZzrfHV2y0axmEpqzQlsFGeV+SCv5haB41fcnvN8tJ8WrlgikSP/30w6M6VgbW4+cQ6zKWaPCQZWN4ahcgoAZt7fHtBW7MWlgY7R/aZHpMvQU53GdGZNi3nqzqT3JdrHksR4ovFyi6QSxSlyElzxObKO8yB9TZkoM1uw5hS51U7UzEEyCOuIaM2aMYSMURRGff/45Pv/8c8UxQRBIcfYz8gFW6HYVSvj2ONtTF8+E3pQR3qviesiLvKaJvgmg/Do65VLnGEykfu61kc3wxZr9GM+pLNzcoRbmrjuAzYe0w+99cXcHW2QkKgeFl7RtKpUOiMr+dglCeZxZGzpHO7rXMLfLb32arx671U6crMlQwTTnECYiwMGlH+9bD5kpMejTsKriuGJSy8bVQrcgoENOFXTwYX+5WYsnr51Lgrd/hUAQEebSDVEq9XfBkM0fyBeOCi+VYMsLfRHJEaKVxeWSUp8il4QyQVWcn3322ZCevalMVJTnxHMZdplm8ZQztGVNU2XKn4ORQ6iK8cT8w9PXNMCL3/yFl69rgr/N3uh1Pq9qHLYdMW+ypoU0sBjSoiaGtOB/5lHhbvz7hhbo+epSzTS1q8T6LB9RedBzrMVa9Snrx+xT5JyuEvoqnx0rZ3bGzyV8I9Bjn5iIMPYqrQy3jX4p7CjHtOLsFftcdT0OGW96rG4qzIqzclIvKghhLysCQVWcJ06cGMzqCQPkptoVZTsEj4GTfSZQ5vMYZZGf15spJfS5rUs2RrbJRHxUOFNxtjyTqjHe9WUPstEEDH38CDMU69gBK8NRXfnDR9PjUMPO1V6rBrUVzVS7WmIU0uIjER3uxt6TF4ItTkgidxrmctnpi8X3cnzdL6v2qh3s8Wa4W8DlEhGdrjiX9XX7hWOw0Ry+Mu+Rps1xhCZKU+2K8ZLwvOu2TSj6YWZSXmSawf5X8pqoT3yU9j7jqxpUxZ8HzyKNI+43D758ZOgxEnai57VXOYiWTLXl5/0kVAVC0PzBT6nqGYX69zfM7cKKCT3hEgTkPPltsMUJSeQLGW6bXsTRHWrZUo7ZsQbTVFuhnAa3vf/0SHf8suM4rrtiEVhRnIO5bLzHobzy7iuOWrI6cuQI5s2bh927d2um2b17N+bNm4ejR48GUDI2hYWFePbZZ5GXl4eoqChUr14dt956Kw4cOBBs0WyhNIjhqPwFXzgqu0y1bSlGgSAI+PaBLphzT0ckG4RZqCjPLBjc1yMXr41shm/u72xLeb5MYtAECGEner4RtE21CW4UnsmtoY6fG2azU7ZgEO52kfd/H8hNi/P8nRgdbsv3/REbwj4B5tunWnbhyv+0zgeazJQYXN82y2PVp3SaGLpt2KWcnSAs4ijFecqUKRgyZAguXtQOK1NYWIghQ4bgjTfeCKBk3ly8eBE9e/bECy+8gIKCAgwaNAiZmZmYOnUqWrRogV27vD3rhhqKcFQV5CXj+W6fLyq2qS7zN43HQq9h9QS05Ih7WlGeGYvpt7ZFbnocZt/d0XNMHfLCFyLCXBjSoibSE+yJW0srzoRTeFRnsMxygiNonLeK05uzz3uc5X9bfHnv6Jqt+F2ZzSIrE3oREu7qnoOe9dMxeVhThLldtryLdimBZlfAvRRnrxVnZxHsFXC7cHFO6lWMq/UfjlKcv/vuOzRq1AgNGjTQTNOwYUM0atQI33zzTQAl8+bFF1/Er7/+ig4dOmDbtm347LPP8Ntvv+HVV1/FsWPHcOuttwZVPjuoiKbaPNdhn+JsPo96pYFg0zUvDQvHd0OrWuUTCJnJMUGUSB9fvKBWZpMown6iwt24r0fulb+VQwDlHmdB8V+gYk/iSCvxd3bVd7pohPweFV6y9i1pkZWMP57r4/lNK7XBhSdeuB2PaNKgRprnYiLC8MGYNhjROhOAPe+iXc3KvFdt/fROW9WtKNtV5LLHRGj7RqFRqD6OUpz37NmDvDzjMC1169bF3r17AyARm0uXLuHf//43AOCtt95CXFy5Cc348ePRtGlTLF26FL///nuwRLQFsQI6Bwuk/m9F4bHTKUxFmezgRayg3X2FefcIx3Bfz1y8MLgxfny4m+I40yO0ze2va14aAG+lPdi8Pqo55t/fGXd1yzZOrMOmA+Wh45b4EFs+MTocuellYwv5BCEReP57S2u0z07B34c01kzTr7F+eEgeUg38ltiNbSvOPn6kXIKgHC857JtXEb1qj+5YO3iChDiOcg5WUqIdJkOOIAgoKiryszTaLF++HGfOnEFOTg5atGjhdX7YsGHYsGEDvv76a7Rq1SoIEtqDYsU5hDsLOTz9u12LvlZumdopjG8C2FdURSYnLRY7j523pSx/qO7qCZB7uufgt90n8fueU36ojagMRIW7cXN7b8dASlNtxoqzDXXf3L4WUuMi0bq2s5TBcLcLjWsk+lzO/lPlXqN9tSBa8GAXXC4REa2zOkT4n4SocMy8o4Pf6zEzZrDjXbRrWGc+HJX3b5dz9WbIJQrliWy57PGR2s5RQ/gSA4Kjpnyzs7OxcuVKFBdrmzcVFxdj5cqVyMrKCqBkSv744w8AQMuWLZnnpeMbNmwImEz+QP7JD+XOQg7PBIBdyo+V2Vw7TbUryCPjxuqtc/qkkPrdu6dHLlIMHMMRhBVYJol29/1hbheubVYd1RKj7S3YgfjanYe5XaQ0VzD0Pjdmxgz2mGrb83L7HI4Kzl6osdMbdTCRT3A47BaHFI5SnK+99locPnwYEyZM0Iyl+MQTT+Dw4cMYOHBggKUrRzITr1mzJvO8dHzPnj0Bk8kfKL1qV4y3LJBXYfQtYc3Sxuo4BzFLw2oJtpVFBBFVM3EJFWcii3AYjP3MAuMYwUdFi8dM+Bczr5ejnIOZXnEWvH4v3Va+rcFp37eK0gdWlHF8sHGUqfYjjzyC6dOn47XXXsOPP/6IcePGISenzFHHzp078f7772PTpk3IyMjAY489FjQ5CwoKAAAxMWxnRLGxsQCAc+fOMc8XFRUpTM3Pnj3LTBdsKqKfKp5Lsuu6jT5K8rMvDm6MFTuPY3DzGvZUDiA9IQqLH+2OOBuVcSdj9bE5/VOibkcuQXCc8xSiYsBaWVEOYqndmaGi+l0gGNjwaphRbKx+AlLjInC84BKA4DkHUyNA6ZTVaZ83Z5uR8+OuIBMAwcZRI+qUlBT88MMPGDJkCDZu3IiHH35YcV4UReTl5WH27NlITU0NkpS+89JLL2HSpEnBFsMQ6aPvtNk/fxOMwc5N7WvhJsaeQ1+pkxpre5kVDSsfkOaZSbbLoYVaSS7bD1bJXkoiIMhXsVyu8qOENcipF6FGb2I+EN16bGQY5t7TCWFuwbYVSPNetVW/BZUZscP6HIU8zhLNFC6ZjTGNIazjKMUZABo0aIA///wTc+bMwcKFC7Fv3z4AQGZmJnr16oWhQ4fC7Q7unh/Ji/aFCxeY58+fL3M0FB8fzzz/xBNPYPz48Z7fZ8+eRWZmps1S+o7UwVck845AxsOkjimwWN7jbOFLGMipFbV0LkHwKbwVQWhhtOJMXZo5OueG7gQ/EXjM7XG2/jJmptgbutFX52CAgDC3c1dD5fKE8riON7RgA9rmp4vjFGcAcLvdGD58OIYPHx5sUZhIjsn279/PPC8dr1WLvYIYGRmJyMjAhh2wgkdxDq4YthIVbjzpYp+ptv75EO5/HYlVSwFLz0GjkWj5ZvAFtqm27dUQBDMcld1etSsToTzIJgJPIJqLP6pwu8y5S1JPVpetOLtkv5373jhXMmOUfbn2lfCMkyszjnIOFio0a9YMALB27Vrmeel406ZNAyaTPyg31Q7lrsKbblfiiGphm1dt0m4CSwCXgQNqzM90DkZti7AfgaEkU1PzP7Pv7hhsEYLOVfXTgy2CT/C+JnrvkznnYNbwx7crLtKNa5tV507vFY4KSmtAp3U5FSWOM3nVtgdHrjgDZY61du7ciXPnzmmu4nTt2jXAUpXRqVMnJCYmYufOnVi/fj2aN2+uOD9r1iwAZV7CQxmPR9AK9oIFSp8N5Q62MmHmOaXERuDk+UvoXi9wgzx1exXIORjhJ1gDRKV5H7U7I5RhdfjytKqVjD+e7UOhpyo5gXAOFu2H1cSYiDD8Y0gTLPrrCC5cKjGdXxCUw0wndzMOFs0Qsh6yB8cpzps2bcJDDz2EJUuWGJo9lpSYf0HtICIiAvfddx/+/ve/495778UPP/zg8aQ9ZcoUbNiwAd26dUOrVq2CIp9dVImNwKe3t3OcowZfMfw42WRuW7HumvMJhFft7x7sgl+2H8eAZtUs1mYeVnslYwbCH8j7+vJwVEESJkSRbxkxowglxoT7QxwiQNjhP8XMu2a1Nn+Y4boEICLMhZZZyfhlx3HD9F7OwSBAEETFb6cSyv2hizExSpjHUYrz9u3b0blzZ5w9exadOnXCoUOHsHv3bowaNQq7du3C2rVrUVxcjIEDByIpKSmosj799NNYuHAhVqxYgbp166JLly7Ys2cPfvvtN6SlpeGDDz4Iqnx2EBXuRseciufchPV9G9isOub9cRCAfaZMRh9SJ38cQpFhrWrivz/vQousJFP5zHw/qiZE4bpW7Pjt/oLVjOKiHNV1ExUEVtgVgXGM4IMmuPgJ9XF89aRon8vwp3OwqgmROHK2CL0a6FtLDWpeHV+tP2iqbEluXpG8TLWF0ImVHMoKJ5lq24Oj9ji/+OKLOHfuHKZOnYply5ahS5cuAIBPPvkEK1euxJ9//onOnTtj8+bNmDJlSlBljYqKwuLFi/HMM88gJiYGX375Jfbs2YMxY8Zg7dq1yM7ODqp8hB6BWcELczvq9arw5FWNx+9P98IXd3YImgz+iH0ezmhHD/Ssi+aZSXhhUCP7KyQqLSzvsbyeWAlvaHK04vPY1fXQtGYi7uqe43NZpvY4m2xaX97bCZOHNcXtXfXHpq+PbI7nrm1oqmyWI0GDHF75Q+VNCeU+kOXDgjCPo5YtfvrpJzRo0ACjR49mns/NzcVXX32F7OxsPPPMM/j3v/8dYAmVREdH4/nnn8fzzz8fVDkIc7A6PnmHb5fyc+TsRQNB7KmHKKdKnHlv9U7fL8xSnJNjI/DlvZ2CIA1RkZG/C9LqhLPfDudRXCo31Q6iIERAuLdHLu7tkWtLWf401a6WGI0RrY3DngqCgOSYCFNlSwrZhUvFXOnV2zDVptpOo6IonBXFyVmwcdSS2NGjR9GwYflMV3h42Z6fixfLFZCkpCR0794d8+fPD7h8RMWAtbrsj07k9z2nbC+TsJ9Q+H7EktMgIsAww1GFwLsSbC6XlHr+pttFmMHMJO7gFjVQPTEKIzmUYbOYfc+lMdXqfL4xT6laRxb8s3jhD0JZ4WRtxSHM4yjFOSUlBUVFRYrfALBnzx6vtEePHg2YXETFgmU+J+9QrMYDVnNfT3tmoQn/EgofkOgIRxkHERUUuZWMtOK86/h52TFHDRkcSXGJNedgROW4V3aZ78dHheOXv/XEy8PsD3tq1jO22WtSj7EE0KRcILhQVP5cnW5p52Qc9RWsU6eOQklu3rw5RFHEZ5995jl2/PhxLFmyBFlZWcEQkagAsMZ+t3So7fnbrtnOhCjykhoS0AeEIAAAK3ed8PzNGliFu+ldMaJUJFNtQhuXjkMVs8qMXlm+sO/kBXNymBRDveIsCAL5AwgAc9YdKP9hcLvNOlmtTDhKce7Tpw82bdrkUZ6vvfZapKam4vnnn8eoUaPwyCOPoE2bNjhz5gxGjBgRZGmJUIXVQTepmRiQumfe0d7zNynWFY/wMEd1qQRhGdYg/njBpSBIElrIlQJSBQg1ekqmUyZazMph1rKitNR7xZlelsBi9Mhu6VArMIKEII4a5d1888147LHHcOTIEQBAbGwsZs6ciaSkJHz++ed47bXXsGfPHvTq1QtPPfVUkKUlQhXDMM5+rDvc7cK0sW1QPyMeU8e08WNNBC92fq/v6JKN3PQ4PNonz8ZSCSIwyK1t3IwRftFlcyaclRG54yMyh/Tm9ZHNgy1CUJHrjOoxQKg2F7Nyl6qdgzncq7aTZbOK0TVd27S65rm4yMq9dcxRV5+Tk4OXXnpJcaxnz57Ys2cPli1bhlOnTiEvLw+tWrUKkoRERcBodrRZzSSb6mEdFdG9Xjq619OPpUgEDjsHK8mxEVg4vhsA4F8/bLOvYL9O5xBEGfJBPWtlrGNuauCECVEUK84VccTtI93y0oItQlCRr7b2qJ+OhtUSsPnQWQChO9FiVm71driyPc7OvfbE6HLrwIgKEmbU6H7rhVOVW05WRhylOM+bNw/h4eHo16+f4nhsbCz69u0bJKmIisap8+Xmhte1rInr25Z5pVz0SDdsOnAGVzeq6re6newtsrIS5qd9YgQRaihWSxnvhdvBg1unIL+HtG/Tm8r+CUyLV4ZMlPtccUprMdtuze9xVq84C44eHMVGhmHefZ3gEgREVJDtWFbb2r09ctC4RmC2NjoVRynOQ4YMQZ8+fbwUZ4Kwk192HPf8fUfXbNTLiAcA5KTFISctzq91O/fTUHn5x5AmuP5/v+F+8oJOVHJKDcyMaY7JGEUfT/eLm8oyJ/NwrzwcOlOI61rWBKAO9+aMm+DvPc6sFee4SGeHXGxqkyWiU7Bq3eDg+Y2A4SjFOS0tDcnJycEWg6hE+HMgyCqaOh3nUbdqPFY/dZXtg5a8qnHYdqQAXSu5aSIROsjNjFmry04Z2DuZklL55EMQBXEoYiX/CCbGhOPdm1t7fgsKxTkYEvmOltx5VdkLEeoVZwjAA1fVxYxV+wDYFxKU0CZU25oTcJTi3L17d6xatQqiKNIHmggIgW5mXh8MwhH4o7/5eFw7zF13ACNaZ9peNkH4A/n+S9YrQYqgMaJijzPdMEIf+TvllD3OZqXQMu3+4s6OzOPqcFQuQUBqXCQzLeEsaATrMK/aL7zwAo4fP46HH34YFy9eDLY4RKUgsB8q0psrD+kJUbizWw6SYyOCLQpBcCGf2GN51XbKwN7JKOI4B1EOp0KfQCUKU+0gyqGA4z3vkF3F87fWhFpiDDvkptceZzjo2iswkj8fwPqiEY1hHbbiPGPGDPTv3x9vvvkmZs6ciV69eiErKwtRUVFeaQVBwDPPPBMEKYmKRKDHgWSCRBCEUzEy1SaMub1LNt5YtB0AmUOaISrc2Xtc/YVc6XRqe3ns6np45futimOjO9bGyl0nAFjwqq36LQg0KRcImmcmeczhrToupDFskBXn7OxsDB8+HC+//DIAYOLEiRAEAaIo4ujRo/j0008185LiTNhBwLtq6nMIgnAoitVSRudI3Zcxg1vU8CjOpAx4ozUh06tB5QzRKDfnd0p7UUtxb49cLNh0GBsPnGGnNym2ep+7AMGxkwYVCbmybHnbDX0Egqs45+fn49ixY57fU6dODaI0RGVh8rCmeHzWBgD+3YMWE+H9elGfQxCEUzHan0s+Goyp7M6vjEiOjcBN7bPw8a97PcemjmmD7vUqpxNFJ/oNkL/6beukeB1T/zY7jipVbXIWBPIHEBB8eGYS1Ls5zFR79OjRwRaBqAQkyYLZ+7Orzkj03mJAEFagsTgRCOpWjcOB04WKY/GRYThXVAwASI6h/fpmIF2AzYuDm+Dz1ftxqaQUANCjfuVcbQaUq8ys2OnBQP69+ceQJsw0cknNx3FWleWMy67wCBp/m4EmBh3mHIwgAoEybqJ/6xrYrLriN/U5hBWo2RCBIMLtPSSQ95Esh2GEEvm7anUfYWWA9kqW4UTnYMp47uw0ijBaJiX3tlxxypVXbOwIfUZjWFKciUqIS9bq/b2nSP3RSY2nFRuCIJwJa0xEAyVzyO8XzTNoQ+2qDKXJc/DkkFOsXhIGcPrCZcVvX1acvcI4O+S6KzqKFWcy1bZM0E21169fj+eff95S3meffdZmaYjKQCAdcMg7mRcHN0b9jISA1U0QBGGGIS1q4MfNR1CvanywRakQ0L5NbWgAXobLgc7BWIrw3pMXFL/lopo1MWeFo5JDkyr+wY7mRc/GAYrzH3/8gT/++MNUHlEUIQgCKc6EJQJpqi3vZG5qX8u/lREEQfhAv8YZmH9/Z2SnxXqO0TjJLBTHmeBHEY4qeGIouKVDbby1eKduGl/GTkZ7nJ0ygVDRsEVxpi9C8BXnnJwcdOrUKdhiEJUIpeLs3w6auhiCIEIFQRDQuEai4hg5gzFHRmK0528a/2tzVf10/LD5CHLT44ItSlAJ5HiEl0SZA1UtlKGNvOWWvHGz8F5xVuZ3yG2ocNjhc4E+Bw5QnDt37owPPvgg2GIQlYhAzvBS+BaCIEIZ6sHMERcZhl/+1gMRYS7HKEJO5JVhzdBu7X5c27RasEUJKnY4bLIbs3KwLLXfH91aM71XHGd1qCtz1ROcOKV9hTpBV5wJItDI9+P4vSOhUSdBECEMzf2Zp2ZyTLBFcDyJMeEY17lOsMUIOoq9wg7RbLhWJg2cmsVHaa9aR0coVQ91dofcBoJgQl61iUqHMvyDv021adRJEEToQn0YQfgPO2Lr2g2P4qr0ql32q0G1Muench8JLG5om6Wqz0t1NhaAMI0dFjC0dYcUZ6IS4jKYKbUT6mMIgghlqA8jiMDglJVWHjEExt7s90e3xp3dsvHRuHa6eaMj3AZlcwhABIxrmpRvp6DPAZlqE5WQS8Wlnr9JcSZCAZrlJYJFkay/JAjCXpRxnJ2hMfJ8bVgr5dWTovFEvwam6yOv2oHBalz5qglRnr9pKBJkxbm0lD7IRODZcOCM529/m2rf2rkOFvx5GFfVT/drPQRBEARBhBbyMYhT9MUwl4BaVWJQcLEYmSns/frKvdm+1Sfdg9gIN85fKkGLrCTfCiSYWB3vyrfrkMNbWnEmKiF9GlbFP7/bAsD/H6q2dVKw+qleqBIb4d+KiAoNfaoIgiAqNk5ZaRUEAT890h2loohwN3tHp1Lh901uKfuap3vjwqViVImL9Kk8go3Vx0S6shLa42ySLVu24OWXX0aPHj2QmpqK8PBwZGRkYOjQoVi2bFmwxSM4iAov318TiM9UWnykwpM3QRAEQRCEwlQ7eGJ44XYJmkoz4PuK862dyj2qS9mjI9ykNPsRs49JWvDp06iq5xjp0LTibJpevXrhwIEDiIuLQ/v27ZGSkoLNmzdj7ty5+PLLLzFlyhQ89NBDwRaT0MGJe4oIgiAIgqi8hOpwxMo4qleDdHywfPeV/HZLRLAwe58XP9Yd+05eQKPqiZ5jtPpMK86mqV+/PqZPn45jx47hxx9/xGeffYaNGzfinXfegSiKePTRR7F58+Zgi0lwQv01QRCENlHhNEwgCH9RUlquiTjFVJsHhXMwX/c4h9B1VyYSosIVSnMZpDnTF9EkCxcuxM0334yoqCjF8TvvvBN9+vRBSUkJvvjiiyBJR/DgRGccBEEQTiQ5hvwzEIS/+GHzkWCLYA2FqbaFgZRDTdQrMvZMUNDTIsXZRpo1awYAOHjwYJAlIfRQ7imiToAgCIIgiOASWivOsgUI3wsjAkBe1XjLeR/ulYeMhCg8eFVdGyUKTWiPs43s2rULAJCRkRFkSQiCIAiCIIhQIYT0ZoWsYTpOxDTzKxTvELrwEKZOaixm3tEeqRYcsD3Yqy4euCqXzOpBirNt7Ny5E/PnzwcADBw4MMjSEHrQa0+EGuSQgyAIomITSmMTuazhbvOS2xkHmuCnfXYVy3lJaS6DFGcbKC4uxpgxY1BUVISRI0eiVatWuumLiopQVFTk+X327Fl/i0gQBEEQBEE4iKyUGOw9eQFAiJlqy2QtulzqY1m+SkMQgaPSKc5DhgzBX3/9ZSrP9OnT0bZtW83zDzzwAH755RdkZ2fjP//5j2F5L730EiZNmmRKBsI/iOQhkCAIQhOydiAI/9G3cQb++3PZNr9QUiDlsi7ddgwj2mSay6/4O4QunKj0VDrFeffu3di6daupPBcuXNA89/e//x1vv/02qlatiu+//x4pKSmG5T3xxBMYP3685/fZs2eRmWmu0yF8QNZH06CQIAiCIIhgoAzrFJoK5EiTSrOaEL1sopJS6RTn9evX21bWO++8g6effhqJiYlYsGABcnNzufJFRkYiMtL85nzCHmIjypt9JMUoJQiCIAgiGISI0tivcQa+23QYANCnYVWF2PFR5lUJ+SRBiNwCggBQCRVnu5g5cybuvfdexMTE4JtvvkHz5s2DLRLBSWxkGP53S2sIAGIi6BUgnI9IphEEQRAVjouXSoItAhf39cz1KM7PDGiIo+cues75vFJOS85ECEHLbRb49ttvccsttyAsLAxz585Fp06dgi0SYZLeDauiV8OqwRaDIAjC0ZAfCILwHx/9uifYInARGeb2/O12CYCPcZzluvKKHcetC0YQAYYUZ5MsX74cw4YNgyiK+Oyzz9CnT59gi0QQBEEQfmFwixoAgKY1E4MsCUFUPEpDZF4qTBYzyiUICsXX1wXjLYfP+VYAQQQQslM1yYABA1BYWIg6dergyy+/xJdffumVpnPnzrjtttsCLxxBEBWSfwxtgvs+XYeHetUNtihEJeOR3vXQMisZ7etYj/9JEERoEyaL1exy+e4VW56D4jgToQQpziY5ffo0gDLv3Lt379ZMR4ozQRB2MaBpdXTNS0NCVHiwRSEqGRFhLlzdKCPYYhAEEUTC3eUGqmUrzjJTbQuKrzyPmzRnIoQgxdkk5KSHIIhgQEozQRAEEQzkyq3dai4pzkQoQXucCYIgCIIgCIJg4pItEZeK6vjTVkpU7pkmiFCBFGeCIAiCIAiCIJi4FYqziM2Hznp+W9njLIcUZyKUIMWZIAiCIAiCIAgmcVFhnpXllNgIXJDFn/Z1j3N4GCnOROhAe5wJgiAIgiAIgmDidgn4c9LVEMUyR2HFJaWec74uGIe5aA2PCB1IcSYIgiAIgiAIQpOYiHKVIS6q/G9fw1GRczAilKBpHoIgCIIgCIIIMHd3zwm2CJa4qn5Vz9/WTLXLM4WR4kyEEKQ4EwRBEARBEESAqZ8RH2wRLCG3rvZV7ZXHiCYIp0OtlSAIgiAIgiACTEpsRLBF8BkrK86louj5O8xNK85E6EB7nAmCIAiCIAgiwHTOTcWdXbNRv1porTwr9zWbV3xLS8sV53ByDkaEEKQ4EwRBEARBEESAEQQBT/RvEGwxTCNfZbay4lwiU5yjItw2SEQQgYGmeQiCIAiCIAiCMI0VQ+sSman2Az1z7ROGIPwMKc4EQRAEQRAEQXChMNS2sORcWh4GGs0yk3yWhyACBSnOBEEQBEEQBEGYxtcVZ7cVW2+CCBKkOBMEQRAEQRAEwYWVVWY5cq/aLorjTIQQpDgTBEEQBEEQBMGFr6qu3Ks2QYQSpDgTBEEQBEEQBGEaK4vPxaQ4EyEKKc4EQRAEQRAEQXDh67ZkWnEmQhVSnAmCIAiCIAiC4ELw0VibVpyJUIUUZ4IgCIIgCIIgTGNFiS4hxZkIUUhxJgiCIAiCIAiCDx9NtdtnVwEAJEaH2yAMQQSOsGALQBAEQRAEQRBEaODrHueMxCisevIqxEeR4kyEFqQ4EwRBEARBEARhGqtKdHpClL2CEEQAIFNtgiAIgiAIgiC48DWOM0GEKqQ4EwRBEARBEATBheCrrTZBhCikOBMEQRAEQRAEQRCEDqQ4EwRBEARBEATBBa03E5UVUpwJgiAIgiAIgjANWW0TlQlSnAmCIAiCIAiC4IKUZaKyQoqzDbzwwgsQBAGCIODjjz8OtjgEQRAEQRAE4RdEMdgSEERwIMXZR7Zu3Yq///3v5GGQIAiCIAiCqFTQ+JeoTJDi7AOiKOKOO+5AUlISBg4cGGxxCIIgCIIgCIIgCD9AirMPvPfee/j555/x6quvIikpKdjiEARBEARBEIRfIUttorJCirNFDh8+jMcffxxXXXUVbrzxxmCLQxAEQRAEQRABhQy1icoEKc4WeeCBB1BYWIi333472KIQBEEQBEEQREBwy/Y1h7lJdSYqD2HBFiAUmT9/Pr744gtMmjQJdevWDbY4BEEQBEEQBBEQoiPcuLdHDi4VlyI9PirY4hBEwCDF2SQFBQW45557kJeXh7/97W+WyigqKkJRUZHn99mzZ+0SjyAIgiAIgiD8ymNX1w+2CAQRcCqd4jxkyBD89ddfpvJMnz4dbdu2BQA8+eST2LdvHxYtWoTIyEhLMrz00kuYNGmSpbwEQRAEQRAEQRBEYKl0ivPu3buxdetWU3kuXLgAAFi1ahXeeust3HzzzejZs6dlGZ544gmMHz/e8/vs2bPIzMy0XB5BEARBEARBEAThPyqd4rx+/XrLeb/99luUlpZi48aN6N69u+Lcli1bAAB///vf8d5776Fv376YMGECs5zIyEjLq9UEQRAEQRAEQRBEYKl0irMd6CnfW7ZswZYtW1C7du2AyUMQBEEQBEEQBEH4DwpHZYKJEydCFEXmv9GjRwMAPvroI4iiiGnTpgVXWIIgCIIgCIIgCMIWSHEmCIIgCIIgCIIgCB1IcSYIgiAIgiAIgiAIHUhxJgiCIAiCIAiCIAgdBFEUxWALUdk5e/YsEhMTcebMGSQkJARbHIIgCIIgCIIgiJAgULoUrTgTBEEQBEEQBEEQhA6kOBMEQRAEQRAEQRCEDqQ4EwRBEARBEARBEIQOpDgTBEEQBEEQBEEQhA6kOBMEQRAEQRAEQRCEDqQ4EwRBEARBEARBEIQOYcEWgACkiGBnz54NsiQEQRAEQRAEQRChg6RD+TvKMinODuDcuXMAgMzMzCBLQhAEQRAEQRAEEXqcO3cOiYmJfitfEP2tmhOGlJaW4uDBg4iPj4cgCMEWR8HZs2eRmZmJffv2+TWgOEEYQW2RcBLUHgmnQG2RcArUFolgIYoizp07h+rVq8Pl8t9OZFpxdgAulws1a9YMthi6JCQkUCdIOAJqi4SToPZIOAVqi4RToLZIBAN/rjRLkHMwgiAIgiAIgiAIgtCBFGeCIAiCIAiCIAiC0IEUZ0KXyMhIPPfcc4iMjAy2KEQlh9oi4SSoPRJOgdoi4RSoLRIVHXIORhAEQRAEQRAEQRA60IozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM8GksLAQzz77LPLy8hAVFYXq1avj1ltvxYEDB4ItGuFQLly4gC+//BLjxo1DvXr1EBUVhdjYWDRr1gzPP/88CgoKNPNOmzYNbdu2RVxcHFJSUtC/f3+sWLFCt77ly5ejf//+SElJQVxcHNq2bYvp06fr5tm/fz/Gjh2L6tWrIyoqCnl5eXjuuedw8eJFS9dMhA4nTpxAeno6BEFAbm6ublpqj4S/OHbsGB599FHUq1cP0dHRSElJQcuWLfHYY48x03/99dfo1q2bJ7xP9+7d8c033+jW8eeff2L48OFIS0tDdHQ0mjRpgtdffx2lpaWaeU6dOoUHH3wQtWrVQmRkJGrVqoWHHnoIp0+f9uVyCYeyevVqjBgxAtWrV0d4eDiSkpLQpUsXTJ06FawdnCUlJXjttdfQpEkTREdHIy0tDSNGjMBff/2lW0+g2i9BBAyRIFQUFhaK7du3FwGI1apVE0eMGCG2bdtWBCCmpaWJO3fuDLaIhAP53//+JwIQAYgNGjQQhw8fLl599dVifHy8CECsX7++eOTIEa98Dz74oAhAjI6OFgcNGiReffXVYlhYmOh2u8W5c+cy65o1a5bodrtFQRDEbt26idddd52YlJQkAhAfeeQRZp7t27eLqampIgCxcePG4ogRI8Ts7GwRgNipUyfx4sWLdt4OwmGMHj1aFARBBCDm5ORopqP2SPiLNWvWiFWqVBEBiI0aNRJHjhwp9uvXT6xVq5bodru90r/22msiADEsLEzs27evOGjQIDE6OloEIL755pvMOlasWOFJ07ZtW3HEiBFiRkaGCEAcPny4WFpa6pXn2LFjYm5urghAzM7OFkeMGCE2atRIBCDm5eWJJ06csP1eEMFD6q8AiC1bthRHjBgh9ujRQwwLCxMBiDfccIMifUlJiThkyBARgJiUlCRed911Yrdu3URBEMSYmBjxt99+Y9YTqPZLEIGEFGfCi6eeekoEIHbo0EE8d+6c5/irr74qAhC7desWPOEIxzJt2jTxjjvuEDdv3qw4fvDgQbFFixYiAPH6669XnPvxxx9FAGKVKlXEbdu2eY6vWLFCjIiIEJOSksRTp04p8pw4cUJMSEgQAYizZ8/2HD98+LBn8Ld48WIv+Tp16iQCEB944AHPscuXL3sGBM8995z1iycczcKFC0UA4h133KGrOFN7JPzF0aNHxdTUVDEmJkb86quvvM6rlY8tW7aIbrdbjIyMFFesWOE5vnXrVrFKlSpiWFiYuH37dkWeS5cuiXXq1BEBiFOmTPEcP3funNihQwcRgDh16lSvum+88UYRgDh06FDx8uXLnuP333+/CEAcPXq0xasmnMbly5fF9PR0EYD4ySefKM5t3rxZTElJEQGIP/30k+e4NClet25d8fDhw57js2bNEgGIubm5inYjioFtvwQRSEhxJhQUFRWJiYmJIgBx7dq1XuebNm0qAhDXrFkTBOmIUGXFihUiADEyMlIsKiryHO/Xr58IQHzttde88jzwwAMiAPFf//qX4vjLL78sAhAHDRrklWfOnDkiAHHAgAGK47/99psIQExPT/dayTt8+LAYHh4uJicne338idDnwoULYk5OjtiwYUNx27ZtuooztUfCX9x9990iAPGtt94ylf7BBx/0OjdlyhQRgHjfffcpjn/22WciALFZs2ZeeX7//XePdYOcgwcPii6XS4yIiFAoRaIoihcvXhTT0tJEt9vNtBYiQo+NGzeKAMR69eoxz0v93Msvv+w51qBBAxEA0+Jm4MCBIgBx1qxZiuOBar8EEWhojzOhYPny5Thz5gxycnLQokULr/PDhg0DULZvhSB4adasGQCgqKgIJ06cAFC2j/6nn34CUN6u5Gi1NWl/FCvPNddcg6ioKCxcuFCxT1TKc+2113rFl6xatSq6dOmCU6dO4ZdffrF0fYRzmTRpEnbt2oV33nkH4eHhmumoPRL+orCwEB9//DFiY2MxduxYrjx67cpKW2zZsiWys7OxadMm5Ofne44vWLAApaWl6NKlC6pWrarIExkZiWuvvRYlJSX49ttvueQmnA1vfOUqVaoAAHbv3o2//voL0dHRuOaaa7zSWWmLdrZfggg0pDgTCv744w8AZZ0UC+n4hg0bAiYTEfrs2rULABAeHo6UlBQAwNatW1FUVIS0tDTUrFnTK49WW9NroxEREWjcuDEuXryIbdu2ceXRq4sIbTZs2IBXX30VY8eORZcuXXTTUnsk/MWaNWtw7tw5tGjRAtHR0fjuu+8wfvx43HPPPXj99ddx8OBBRfrTp09j7969AMCcwM7MzERqair27NmDs2fPeo5baVfUFisX2dnZyMnJwdatW/Hpp58qzv3111/4+OOPkZycjCFDhgAobx+NGzdmTjyy2kcg2y9BBBpSnAkFUmfHGjjKj+/ZsydgMhGhzxtvvAEA6Nu3r2fG26itxcbGIikpCadOncK5c+cAAGfPnsWZM2d087HaKLXrykdpaSluu+02JCUlYfLkyYbpqT0S/mLz5s0AgPT0dAwePBj9+/fHa6+9hrfffhsPP/wwcnNzMWPGDE96qX0kJycjNjaWWaZd7YraYuXC7Xbjww8/RFJSEm688Ua0atUKo0aNQs+ePdG0aVPUrFkTixYt8kxw+9KmAtF+CSLQkOJMKJBCBsXExDDPS52gNHAkCCO+/fZbvP/++wgPD8cLL7zgOW7U1gDv9iYPaWWmjVK7rny8+eabWL16NV555RWP2aEe1B4Jf3Hq1CkAwLx587BgwQK89dZbOHr0KPLz8/Hoo4+isLAQo0ePxvr16wFYa4s8+agtEgDQqVMnLF26FNnZ2Vi7di0+++wzLF68GC6XC71790Z2drYnrT/alNV81BYJJ0CKM0EQfmPLli246aabIIoiXnnlFc9eZ4LwJ3v37sXTTz+Nbt26YcyYMcEWh6jkSPFni4uL8fzzz+Oee+5BWloaatWqhVdeeQXDhw/H5cuX8corrwRZUqIyMGPGDLRt2xaZmZn47bffUFBQgG3btmHMmDF49dVX0bNnTxQVFQVbTIJwJKQ4Ewri4uIAABcuXGCeP3/+PAAgPj4+YDIRocmBAwfQt29fnDp1CuPHj8eDDz6oOG/U1gDv9ibl0cvHaqPUrisX9957Ly5duoR33nmHOw+1R8JfyNsJyzmYdGzp0qWK9GbaIk8+aovE9u3bMXr0aKSmpmL+/Plo27YtYmNjUbduXbz77rsYMGAA1q5diw8++ACAf9qU1XzUFgknQIozoSArKwsAsH//fuZ56XitWrUCJhMRepw8eRJ9+vTBnj17MHbsWPzrX//ySmPU1s6fP4/Tp08jOTnZ86FMSEhAYmKibj5WG6V2XbmYP38+YmJicNddd6F79+6ef6NGjQJQNqkjHTt8+DAAao+E/5CeY0xMDNLS0rzO165dGwBw9OhRAOXt49SpUx5lQY1d7YraYuVi5syZuHz5Mvr27auY0JEYMWIEAODnn38G4FubCkT7JYhAQ4ozoUAypV27di3zvHS8adOmAZOJCC0KCgrQr18/bN68GUOHDsX//vc/CILgla5evXqIjIzEsWPHcODAAa/zWm1Nr41evnwZmzZtQlRUFPLy8rjy6NVFhC6nT5/G0qVLFf9+++03AMDFixc9x6QwUdQeCX8heRYuLCxkmsCePHkSQPmKW1JSkkeJWLdunVf6ffv24fjx46hVqxYSEhI8x620K2qLlQtJ+ZQm/NRIx6V9+VL72LRpEy5fvuyVntU+Atl+CSLQkOJMKOjUqRMSExOxc+dOj6MSObNmzQJQFn+UINQUFRVh0KBBWLVqFa6++mrMmDEDbrebmTY6Oho9e/YEAHzxxRde57XamhRLUjovZ/78+bh48SJ69eqFqKgorzxff/2118D1yJEjWLZsGZKTk9GpUyfeSyUcjCiKzH+7d+8GAOTk5HiOSat91B4Jf5GVlYVmzZpBFEWPObYc6Zg8dI9eu7LSFtetW4ddu3ahcePGnjYPlEU6cLlcWLZsmWfFW6KoqAhff/013G43+vfvz3OphMPJyMgAUBYijcXq1asBlFtB1KlTBw0aNEBhYaEnzrIcK23RzvZLEAFHJAgVTz31lAhA7Nixo1hQUOA5/uqrr4oAxG7dugVPOMKxFBcXi0OGDBEBiF26dBHPnz9vmOfHH38UAYhVqlQRt23b5jm+YsUKMTIyUkxKShJPnTqlyHPixAkxISFBBCDOnj3bc/zIkSNibm6uCEBcvHixV12dOnUSAYgPPvig59jly5fFoUOHigDE5557zuwlEyHG7t27RQBiTk4O8zy1R8JffPLJJyIAsUmTJuLBgwc9x9etWyempKSIAMTPP//cc3zLli2i2+0WIyMjxZUrV3qOb9u2TaxSpYoYFhYmbt++XVHHpUuXxDp16ogAxClTpniOFxQUiB06dBABiFOnTvWS7cYbbxQBiNddd514+fJlz/EHHnhABCCOHj3ahjtAOIHff/9dBCACEP/zn/8ozq1cuVKMjY0VAYg//vij5/j//vc/EYBYt25d8ciRI57js2fPFgGIubm5inYjioFtvwQRSEhxJrwoLCwU27VrJwIQq1WrJo4YMcLzOy0tTdy5c2ewRSQcyOuvv+75IA8ZMkQcPXo089+xY8cU+R588EERgBgTEyMOGjRI7NevnxgWFia63W5x7ty5zLpmzZolulwuURAEsUePHuKwYcPEpKQkEYA4fvx4Zh7pgy0NXkeOHClmZ2d7JokuXrxo9y0hHIaR4iyK1B4J/zF69GgRgJiUlCT2799f7NGjhxgZGSkCEG+//Xav9FOmTBEBiGFhYWK/fv3EQYMGidHR0SIA8f/+7/+YdSxfvtyTpl27duKIESPEatWqiQDEYcOGiaWlpV55jh07Jubk5HjejZEjR4qNGzf2KEsnTpyw/V4QwePRRx/1fKsbNWokDh8+XOzUqZPocrlEAOIdd9yhSF9SUuKZFE9OThaHDRsmdu/eXRQEQYyOjhZ//fVXZj2Bar8EEUhIcSaYXLhwQXzmmWfEnJwcMSIiQszIyBDHjBkj7tu3L9iiEQ7lueee83yM9f7t3r3bK+/UqVPFVq1aiTExMWJSUpLYt29fcfny5br1/fLLL2Lfvn3FpKQkMSYmRmzdurU4bdo03Tx79+4Vx4wZI2ZkZIgRERFibm6u+Mwzz4iFhYW+XDoRIvAozqJI7ZHwD6WlpeJ///tfT9uKjY0VO3TooNtO5s2bJ3bp0kWMi4sT4+LixC5duohff/21bj2bNm0Sr7vuOrFKlSpiVFSU2KhRI3HKlCliSUmJZp4TJ06I999/v5iZmSlGRESImZmZ4gMPPOBlYUFUDObMmSP26dPHs/qbnJws9ujRQ/z000+Z6YuLi8VXX31VbNSokRgVFSVWqVJFHDZsmPjnn3/q1hOo9ksQgUIQRVH01dybIAiCIAiCIAiCICoq5ByMIAiCIAiCIAiCIHQgxZkgCIIgCIIgCIIgdCDFmSAIgiAIgiAIgiB0IMWZIAiCIAiCIAiCIHQgxZkgCIIgCIIgCIIgdCDFmSAIgiAIgiAIgiB0IMWZIAiCIAiCIAiCIHQgxZkgCIIgCIIgCIIgdCDFmSAIgiAIgiAIgiB0IMWZIAiCCHkEQVD8c7lcSExMRPv27fH666/j8uXLwRaRybRp0yAIAiZOnBhsUUwjCAJq164dbDH8xpgxYyAIApYsWRJsUQiCIAgHQIozQRAEUWEYPXo0Ro8ejRtvvBFNmzbF6tWr8fDDD6Nfv34oLi4OtniOo3bt2hAEwbbylixZAkEQMGbMGNvK9Bd2XztBEARRsQkLtgAEQRAEYRfTpk1T/P7tt9/QvXt3LFq0CDNnzsRNN90UHMEqIH/99RfCw8ODLYbfeOmllzBhwgRkZWUFWxSCIAjCAdCKM0EQBFFhadeunWf18/vvvw+uMBWM+vXrIycnJ9hi+I1q1aqhfv36iImJCbYoBEEQhAMgxZkgCIKo0DRq1AgAcPToUa9zoihixowZ6NmzJ5KTkxEVFYUGDRpg4sSJuHDhglf6HTt2YOLEiejQoQMyMjIQERGBmjVr4pZbbsG2bds0ZVi+fDl69eqF+Ph4JCUl4eqrr8Zvv/2mK/eJEyfw2GOPoW7duoiKikJKSgr69u2LH374wSttfn4+BEFA9+7dmWVNnDgRgiB4VuQlk+o9e/YAUO4R5923rE47ZswY9OjRAwDw4YcfKspU7+Het28f7rvvPuTk5HiubcCAAVixYoVXPXLz78OHD+O2225DzZo1ERYWhtdffx0AcOjQIUyePBndunVDjRo1EBERgYyMDAwdOhSrV69mlmd07Xp7nPft24c777wTtWrVQmRkJNLT05l1AcpnU1hYiAkTJnjy5ebm4uWXX4Yoihx3nCAIgggmZKpNEARBVGjOnTsHAEhPT1ccLy0txU033YQZM2YgLi4OrVu3RnJyMtasWYNJkybhu+++w5IlSxAdHe3J895772Hy5Mlo3Lgx2rRpg8jISGzevBkfffQRvvrqKyxbtgxNmzZV1DN//nwMGTIExcXFaNu2LbKzs/HHH3+ga9eumnuBDxw4gK5du2LXrl3IysrC4MGDcezYMSxcuBDff/89pkyZgocfftjyPcnIyMDo0aMxa9YsnD9/HqNHj/acS01NtVRm586dcfjwYXz//ffIyclB586dPeeaN2/u+XvlypW45pprcOrUKdSrVw/XXHMNjh07hu+//x4LFizAJ598gpEjR3qVf+zYMbRp0wbFxcXo3LkzLl686FkN/uqrr/C3v/0N9erVQ9OmTZGQkIDt27dj7ty5mD9/PubPn48+ffrYcu0bN25Ez549cfz4cdSrVw9Dhw7F3r17MXfuXHz99df49NNPMXz4cK98ly5dQp8+fbB582Z0794d58+fx9KlSzFhwgScO3cOL774Ive9JgiCIIKASBAEQRAhDgBR65PWtWtXEYD48ccfK45PnjxZBCB2795dPHTokOd4UVGROG7cOBGA+Le//U2RZ+XKleKuXbu86vjggw9EAGKPHj0Ux8+ePSumpaWJAMQPPvjAc7y0tFT829/+5pH7ueeeU+QbMGCACEC84YYbxKKiIs/xZcuWiTExMaLb7RbXrVvnOb57924RgNitWzfmPXjuuedEAOLUqVMVx2vVqqV534wAINaqVUtxbPHixSIAcfTo0cw8Z86cEatVqya63W6v57F69WoxOTlZjIuLE48ePepVJgBxyJAhYmFhoVe5GzZsEDdt2uR1fMGCBWJERISYk5MjlpaWKs4ZXfvo0aNFAOLixYs9x0pLS8UmTZqIAMTHH39cUeasWbNEl8slxsXFiQcPHvQcl56N9HzOnDmjuGa32y3GxMSI586d05SFIAiCCD5kqk0QBEFUOEpLS7Fz507cfffd+PnnnzFo0CDFKmZxcTEmT56M2NhYzJw5ExkZGZ5zERERePPNN5GRkYH//ve/KC0t9Zxr37496tSp41Xf2LFj0alTJyxZsgRnzpzxHJ81axaOHTuGrl27YuzYsZ7jgiDghRdeQM2aNb3K2rVrF+bPn4+4uDi8+eabiIiI8Jzr3Lkz7rrrLpSUlOCtt96yfoOCxAcffIBDhw7hoYcewo033qg417p1azzzzDMoKCjAxx9/7JU3MjISb775JqKiorzONWnSxGOSL+fqq6/G8OHDsXPnTmzatMln+ZcsWYKNGzciKysLL774osIr93XXXYfBgwejoKAAH3zwgVdel8uFd999FwkJCZ5jrVu3Rr9+/XDhwgWsWbPGZ/kIgiAI/0Gm2gRBEESFgRVe6Pbbb8e7776rOLd27VocP34cvXv3RtWqVb3yREdHo1WrVvjmm2+wfft21KtXz3OuoKAAX3/9NdavX4+TJ096YkQfOnQIoihi586daNmyJQBg2bJlAIBRo0Z51REeHo5hw4Z59ulK/PLLLwCAvn37IiUlxSvfzTffjClTpnjKDiWk/dlDhw5lnu/SpQsAYNWqVV7nWrZsiRo1amiWXVRUhAULFmDVqlU4duwYLl26BKDMtBoAtm/fjiZNmvgkv3TPR4wYwfQofvPNN2POnDnMZ1OrVi1FO5LIy8sDUNZ+CIIgCOdCijNBEARRYZD2q168eBF//PEHtmzZgv/973/o2LGjYj9xfn4+AODHH380jOUr7WUFgJ9++gmjRo3CsWPHNNNLe6oB4ODBgwDKlCYWLEdcUh4tJ13S8QMHDujK7USk+96pUyfddMePH/c6phcWauPGjRg4cKCnfBby52IVX54Ny7oAAOLj4wGUKf4EQRCEcyHFmSAIgqgwqOM4v/LKK3j88cdx7733okePHh4FVjK/zs3NNVTiqlSpAqBspXnEiBE4efIknn32WYwaNQq1atVCdHQ0BEHADTfcgBkzZvjdQ7KRos9Cbm4eTCQ5hg0bhtjYWM109evX9zrGMtEGyjyjjxgxAvn5+bjrrrtw1113ITs7G3FxcRAEAU8++SReeumlgHiu1ns2LhftjiMIgghlSHEmCIIgKiyPPfYYFi5ciB9++AGTJk3y7D2VVv/q16/vpWxrsWzZMpw4cQLDhg3DpEmTvM7v2rXL61i1atUAwBP6SA3rePXq1XXzSKuqcrNlaR90QUEBM8++ffuYxwNNzZo1sXXrVkyYMAGtWrWypcwtW7Zgy5YtaN26Nd5++22v86znYhUrz4YgCIKoGND0J0EQBFGh+ec//wkA+OijjzwKT5s2bZCYmIilS5fi5MmTXOWcOnUKANvkdseOHVi7dq3XcWnP7ueff+51rri4GLNnz/Y6LoVxWrBgAU6fPu11XnKcJZUNlIVRCgsLw+7du1FcXKxIf/nyZSxdupR5TZLCrc5jFaPyevfuDQCYO3euLfUB+s/l1KlT+PHHH5n5rFy7dM+/+OILlJSUeJ1nPRuCIAiiYkCKM0EQBFGhadGiBQYPHuzxpA2UeWh+/PHHce7cOQwdOpS5KnngwAF89NFHnt+SE6c5c+Yo9jifPn0a48aN8zgJkzN8+HBUqVIFS5YswYcffug5LooinnvuOezdu9crT3Z2Nq655hqcO3cODz74oKLclStX4u2334bb7ca9997rOR4REYEOHTrg5MmTCm/bxcXFeOSRR7B7927mvZFWULdu3co8bxaj8u68806kp6dj8uTJXh7LJXm///57Ux6wc3Nz4XK58NNPP2H79u2e4xcvXsRdd92lOTFi5dq7d++OJk2aID8/H88++6zC/Hvu3LmYM2cO4uLicOutt3KXSRAEQYQGpDgTBEEQFZ6JEydCEAR88MEHOHz4MABgwoQJuPnmm7F06VI0aNAA7du3x/XXX4/rrrsOjRs3RmZmJl599VVPGa1bt0bv3r2xd+9e5OXlYciQIRgyZAjq1KmDgwcPYtCgQV71xsfH4/3334fb7caYMWPQvn173HDDDWjcuDFeeeUV3H777Ux53333XdSpUwfTp09H3bp1cf3116NXr17o0qULzp8/j8mTJ6N58+aKPM8++yxcLhceeughdOzYEUOHDkVOTg5mzJjhcZqmZuDAgQCAq666Ctdffz1uu+02TJgwwcotBlDmHKtp06ZYs2YN2rZti7Fjx+K2227DvHnzAABJSUn46quvkJiYiDvvvBO1a9dG//79ceONN+Kqq65CWloa+vbtix07dnDXmZ6ejnHjxuHs2bNo1qwZBgwYgOHDh6N27dr46aefFE7hfL12QRDwySefoEqVKvjHP/6BRo0a4YYbbkDnzp0xdOhQuFwuvP/++x4TfYIgCKLiQIozQRAEUeFp1qwZhgwZgosXL2LKlCkAypw1TZ8+HV999RV69+6N3bt3Y/bs2fjll18QFRWFxx57zCse71dffYWnnnoKaWlp+O677/D7779j1KhR+PXXX5GUlMSse9CgQVi8eDF69OiBTZs24ZtvvkG1atWwdOlSdOzYkZmnRo0aWL16NR555BGEhYVhzpw5+P3333HVVVfh+++/x/jx473y9OrVC/PmzUObNm2wdu1aLF26FO3bt8fq1as1vUA/8MADePrppxEXF4fZs2fj/fffx8yZM/lvLIPZs2dj8ODB2LVrF6ZPn473339fYcbevn17bNy4EY8//jgSEhKwdOlSfPnll9izZw+6deuGadOmoVevXqbqfPvtt/Hqq6+iTp06WLRoEZYtW4ZevXphzZo1mh7NrV57kyZNsHbtWtx+++0oKCjArFmzsHXrVgwePBjLly/HiBEjTMlOEARBhAaCGAg3kwRBEARBEARBEAQRotCKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEARBEARBEARB6ECKM0EQBEEQBEEQBEHoQIozQRAEQRAEQRAEQehAijNBEAQREPbu3YuxY8ciKysLEREREAQBSUlJwRaLIAiCIAjCEFKcCYIggsDEiRMhCILn38yZMw3zXHPNNYo8+fn5/hfUJs6cOYNOnTph2rRp2LdvH2JiYlC1alVUrVo12KIRlYji4mIsWrQIr7zyCkaNGoW8vDy4XC4IgoAxY8Zwl3Pu3DlMnDgRTZo0QVxcHBITE9GmTRu8+uqruHTpkmm57rrrLs97Xbt2bdP51cyaNQvXXnstqlevjoiICMTGxqJevXq4/fbbsX79es18p0+fxldffYVnn30WAwYMQLVq1TxyTZs2zWe5unfvDkEQ0L17d8O006ZNC8m+jiCIiktYsAUgCIIggKlTp2LUqFGa5w8ePIjvv/8+gBLZy4wZM7B//34kJydjxYoVqF+/frBFIioh+/fvR69evXwqY8+ePejevbtHmYuJiUFRURHWrFmDNWvW4JNPPsGiRYuQnJzMVd7ixYvx3//+1yeZJIqKijB8+HB8/fXXnmNxcXG4dOkStm3bhm3btuGDDz7Av/71Lzz88MNe+b/88kuMHTvWFlkIgiAqGrTiTBAEEURSU1MRGxuLhQsXYv/+/Zrppk+fjpKSEltWo4LBxo0bAQA9e/YkpZkIKvHx8ejcuTMefPBBfPjhh2jevDl33uLiYlx77bXIz89HtWrV8OOPP+L8+fO4cOECZs6cifj4eKxbtw433XQTV3kXLlzA7bffjrCwMLRu3driFZXzj3/8w6M033PPPdi/fz/OnTuHwsJCrFmzBp07d0ZpaSkeeeQR/P7778wyMjIy0K9fPzz11FOYM2eOzzIRBEFUFEhxJgiCCCKxsbEYNmwYSktLdU0hp06dCgCmzEmdxIULFwCUrX4RRLDIysrCmTNnsGzZMrz++uu45ZZbkJiYyJ3/ww8/9EwCzZ4927N67XK5MHLkSLz77rsAgG+//RaLFi0yLO+pp57Czp078fjjj6NRo0YWrkjJ9OnTAQDdunXDW2+9hRo1anjka9WqFebPn4+4uDiIoohZs2Z55b/55ptx6NAhfPvtt3jxxRcxZMgQn2UiCIKoKJDiTBAEEWQk00gtxfmXX37Btm3bkJ2dja5du+qWtWnTJkycOBE9e/ZETk4OoqOjkZCQgBYtWuDpp5/G8ePHmfnuvvtuj7Murf2Eb7/9NgRBQFhYGH7++Weua5P2NErX9uGHHyr2aUvHlyxZ4jkGAOvWrcONN96ImjVrIjw83GtP5KVLl/Cf//wHPXr0QGpqKiIiIpCRkYFBgwbhu+++05WpsLAQL774Iho2/H/27ju+6Tr/A/grq0nTke4yWlooowUEWUVELMMTBBVBQT25Y4ie4kBx3HFOxN/pCXIOHHeegpNzgAPl9JQlstpaZLZldQGlg7ZJmzZpxuf3R5rY2kFHmm/avJ6Px/dB+p3vb/h2vPMZ78Hw9/dHVFQUpk+f7kp04uPjmxzT+dsYm+PcZ8eOHU1ut9vt+PDDDzF9+nRER0fDz88PkZGRuPrqq7FhwwYIIZo8rn5ctbW1WLVqFYYPH46AgADodDpMnjwZ3377bYuxAcD+/fuxcOFC9O/fH1qtFsHBwRg8eDAWLVrUYDjAX/7yF8hksosmdAaDAYGBgW4bB9uZnOOZ2+vdd98FAEyaNAnjxo1rtP2WW25B3759AfyaxDZn3759eOWVVzBw4EA8/vjj7Y6pvsLCQgBotvVap9Nh4MCBAICqqqpG2xUKhVvi6GzO74WLLa0ZS01E1GqCiIg87qmnnhIARFxcnLDb7SIhIUEAEDt37my076JFiwQA8cwzz4jt27cLAAKAyMnJabRvXFyca7tGoxFhYWFCJpO51vXu3VtkZWU1Oq66uloMGTJEABDjxo0TFoulwfbDhw8LjUYjAIgnn3yy1fc5a9YsER0d7TpWo9GI6Oho1/Kf//xHCCEa3Ndnn30mVCqVACCCg4OFRqMRKSkprnPm5ua6YgUgZDKZ0Ol0rq8BiLvuuqvJeC5cuCBGjBjh2k+pVIqQkBDXeV5//XXXe7hu3boGx9aPsSXOfbZv397k9a+88soGsf429uuvv16YzeZGxzrjevXVV8XYsWMFAKFSqURgYGCD9+Ltt99uMi6r1Sruv//+BtcKCAgQoaGhrmdEp9O59j99+rRr/a5du5q93zfeeMN1bHV1dYvvjTdKSUkRAMT8+fNb3M9oNAq5XC4AiBdeeKHZ/e6++24BQPTo0aPZfUwmk0hKShIymUzs2LFDCCHE/PnzXT8T2ispKUkAEBMnTmxye0VFhet5Wb9+favO6XxWfvv90B7O97r+93Nz1q1b1+zPutGjRzf4OfLbRalUtvo6REStxRZnIiKJ1Z/R95133mmwzWg04pNPPoFcLm9VN+2UlBSsX78eeXl5qKmpwYULF2AymfDDDz8gOTkZZ8+exe9///tGx/n7++M///kP/P39sXfvXjz11FOubTU1NbjllltgMpkwfvx4PPnkk62+t02bNuH8+fO4+eabAQA333wzzp8/71qc6+tbsGABfve73yEzMxN6vR41NTV46623XO/HtGnTcPToUUycOBE7duxATU0NKioqUFFRgTVr1iAwMBBvvvkmXn755UbnXrx4MQ4cOAC1Wo0333wTlZWVKC8vR25uLm644QYsXboUJSUlrb6/trDZbJg9ezZ+/PFHXHrppdi8eTOMRiMqKipQVVWFd999F1FRUfjqq6/w5z//udnzPPnkkzhz5gy++OILGI1GVFZWIisrC5dddhmEEFi6dCn0en2j4/7617/ilVdeAQAsWrQI2dnZqKqqQllZGcrLy/HFF19g2rRprv379u2LqVOnAoDr/W+Kc9sf/vAH+Pv7t+u96QoyMzNht9sBAEOHDm12P+e28+fPo6ysrMl9nnnmGWRmZuL2229HSkqK22K8++67ATh6R9xzzz04e/YsAEAIgYyMDFx77bWoqqrCuHHjWj0O2xulpaU1+DlSf1m3bp2r18aMGTMkjpSIuhWJE3ciIp9Uv8VZCCHy8/OFXC4XAQEBorKy0rXfO++8IwCI3/3ud0IIcdEW55ZUVlaK6OjoFlsQna2HcrlcbNu2TQghxJ/+9CcBQISEhIi8vLy236z4tTWtuVa9+veVnJwsrFZrk/s988wzrpak2traJvfZtGmTACAiIiIatJzv37/fdY2mWmWtVqu44oormm1h62iL83vvvScAiMTERFFRUdHksenp6UImkwk/Pz9RVFTUYJuzxVmtVovMzMxGxxYXF7ta9j/44IMG27Kzs12tpY8++miL8df3xRdfCADC399flJeXNxmv834PHTrU6vMKIUROTk6D1u+2Lk899VSbrtec1rY4f/XVV65rHzx4sNn9nO8ZAHH48OFG2zMyMoRSqRTR0dEN3lN3tDjbbDbx6KOPuv6vAYjAwEDh5+fnagX/y1/+0qaeAc19P7SH871WqVQtthhHR0eL4ODgNv+sO3jwoAgKChIAxIIFCzocLxFRfWxxJiLyArGxsbjqqqtcLcxOzknBFi1a1OFrBAYGulq3fvrppyb3ueuuuzB79mzY7XbMmzcP//rXv1wTHr311lvo06dPh+O4mEceeaTZsZZvv/02AGDZsmVQqVRN7nPDDTcgODgYpaWlDWYOdtbKjo2NbbLkjkKhwBNPPNHR8JvljP3uu+9udkKqUaNGYciQIaitrcX27dub3Oemm25qcmbyyMhI17jbQ4cONdj27rvvwm63Izw8HCtWrGh1zNdeey1iYmJQU1OD999/v9F2Z2vzuHHjcMkll7T6vIDj/XbW8m7P4umJ5iorK12vtVpts/vV31b/GMAxK/eiRYtgtVrxyiuvICQkxK0xyuVyPPfcc3jnnXdc709VVZWrtrTJZIJer4fRaHTrddvKYrGgqKioxcVgMLTpnIWFhbj22mtRWVmJlJQU188tIiJ3YeJMROQlnMmcs7v2yZMnsWvXLoSGhuKGG25o9Xm+/vpr3HzzzejXrx8CAgIaTJbjTMpbKn3173//G3369MG5c+fwpz/9CYCji/NNN93Uzjtrm/Hjxze5/uzZs8jLywMA3H777ejRo0eTS8+ePV0THzn3B4D09HQAv05Y1pQrr7wSSqXSnbcDwNFNe9++fQCAp59+utnYe/Togezs7Eax1zd27Nhmr9OrVy8AaNRFeM+ePQCA3/3ud9BoNK2OW6FQ4I477gDQuLu20WjERx99BAC48847W31Op9jY2Ga727Zmefjhh9t8Tak9//zz+OWXX3Dttddi7ty5bj9/aWkppkyZggULFmDcuHH46aefUFFRgcLCQmzatAmRkZF44403MHbsWFc3bimkpKRACNHi4vzQsDWqq6tx3XXXoaCgAP3798emTZvg5+fXiXdARL7I/X8dEBFRu8yaNQuhoaHYvXs3Tpw44Zqh+NZbb21VsuNsJd6wYYNrnVKpRGhoqOuPSL1eD5PJ1GKLU2hoKF577TVcd911AIB+/fo1OV64s0RFRTW5/ty5c67Xzc0O/lvOMlgAUFxcDACuEj1N0Wg0CA8PR1FRUavO31plZWUwm80AgPLy8lYdUz/2+oKCgpo9xpn0WyyWBuvPnz8PAIiLi2vVtetbvHgxVq5cicOHD2Pfvn247LLLADha8CsrKxESEtLkWPXupv773tz/zW+31T/m2LFjWLlyJQIDA/H66693Sozz58/Hjh07kJKSgu+++871AZFOp8OsWbMwfvx4DBkyBKdPn8Zf/vKXJnsRdDV2ux2///3v8fPPPyM0NBTffPMNwsLCpA6LiLohtjgTEXkJtVqNW2+9FYCj1ddZzqapbsVNefvtt7FhwwYoFAo8+eSTOHHiBMxmM8rKylytdM5WY9FMySOn+q2LZ8+excmTJ9tzS+3SXDdtm83mep2ZmXnRFishhNfUva4f+3//+99Wxf7000+77fodKcHUq1cvXH/99QCAf/3rX671zmdk3rx53XpSMCdnaz6AFltr62+rf8w999yD2tpaPPbYYwgNDUVVVVWDxWq1AnB8bzrX/fYDkJZkZmZiy5YtAICHHnqoyf/zqKgo/PGPfwTgmLjvYj8HuoJHHnkEX375JVQqFTZu3Ogqt0VE5G5MnImIvIgzSX7ppZdw5swZDB06tNmarL/lHMO7ePFirFixAv3794dc3vDHvLPlsSVr167FV199BYVCgcGDB8NsNuOWW25psZXNE3r06OF63Vw35pY4W7JbSnrMZjMuXLjQ5Lb6XbhNJlOT+zQ1mzUAhIeHu45vT+wd5Xzv2nvtu+66CwDwySefwGAw4PDhw9i/fz8AuLrzt1VBQUGLXdYvtqxevbpd122vpKQk1/fTkSNHmt3Pua1Hjx4NWj5zcnIAAMuXL0dQUFCj5cMPPwQA5Ofnu9a99tprrY7v2LFjrtcJCQnN7jdgwAAAjpZxZy+Mruqf//wn1qxZA8BRZ37SpEkSR0RE3RkTZyIiLzJ69Ghccsklrsl82jIpWEFBAQBgxIgRTW6vqqpyJTvNOXz4MB555BEAjrJHW7ZsQUhICDIzM/Hggw+2OpbOEB8f7+pmvXnz5jYf7/wAYufOnc22tP3444+ulr/fCg0Ndb12vte/1dz7q1KpkJycDKB9sXfU5ZdfDgD4/vvvm036W3LVVVehf//+MBqN+PDDDxtMCtZSaaaW2Gy2i04Q1dLiHMfuKVqt1jX+/ttvv21yHyEEvvvuOwDA1Vdf7bHYADT4kKylD0jqD0Pw9ARr7vS///0P9957LwBHq/Ptt98ucURE1N0xcSYi8jJ///vf8dBDD+Ghhx5qU61V50zNBw8ebHL7ypUrG83yW1/9es1XXHEFHnvsMcTFxbm65/7rX//Cxo0b23An7uecqOrtt9/GgQMHWtz3txNkOcfh5ufn49133220v91ux7PPPtvs+QYOHOjqktzU+2C32/Hcc881e7xzAq0tW7a4utS2NvaOWrBgARQKBS5cuNCgRndryWQyV8vy66+/jg8++ABA+yYFc4qPj29Vl3VPdGVvrfnz5wMAtm/f3uSHJJ9++ilOnz4NAK4u0U65ubkt3o/z3HFxca51DzzwQKtjGzlypOv1G2+80eQ+RqPRNQRk2LBhCAgIaPX5vcnRo0cxZ84cWK1W3HDDDXj++eelDomIfAATZyIiL3PNNddg9erVWL16NSIjI1t93LRp0wA4xp7+61//crVanz9/Hg8++CBeeOEFhIeHN3v8gw8+iGPHjiEkJAQffviha6zxnDlzXK05d9xxR7OtrZ7w0EMP4ZJLLoHJZMKkSZOwdu3aBl2rKyoq8N///hd//OMfMWHChAbHjh071jVW9+6778Zbb73lmrArPz8fN998M/bu3dtsqSGVSoUbb7wRAPC3v/0Nn3zyies9zs7OxqxZsxqVgapv3rx5uOqqqyCEwKxZs/Dss882mPDMaDRi+/btuOeee9CvX792vDvN69+/v6snwQsvvIDFixfjxIkTru0GgwEff/wxZs2a1ew5Fi5cCLVajSNHjqC8vLzLTgqm1+tRWlrqWpzjiM1mc4P1TXW7nz9/Pi655BIIIXDjjTdi69atABwfmnz66aeuD3auueYaTJkyxe2xP/30064Z8nNzcxtsi4uLc03ot3nzZvzhD3/AqVOnIISAxWLBnj17MHHiRFdi/9BDDzV5jfrvQf1J+Kqqqhqsl2roRmlpKWbMmAGDwYCRI0figw8+aDQkhYioU7irIDQREbXeU089JQCIuLi4Nh23fft2AUAAEDk5OQ22lZeXi8TERNd2uVwuQkJChEwmEwDEn/70JzF//nwBQMyfP7/BsRs3bnQd9+mnnza6rtFodJ17woQJwmq1tinu5q7b1H1dzNmzZ8Vll13m2l8mk4mQkBARHBzsWgdA9O/fv9GxpaWlYvjw4a59VCqVCAkJcZ3ntddeE3FxcQKAWLduXaPjCwoKRK9evRoc77xuUFCQ2LFjh2vb9u3bGx2v1+vFtdde2yDO4ODgBv9PAIRSqWx0bEtxObX0PlutVnHPPfc0uHZgYKAIDQ11XVun07Xwzgsxb94817H33ntvi/t6q5SUlAbvQXNLSkpKk8fn5OSI+Ph4135arVZoNBrX1yNGjBBlZWVtjsv5f9fSzwTnz42mvv+FEKKkpESMGjWqwX1otVqhVCobrHvkkUeavUZr3hsA4qmnnmrzPTrf++be2/rWrVvX5L3W/1kRHBwsoqOjm11mzZrV5hiJiJrDj+iIiLqJkJAQ7NmzBw888ADi4+OhUCigVCoxceJEbNiwAW+++WaTxxUUFGDx4sUAHPWRm6rXrNVqsWHDBqjVauzatavFLs2drVevXvjpp5+wYcMGXH/99ejZsyeqq6tRW1uL+Ph4XHfddXjppZfw448/Njo2PDwce/bswYoVK5CYmAi5XA6lUolp06bh+++/x5IlS1q8dkxMDPbv34/Fixe7xlsHBgbij3/8IzIyMpCSktLi8cHBwdi8eTO2bNmCm2++GX369IHZbEZ1dTV69+6Nq6++Gs8995yrlrM7KRQKrF27Fj/99BNuu+029OnTBxaLBUIIDB48GLfffvtFu+LPmTPH9bq9k4J1dfHx8Th06BCefPJJDB06FDKZDCqVCqNGjcLq1auxb9++BuPhPSkiIgL79u3Dv//9b0ydOhXR0dGwWCxQKpXo168f5s2bh127duGFF16QJD53MxgMLY6Dd/eQByLybTIhukEtAiIiIjeJj49HXl4e1q1b5zXlrLzFfffdh7Vr12LcuHHYs2eP1OEQERF5DFuciYiI6KIMBoNrYqm7775b4miIiIg8i4kzERERtchsNmPp0qUwGAyIjY3tkpOCERERdYRS6gCIiIjIO7300kt46aWXUFxcjJqaGgDAmjVr4OfnJ3FkREREnsUWZyIiImpSRUUF8vLyIITApZdeio8//rjJyeOIiIi6O04ORkRERERERNQCtjgTERERERERtYBjnL2A3W7HuXPnEBQUBJlMJnU4REREREREXYIQApWVlejVqxfk8s5rF2bi7AXOnTuH2NhYqcMgIiIiIiLqkgoKChATE9Np52fi7AWCgoIAOP6zg4ODJY6GiIiIiIioa3CWSnTmVJ3FZxLnNWvW4KeffsLhw4dRXFwMk8mEHj16ICUlBY888gguueSSVp8rPj4eeXl5zW7PzMxEYmJiq8/n7J4dHBzMxJmIiIiIiKiNOnvIq88kzn/7299gNBoxbNgwV5J89OhRvP/++/jPf/6DTZs24dprr23TOefPn9/kep1O1+F4iYiIiIiIyDv4TDmq3bt3Y9SoUdBoNA3Wv/7667jnnnsQHR2NM2fOQKm8+GcJzhZnd711BoMBOp0Oer2eLc5ERERERESt5KlcymfKUY0fP75R0gwAS5YsQUJCAoqKinDs2DEJIiMiIiIiIiJv5jOJc0tUKhUAwM/PT+JIiIiIiIiIyNv4zBjn5rz//vvIzs7GgAEDMGDAgDYdu2rVKpw6dQpqtRpDhgzBrFmzEBkZ2UmREhERERERkRR8Zoyz06pVq3D06FEYjUZkZmbi6NGj6NWrF7766iuMGjWqVedoblZtrVaLV199FYsWLWpTTBzjTERERERE1HaeyqV8rsX5u+++w9atW11fx8XF4b333mt10gwA119/PSZNmoRRo0YhMjISp0+fxjvvvIOXX34ZixcvRnh4OGbOnNns8WazGWaz2fW1wWBo380QERERERFRp/O5FmeniooKHD58GM888wx++OEHPPvss3jsscc6dM633noLd955JwYNGoSsrKxm93v66aexYsWKRuvZ4kxERERERNR6nmpx9tnE2clisWDcuHHIyMjA/v37MWbMmHafy263o2fPniguLkZOTg7i4+Ob3K+pFufY2FgmzkRERERERG3AclQeolKpcPPNN0MIgc2bN3foXHK5HAkJCQCAwsLCZvdTq9UIDg5usBAREREREZF38vnEGQAiIiIAACUlJR0+V3l5OQAgICCgw+ciIiIiIiIi6TFxBrBz504AcLUWt9fRo0eRnZ0NrVaLxMREd4RGREREREREEvOJxHn37t349ttvYbfbG6y3WCx49dVX8f7778Pf3x8333yza9vatWuRmJiI5cuXNzhmy5Yt2LZtW6NrHDp0CHPmzIEQAosXL4afn1/n3AwRERERERF5lE+Uozpx4gQWLlyIiIgIjBo1CuHh4SgtLcXhw4dRWFgIjUaD9evXIzY21nVMaWkpsrOzG41VTk1NxYoVKxAXF4fhw4dDq9Xi9OnTyMjIgNVqxcSJE/H88897+haJiIiIiIiok/hE4pySkoK//vWv2LlzJw4dOoTS0lL4+fkhPj4eN910E+6//37079+/VeeaOnUqCgoKkJaWht27d7tmb7viiitw2223YeHChVAoFJ18R0REREREROQpPl+Oyht4agp16nxGoxGBgYEAgKqqKk4SR0RERETUiViOioiIiIiIiMgLMHEmIiIiIiIiagETZyIiIiIiIqIWMHEmIiIiIiIiagETZyIiIiIiIqIW+EQ5KiJPUSgUuOmmm1yviYiIiIio62PiTORGGo0Gn376qdRhEBERERGRG7GrNhEREREREVELmDgTERERERERtYCJM5EbGY1GyGQyyGQyGI1GqcMhIiIiIiI3YOJMRERERERE1AImzkREREREREQtYOJMRERERERE1AImzkREREREREQtYOJMRERERERE1AImzkREREREREQtUEodAFF3olAoMH36dNdrIiIiIiLq+pg4E7mRRqPBN998I3UYRERERETkRuyqTURERERERNQCJs5ERERERERELWDiTORGRqMRAQEBCAgIgNFolDocIiIiIiJyA45xJnKz6upqqUMgIiIiIiI3YoszERERERERUQuYOBMRERERERG1gIkzERERERERUQuYOBMRERERERG1gIkzERERERERUQs4qzaRG8nlcqSkpLheExERERFR18fEmciN/P39sWPHDqnDICIiIiIiN2KTGBEREREREVELmDgTuVneBSP+d/S81GEQEREREZGbMHEmciOj0YgrHn0Hd77/M749mC91OERERERE5AZMnIncSF9jgTwiHgCwNbtU2mCIiIiIiMgtmDgTudEvZwyQyRzfVjtPXIAQQuKIiIiIiIioo5g4E7lRRr7e9bq4shZHzhokjIaIiIiIiNyBiTORG/1cUAEAsFtMAICtWUUSRkNERERERO7gM4nzmjVrMHv2bAwYMAA6nQ5qtRpxcXH44x//iMOHD7f5fOXl5Vi6dCni4uJc53rggQdQUVHh/uCpSzBZbDhyrhIAUJn+FQBga2axlCEREREREZEb+Ezi/Le//Q3//e9/ERYWhilTpmDGjBnQaDR4//33MWrUKHz99detPldpaSmSk5PxyiuvQKlU4oYbbkBQUBBefvlljB07FmVlZZ14J+StDp3Rw2ITsFaVwfCzI3E+fFaPIoNJ4siIiIiIiKgjfCZx/vLLL1FeXo79+/dj06ZN2LRpE7Kzs/Haa6/BYrFg8eLFsFqtrTrXAw88gJMnT2L27NnIzs7Gxx9/jCNHjuC+++7D8ePHsWzZsk6+G/JGabmOD0y0xkKMTOqPYb2DAQDbstjqTERERETUlflM4jx+/HhoNJpG65csWYKEhAQUFRXh2LFjFz1PYWEhNmzYAD8/P7z++utQKpWubatWrUJkZCQ++OADFBczWfI1zsR5+eI5SEtLw+8G9wAAbM3kOGciIiIioq7MZxLnlqhUKgCAn5/fRff99ttvYbfbMWHCBERHRzfYplarcd1118Fms2HLli2dEit5J5td4Oe8cgDAmPgwAMDkpCgAwE8nS2Gy2CSLjYiIiIiIOsbnE+f3338f2dnZGDBgAAYMGHDR/Q8ePAgAGDlyZJPbnesPHTrkviDJ62Wfr0SlyYpAtRKJPYIAAIN7BqOnTgOTxY49p0oljpCIiIiIiNpLefFdupdVq1bh6NGjMBqNyMzMxNGjR9GrVy9s2LABCoXiosePm0G4AAB2s0lEQVTn5+cDAGJiYprc7lyfl5fnvqDJ66XnObppD+8dhP4J/QAAx44dw+TEKHy4Px8/ZBZjcmJ0S6cgIiIiIiIv5XOJ83fffYetW7e6vo6Li8N7772HUaNGter4qqoqAIBWq21ye0BAAACgsrKy2XOYzWaYzWbX1waDoVXXJu+VmuNInEf20eGjug9NhBC4KikaH+7Px7bMYogbBGQymZRhEhERERFRO/hcV+0ffvgBQgiUl5fjxx9/xIABA5CSkoL/+7//81gMzz33HHQ6nWuJjY312LXJ/YQQronBRsbqGmwblxAOf5UC5w0mHD3HD0iIiIiIiLoin0ucnUJCQjBhwgRs2bIFo0aNwhNPPIG0tLSLHhcYGAgAqK6ubnK70WgEAAQFBTV7juXLl0Ov17uWgoKCdtwBeYsz5TUoMpihUshwSV0JKieNSoHx/SMAsCwVEREREVFX5bOJs5NKpcLNN98MIQQ2b9580f379OkDADhz5kyT253r4+Limj2HWq1GcHBwg4W6Lmdr89DeOvirGo+Tv6pudm2WpSIiIiIi6pp8PnEGgIgIR4tgSUnJRfcdPnw4ACAjI6PJ7c71w4YNc1N05O2ciXNyXRmq35qc6EicD57Ro9hg8lhcRERERETkHkycAezcuRMAkJCQcNF9p02bBrlcjl27dqG4uGHXW7PZjM2bN0OhUGD69OmdEit5n7RcR/3m0c0kzlHBGgyLcYx93p7N7tpERERERF2NTyTOu3fvxrfffgu73d5gvcViwauvvor3338f/v7+uPnmm13b1q5di8TERCxfvrzBMT179sStt96K2tpaLFmyBFar1bXt0UcfRUlJCebNm4eoqKjOvSnyCmXGWpwsdsy0PjouFDKZDIMHD8bgwYMbzKA9pa4U1Q+ZTJyJiIiIiLoanyhHdeLECSxcuBAREREYNWoUwsPDUVpaisOHD6OwsBAajQbr169vMLt1aWkpsrOzUVhY2Oh8L730Evbt24eNGzciMTERo0ePxtGjR3HkyBEMGDAAa9as8eTtkYTS67ppD4gKRGiAHwA/HD16tNF+U5Ki8I8fjuOnE6UwWWzQNDEWmoiIiIiIvJNPtDinpKTgr3/9KwYNGoRDhw7h008/xe7duxEWFob77rsPhw8fxty5c1t9voiICKSmpuK+++5DbW0tPv/8c+j1etx///1ITU1FWFjTXXap+3GObx7Tt+X/8yG9gtEjWIMaiw17T1/wRGhEREREROQmMiGEkDoIX2cwGKDT6aDX6znDdhdzw2u78UtBBf5x83DMGhHT4r5//fwwPtqfj3mX9cGzN1zioQiJiIiIiLovT+VSPtHiTNQZqmutOHJWDwAYUzcxWHV1NYYMGYIhQ4Y0qvU9pW527W2ZxeDnVUREREREXYdPjHEm6gy/FFTAahfoqdOgd4g/AEAIgWPHjrle1ze+fwQ0KjnO6U3ILKzE4F7sXUBERERE1BWwxZmondJyHGWoxsSHNZhBuzkalQLjExw1w7dmFnVqbERERERE5D5MnInaKT2vbmKw+NBWHzMlyVGWamsWy1IREREREXUVTJyJ2sFqsyMjr67F+SIzatc3uW6c88EzFSipNHdKbERERERE5F5MnInaIbOwEsZaG4I1SgyMCmr1cT10GgztHQwhgO3ZbHUmIiIiIuoKmDgTtYOzfvPo+DDI5Rcf31zflMS67toc50xERERE1CUwcSZqh18T54bjm2UyGeLi4hAXF9fshGFTkhzdtXedKIXJYuvcQImIiIiIqMOYOBO1kRACabmO8c3J8Q3HN2u1WuTm5iI3NxdarbbJ44f20iEqSI3qWhv255R1erxERERERNQxTJyJ2ij3QjVKq8zwU8pxSYyuzcfL5TJXqzO7axMREREReT8mzkRt5OymfWlMCNRKRbvOMdk1zrkYQgi3xUZERERERO7HxJmojdJymh7fDAA1NTUYM2YMxowZg5qammbPcUX/CKiVcpytqEF2UWWnxUpERERERB3HxJmojdJbqN9st9uRnp6O9PR02O32Zs/h76fA+P4RABytzkRERERE5L2YOBO1QXGlCTmlRshkwMg+jVuc22JyIsc5ExERERF1BUycidrg57rZtBN7BEPnr+rQuZwThB0oqEBplbnDsRERERERUedg4kzUBql1E4ONaWJ8c1v11PljcM9gCAHsyC7p8PmIiIiIiKhzMHEmaoP0uhbnMfGNxze3x1UsS0VERERE5PWYOBO1UpXZiqPn9ADclzhPSXKUpfrxeAnMVptbzklERERERO7FxJmolQ7kl8MugNgwf/TQaZrdLyIiAhEREa065yW9dYgMUsNYa0NqXZkrIiIiIiLyLkyciVrJWb95TFzzrc0BAQEoKSlBSUkJAgICLnpOuVyGyYOc3bVZloqIiIiIyBsxcSZqpbTc5us3d8TkunHOP2QWQQjh1nMTEREREVHHMXEmaoVaqx0HCpwTg3V8Ru36JgyIgJ9SjjPlNThRXOXWcxMRERERUccxcSZqhaPn9DBZ7AjVqpAQGdjsfjU1NZg4cSImTpyImpqaVp1b66fE5QnhABytzkRERERE5F2YOBO1Qlpd/ebR8WGQyWTN7me327Fz507s3LkTdru91eefkujorr2N45yJiIiIiLwOE2eiVnCOb052Uxmq35pcV5YqI78cZcbaTrkGERERERG1DxNnoouw2wXSXS3O7h3f7NQ7xB9JPYNhF8D2LLY6ExERERF5EybORBdxurQK5dUWaFRyDO2t67TruLprM3EmIiIiIvIqTJyJLiI1x9FNe0RsKFSKzvuWmVJXlmrn8RLUWls/PpqIiIiIiDoXE2eii3B203Z3GarfGh4TgohAP1SZra7JyIiIiIiISHpMnIkuItWZOPdt3cRgWq0WWq22zdeRy2WYNMjR6syyVERERERE3oOJM1ELCvU1OFNeA7kMGNHn4i3OAQEBMBqNMBqNCAgIaPP1ptTNrr01sxhCiDYfT0RERERE7sfEmagFzjJUQ3rpEKhWdvr1JgyIgJ9Cjvyyapwqqer06xERERER0cUxcSZqQWeXofqtALUSlyWEAwB+yOTs2kRERERE3oCJM1ELUnMciXNyfOvGN5tMJsyYMQMzZsyAyWRq1zVdZamYOBMREREReQUmzkTN0NdYkF1UCQAY3crE2WazYcuWLdiyZQtsNlu7russS5WeV4ZyY227zkFERERERO7jE4lzdXU1vvjiC9x+++0YNGgQNBoNAgICMHz4cDzzzDOoqmrbWNL4+HjIZLJml6ysrE66E/KkjLxyCAH0jQhAZJDaY9eNCdUisUcQ7ALYcZytzkREREREUuv82Y68wEcffYQ77rgDAJCUlITrr78eBoMBe/bswVNPPYUNGzZg586diIqKatN558+f3+R6nU7X4ZhJes5ayqPjPDO+ub7JiVHIOl+JrZnFmDUixuPXJyIiIiKiX/lE4qxSqXDnnXfigQceQFJSkmt9YWEhZsyYgQMHDuCBBx7ARx991Kbzrl+/3s2RkjdJa2P9ZneakhSN13ecws7jJbDY7FApfKJzCBERERGRV/KJv8bnz5+Pf/7znw2SZgDo2bMnXnvtNQDApk2bUFvL8aTkYLLYcLBADwAY08rxze50aWwIwgP8UGmyuhJ4IiIiIiKShk8kzi0ZPnw4AMBsNuPChQsSR0Pe4vBZPWptdkQEqhEfrvX49RVyGSYOcgwd2MrZtYmIiIiIJOUTXbVbcvr0aQCO7txhYW1rWVy1ahVOnToFtVqNIUOGYNasWYiMjOyMMMnDXN2040Mhk8kkieGqpChszDiDrZlFeHxGkmRxEBERERH5Op9PnF9++WUAwLRp06BWt23m5EcffbTB1w8++CBeffVVLFq0yG3xkTTScpyJc9s+TAkICIAQwi0xXDEgAiqFDLkXqnG61IiEyEC3nJeIiIiIiNrGp7tqb9myBW+//TZUKhVWrlzZ6uOuv/56bNq0CXl5eaiursaRI0ewbNkymM1mLF68GF9++WWLx5vNZhgMhgYLeQ+7XSA9rxyANOObnYI0KlzWLxwAsDWzSLI4iIiIiIh8nc8mzllZWZg3bx6EEFi1apVrrHNrvPLKK5g1axb69OkDf39/DBkyBC+++CLeeOMNCCHw5z//ucXjn3vuOeh0OtcSGxvb0dshN8ouqkSlyYoAPwWSegZJGsuURMc45x84zpmIiIiISDI+mTifPXsW06ZNQ3l5OZYtW4alS5e65by33347oqKikJ2djdzc3Gb3W758OfR6vWspKChwy/XJPdLrxjePjAuFso1loEwmE+bMmYM5c+bAZDJ1OJYpSdEAgJ/zylFRzVnfiYiIiIik4HOJc1lZGa6++mrk5eVh4cKFWL16tdvOLZfLkZCQAMBRI7o5arUawcHBDRbyHqm57e+mbbPZ8Nlnn+Gzzz6DzWbrcCyxYVoMjA6EzS6w83hJh89HRERERERt51OJc1VVFa655hocO3YMs2fPxltvveX2mYrLyx1JV0BAgFvPS54hhHBNDDY6PlTiaBwmJzpanVmWioiIiIhIGj6TOJvNZsycOROpqamYOnUqNmzYAIVC4dZrHD16FNnZ2dBqtUhMTHTruckzzpTX4LzBBKVchhGx3pE4X5XkGOe8I7sYFptd4miIiIiIiHyPTyTONpsNt956K7Zt24YJEyZg06ZN8PPza/GYtWvXIjExEcuXL2+wfsuWLdi2bVuj/Q8dOoQ5c+ZACIHFixdf9PzkndLzHK3NQ3vr4O/n3g9W2mtEn1CEalUwmKxIr+tGTkREREREnuMTdZzXrl2Lzz//HAAQERGBJUuWNLnf6tWrERERAQAoLS1FdnZ2o7HKqampWLFiBeLi4jB8+HBotVqcPn0aGRkZsFqtmDhxIp5//vnOvSHqNKk5jsQ0ua90Zah+SyGXYdKgKGw6cBbbsoowLiFc6pCIiIiIiHyKTyTOznHHAFwJdFOefvppV+LcnKlTp6KgoABpaWnYvXs39Ho9goODccUVV+C2227DwoUL3d4FnDzHOaP26Djv6KbtNCUpGpsOnMXWzGI8NmOw1OEQEREREfkUmRBCSB2ErzMYDNDpdK4knKRRbqzFiJXfAwAynvgdwgLa3t3eaDQiMDAQgGMyOndNEldpsmDEM9/DahfY9lAK+kUGuuW8RERERERdmadyKZ8Y40zUGul5jp4J/aMC25U0A4BWq0VVVRWqqqqg1WrdFluQRoWx/Rzdx7dlcXZtIiIiIiJPYuJMVCetrpt2e+o3O8lkMgQEBCAgIMDtpc6m1JWl+iGzyK3nJSIiIiKiljFxJqrza+LsXeObnabUlaVKyy2HvsYicTRERERERL6DiTMRgJpaGw6f0QPoWIuz2WzGggULsGDBApjNZneFBwCICw9A/6hA2OwCO4+XuPXcRERERETUPCbORAB+KaiA1S7QI1iDmFD/dp/HarXi3Xffxbvvvgur1erGCB2crc7b2F2biIiIiMhjmDgToV437b5hbh+b7E7Occ7bs0tgtdkljoaIiIiIyDcwcSaC949vdhrZJwQhWhX0NRb8nFd+8QOIiIiIiKjDmDiTz7Pa7MioS0I7Mr7ZE5QKOSYOjATAslRERERERJ7CxJl8Xtb5ShhrbQjSKDEwOkjqcC5qShLLUhEREREReRITZ/J5qTmObtqj40KhkHvv+GanKwdGQimX4VSJEbmlRqnDISIiIiLq9pg4k89Lz6tLnL28m7aTzl/l6lK+ld21iYiIiIg6HRNn8mlCCKTmOMY3J/fteOKs1WpRXFyM4uJiaLXaDp+vOc6yVFvZXZuIiIiIqNMxcSaflnehGqVVZvgp5Likt67D55PJZIiMjERkZGSnlrW6qm6cc2pOGQwmS6ddh4iIiIiImDiTj0utK0M1PFYHjUohcTStFx8RgH6RAbDaBX48XiJ1OERERERE3RoTZ/Jp6bnuHd9sNptxzz334J577oHZbHbLOZvjbHXemslxzkREREREnYmJM/m0tNy68c1uSpytVitef/11vP7667BarW45Z3MmJzrGOW/PLobNLjr1WkREREREvoyJM/mskkozckqNkMmAkXGhUofTZqPjQqHzV6Gi2oKM/HKpwyEiIiIi6raYOJPPcnbTHhQdBJ2/SuJo2k6pkGPioEgA7K5NRERERNSZmDiTz3J20x7TReo3N8XZXZtlqYiIiIiIOg8TZ/JZaXUtzmPcUL9ZKhMHRkEhl+FEcRXyL1RLHQ4RERERUbfExJl8UpXZiqPn9ACAMfFdb3yzk06rwui68dlbs9jqTERERETUGZg4k086kF8OuwBiQv3RU+cvdTgdwrJURERERESdi4kz+aTOGt/s7++PnJwc5OTkwN/fMwn5lCTHOOf9ORdQabJ45JpERERERL6EiTP5pLScuvHNbk6c5XI54uPjER8fD7ncM99e/SID0TciABabwK4TpR65JhERERGRL2HiTD7HYrPjQIGzxbnrjm+ub0rd7No/cHZtIiIiIiK3Y+JMPufIWT1MFjtCtSr0jwp067lra2vxyCOP4JFHHkFtba1bz92SyXXdtXdkl8BmFx67LhERERGRL2DiTD4nvW5886i4MMhkMree22KxYPXq1Vi9ejUsFs+NNx4TH4YgjRJlxlr8UteaTkRERERE7sHEmXxOal395uS+3aObNgCoFHJMHOTsrs3ZtYmIiIiI3ImJM/kUIQTS6xLn0W6eGExqznHO25g4ExERERG5FRNn8imnSowor7ZAo5JjaC+d1OG41cRBkVDIZcguqkRBWbXU4RARERERdRtMnMmnpNW1Nl8aGwI/Zfd6/EO0fhgV5+h+vi2Lrc5ERERERO7SvTIHootwJs7J3aybthPLUhERERERuR8TZ/Ipad10fLPTlKRoAMD+02WoMlsljoaIiIiIqHtg4kw+47zehIKyGshlwMi4zplR29/fH0eOHMGRI0fg7+/fKddoSUJkAOLCtai12fHTiRKPX5+IiIiIqDti4kw+w9naPLhXMALVyk65hlwux5AhQzBkyBDI5Z7/9pLJZJiS6Gh1ZlkqIiIiIiL3YOJMPsNZhmpMN+2m7XRVkmOc8/asYtjtQuJoiIiIiIi6Pp9InKurq/HFF1/g9ttvx6BBg6DRaBAQEIDhw4fjmWeeQVVVVZvPWV5ejqVLlyIuLg5qtRpxcXF44IEHUFFR4f4bILdIzS0H0LmJc21tLZ5++mk8/fTTqK2t7bTrtGR0fBiC1EpcMNbilzMVksRARERERNSdyIQQ3b5J6t///jfuuOMOAEBSUhKGDh0Kg8GAPXv2oLKyEomJidi5cyeioqJadb7S0lKMGzcOJ0+eRL9+/TB69GgcPXoUR48excCBA7F3716EhbU+OTMYDNDpdNDr9QgODm7XPVLLDCYLhq/4H4QAUh+bgqggTadcx2g0IjAwEABQVVWFgICATrnOxdzzUQa+OVSIeyYl4JGpiZLEQERERETU2TyVS/lEi7NKpcKdd96JY8eO4dixY/jkk0/w7bffIjs7GyNGjEBWVhYeeOCBVp/vgQcewMmTJzF79mxkZ2fj448/xpEjR3Dffffh+PHjWLZsWefdDLXLz3nlEAKID9d2WtLsTZxlqbZynDMRERERUYf5RItzS/bu3YvLL78carUaBoMBfn5+Le5fWFiImJgYKJVK5OfnIzo62rXNbDYjNjYWZWVlOHfuXKtbsNni3PlWfZeF17afwk2jYrB6zvBOu463tDiXG2sx6tnvYRfAT3+ehJhQrSRxEBERERF1JrY4e8jw4Y4kymw248KFCxfd/9tvv4XdbseECRMaJM0AoFarcd1118Fms2HLli2dEi+1T1qOY3xzcjefGMwpNMAPo+pKbm3PYqszEREREVFH+HzifPr0aQCO7tytGZd88OBBAMDIkSOb3O5cf+jQITdFSB1lttpck2SNju+c+s3eaDLLUhERERERuYXPJ84vv/wyAGDatGlQq9UX3T8/Px8AEBMT0+R25/q8vDw3RUgddfiMHrVWOyIC/dA3Qpqu01JwlqXae+oCjGarxNEQEREREXVdPp04b9myBW+//TZUKhVWrlzZqmOcpau02qbHjDrHtFZWVjZ7DrPZDIPB0GChzpNWV4ZqdFwYZDKZxNF4Tv+oQPQJ06LWZsdPJ0ulDoeIiIiIqMvy2cQ5KysL8+bNgxACq1atco119oTnnnsOOp3OtcTGxnrs2r4oLbcMADCmb+ePb9ZoNEhNTUVqaio0Gmln75bJZJjsml27SNJYiIiIiIi6Mp9MnM+ePYtp06ahvLwcy5Ytw9KlS1t9rHPG5Orq6ia3G41GAEBQUFCz51i+fDn0er1rKSgoaEP01BZ2u0C6M3H2wPhmhUKBMWPGYMyYMVAoFJ1+vYu5KskxznlbVgnsdp+eQJ+IiIiIqN2UUgfgaWVlZbj66quRl5eHhQsXYvXq1W06vk+fPgCAM2fONLnduT4uLq7Zc6jV6laNp6aOO15cCYPJCq2fAoN7+l6pr+S+YQhUK1FaZcahs3pcGhsidUhERERERF2OT7U4V1VV4ZprrsGxY8cwe/ZsvPXWW20e8+rs0p2RkdHkduf6YcOGdSxYcgvn+OaRfUKhVHT+415bW4tVq1Zh1apVqK2t7fTrXYyfUo4rB0YAYHdtIiIiIqL28pnE2Ww2Y+bMmUhNTcXUqVOxYcOGdnWlnTZtGuRyOXbt2oXi4oZlfsxmMzZv3gyFQoHp06e7K3TqgLQcZzdtz9RvtlgsePTRR/Hoo4/CYrF45JoXM6WuLNVWlqUiIiIiImoXn0icbTYbbr31Vmzbtg0TJkzApk2b4Ofn1+Ixa9euRWJiIpYvX95gfc+ePXHrrbeitrYWS5YsgdX6a5mfRx99FCUlJZg3bx6ioqI65V6obTw5vtlbTRwUCZkMOFZowLmKGqnDISIiIiLqcnxijPPatWvx+eefAwAiIiKwZMmSJvdbvXo1IiIc3VpLS0uRnZ2NwsLCRvu99NJL2LdvHzZu3IjExESMHj0aR48exZEjRzBgwACsWbOm826GWu1MeTXO6U1QymW4tE+I1OFIJjxQjZF9QvFzXjm2ZhXjD5c1P/6eiIiIiIga84nEuby83PXamUA35emnn3Ylzi2JiIhAamoqnn76aXzxxRf4/PPPER0djfvvvx8rVqxASEiIO8KmDkqvG988pLcOWj+feNSbNTkxCj/nlWNbZhETZyIiIiKiNpIJIVijRmIGgwE6nQ56vR7Bwb4383Nn+evnh/HR/nzcMaEvHpsx2CPXNBqNrpJlVVVVCAgI8Mh1Lyb7fCWmvvQj/JRy/PLk73z+gwQiIiIi6h48lUv5xBhn8k3O8c2jPTQxmDcbGB2ImFB/1Frt2H3ygtThEBERERF1KUycqVsqN9bieFEVAGB0nO9ODOYkk8kwJdExYR3LUhERERERtQ37a1K39HOeY3xzQmQAwgPVHruuRqPB9u3bXa+9yZSkaLy7Nw9bs4phtwvI5W2rYU5ERERE5KuYOFO3lFbXTTu5r2e7aSsUCkycONGj12ytsf3CEOCnQEmlGUfO6TEsJkTqkIiIiIiIugR21aZuyZk4j47j+GYntVKBCQMiAQA/ZBZLHA0RERERUdchaeL8zDPP4KuvvpIyBOqGTBYbDp/VA/B8i7PFYsFrr72G1157DRaLxaPXbo0pSY5xztuyOM6ZiIiIiKi1JE2cnXWQidzpl4IKWGwC0cFqxIT6e/TatbW1uPfee3HvvfeitrbWo9dujUmJUZDJgCNnDTivN0kdDhERERFRl8Cu2tTtpOU4ummPiQ+DTMYJsOqLCFTj0tgQAMBWtjoTEREREbUKE2fqdtLqZtQew/rNTboqKRoAsI3jnImIiIiIWoWJM3UrNrtABhPnFk2uq+f808lS1NTaJI6GiIiIiMj7SV6OqqqqCvn5+e06tk+fPm6Ohrq6zEIDqsxWBKmVGNQjSOpwvFJijyD0DvHH2Yoa7D5ZiqsGR0sdEhERERGRV5M8cd64cSM2btzY5uNkMhmsVmsnRERdmbMM1aj4UCjkHN/cFJlMhsmJUXh/Xx62ZhUzcSYiIiIiugjJu2oLIdq12O12qUMnL5Sey27arVG/LJUQQuJoiIiIiIi8m+SJ84IFC2C329u1ENUnhEBq7q8zaktBrVbj66+/xtdffw21Wi1JDK1xWb9waP0UKDKYcfScQepwiIiIiIi8muRdtYncJb+sGiWVZvgp5BgWo5MkBqVSiRkzZkhy7bbQqBS4on8E/nesCD9kFmFob2neLyIiIiKirkDyFmcid0mtq988LEYHjUohcTTez1mWaivLUhERERERtYiJM3UbzvHNoyUc32yxWLB+/XqsX78eFotFsjhaY1JdWarDZ/UoMpgkjoaIiIiIyHsxcaZuwzmjdnLfUMliqK2txcKFC7Fw4ULU1tZKFkdrRAapMTw2BACwLYutzkREREREzZE0cV63bh0WL14sZQjUTZRWmXG61AiZDBjVhzNqt9ZVda3O7K5NRERERNQ8SRPnd999Fz/99FOT2/Lz81FWVubhiKirSq9rbR4UHQSdViVxNF3H5LqyVD+dLIHJYpM4GiIiIiIi7yRp4rxjxw5kZWU1ua1v37545JFHPBwRdVVprvHN0nXT7ooG9wxGL50GJosde06VSh0OEREREZFX8toxzkIICCGkDoO6iDSJ6zd3VTKZzNXqzO7aRERERERN89rEmai1jGYrjp4zAGDi3B5TEh1lqbZlFfPDKiIiIiKiJjBxpi7vQH4FbHaB3iH+6BXiL3U4Xc64hHD4qxQo1JtwrNAgdThERERERF5HKXUARB31azdt6cc3q9VqfPLJJ67XXYFGpcD4/hH4IbMIWzOLMaSXTuqQiIiIiIi8ClucqctzJc59pe+mrVQqMWfOHMyZMwdKZdf5XOoq1zjnIokjISIiIiLyPpInzu+++y4UCkWjRSaTNbtNoVB0qaSEOo/FZseB/AoAHN/cEZPr6jkfPKNHcaVJ4miIiIiIiLyL5Imzc/bsti52u13q0MkLHD1nQI3FhhCtCv0jA6UOB1arFZ9++ik+/fRTWK1WqcNptahgDYbFOLpob8/i7NpERERERPVJ2mzL5Jc6Kr2um/bouFDI5TKJowHMZjPmzp0LAKiqqupSPSOmJEbj0Bk9tmYW4+YxfaQOh4iIiIjIa0je4kzUEak5rN/sLlPqxjnvOlEKk8UmcTRERERERN6DiTN1WUIIpOeVAwBGM3HusCG9gtEjWIMaiw17T1+QOhwiIiIiIq8haeI8efJkvPDCC01uy8/PR1lZmYcjoq7kVIkRZcZaqJVyXNKbJZQ6SiaTYXJdq/O2TI5zJiIiIiJykjRx3rFjB7Kysprc1rdvXzzyyCMejoi6Euf45ktjQ+CnZOcJd5iS+GtZKiGExNEQEREREXkHr802nLNnEzUntS5xTvaC+s3dxfj+EdCo5DinNyGzsFLqcIiIiIiIvILXJs5EF5Oey/HN7qZRKXBF/wgAwLasIomjISIiIiLyDj6TOP/88894/vnnMXv2bMTExEAmk0Ema1/5ovj4eNfxTS3NdT8n9ykymJBfVg25DBjZJ0TqcFz8/Pywbt06rFu3Dn5+flKH0y6TE6MBAD9wnDMREREREQCJ6zh70sqVK/Hll1+69Zzz589vcr1Ox4mqOltaXTftpJ7BCNKoJI7mVyqVCgsWLJA6jA6ZXDfO+eCZCpRUmhEZpJY4IiIiIiIiaflM4jxu3DgMGzYMY8aMwZgxYxAfHw+z2dyhc65fv949wVGbpbF+c6fpodNgaO9gHDlrwPbsYswdHSt1SEREREREkpIJCWfgksvl7e4uLZPJYLVa231tjUYDs9ncrgnI4uPjkZeX57bJywwGA3Q6HfR6PYKDg91yzu5u+su7cKzQgNd+PxIzhvWUOhwXq9WK7777DgAwdepUKJVd87Opf3x/HC9vPYGpQ6Lxzz+MljocIiIiIqImeSqXkvyv+vYmn5xx23cZTBZknjcAAMbEh0ocTUNmsxnXXnstAKCqqqrLJs5XJUXj5a0nsOtEKcxWG9RKhdQhERERERFJRtK/6u12u5SX77BVq1bh1KlTUKvVGDJkCGbNmoXIyEipw+r2MvLKIQQQF65FVLBG6nC6pSG9ghEVpEZxpRn7TpchZSCfayIiIiLyXV2zOcxLPProow2+fvDBB/Hqq69i0aJFEkXkG5wTg3F8c+eRy2WYkhSFDakF2JZZxMSZiIiIiHyaz5Sjcqfrr78emzZtQl5eHqqrq3HkyBEsW7YMZrMZixcvvujs3WazGQaDocFCrZdWV7/Z27ppdzdT6pWl4tAIIiIiIvJlTJzb4ZVXXsGsWbPQp08f+Pv7Y8iQIXjxxRfxxhtvQAiBP//5zy0e/9xzz0Gn07mW2FjOWtxaZqsNvxRUAABGs8W5U43vHwG1Uo6zFTXILqqUOhwiIiIiIskwcXaj22+/HVFRUcjOzkZubm6z+y1fvhx6vd61FBQUeC7ILu7IWT1qrXaEB/ihX0SA1OF0a/5+CozvHwEA2JpZLHE0RERERETSYeLsRnK5HAkJCQCAwsLCZvdTq9UIDg5usFDrpOY4ummPjg9tdykzar3JiVEAgK2ZRRJHQkREREQkHU4O5mbl5Y7ELiCAraGdId3LJwbz8/PD2rVrXa+7uilJUXj8C+BAQQUuVJkRHqiWOiQiIiIiIo9j4uxGR48eRXZ2NrRaLRITE6UOp9ux2wXS85wTg3ln4qxSqXDPPfdIHYbb9NT5Y0ivYBw9Z8D27BLcNCpG6pCIiIiIiDyOXbWbsXbtWiQmJmL58uUN1m/ZsgXbtm1rtP+hQ4cwZ84cCCGwePHibtHa6G1OFFdBX2OB1k+BIb3Yvd1TprC7NhERERH5OJ9pcf7mm2+wcuVK19e1tbUAgMsuu8y17oknnsCMGTMAAKWlpcjOzm40Vjk1NRUrVqxAXFwchg8fDq1Wi9OnTyMjIwNWqxUTJ07E888/74E78j2pdd20R/QJgVLhnZ/52Gw27Nq1CwAwYcIEKBQKiSPquClJ0Xhl20n8eLwEtVY7/JTe+d4TEREREXUWn0mcS0pKsH///kbr668rKSm56HmmTp2KgoICpKWlYffu3dDr9QgODsYVV1yB2267DQsXLuwWyZI38vbxzQBgMpkwadIkAEBVVVW3GOt+SW8dIoPUKKk0Y3/OBUwYECl1SEREREREHiUTQgipg/B1BoMBOp3OlYRT08Y/vw1nK2rw4eKxrjJJ3sZoNCIwMBBA90mcAeDPnx3Cx+kFWHB5PJ6+fojU4RARERERAfBcLsU+l9QlnK2owdmKGijkMozoEyJ1OD5nSlLdOOesIvCzNiIiIiLyNUycqUtwdtMe2isYWj+fGWHgNa4YEAE/pRwFZTU4UVwldThERERERB7FxJm6hNQc7x/f3J1p/ZS4PCEcALA1s1jiaIiIiIiIPIuJM3UJ6bmO+s2jmThLZkpSNACWpSIiIiIi38PEmbxeRXUtsosqAQBj4kMljsZ3Ta6r55yRX44yY63E0RAREREReQ4Hi5LX+znP0drcLzIA4YFqiaNpmUqlwgsvvOB63Z30DvFHUs9gZBYasCO7GLNHxkgdEhERERGRRzBxJq+XWjcxWHIX6Kbt5+eHRx55ROowOs2UxChkFhqwNZOJMxERERH5DnbVJq/H8c3ew1mW6sfjJai12iWOhoiIiIjIM5g4k1czWWw4dKYCQNdocbbZbEhLS0NaWhpsNpvU4bjd8JgQRAT6odJsRVpdTwAiIiIiou6OiTN5tYMFFbDYBKKC1IgN85c6nIsymUxITk5GcnIyTCaT1OG4nVwuw6RBjlbnHzi7NhERERH5CCbO5NWcrZpj+oZBJpNJHA0B9ctSFUMIIXE0RERERESdj4kzebW0uvHNY+JYhspbTBgQAT+FHPll1ThVUiV1OEREREREnY6JM3ktm10go64U1Zi+3j++2VcEqJW4LCEcgKPVmYiIiIiou2PiTF4r67wBlWYrgtRKJPYIljocquequtm1mTgTERERkS9g4kxeKy3HMb55ZFwoFHKOb/YmkxMdiXN6XhnKjbUSR0NERERE1LmYOJPXSnN2047n+GZvExOqRWKPINgFsPN4idThEBERERF1KqXUARA1RQjhanEe0wXqNzupVCo89dRTrtfd2ZSkKGSdr8QPmUW4YURvqcMhIiIiIuo0TJzJKxWU1aC40gyVQobhsSFSh9Nqfn5+ePrpp6UOwyMmJ0bjte2nsPN4CSw2O1QKdmAhIiIiou6Jf+mSV0qtq988LCYEGpVC4mioKZfGhiA8wA+VJqur3jYRERERUXfExJm8UnpdIja6i41vttvtOHr0KI4ePQq73S51OJ1KIZdh4iDOrk1ERERE3R8TZ/JKzhbn5C40vhkAampqMHToUAwdOhQ1NTVSh9PpnGWptmUxcSYiIiKi7ouJM3mdC1VmnC4xAgBGxXWtFmdfM2FgJFQKGXJKjThVUiV1OEREREREnYKJM3mdtFxHGapB0UEI0fpJHA21JFCtxGX9wgEAWzOLJI6GiIiIiKhzMHEmr9NVxzf7qimJHOdMRERERN0bE2fyOs4ZmpP7dq3xzb5qSlI0ACA9rxz6aovE0RARERERuR8TZ/Iq1bVWHDlnAACM7mITg/mq2DAtBkYHwmYX2HGcrc5ERERE1P0wcSavciC/Aja7QO8Qf/QO8Zc6HGolZ6szu2sTERERUXeklDoAovrSuvj4ZpVKhYcfftj12ldMSYzCGztOYUd2MSw2O1QKfiZHRERERN0HE2fyKs7EeUwX7abt5+eHVatWSR2Gx43oE4pQrQrl1Rb8nFfummmbiIiIiKg7YLMQeQ2LzY4D+RUAum7i7KsUchkmDXLOrs2yVERERETUvTBxJq9x7JwB1bU26PxVGBAVKHU47WK325Gbm4vc3FzY7Xapw/Eo1zjnLI5zJiIiIqLuhYkzeQ3X+Oa4UMjlMomjaZ+amhr07dsXffv2RU1NjdTheNSVAyOglMtwusSInFKj1OEQEREREbkNE2fyGq7xzazf3CUFaVQY28/xf8fu2kRERETUnTBxJq8ghEB6bjkAYEwXnVGbgCmJLEtFRERERN0PE2fyCqdLjbhgrIVaKcfQ3jqpw6F2mpLkmCAsLbcM+hqLxNEQEREREbkHE2fyCul13bSHx4ZArVRIHA21V1x4APpHBcJqF9h5vETqcLq1XSdKMPefe/Hkl0dQa/WtieiIiIiIPM1nEueff/4Zzz//PGbPno2YmBjIZDLIZO2fgKq8vBxLly5FXFwc1Go14uLi8MADD6CiosJ9QfuQ1BxHN+1klqHq8pytzts4zrlTnCyuwqL1afjD26lIzSnDe3vzsGh9GipNbOEnIiIi6iw+kzivXLkSy5cvx+eff46zZ8926FylpaVITk7GK6+8AqVSiRtuuAFBQUF4+eWXMXbsWJSVlbkpat+Rnlc3ozbHN3d5znHO27NLYLWxJdRdyo21eOrLI5j60o/YllUMpVyGG0fGQOunwE8nSzH3n/tQZDBJHSYRERFRt6SUOgBPGTduHIYNG4YxY8ZgzJgxiI+Ph9lsbte5HnjgAZw8eRKzZ8/Gxx9/DKXS8Tbef//9ePXVV7Fs2TKsX7/ejdF3b8UGE/IuVEMuA0bFde3EWalUYsmSJa7XvmhknxCEaFWoqLYgI78CyZwlvUNqrXa8tzcXr2w9AYPJCgC4KikKy6cnISEyEAsuj8fC9WnILDRg9ut78O6iMegfFSRx1ERERETdi0wIIaQOQgoajQZmsxltvf3CwkLExMRAqVQiPz8f0dHRrm1msxmxsbEoKyvDuXPnEBUV1apzGgwG6HQ66PV6BAcHtyme7uCbQ4W456MMDO4ZjC1LJ0gdDrnBgx//gs8PnMWfruyH5dOTpA6nSxJC4PtjRXjuv1muutiJPYLwxLWDMb5/RIN9C8qqMf+dVJwuNULnr8K/54/GGA57ICIiIh/gqVzKZ7pqu8u3334Lu92OCRMmNEiaAUCtVuO6666DzWbDli1bJIqw63HWb2bLZPcxOdHxodEPHOfcLkfP6fH7t/bjzvd/Rk6pERGBajw/+xJ8c/+ERkkzAMSGafHZ3ZdjRJ8Q6GssuO3f+/Hfw4USRE5ERETUPTFxbqODBw8CAEaOHNnkduf6Q4cOeSymrs6ZOHeH8c1CCJSUlKCkpKTNvRm6kysHRkIpl+FUiRG5da2ldHHFBhMe/ewgrn31J+w9fQF+SjmWTEzAjkcm4pbkPlDIm5/QMCzADx8tvgy/GxyNWqsdSz7KwPrdOR6MnoiIiKj7YuLcRvn5+QCAmJiYJrc71+fl5Xkspq6s0mRBZqEBALpF19Lq6mpERUUhKioK1dXVUocjGZ2/yvX/uTWrWOJovJ/JYsPabScwcfUOfJJ+BkIA1w3vhW0PpeDRaYkIVLduvLy/nwJvzhuFeZf1gRDA05uP4bktmbDbffdDHCIiIiJ38M3ZizqgqqoKAKDVapvcHhAQAACorKxs9hxms7nBxGQGg8GNEXYtGfkVsAugT5gW0cEaqcMhN5qSFIW9py9gW1YRbr+ir9TheCUhBL46eA5//28WzukdM2JfGhuCJ64d3O6J8hRyGVbOHIqeOn+s+i4b//zxNAr1JqyaM4w10omIiIjaiS3OEnjuueeg0+lcS2xsrNQhSSYtx9FNuzu0NlNDVyU55gDYf7oMBtYYbuTnvHLMen0Plv7nF5zTm9BLp8HLt1yKTXdf3uHZ5WUyGe6Z1B8vzhkOpVyGrw6ew8J1afx/ICIiImonJs5tFBgYCADNdsM1Gh3jOYOCmi8Hs3z5cuj1etdSUFDg/kC7COf45jHdYHwzNRQfEYB+kQGw2gV+PF4idThe40x5Ne79KAM3vrEHvxRUQOunwMNXD8S2hydi5qW9IW9hHHNb3TgqBu8sGIMAPwX2nLqAuW/uxXk9az0TERERtRUT5zbq06cPAODMmTNNbneuj4uLa/YcarUawcHBDRZfZLba8EtBBQBgDGfU7pacrc7bMjnOucpsxQvfZmHyizvx9aFCyGTA3NEx2PHwRNw7eQA0qs7pRn3lwEh8/KdxiAxSI+t8JWa/vhvHi5ofSkJEREREjTFxbqPhw4cDADIyMprc7lw/bNgwj8XUVR05a4DZakd4gB/6RQRIHQ51gil1Zam2ZxfD5qMTVNnsAv9JzcfEVTvw+o5TqLXacVm/MGy+9wq8cNNwRHlgbP/Q3jpsuvty9IsMwDm9CTe9sQf7T1/o9OsSERERdRdMnNto2rRpkMvl2LVrF4qLG7aimc1mbN68GQqFAtOnT5cowq6jfhkqmcx93VPJe4yKC4XOX4XyagsO5JdLHY7H7T5Zihmv7MJfNh1GaZUZ8eFa/OsPo7DhjsswtLfOo7HEhmmx8S7H+GmDyYo/vJ2Kbw6x1jMRERFRazBxbsbatWuRmJiI5cuXN1jfs2dP3HrrraitrcWSJUtgtVpd2x599FGUlJRg3rx5iIqK8nTIXU56bvebGEypVGL+/PmYP38+lEpOWq9UyDFxUCQA4Acf6q59uqQKi99Nw23/3o+s85UI1ijx+Iwk/O/BFFw9pIdkHxSFBvjhw8VjcfXgaNTa7Lh3Qwbe/om1nomIiIguxmf+sv/mm2+wcuVK19e1tbUAgMsuu8y17oknnsCMGTMAAKWlpcjOzkZhYeMWmZdeegn79u3Dxo0bkZiYiNGjR+Po0aM4cuQIBgwYgDVr1nTy3XR9drtAWq6jBbI7Jc5qtRrr16+XOgyvMjkxCl/+cg5bM4vwl2sSpQ6nU1VU1+LlrSfw/t48WO0CCrkMf7gsDkunDEBogJ/U4QEANCoF3pg3Cis2H8V7e/Ow8utjOK+vwfJrktw6MRkRERFRd+IziXNJSQn279/faH39dSUlrZv5NyIiAqmpqXj66afxxRdf4PPPP0d0dDTuv/9+rFixAiEhIe4Ku9s6WVIFfY0F/ioFBvfyzcnRfMXEgVFQyGU4UVyF/AvV6BPedA30rsxis+P9vXl4eesJ6GscJZ8mJ0bhr9OT0D8qUOLoGlPIZVhx/RD01Pnj799m4a1dOThvMGM1az0TERERNUkmhPDNGXu8iMFggE6ng16v95kZtj/Yl4fHvziCyxPC8dEdl138gC5CCOEqVabVajl2u84t/9qLfafL8NR1g7FwfF+pw3EbIQS2Zhbjb1sycbrUUYpuUHQQHr82CRMGREocXet8fuAMHvn0EKx2gcv6heGffxgNnb9K6rCIiIiIWsVTuRTHOJMkuuP4ZsBR3zswMBCBgYHN1vr2RVMSHWWptnajcc6ZhQbMe3s/Fr+XjtOlRkQE+uFvsy7BN/df0WWSZgCYNSIG6xcmI1CtxL7TZZj75l4U6mukDouIiIjIqzBxJkl0x/HN1LwpSY7J8vbnXEClySJxNB1TXGnCXzYewoxXdmH3yQvwU8hxV0oCtj88Eb8f2wdKRdf7sXrFgAh8/KfLEBWkRnZRJWa/vgfZ51nrmYiIiMip6/2FR13e2YoanK2ogUIuw4g+IVKHQx7QLzIQfSMCYLEJ7DpRKnU47WKy2PDa9pOYtGoH/pNWALsAZgzria0PpeAv1yQiSNO1uzcP6aXDpiWXIyEyAIV6E256cw/2nmKtZyIiIiKAiTNJwNlNe0ivYASofWZ+Op83JdHR6tzVumsLIfDVwXOY8uJOrPouG8ZaG4bH6PDZXePw2u9HIjas+0x2FhOqxca7L8eY+FBUmqyY/04qNh88J3VYRERERJJj4kwel9ZNxzdTy6YkOcY5b88uhs3eNeYkPJBfjhvf2IP7NxzA2Yoa9NRp8I+bh+PzJeMxups+vyFaP7x/+1hcM7QHam123LfhAP6967TUYRERERFJis195HFpOc7xzaESR0KeNDo+FEEaJcqMtfiloByj4rw38TxbUYMXvs3Cl784Wlv9VQrcPTEBd0zoB3+/7l+uSaNSYO3vR2Ll18ewfk8unv0mE+cqTHh8Bms9ExERkW9i4kwepa+2ILvIMelQd22xo6apFHJMHBSFzQfPYWtmsVcmzkazFW/sOIW3dp2G2WqHTAbcODIGj0wdhOhgjdTheZRCLsNT1w1GT50Gz/03C+/szkFRpQkvzhkOjar7f3hAREREVB8TZ/Ko9DxHN+1+EQGICFRLHI37KRQK3HTTTa7X1NBVSb8mzo9OS5Q6HBebXWDjz2ew6n/ZKKk0AwCS+4bhyWsHY2hvncTRSUcmk+FPKQnoodPg4U8P4ptDhSipNOOtP4yGTtu1J0MjIiIiagsmzuRR3b0MlUajwaeffip1GF4rZWAkFHIZsosqUVBW7RUTa+05VYpnv87EsUIDACAuXIvl1yRh6pBoyGTslgwAMy/tjchANf70/s9IzSnDnH/uwfqFyegV4i91aEREREQewcnByKOcE4ON5vhmnxSi9cOoOMf//bYsaWfXzik14o730vH7t/bjWKEBQRolHpuehP89eCWmDe3BpPk3Lu8fgU/uGofoYDWOF1Vh1uu7kVn3YQMRERFRd8fEmTzGZLHh0JkKAI5usOSbnGWpfsgskuT6+moLVn59DFf/Yye+P1YEhVyGP46Lw46HJ+KOK/tBrWQX++Yk9QzGpiXjMSAqEEUGM+a+uRd7TnXNutxEREREbcHEmTzmYEEFLDaByCA1+nhBF93OYDQaIZPJIJPJYDQapQ7HKznLUu0/XYYqs9Vj17XY7Fi/Owcpq7fj7Z9yYLEJTBwUiW+XTsAzM4civBuOue8MvUP88dldlyM5PgyVZket5y9/OSt1WERERESdiokzeUx6nmN8c3J8GLvB+rCEyADEh2tRa7PjpxMlnX49IQS2ZRVh2ks/4unNx1BRbcGAqEC8uygZ6xcmY0B0UKfH0N3otCq8d3syZlzSExabwNL//IJ//XgKQnSN+txEREREbcXEmTwmNYfjm8kxU/PkREer89bMzh3nnHXegD++k4pF69NxqsSIsAA/PHvDUPx36QSkDIzs1Gt3dxqVAq/eOgKLxvcFAPxtSxZWbD4Gm53JMxEREXU/nFWbPMJmF8jI694zalPrXZUUhXd252B7djHsdgG53L09EEqrzHjxf8fxcVo+7ALwU8ixcHw87pncH8EallFyF7lchievG4xeIRo8+00m1u/JRZHBhH/cfClrPRMREVG3wsSZPCLrvAGVZisC1Uok9QyWOhyS2Oj4MASplSitqsUvZyowso97eiGYLDas252L17afdI2fvmZoD/zlmkTEhQe45RrU2OIJ/RAVrMHDnxzEf4+cx4WqVPzrj6MQovWTOjQiIiIit2BXbfKI9Lr6zSPjQqFwc+sidT1+SjmuHOToKr3NDd21hRD4+tA5XLVmJ/7+bRaqzFZc0luHT/40Dm/MG8Wk2QOuH94L7y5KRpBGidTcMtz05l6craiROiwiIiIit2DiTB6RWle/eUwcxzeTw1VJ7ilLdbCgAnPe3It7PzqAM+U1iA5W48U5w/HlPeNZ9szDxiWE49O7xqFHsAYni6sw67XdOHaOtZ6JiIio62NXbep0QgikOxPnbp7IKBQKTJ8+3fWamjdxYBTkMiDrfCXOlFcjJrRtJcoK9TV44dtsfH7AUQpJo5LjT1cm4E8p/aD14482qST2CMamJZdjwbpUHC+qwtx/7sU//zAK4/tHSB0aERERUbuxxZk6XUFZDYoMZqgUMlwaGyJ1OJ1Ko9Hgm2++wTfffAONRiN1OF4tNMAPo+p6IGzPan13baPZijX/y8ak1TtcSfPskb2x4+FJePB3A5k0e4FeIf749K7LMbZvGKrMVixYl4ovDrDWMxEREXVdTJyp06XVtTZf0lvHmXapgSlJjrJUP7RinLPdLvBpegEmrd6BV7adhMlix5j4UHx173ismXspeuj4QYU30fnX1Xoe5qj1/MDHv+CNHaz1TERERF0Tm2ao0zkTZ5ahot+akhiF5/+bhb2nLsBotiJA3fSPpH2nL+DZb47hyFnHeNnYMH/89ZokTBvaAzIZJ5vzVmqlAq/eMgI9gzX49085+Pu3WTivr8GT1w3hJIFERETUpTBxpk7nS4mz0WhEVJRj0qvi4mIEBHA255b0jwpEnzAt8suq8dPJUkwd0qPB9txSI577bya+O+qYQCxIrcS9k/tjwfh4qJXsvdAVyOUyPH7tYPQM8cez3xzDu3vzcN5gwsu3jGAPFCIiIuoy2FWbOtWFKjNOlRgBwDWetburrq5GdXW11GF0CTKZDJMTHR80bK03u7a+xoL/++YYfvePnfjuaBHkMuC2sX2w/ZGJ+FNKApPmLuj2K/ri1VtHwE8hx3dHizDv3/tRbqyVOiwiIiKiVmHiTJ0qPc9Rv3lgdCBCA/wkjoa80VV145y3ZZWg1mrHe3tzMXHVdry1KwcWm8CVAyPx7QNX4v9mXYKIQLXE0VJHXDusF967PRnBGiXS88px45t7UFDGD5mIiIjI+zFxpk7lLEM12ge6aVP7JPcNQ6BaidIqMyat3oEnvzyK8moL+kcFYt3CMXhvUTIGRgdJHSa5yWX9wvHZ3Zejp06D0yVGzH5jD46e00sdFhEREVGLmDhTp0rNdbQ4JzNxpmb4KeW4cqCjxu/ZihqEalV4ZuYQ/HfpBEwaFCVxdNQZBkYHYdOSy5HYIwgllWbMfXMvdp0okTosIiIiomYxcaZOU11rxdGzjpak0fG+Mb6Z2ufOKxMwKDoId0zoix2PTMIfx8VDpeCPp+6sp84fn9w1DuP6hcNYa8PCdWnYlHFG6rCIiIiImsS/TKnT/JJfAatdoJdOg5hQrdThkBe7NDYE3z14JR6bMRg6f5XU4ZCHBGtUWL9oDK4f3gtWu8CyTw7ite0nWeuZiIiIvA7LUVGnSavrpu1L45vlcjlSUlJcr4moZWqlAi/dfCl66jT454+nseq7bBTqa7Di+qGs9UxEREReg4kzdRpX/ea+vpM4+/v7Y8eOHVKHQdSlyOUyLJ+ehB46DZ75+hg+2JePYoMZr9zKWs9ERETkHdgkRp3CarMjI9/R4jyG45uJqBUWju+L138/En5KOf53rAi/f2sfaz0TERGRV2DiTJ3iWKEB1bU2BGuUGBjFUkJE1DrXXNITH9w+FsEaJTLyK3DjG6z1TERERNJj4kydov74ZrkPjVM0Go2IjIxEZGQkjEaj1OEQdUnJfcOw8e7L0TvEH6dLjZj1+h4cOctaz0RERCQdJs7UKdJy6sY3+9DEYE6lpaUoLS2VOgyiLm1AvVrPpVVm3PzPvdh5nLWeiYiISBo+lTjX1NTgySefxMCBA6HRaNCrVy8sWrQIZ8+ebdN54uPjIZPJml2ysrI66Q66BiEE0vOciTPHNxNR+0QHa/DJXeMwvr+j1vPt69PwaXqB1GERERGRD/KZWbVNJhMmT56Mffv2oWfPnpg5cyZyc3Oxbt06fP3119i3bx/69evXpnPOnz+/yfU6nc4dIXdZOaVGlFbVwk8pxyUxvv1eEFHHBGtUWLcgGY9+dhBf/HIOj3x2CEUGE+6Z1B8yme8MAyEiIiJp+Uzi/Oyzz2Lfvn0YN24c/ve//yEwMBAAsGbNGjz00ENYtGhRm8sIrV+/3v2BdgPpdeObL40JgVrJUjJE1DF+SjnWzL0UPXT+eHPnKaz+33Gc05vwzPVDoFT4VMcpIiIikohP/MVRW1uLtWvXAgBee+01V9IMAMuWLcOwYcOwc+dO/Pzzz1KF2K2kuuo3s5s2EbmHXC7DX65JxIrrh0AmAz7an4+7PvgZNbU2qUMjIiIiH+ATifPu3buh1+uRkJCAESNGNNp+0003AQA2b97s6dC6pfS6xHm0D04MRkSda/7l8XjjtpFQK+X4IbMYt761DxeqzFKHRURERN2cT3TVPnjwIABg5MiRTW53rj906FCbzrtq1SqcOnUKarUaQ4YMwaxZsxAZGdmxYLu44koTci9UQyYDRsX5XouzXC7H6NGjXa+JyP2mDe2JDxercfu76filoAI3vbkX7y5MRp9wrdShERERUTflE4lzfn4+ACAmJqbJ7c71eXl5bTrvo48+2uDrBx98EK+++ioWLVrUjii7B+f45sQewQjWqCSOxvP8/f2RlpYmdRhE3d7o+DBsvHsc5r+ThpxSI2a/sRvvLBiDYTEhUofWrQghYDBZUW6sRXSwBv5+nLeCiIh8k08kzlVVVQAArbbp1oiAgAAAQGVlZavOd/3112PSpEkYNWoUIiMjcfr0abzzzjt4+eWXsXjxYoSHh2PmzJnNHm82m2E2/9q10GAwtPZWvF5qXf3mZJahIqJO1j8qCJ8vuRwL1qXhWKEBt/xrH167bSQmDYqSOrQuw2y14bzehLMVNThXYUJhRQ3O6R2vz1XU4FxFDYz1xpFHBakRHx6APuFaxIVpERcR4Pg3XIsQrZ+Ed0JERNS5fCJxdrdXXnmlwddDhgzBiy++iMTERNx5553485//3GLi/Nxzz2HFihWdHaYknPWbOb6ZiDwhKliDj/90GZZ8mIFdJ0qx+N10PDf7EswdHSt1aJKz2wVKq8y/JsX6GpytqEFhhcmVHJe2cny4RiWHyWJHcaUZxZVm1ySQ9en8VYgL16JPmLZBch0fEYCoIDXLhxERUZfmE4mzcxbt6urqJrcbjUYAQFBQUIeuc/vtt+Pxxx9HdnY2cnNzER8f3+R+y5cvx7Jly1xfGwwGxMZ2/T/yKk0WHDvnaD0f46OJc3V1NQYPHgwAOHbsWLO9HIjIfYI0Krw9fwz+svEQNh04i0c/O4TCChPun9K9az0bTBZXq7CzhbiwrvW4UF+D83oTLDZx0fP4qxToGaJB7xB/9NL5o2eIBr3qXvcK0aCnzh/+fgpUVNci90I18i4YkX+hGrkXqpFfZkTehWoUV5qhr7Hg0Bk9Dp3RN7qGRiVHnzAt4sJ/baGOCw9AXLgWvUL8oWJZMSIi8nI+kTj36dMHAHDmzJkmtzvXx8XFdeg6crkcCQkJKC4uRmFhYbOJs1qthlqt7tC1vNGB/ArYBRAb5o8eOo3U4UhCCOEaKy/Exf9gJSL38FPK8eLc4eih0+D1Hafwjx+O47yhBitnDu2StZ6dXajrd5k+p3cmx45Eucpsveh5FHIZooPU6BXij54hjkS4d4g/etYlxb10/gjRqlr1AUOI1g+Xav1waWxIo23VtVbkl1Uj70J1XVJtdH19tqIGJosdx4uqcLyoqskYe4f41yXTWsSFBbgS6z5hWo6rJiIir+ATifPw4cMBABkZGU1ud64fNmxYh69VXu6YHMs5btqXpDnrN/toazMRSUsmk+HRaYnoGeKPp748gg2pBSg2mPHq70dA6+c9v+6cXaidiXDDFuManG1DF+pQrcqRFOv80TtEU5cc+6OXztFqHBWk9sgHB1o/JRJ7BCOxR3CjbRabHWfLa5BX5mitzrtQXbc4kmuz1Y78smrkl1Vj14nG544OViMuzNH1Oz5ciz51rdbx4QHQaX1vEkoiIpKG9/wl0YnGjx8PnU6HU6dO4ZdffsGll17aYPtnn30GALjuuus6dJ2jR48iOzsbWq0WiYmJHTpXV8TEmYi8wR8ui0NUkBr3bziArVnFuPWt/Xh7/mhEBHqmp4/BZHGMI3ZNtOUYV3y27uvWdqHWqOR13aX90bMuEe4d8mtX6p46jVd9INAclUKO+IgAxEcEAGhYstFuFyiuNP+aUJc1TKwNJiuKDGYUGVoeV+3sAu5Irh0t1hxXTURE7uT9v3HdwM/PD/feey/+7//+D/fccw/+97//uVqE16xZg0OHDiElJQWjRo1yHbN27VqsXbsWs2bNwnPPPedav2XLFmg0GkyePLnBNQ4dOoRbbrkFQggsXrwYfn6+NbtordWOA/kVAJg4E5H0pg7pgY/uGIvb303HwYIK3PjGHry7MLkueWu/WqvdNQt1of43XajrkuXKVnShlsuA6OBfE+De9ZJj5xLayi7UXZlcLkMPnQY9dBqM7RfeaHtHx1U7W6p/OwN47xD/LtmFn4iIpOMTiTMAPP744/jhhx+wZ88eDBgwABMmTEBeXh7279+PyMhIvPPOOw32Ly0tRXZ2NgoLCxusT01NxYoVKxAXF4fhw4dDq9Xi9OnTyMjIgNVqxcSJE/H888978ta8wpFzepitdoQF+CEh0ve6qROR9xkVF4aNd1+O+e+kIu9CNW58Yw/eWTAGw5sYowvUdaE2ml1lmc7WTbZVPzkuqWxdF+oQrerX7tM6ZzL8a2Ic7aEu1F1da8dVO1us88sc46vPVZhgstiRXVSJ7KLGpSaVchl6h/q7ZgB3zQYe4RhXrVFxXDURETXkM4mzRqPB9u3b8dxzz+Gjjz7CF198gbCwMCxYsAArV65ETExMq84zdepUFBQUIC0tDbt374Zer0dwcDCuuOIK3HbbbVi4cCEUCt/7hZtWV795dFxot28hIaKuIyEyEJuWXI6F69Jw9Jyj1vNjM5Igk+E33alNOK83odZmv+g5/ZRyxwzU9ZPiBq3FXaMLdVfX0XHVznW7TpQ2Ot45rto5YVmf8ADE101cxnHVRES+SSY49a/kDAYDdDqdKwnviha/m44fMovw2PQk3HFlP6nDkUx1dTXGjBkDAEhLS2M5KiIvUWW2YsmHGfjxeEmL+8lkQHSQxjWOuEEX6rqZqMMC/PgBYRfmHFedW9f9O6/M6OgCXm9cdUt0/qoGk5TVL63FcdVERJ7nqVyKibMX6OqJs90uMPLZ71FRbcEX94xvsksdEZHULDY7Vn2XjT2nShskx73qdaWODtawprCPu9i46pb4qxTo45qkTIu+EYHoH+VYwgJ8a+4TIiJP8VQuxb5k1GGnSqpQUW2Bv0qBIb26XuJPRL5BpZDjr9OTpA6DvFx7x1WfLa9BjcXW7Lhq5xwg/aMCkRD5a0LdS+cPuZyt1ERE3o6JM3WYs0TIiD4hbKkhIqJuqzXjqnPrxlHnllbjdGkVThZX4Ux5DcqMtSgz1iItt7zBcf4qBfrVJdT96yXUceEB8FPydyoRkbdg4kwdll73R8BolqHiGGciIh/VsF51QzW1NpwqqcKpEkci7VxyLxhRY7Hh6DkDjp4zNDhGIZchLlz7a+t03b8JUYEIVPPPNyIiT+NPXuqw1LoZtZOZOEMIgWPHjrleExER+fspMLS3DkN76xqst9rsyC+rdiTSdUn1qeIqnCoxospsxekSI06XGPH9saIGx/XUaVwJdUK9pDoikBPXERF1FibO1CHn6mqdKuQyjOgTInU4REREXYZSIUe/yED0iwzE1fXWCyFQZDDXtUxXupLqk8VGlFaZUag3oVBvwk8nG5bS0vmrXOOoXUtkEHqH+kPBcdRERB3CxJk6JK1ufPOQXsEIYNcxIiKiDpPJZOih06CHToMrBkQ02KavtuBkiaNl+mS9rt8F5dXQ11iQkV+BjPyKBseolY4E/bdJdd+IAKiVCg/eGZHn1VrtqKiphb7agooaCyqqLdDXWKCQO+YY0KgU0Pop4a9SwN9PDo1KUfdaAY1Swcn7yIWZDnWIa3xzHLtpExERdTadVoVRcaEYFRfaYL3JYsPpEuOv46jrkuvTpUaYrXZkFhqQWdhwHLVcBvQJ0zbs9l23BGtUnrwtootqKgEur3Z+XYvyaovrdUW1pW6phbHW1qHrqpVy+PvVJdN1ibbz619fy6H1U9ZLuuWN9vVXKaCp99rfT+HaX6WQcZhFF8DEmTrE2eI8Jj70InsSERFRZ9GoFBjcKxiDf1MW0mYXKKgbR10/qT5ZXIVKkxW5dbWqt2YVNzguKkjdoGyWc4kKUvMPfOoQKRJgmcwxlCHEX4UQrR+C/VWw2wVqLDbU1Npgstgcr+u+NlvtrmPNVjvMVjsqYHHH7TdJIZe5kumGSba8UZLd6OvfJPHaRkm9Y1Er5Ww97yAmztRu+mqLq1YlZ9QmIiLyPgq5zDXb91WIdq0XQqCk0tygddqZUBcZzCiudCx7T19ocL4gtRIJv6lF3T8qELGh/lCyJKVP8YYEOETbwmutX93XKgRrVG1KGu12AZPVkUTXWOoS61p7g+S6xmJ1rTNZft23xmKDqbZhIt4wMbfDZLGhutYKe908sja7QJXZiiqztd3vTWtonIl4C63frmS8mZbyq4dE+2z5WSbO1G4/55dBCKBvRAAig9RSh+MVZDIZ4uLiXK+JiIi8kUwmQ1SwBlHBGlzev+E4aoPJ4kikXUm1owt43gUjKs1W/FJQgV8KKhoc46eQo29EABKiAtA/8tdu3wmRgdCoOI7am3XnBLi95HIZtH5KaP06L1USQsBiE/US8d8m6vUS8WYTc3vDxPw356mx2FBbr/XcZLHDZLGjvAOt59nPTnPH7XdJTJyp3dLqxjezm/avtFotcnNzpQ6DiIio3YI1KozoE4oRfRr+fjdbbcgtrf61FnWJs3xWFcxWO7KLKl090ZxkMiAm1N/RQv2bVuoQrZ8nb6vb6yoJcKjWD0Eapc93G5bJZPBTyuCnlEPn33lzCtjsokFi/dsk21SvJbypJL2mtuG+fj7a2gwwcaYOSKur38xu2kRERN2fWqnAoB5BGNQjqMF6u13gbEXNrwl1vW7f+hoLCspqUFBWgx3ZJQ2Oiwj0Q0Jkw1rUUcFq2O2AXQgIAdiEqHstYKtbbxfCtY9rP3vdelFvH+GIrcnXrvM0PsZmd1zvt6/tQsBWdz27vd5rIequj7p9HbG6XgvUu4df97PVu3b9e6h/383fw6/7Wax26GssTICpSQq5DAFqJavfuAHfQWoXk8WGQ2f0AIBkJs5EREQ+Sy6XITZMi9gwLSYlRrnWCyFwwVjbIKF2TlBWqDehtKoWpVVl2F/3QTx1HBNgos7DxJna5dAZPWptdkQEqhEXrpU6HK9RU1ODK6+8EgDw448/wt/fX+KIiIiIpCGTyRARqEZEoBqX9QtvsK3KbMXpenWona3U+moL5HIZ5DJALpM5FrnjtUImg6zB+nr71Xtdfz+FvOFrucwRl6LuvLK6cynq9pHJZFDIG7/+dUHdterH+Jt1jbY7Xjtiqbte/WvXu55chrr4fz1GXnc/DV7Xu2el3NHVlwkwUedi4kzt4ixDldw3lJNg1WO325Genu56TURERI0FqpUYFhOCYTEhUodCRNQqvju6mzrEmTiPjmM3bSIiIiIi6t6YOFOb2ewCP+c5ZtRO7svEmYiIiIiIujcmztRm2ecrUWmyIsBPgcTfzKxJRERERETU3TBxpjZLz3N00x4ZFwqlD9dyIyIiIiIi38Csh9osta5sxBiWoSIiIiIiIh/AWbWpTYQQronBmDg3LSIiQuoQiIiIiIjIjZg4U5ucKa9BkcEMlUKGS2NDpA7H6wQEBKCkpETqMIiIiIiIyI3YVZvaxNnaPLS3Dv5+ComjISIiIiIi6nxMnKlN2E2biIiIiIh8DRNnapO0XEf9ZibOTaupqcHEiRMxceJE1NTUSB0OERERERG5Acc4U6uVGWtxsrgKADA6LlTiaLyT3W7Hzp07Xa+JiIiIiKjrY4sztVp6XTftAVGBCA3wkzgaIiIiIiIiz2DiTK3mHN88mt20iYiIiIjIhzBxplZzjm9O7stu2kRERERE5DuYOFOrVNdaceSsHgAwOo4tzkRERERE5DuYOFOr/FJQAatdoKdOg5hQf6nDISIiIiIi8hjOqk2tkpbj6KY9Oj4MMplM4mi8m1arlToEIiIiIiJyIybO1CrpeY6JwZLjOb65JQEBATAajVKHQUREREREbsSu2nRRVpsdGXm/tjgTERERERH5EibOdFGZhZUw1toQpFFiUHSQ1OEQERERERF5lE8lzjU1NXjyyScxcOBAaDQa9OrVC4sWLcLZs2fbfK7y8nIsXboUcXFxUKvViIuLwwMPPICKigr3By6xVGf95rhQyOUc39wSk8mEGTNmYMaMGTCZTFKHQ0REREREbuAzibPJZMLkyZOxcuVKVFVVYebMmYiNjcW6deswYsQInD59utXnKi0tRXJyMl555RUolUrccMMNCAoKwssvv4yxY8eirKysE+/E89LrEucxfdlN+2JsNhu2bNmCLVu2wGazSR0OERERERG5gc8kzs8++yz27duHcePG4fjx4/j444+xf/9+vPjiiygpKcGiRYtafa4HHngAJ0+exOzZs5GdnY2PP/4YR44cwX333Yfjx49j2bJlnXgnniWEQJozceb4ZiIiIiIi8kEyIYSQOojOVltbi6ioKOj1emRkZGDEiBENtg8fPhyHDh1Ceno6Ro0a1eK5CgsLERMTA6VSifz8fERHR7u2mc1mxMbGoqysDOfOnUNUVFSr4jMYDNDpdNDr9QgODm77DXainFIjJq3eAT+lHIefvhpqpULqkLya0WhEYGAgAKCqqgoBAQESR0RERERE1H15KpfyiRbn3bt3Q6/XIyEhoVHSDAA33XQTAGDz5s0XPde3334Lu92OCRMmNEiaAUCtVuO6665zddftDtJyHK3Nw2N0TJqJiIiIiMgn+UTifPDgQQDAyJEjm9zuXH/o0CGPnqsrYDdtIiIiIiLydT6ROOfn5wMAYmJimtzuXJ+Xl+fRc3UFTJyJiIiIiMjXKaUOwBOqqqoAAFqttsntznGolZWVHjmX2WyG2Wx2fW0wGC56XSkUV5qQe6EaMhkwMi5U6nCIiIiIiIgk4RMtzt7mueeeg06ncy2xsbFSh9SkKpMVVyVFYWzfMOj8VVKH0yUEBARACAEhBCcGIyIiIiLqJnwicXbOclxdXd3kdqPRCAAICgryyLmWL18OvV7vWgoKCi56XSn0iwzEv+ePwX/uHCd1KERERERERJLxia7affr0AQCcOXOmye3O9XFxcR45l1qthlqtvui1iIiIiIiISHo+0eI8fPhwAEBGRkaT253rhw0b5tFzERERERERkffzicR5/Pjx0Ol0OHXqFH755ZdG2z/77DMAwHXXXXfRc02bNg1yuRy7du1CcXFxg21msxmbN2+GQqHA9OnT3RI7ERERERERScsnEmc/Pz/ce++9AIB77rnHNQ4ZANasWYNDhw4hJSUFo0aNcq1fu3YtEhMTsXz58gbn6tmzJ2699VbU1tZiyZIlsFqtrm2PPvooSkpKMG/ePERFRXXyXREREREREZEn+MQYZwB4/PHH8cMPP2DPnj0YMGAAJkyYgLy8POzfvx+RkZF45513GuxfWlqK7OxsFBYWNjrXSy+9hH379mHjxo1ITEzE6NGjcfToURw5cgQDBgzAmjVrPHVbRERERERE1Ml8osUZADQaDbZv344nnngCWq0WX3zxBfLy8rBgwQJkZGSgX79+rT5XREQEUlNTcd9996G2thaff/459Ho97r//fqSmpiIsLKwT74SIiIiIiIg8SSaEEFIH4esMBgN0Oh30ej2Cg4OlDoeIiIiIiKhL8FQu5TMtzkRERERERETtwcSZiIiIiIiIqAVMnImIiIiIiIhawMSZiIiIiIiIqAVMnImIiIiIiIhawMSZiIiIiIiIqAVMnImIiIiIiIhawMSZiIiIiIiIqAVKqQMgQAgBwFG8m4iIiIiIiFrHmUM5c6rOwsTZC1RWVgIAYmNjJY6EiIiIiIio66msrIROp+u088tEZ6fmdFF2ux3nzp1DUFAQZDKZ1OE0YDAYEBsbi4KCAgQHB0sdDnVhfJbInfg8kbvwWSJ34vNE7sTnqXWEEKisrESvXr0gl3feSGS2OHsBuVyOmJgYqcNoUXBwML9hyS34LJE78Xkid+GzRO7E54ncic/TxXVmS7MTJwcjIiIiIiIiagETZyIiIiIiIqIWMHGmFqnVajz11FNQq9VSh0JdHJ8lcic+T+QufJbInfg8kTvxefIunByMiIiIiIiIqAVscSYiIiIiIiJqARNnIiIiIiIiohYwcaYm1dTU4Mknn8TAgQOh0WjQq1cvLFq0CGfPnpU6NJJAdXU1vvjiC9x+++0YNGgQNBoNAgICMHz4cDzzzDOoqqpq9tj169cjOTkZgYGBCAsLw/Tp07Fnz54Wr7d7925Mnz4dYWFhCAwMRHJyMt577z133xZ5iQsXLiAqKgoymQz9+/dvcV8+T9SckpISPPzwwxg0aBD8/f0RFhaGkSNH4pFHHmly/82bNyMlJcVV5mXixIn45ptvWrzG0aNHMWfOHERGRsLf3x+XXHIJXnrpJdjt9s64JZJIWloa5s6di169ekGlUiEkJAQTJkzAunXr0NQIR5vNhn/84x+45JJL4O/vj8jISMydOxeZmZktXqc9zyB5l59//hnPP/88Zs+ejZiYGMhkMshksose56nfZWfOnMHChQvRq1cvaDQaDBw4EE899RRMJlOb7pPqCKLfqKmpEZdddpkAIHr27Cnmzp0rkpOTBQARGRkpTp06JXWI5GFvvfWWACAAiKSkJDFnzhwxdepUERQUJACIxMREUVRU1Oi4pUuXCgDC399fzJw5U0ydOlUolUqhUCjE559/3uS1PvvsM6FQKIRMJhMpKSnixhtvFCEhIQKAeOihhzr5TkkK8+fPFzKZTAAQCQkJze7H54mak56eLsLDwwUAMWTIEHHzzTeLa665RsTFxQmFQtFo/3/84x8CgFAqlWLatGli5syZwt/fXwAQr776apPX2LNnj2uf5ORkMXfuXNGjRw8BQMyZM0fY7fbOvk3yAOfPDABi5MiRYu7cuWLSpElCqVQKAOL3v/99g/1tNpuYNWuWACBCQkLEjTfeKFJSUoRMJhNarVbs37+/yeu05xkk7zNz5kzX30f1l5Z46nfZiRMnREREhAAghg4dKubOnSv69esnAIjx48cLk8nU0dv3OUycqZHHHntMABDjxo0TlZWVrvUvvviiACBSUlKkC44ksX79enHnnXeKY8eONVh/7tw5MWLECAFA3HrrrQ22ff/99wKACA8PF8ePH3et37Nnj/Dz8xMhISGivLy8wTEXLlwQwcHBAoDYuHGja/358+dF//79BQCxfft2t98fSeeHH34QAMSdd97ZYuLM54maU1xcLCIiIoRWqxVffvllo+2/TVyysrKEQqEQarVa7Nmzx7U+OztbhIeHC6VSKU6cONHgmNraWtG3b18BQKxZs8a1vrKyUowbN04AEOvWrXPvjZHHWSwWERUVJQCIDz/8sMG2Y8eOibCwMAFAbNu2zbXe+cHygAEDxPnz513rP/vsMwFA9O/fX1gslgbnas8zSN7p+eefF0888YT46quvRGFhoVCr1S0mzp78XTZ+/HgBQNx///2udRaLxfVBz1NPPdXu+/ZVTJypAbPZLHQ6nQAgMjIyGm0fNmyYACDS09MliI680Z49ewQAoVarhdlsdq2/5pprBADxj3/8o9Ex999/vwAgVq9e3WD93//+dwFAzJw5s9ExmzZtEgDEtdde6+5bIIlUV1eLhIQEMXjwYHH8+PEWE2c+T9Scu+++WwAQr732Wpv2X7p0aaNta9asEQDEvffe22D9xx9/LACI4cOHNzrm559/drXoUNd2+PBhAUAMGjSoye3OnzV///vfXeuSkpIEgCZbCq+//noBQHz22WcN1rfnGaSu4WKJs6d+l+3fv18AEFFRUY1als+fPy9UKpUIDQ1t9KEOtYxjnKmB3bt3Q6/XIyEhASNGjGi0/aabbgLgGJdDBADDhw8HAJjNZly4cAGAY4z8tm3bAPz6zNTX3HPkHNvV1DEzZsyARqPBDz/8wLE53cSKFStw+vRpvPnmm1CpVM3ux+eJmlNTU4MPPvgAAQEBWLhwYauOaem5aM+zNHLkSPTr1w9HjhxBbm5uW8InL9PaWrnh4eEAgJycHGRmZsLf3x8zZsxotF97nif+ndV9efJ3mfOY6667rtFzHR0djQkTJqC8vBw//fRTB+7I9zBxpgYOHjwIwPGHQFOc6w8dOuSxmMi7nT59GgCgUqkQFhYGAMjOzobZbEZkZCRiYmIaHdPcc9TS8+fn54ehQ4fCZDLh+PHjbr0H8rxDhw7hxRdfxMKFCzFhwoQW9+XzRM1JT09HZWUlRowYAX9/f/z3v//FsmXLsGTJErz00ks4d+5cg/0rKiqQn58PAE1+OBwbG4uIiAjk5eXBYDC41vN3o2/o168fEhISkJ2djY8++qjBtszMTHzwwQcIDQ3FrFmzAPz6XAwdOrTJD/+aei7a+wxS1+fJ32X8mdU5mDhTA84f5k19Q9dfn5eX57GYyLu9/PLLAIBp06a5PtW82HMUEBCAkJAQlJeXo7KyEgBgMBig1+tbPI7PX/dgt9uxePFihISE4IUXXrjo/nyeqDnHjh0DAERFReGGG27A9OnT8Y9//ANvvPEGHnzwQfTv3x8bNmxw7e98lkJDQxEQENDkOZt6Lvi70TcoFAq8++67CAkJwW233YZRo0bhlltuweTJkzFs2DDExMRg69atrg+J2/NctPcZpK7Pk7/L+DOrczBxpgacZYW0Wm2T250/5J3f0OTbtmzZgrfffhsqlQorV650rb/YcwQ0fpbql7Ti89e9vfrqq0hLS8OqVatcXR5bwueJmlNeXg4A+Oqrr/Dtt9/itddeQ3FxMXJzc/Hwww+jpqYG8+fPxy+//AKgfc9Sa47js9R9jB8/Hjt37kS/fv2QkZGBjz/+GNu3b4dcLsfvfvc79OvXz7Vve56L9j6D1PV58ncZf2Z1DibORNQuWVlZmDdvHoQQWLVqlWusM1FL8vPz8fjjjyMlJQULFiyQOhzq4pz1k61WK5555hksWbIEkZGRiIuLw6pVqzBnzhxYLBasWrVK4kipq9iwYQOSk5MRGxuL/fv3o6qqCsePH8eCBQvw4osvYvLkyTCbzVKHSUQSYOJMDQQGBgIAqqurm9xuNBoBAEFBQR6LibzP2bNnMW3aNJSXl2PZsmVYunRpg+0Xe46Axs+S85iWjuPz1/Xdc889qK2txZtvvtnqY/g8UXPq/z83NTmYc93OnTsb7N+WZ6k1x/FZ6h5OnDiB+fPnIyIiAl9//TWSk5MREBCAAQMG4J///CeuvfZaZGRk4J133gHQvueivc8gdX2e/F3Gn1mdg4kzNdCnTx8AwJkzZ5rc7lwfFxfnsZjIu5SVleHqq69GXl4eFi5ciNWrVzfa52LPkdFoREVFBUJDQ10/tIODg6HT6Vo8js9f1/f1119Dq9XirrvuwsSJE13LLbfcAsDxoYxz3fnz5wHweaLmOf/vtFotIiMjG22Pj48HABQXFwP49VkqLy93/eH4W009F/zd6Bv+85//wGKxYNq0aQ0SFqe5c+cCAH788UcA7Xsu2vsMUtfnyd9l/JnVOZg4UwPO7rYZGRlNbneuHzZsmMdiIu9RVVWFa665BseOHcPs2bPx1ltvQSaTNdpv0KBBUKvVKCkpwdmzZxttb+45aun5s1gsOHLkCDQaDQYOHOiO2yGJVFRUYOfOnQ2W/fv3AwBMJpNrnbO0Bp8nao5zVuKampomu8+WlZUB+LX1JSQkxPUH5YEDBxrtX1BQgNLSUsTFxSE4ONi1nr8bfYMzmXAmKr/lXO8cW+98Lo4cOQKLxdJo/6aei/Y+g9T1efJ3GX9mdQ4mztTA+PHjodPpcOrUKddkKvV99tlnABx14ci3mM1mzJw5E6mpqZg6dSo2bNgAhULR5L7+/v6YPHkyAODTTz9ttL2558hZB9O5vb6vv/4aJpMJV111FTQaTYfuhaQjhGhyycnJAQAkJCS41jlbC/k8UXP69OmD4cOHQwjh6o5dn3Nd/bI/LT0X7XmWDhw4gNOnT2Po0KGuZ5a6ph49egBwlDlrSlpaGoBfezL07dsXSUlJqKmpcdXNra89zxP/zuq+PPm7zHnM5s2bG32oWFRUhF27diE0NBTjx4/vwB35IEH0G4899pgAIC6//HJRVVXlWv/iiy8KACIlJUW64EgSVqtVzJo1SwAQEyZMEEaj8aLHfP/99wKACA8PF8ePH3et37Nnj1Cr1SIkJESUl5c3OObChQsiODhYABAbN250rS8qKhL9+/cXAMT27dvddVvkRXJycgQAkZCQ0OR2Pk/UnA8//FAAEJdccok4d+6ca/2BAwdEWFiYACA++eQT1/qsrCyhUCiEWq0We/fuda0/fvy4CA8PF0qlUpw4caLBNWpra0Xfvn0FALFmzRrX+qqqKjFu3DgBQKxbt67zbpI84ueffxYABADx+uuvN9i2d+9eERAQIACI77//3rX+rbfeEgDEgAEDRFFRkWv9xo0bBQDRv39/YbFYGpyrPc8gdQ1qtVq0lF558nfZ+PHjBQCxdOlS1zqLxSJmz54tAIinnnqqvbfps5g4UyM1NTVi7NixAoDo2bOnmDt3ruvryMhIcerUKalDJA976aWXXH9MzJo1S8yfP7/JpaSkpMFxS5cuFQCEVqsVM2fOFNdcc41QKpVCoVCIzz//vMlrffbZZ0IulwuZTCYmTZokbrrpJhESEiIAiGXLlnngbkkKF0ucheDzRM2bP3++ACBCQkLE9OnTxaRJk1x/wN5xxx2N9l+zZo0AIJRKpbjmmmvEzJkzhb+/vwAgXnnllSavsXv3btc+Y8eOFXPnzhU9e/YUAMRNN90k7HZ7Z98mecDDDz/s+n03ZMgQMWfOHDF+/Hghl8sFAHHnnXc22N9ms7k+WA4NDRU33XSTmDhxopDJZMLf31/s27evyeu05xkk7/P111+LsWPHuhaZTOb6GeFcvv766wbHeOp3mfODGOcHizfffLPo16+fq3HMZDK5++3o9pg4U5Oqq6vFE088IRISEoSfn5/o0aOHWLBggSgoKJA6NJLAU0895fpDoqUlJyen0bHr1q0To0aNElqtVoSEhIhp06aJ3bt3t3i9n376SUybNk2EhIQIrVYrRo8eLdavX99Jd0feoDWJsxB8nqhpdrtd/Otf/3I9GwEBAWLcuHEt/j9/9dVXYsKECeL/27vXoCirPw7g3wcQFlwvCJjKTQRFlBIiYSaDgS6CkoKBIzKopKhA5jipJC9IdELSJLK0ZsBAugASKclYXpiEZmrUhMjRTC5B4N0InUQkZM//hbP7d93dhxXBzfx+ZvYF5/p7FmbY357znEepVAqlUikCAwNFeXm57DynTp0SUVFRws7OTigUCjF58mTx3nvviZ6env6+JDKhPXv2iOnTp2tWf21tbUVISIgoLCzU2/727dsiKytLTJ48WSgUCmFnZyeio6PF6dOnZefpy98g/bvk5+f3+tlI326Uh/W/rKWlRcTHx4tRo0YJS0tL4eHhIdLS0kRnZ+eDXPZjSxJCiAfa601ERERERET0H8bDwYiIiIiIiIhkMHEmIiIiIiIiksHEmYiIiIiIiEgGE2ciIiIiIiIiGUyciYiIiIiIiGQwcSYiIiIiIiKSwcSZiIiIiIiISAYTZyIiIiIiIiIZTJyJiIiIiIiIZDBxJiKix44kSbKv4OBgU4dI96GhoQGWlpZYu3atVnl6ejokSUJ6erps/7Fjx0KSJDQ3N/dp/osXL8La2hrJycl96k9ERP9+FqYOgIiIyFQWLVqkt3zixIkPORJ6EKmpqbC0tERKSopJ5h89ejSWLVuGjz76CKtWrcKECRNMEgcREQ0cJs5ERPTY2rVrl6lDoAdUU1OD0tJSrFy5Eg4ODiaLIyUlBdu3b0daWhp2795tsjiIiGhgcKs2ERERPbI+/vhjAMDChQtNGoejoyNCQkKwd+9eXL582aSxEBFR/2PiTEREZEB8fDwkSUJlZSUOHjyIkJAQDB8+HJIk4dq1a5p2Bw4cQHh4OBwcHGBlZYVx48bhjTfeQFtbm95x//rrL6xYsQJjxoyBQqHApEmTsG3bNgghIEkSxo4dq9Vefa+uoRVy9T26+pw5cwbx8fFwdnaGlZUVnnjiCcTExOD06dM6bXft2qW5J7ilpQWxsbFwcHCAtbU1nnnmGZSXlxt8r86cOYMlS5Zg7NixsLKywsiRIzFt2jRs3boVt2/fBgB4e3tDkiScPXtW7xitra0wNzeHm5sbhBAG51K7ceMGiouLMX78ePj5+fXa/n4EBwf3ei/8vfdEx8bGoru7mzsZiIj+g5g4ExER9aKwsBAzZsxAR0cHZsyYgalTp2oS1XXr1mHGjBmoqKiAp6cnZs+eDQsLC2RnZyMgIEBn9bG9vR3PPfccduzYASEEIiIi4OjoiDVr1mDlypX9GndZWRl8fX1RUFAAe3t7zJ49G25ubigpKYG/vz++//57vf2am5sxdepUHD9+HC+88AJ8fX1RXV2NyMhIHDp0SKf9l19+CV9fX+Tl5cHGxgZz5syBn58fWltbsXbtWty4cQMAsHz5cgDAzp079c6bl5cHlUqFhIQEg18E3K2qqgo3btwYkMPcwsLCsGjRIp1XVFSUpo25ublWH3Uc+/fv7/d4iIjIxAQREdFjBoAw5l/gokWLNG2Li4t16ktKSgQA4e3tLerr6zXlKpVKvPXWWwKAmDdvnlafxMREAUCEhYWJjo4OTfmxY8eEUqkUAISrq6tWn/Xr1wsAIj8/X2+crq6uOtfT1NQkBg8eLJRKpTh8+LBW3bfffisGDRoknJ2dRVdXl6Y8Pz9fc72rV68WPT09mrrs7GwBQAQGBmqNVVdXJxQKhbCwsBBffPGFVp1KpRIHDx4Ut27dEkIIce3aNWFjYyMcHBy05hVCiJ6eHuHi4iLMzc3F+fPn9V7nvd58800BQOTk5OitV79v69evlx1H/f41NTXJtlOpVCIyMlIAEMnJyXrb2NvbCysrK9HZ2WnMJRAR0SOCK85ERPTYMnYLbnh4OObNm6fTPyMjAwBQVFQEDw8PrXHT09Ph4+OD0tJS/PnnnwCAjo4OFBQUwMzMDNu3b4eNjY2mj7+/P1577bV+u7b3338fHR0dyMzMxIsvvqhVFxYWhqSkJLS2tupdHXVzc8OmTZtgZvb/jwkrVqyAra0tjh49in/++UdTnp2djVu3biEhIQGxsbFa40iShOnTp8PKygoAMGzYMMTExODq1av4+uuvtdoeOnQILS0tCA8Px5gxY4y6xpMnTwIAPD09Zdtt2LBBdsv1H3/8YdR8aWlpKCsrQ0hICLZt26a3jaenJ7q6unDmzBmjxiQiokcDT9UmIqLHlqHHUSmVSq2fZ8+erdPmypUr+OWXXzB+/Hh4e3vr1EuShGnTpqG2thbV1dUIDQ1FdXU1Ojs74e/vD3d3d50+8+fPx+bNm/t4NdrUW6pfeeUVvfWBgYH44IMPcPz4ccyZM0erLjg4GJaWllplFhYWcHNzQ01NDdra2jB69GgAQEVFBYD/b8PuTWJiIvLy8pCbm4u5c+dqynNzcwEAy5YtM2oc4M7vAABsbW1l202ZMgU+Pj4G60tLS9HR0SE7RnFxMTIyMuDu7o7S0lJYWOj/CDVixAgAwNWrV2XHIyKiRwsTZyIiemwZe4iTi4uLTpl6Vbq+vr7X+3HVK84XLlwAALi6uuptd++hYA9CHZ+jo6NRsd3NyclJb9shQ4YAALq6ujRlra2tAKD3iwB9pk6diqeffhoVFRVoamqCm5sbLl++jPLycjg5OSEsLMyocQDg+vXrWnEZEhkZifT0dIP1lZWVsolzdXU1Fi9ejCFDhmDfvn2a5FifoUOHAoDW4XFERPToY+JMRETUC4VCoVOmUqkAAKNGjUJoaKhsf0OJcn9Rx6KvzNCqulpAQIBO2d1btAdCYmIili1bhk8++QRvv/02CgoK0N3djcWLF+scuCVn2LBhAIC///57oELFpUuXEBERga6uLpSUlGDSpEmy7dXJ/PDhwwcsJiIieviYOBMREfWBelXW3t7e6JVr9fZmQ/fUGipXb5tWn059t56eHly6dElvfI2NjcjKyoKdnZ1R8fWFs7Mz6uvr0djYKLsd+m6xsbFYs2YN8vPzkZ6ejp07d8LMzAxLliy5r7lHjhwJ4M7jvQZCV1cXIiMjcf78eWzevBkvv/xyr33a29sBAA4ODgMSExERmQYPByMiIuoDJycnTJw4Eb/++ivq6uqM6uPn5wdra2tUV1fj999/16kvLi7W20+dcOub58iRI+ju7tYpf+mllwAAe/fuNSq2vlIfPJaTk2N0n8GDByMuLg4XLlxASkoK6uvrERoaqndLvJwpU6YAgMHnQj+opUuX4tixY4iLi0NKSopRfX777TdYWVnBy8trQGIiIiLTYOJMRETUR2lpaVCpVIiKikJtba1OfVtbm+bQK+DOoWMLFixAT08PXn/9dXR2dmrqTpw4ge3bt+udJygoCADw+eefa5343dTUZPDZz6tXr4a1tTXWrFmDPXv26NR3dXWhtLQU586dM+ZSDVq1ahUUCgVyc3Oxe/durTohBA4fPqx1T7RaYmIigDuncgN3ktT7FRgYCAD46aef7rtvb7Zs2YLPPvsMAQEBWr9DOY2NjWhra4O/v7/e7f1ERPTo4lZtIiKiPoqNjcXp06exadMm+Pn5wcfHB+7u7hBCoLGxESdPnoRSqdRKCjMzM1FVVYVvvvkG7u7uCAoKQnt7O7777jssX74cO3bs0JnH3d0dCxcuxKeffgofHx8EBQXh5s2bOHr0KGbOnImbN2/qbPP28PBAUVERYmNjERUVBQ8PD3h5eWHw4ME4f/48ampq0NHRgZ9//tngYWDGmDBhAvLz87Fw4ULExMRg48aNeOqpp3D9+nWcOnUKra2taG9v1zySSu3JJ5/Es88+ix9//BGjRo3CrFmz7nvuoKAgKJVKVFZW9jl+Q1JTUwHcObFbneTfa+vWrbC3t9f8rI4jPDy83+MhIiLT4oozERHRA8jIyEBVVRWioqJw6dIllJWV4ciRI+jp6UFSUhL27dun1X7EiBH44YcfkJSUBCEEysrK0NLSgnfeeQcffvihwXlyc3Oxbt06DB06FAcPHkRzczNSU1NRVFRksE9ERAROnjyJ5ORkSJKEw4cPY//+/bhy5QpmzZpl1GFXxoiJicGJEycQFxeH69ev46uvvkJ1dTVcXFyQlZWl83gvteeffx4A8Oqrrxp8vJMcpVKJ+fPno6Ghod9XndWHqx04cAAFBQV6X/fec15YWIhBgwYhPj6+X2MhIiLTk4QQwtRBEBER0R2SJMHV1VVrS/Z/kRACXl5eqKurQ0NDA8aNG9encWpra+Hr64sVK1bIfvEw0M6dOwdXV1dER0frbFknIqJHH1eciYiI6KErLS3F2bNnMXPmzD4nzQDg4+ODuXPnIi8vD1euXOnHCO/Pu+++CzMzM2zcuNFkMRAR0cBh4kxEREQPTUJCAqKjo7FgwQKYm5tjw4YNDzxmZmYmuru7sWXLln6I8P5dvHgROTk5WLp0KTw9PU0SAxERDSxu1SYiIvoX+a9v1ZYkCRYWFhg/fjw2btyI6OhoU4dERETUKybORERERERERDK4VZuIiIiIiIhIBhNnIiIiIiIiIhlMnImIiIiIiIhkMHEmIiIiIiIiksHEmYiIiIiIiEgGE2ciIiIiIiIiGUyciYiIiIiIiGQwcSYiIiIiIiKSwcSZiIiIiIiISMb/AGjUYLQ4dp0XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240331120942_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(0*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "delta_freqs = 1e-3*res.delta_freq.fetch_all()['value']\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(delta_freqs)\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'delta_freqs': delta_freqs,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'raman_pulse_durations': raman_pulse_durations,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54ca7727-ae51-434b-a4a9-c9d09e770e36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8752e51c-8330-4fb5-947c-95bb51f030c9",
   "metadata": {},
   "source": [
    "### 10pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1996,
   "id": "367c0f96-3d63-4016-90d0-2ac5ab2fc81d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T22:27:12.844509Z",
     "iopub.status.busy": "2024-03-29T22:27:12.844509Z",
     "iopub.status.idle": "2024-03-29T22:27:16.457567Z",
     "shell.execute_reply": "2024-03-29T22:27:16.456516Z",
     "shell.execute_reply.started": "2024-03-29T22:27:12.844509Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-50000, -43750, -37500, -31250, -25000, -18750, -12500, -6250, 0, 6250, 12500, 18750, 25000, 31250, 37500, 43750, 50000, 56250, 62500, 68750, 75000]\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "#raman_pulse_durations = (1e3+1e6*np.sinhspace(1, 5.4, 15, 5))//4 \n",
    "raman_pulse_durations = np.linspace(-0.2e6,0.3e6,21)//4 \n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    kk = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    delta_duration_shift  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(delta_duration_shift, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "            with for_(kk, 0, kk < 10, kk + 1):\n",
    "                Pauli_swept( 'aX', delta_freq, pulse_duration_adj=delta_duration_shift)\n",
    "                align()\n",
    "                wait(int(1000e3/4))\n",
    "                align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #play('ON',fsv_trigger)\n",
    "            align()\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1997,
   "id": "9ebe3aa4-8166-4f35-9856-0cf23af62035",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-29T22:27:16.460527Z",
     "iopub.status.busy": "2024-03-29T22:27:16.459530Z",
     "iopub.status.idle": "2024-03-30T00:28:26.564476Z",
     "shell.execute_reply": "2024-03-30T00:28:26.563477Z",
     "shell.execute_reply.started": "2024-03-29T22:27:16.460527Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 7200 s                                                       \n",
      "\n",
      "(188, 21, 4)\n",
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n",
      "0.8354078367224115 0.15796239400114878 0.42755552787443346\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240329232713_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            # guess = [2, 30, (-1)**(i//2+1)*0.1, (-1)**(i//2+1) + 0.5, 0.9-i*np.pi]\n",
    "            # # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            # est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            # #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            if(i==3): \n",
    "                q1, q2, q3 = np.polyfit(x, pops[i], 2)\n",
    "                optimum_duration = fine[np.argmax(q1*fine**2 + q2*fine + q3)]\n",
    "                optimum_duration = abs(q2/q1/2)\n",
    "                print(q1, q2, q3)\n",
    "                ax[1].plot(fine, q1*fine**2 + q2*fine + q3, color = colors[i])\n",
    "            # ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "\n",
    "try: \n",
    "    plt_label = \"$\\Delta T_{\\pi} = %.3f$ ms \"%(optimum_duration)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin_rabi.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b923cf-4088-4476-b6b7-f7b48717218f",
   "metadata": {},
   "source": [
    "### Rabi on a (pi/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfd0f92f-30b8-4113-9835-1a10bc20f8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2+30e3)\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.470e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.70e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.40e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(1.2e6/4)\n",
    "# raman_pulse_durations = (1.1e6+ sinhspace(-0.1e6,0.1e6,6,nonlinearity=2))//4\n",
    "raman_pulse_durations = (1e6*np.linspace(1.085,1.155,2))//4\n",
    "# raman_pulse_durations = (1.1e6+np.linspace(-20,20,3)*1e3)//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq,pump_steps = 50, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = 50, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c32fc0d-7f09-49b2-aac5-93a99f3509b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "#ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            vals = np.polyfit(x, pops[i], 1)\n",
    "            ax[1].plot(x,np.polyval(vals, x),\"--\", color = colors[i], alpha=0.5)\n",
    "            #ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        #ax[1].plot(x,pops[i],\"o-\", label = labels[i], color = colors[i])\n",
    "        ax[1].errorbar(x, pops[i], np.sqrt(0.25/len(data)), label = labels[i], color = colors[i],fmt = \"o-\") \n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0.45,0.65)\n",
    "ax[1].set_xlim(0.99*x[0],1.01*x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "#ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = pops[-1]#(data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean(0)))\n",
    "\n",
    "# fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b71277f-8436-4520-8311-67b0e87efe44",
   "metadata": {},
   "outputs": [],
   "source": [
    "pulse1 = plot_flattop_ft(int(2.476e6//4), int(810.466e3), ramp_time = int(0.4e6/4))\n",
    "pulse2 = plot_flattop_ft(int(2.476e6//4), int(810.466e3), ramp_time = int(1.2e6/4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1971ba3f-9b91-4f4e-a4c5-2608f669cc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(pulse1[0],pulse1[1])\n",
    "plt.plot(pulse2[0],pulse2[1])\n",
    "plt.xlim(400e3,415e3)\n",
    "plt.ylim(0,0.3*1e-8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b09294-a2b9-444a-98a1-08e29063bf73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "piece_size = 400\n",
    "pieces = len(data)//piece_size\n",
    "data_vals = []\n",
    "for i in range(pieces):\n",
    "        data_vals.append(data[i*piece_size:(i+1)*piece_size])\n",
    "pieced_data= []\n",
    "for k in range(len(data_vals)):\n",
    "    data_k = data_vals[k]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_k, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    pieced_data.append(pops)\n",
    "    \n",
    "for pops in pieced_data[:]:\n",
    "    for i in range(4):\n",
    "        plt.plot(x,pops[i]+i*0.2,\"o--\", label = labels[i], color = colors[i])\n",
    "plt.ylim(0.5,1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08736f15-6cbb-4c1c-8a93-13bef82a6f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_probs = []\n",
    "N = 100\n",
    "for i in range(N):\n",
    "    PrintStatic(f'bootstrapping {i+1} out of {N}')\n",
    "    mask = np.random.randint(0,data.shape[0],data.shape[0])\n",
    "\n",
    "    pops = []\n",
    "    for i in range(len(x)):\n",
    "        data_nro = data[mask,i]\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops.append([p0,p1,p2,p3])\n",
    "\n",
    "    full_probs.append(pops)\n",
    "full_probs = np.array(full_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a0cae3-32d2-44f2-b2e3-bef282e1cc2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for pops in full_probs:\n",
    "    for i in range(4):\n",
    "        plt.plot(x,pops[:,i]+i*0.1,\"o--\", label = labels[i], color = colors[i])\n",
    "plt.ylim(0.5,0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4de5e7fa-451c-4f43-8fca-177cc5635336",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "#ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            #ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o-\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0.45,0.55)\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "#ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33ce2f10-e3f3-4e22-8843-ad480d05e034",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>60)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            guess = [0.05,100,0.5,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            print(est[2]*2)\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd1d303-89ac-434e-b01c-9b948d7c6018",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for spin_chirp_df in np.linspace(-0.12,0.12,2):\n",
    "    \n",
    "    config['waveforms']['rising_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0]\n",
    "    config['waveforms']['rising_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1]\n",
    "    config['waveforms']['lowering_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1][0]\n",
    "    config['waveforms']['lowering_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1][0]\n",
    "    \n",
    "    fig, axs = plt.subplots(1,2,tight_layout=True,figsize=(10,5))\n",
    "    fig.suptitle('df = %.3f Hz'%spin_chirp_df)\n",
    "    axs[0].set_title('I, %.3f, %.3f'%(config['waveforms']['rising_spin_chirp_wf_I']['samples'][-1], config['waveforms']['lowering_spin_chirp_wf_I']['samples'][-1]))\n",
    "    axs[0].plot(config['waveforms']['rising_spin_chirp_wf_I']['samples'])\n",
    "    axs[0].plot(config['waveforms']['lowering_spin_chirp_wf_I']['samples'])\n",
    "    axs[0].hlines(0, 0, 5000, 'k')\n",
    "    \n",
    "    \n",
    "    axs[1].set_title('Q, %.3f, %.3f'%(config['waveforms']['rising_spin_chirp_wf_Q']['samples'][-1], config['waveforms']['lowering_spin_chirp_wf_Q']['samples'][-1]))\n",
    "    axs[1].plot(config['waveforms']['rising_spin_chirp_wf_Q']['samples'])\n",
    "    axs[1].plot(config['waveforms']['lowering_spin_chirp_wf_Q']['samples'])\n",
    "    axs[1].hlines(0, 0, 5000, 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1ceb671-5ab5-4ed1-bd49-39dfc922b97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "data_all = []\n",
    "\n",
    "for spin_chirp_df in np.linspace(-0.12,0.12,3):\n",
    "    \n",
    "    config['waveforms']['rising_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0]\n",
    "    config['waveforms']['rising_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1]\n",
    "    config['waveforms']['lowering_spin_chirp_wf_I']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[0][::-1][0]\n",
    "    config['waveforms']['lowering_spin_chirp_wf_Q']['samples'] = chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1]-1*chirp_cos_raise(spin_chirp_duration, spin_chirp_amplitude, spin_chirp_df)[1][::-1][0]\n",
    "    \n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.745e6)\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    ### Raman preparation pulses parameters ###\n",
    "\n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a  = int(808.795e3)\n",
    "    nuclear_spin_freq_b = int(810.717e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1\n",
    "    detuned_electron_amplitude_b = 0.2\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.8\n",
    "    detuned_sideband_amplitude_b = 0.8\n",
    "    \n",
    "    ramp_time       = int(3e6/4)\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,12,13))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    print(raman_pulse_durations)\n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_rabi_a'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Readout parameters #######################\n",
    "    \n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    threshold = 100\n",
    "    prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_freq = readout_freqs[-1]\n",
    "    \n",
    "    #readout_freqs = [readout_freqs[0]]\n",
    "    \n",
    "    ####################### Measurement time estimate #######################\n",
    "    total_measurement_time = 3600\n",
    "    one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "    N_repetition = int(1e9)\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        duration_set = declare(int)\n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        \n",
    "        preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        preparation_flag_stream = declare_stream()\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "                save(preparation_flag, preparation_flag_stream)\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "    \n",
    "                ################# Now play the Raman sideband sequence #################\n",
    "                # align()\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                align()\n",
    "                Raman_pulse_chirped(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                \n",
    "                with for_each_(freq_set, readout_freqs): \n",
    "                    align()\n",
    "                    # play('ON',fsv_trigger)\n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "            \n",
    "            preparation_flag_stream.save_all('preparation_flag')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    \n",
    "    better_sleep(total_measurement_time)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    \n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>60)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    # guess = [0.05,100,0.5,1,1*np.pi]\n",
    "    guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        print(guess)\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "    \n",
    "    \n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi        = fine[np.argmin(data_fit)]\n",
    "    T_pio2      = T_pi-T_half_period/2\n",
    "    \n",
    "    std[3]/(2*np.pi*est[0])\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)):\n",
    "            try:\n",
    "                #guess = [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "                est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "                print(est[2]*2)\n",
    "                ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            except: print(\"fit failed\")\n",
    "            ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "            \n",
    "    else:\n",
    "        pass\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    # plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    # ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "        \n",
    "                'spin_chirp_df': spin_chirp_df,\n",
    "                'click_array': data,\n",
    "                'x': x,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6fddc7e-c6fc-48f0-ab6a-c3f8d86243e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    \n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>100)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    # guess = [0.05,100,0.5,1,1*np.pi]\n",
    "    guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        print(guess)\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "    \n",
    "    \n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi        = fine[np.argmin(data_fit)]\n",
    "    T_pio2      = T_pi-T_half_period/2\n",
    "    \n",
    "    std[3]/(2*np.pi*est[0])\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)):\n",
    "            try:\n",
    "                #guess = [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "                est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "                print(est[2]*2)\n",
    "                ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            except: print(\"fit failed\")\n",
    "            ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "            \n",
    "    else:\n",
    "        pass\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    # plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    # ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "        \n",
    "                'spin_chirp_df': spin_chirp_df,\n",
    "                'click_array': data,\n",
    "                'x': x,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99e3a48-3473-4f6c-ab01-6386eb514f22",
   "metadata": {},
   "source": [
    "### 16pi/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1998,
   "id": "2de96691-b75b-40eb-9d3b-c5c1bca4fe74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T00:28:26.568477Z",
     "iopub.status.busy": "2024-03-30T00:28:26.567479Z",
     "iopub.status.idle": "2024-03-30T00:28:30.175788Z",
     "shell.execute_reply": "2024-03-30T00:28:30.173786Z",
     "shell.execute_reply.started": "2024-03-30T00:28:26.568477Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-37500, -32500, -27500, -22500, -17500, -12500, -7500, -2500, 2500, 7500, 12500, 17500, 22500, 27500, 32500, 37500, 42500, 47500, 52500, 57500, 62500]\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "#raman_pulse_durations = (1e3+1e6*np.sinhspace(1, 5.4, 15, 5))//4 \n",
    "raman_pulse_durations = np.linspace(-0.15e6,0.25e6,21)//4 \n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    kk = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    delta_duration_shift  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(delta_duration_shift, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "            with for_(kk, 0, kk < 16, kk + 1):\n",
    "                Pauli_swept( 'aX90', delta_freq, pulse_duration_adj=delta_duration_shift)\n",
    "                align()\n",
    "                wait(int(1000e3/4))\n",
    "                align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #play('ON',fsv_trigger)\n",
    "            align()\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,1,0,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1999,
   "id": "844d62b7-25fd-4915-be08-8184cdbc2396",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T00:28:30.178783Z",
     "iopub.status.busy": "2024-03-30T00:28:30.177784Z",
     "iopub.status.idle": "2024-03-30T02:29:40.492411Z",
     "shell.execute_reply": "2024-03-30T02:29:40.490410Z",
     "shell.execute_reply.started": "2024-03-30T00:28:30.178783Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 7200 s                                                       \n",
      "\n",
      "(186, 21, 4)\n",
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:827: RuntimeWarning: invalid value encountered in multiply\n",
      "  pcov = pcov * s_sq\n",
      "C:\\Users\\manipp102\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py:827: RuntimeWarning: invalid value encountered in multiply\n",
      "  pcov = pcov * s_sq\n"
     ]
    },
    {
     "data": {
      "image/png": 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ArSLpkPTrx2bPkcQd+dOjG5jnLHe92hxQ6VwEwmczUI5zlF90KU+j6HiZ/8ZcCAo2Byn75M8ye+UU0UtnxFP5n9N67c1b99xePZlJ0qEN0sF1+YE645R08Gfzlie6gRTfM79VumGCFBpZ8a/PAqp9cA4LK6GrUK7atc0uU3v37tW2bdsUERGhkSNHFlpu/PjxWrBggRYuXEhw9gNXerp2dO8hSWq7Yb2CIqvHgexzeS08ed4fb81rAAZKndXR6X3mQEAbP5DO7MufXru1GZa7XCXFNjy3dQfCF+pAqDGQVIeAUrO51P8e85a4S/rtczNEn9gu7fjKvNnDzW7OHS83/5Z0Ld+ilPeHvOw083zPTR9Ie1bI/eXcHi61G2UOOtRycNXcR2fjOK8+mvU1v1+U1JrbrG9lV+apwxjzFJYiR/4uoZdOeJzZWt7KbCiUYUin9ngG6aObpdSj0raF5k0ye8g06JQfpJv0NC+5WJpW6YKNHwGg2nfVdjqdatu2rXbv3l1sV23DMLRr1y7VqlVL8+bN02WXXaaePXvq559/LrS+3377TZ06dVK3bt20YUMprjmqAOmqHSAtZs6UFP3e0zy/PP6/ryuqXz/ZgqvBf96+FCjXAAyUOqXA6c5X7i/U6ea5m7/Mlv5YmT89LNa8BE/CteZ/qBV53VKgqjm2NT9En9qTPz002myB7ni5dN5FJZ8znOdcjnOXy7zO8qaPzK7l2an585r2NcNyh3FSuEW/wwC+EChjLkgV970jO90c6K9gF+/Uo4WXi6hVIEifbw5CFh7nuUxu40dy4mG6ageK4OBgvfvuuxo1apSuvfZaPfvss2rdurWOHz+ulStXqkOHDpo1a5Zq1aolSdq/f78kqUmTJkWuL2/6vn37ipyPipP8zTc69vi/3I8P3HKr7A0aqP5DDyp2+HA/VhZAXE5p8f+p6F9Tc6d9+TepVivzS1qprnsa5Ic6bdLiB8xzA/0dUKt6q7hhSAfWmGH5t3kFBhuxSS0HmmG53ahq040L8Ln6Hczb4L9LRzblhui5UtJ+afOn5i0sTmo/ygzRLQeao9AXpyynt5zcbYblXz+SzuzPn16zee4lpP4k1Wrhk5cJWF55WnMrW0X10gmNNNeX17JuGOYAnwfX5rdMH95odvHe+bV5kyTZzMt55Z0nnZ0mff2QynVZLT+o9sFZkvr166cVK1bosssu04YNG9wtxaGhoRo2bJhatmzpXjY11fyVNbKYLsBRUeYvtykpRYxUlysrK0tZWVnux8nJyeV+DdVd8jff6NDkKeYBXEDOsWPm9JkzCM8lceZIG/4nJR/2vlzacem1snRFshUTrs/+6y18nzUt43QJdeb+Q750ujmoRUhE7i0y/689PP+xPbxiAn5xreLJR8zpVvp1uqySDplfpjd+IJ3clT+9ZnMzLHe9yhydE4Bv2GxSowTzNvRR80vqls/MFuCUI+Yo9RvfN1t6OowxQ3Tz/p5fmEvzQ17GGTOcb/rI/FEsT1is1HGc1PUaqWlveo6gegqUMRcq6zQCm80cLCyuiXl+t2SOrH9ss9kandcyfWafdGKbefvlvYqvq4IQnCV9+OGHuuGGG9S7d299+OGH6tixow4fPqxnnnlGzz77rJYvX65Vq1aV+nzokjz55JN69NFHC013padLVu2iYOEBmAynU8eeeLJQaDZnGpLNpmNPPKmYiy6yRLdty5yH7cgwR3Hdt8q8HVzr2f3Om5AoM8C6cvJvRnHnqRiSy2HeKtuqF0q/rD3cM1zbI84K3AWCtsf8AtMKLh8UYrbOB0KreJ6SjnNHprTjS3NU7D3L869FGxJlfqFOuMbstlkRP0IAyGezmYPzxPeULn7CvKzVls/MAfjSE6X1s8xbdH3z+tAdL5dSj+deTqeYH/IG/E06tVva/lX+9dRtQea/A12vNv+tOpdzqoGqpjqMuVAe9lCpcQ/zptvMaanH81ukdy4xg3UAqvbnOO/cuVMdO3ZUvXr1tH37dkVHe45WOXr0aH3xxRd65ZVXdPvtt+uFF17Q5MmTddlll+nzzz8vtL6kpCTVqFFDtWrV0smTJ4vcZlEtzvHx8Tr01VdqOHy4JcKdh60LZHx1n2wFz2GwQFdTw+WS8/RppSxbpqOPTCtx+UYzZij2oiGyhXjpwlYJ/HYedsYZs/Vg3yrzS9ahDYXDbEiU5EgreV0Tvyg8aqRhmEGqYJh2Oc96XNQ0b8uc/Tf3dmK7tOa1kuts3NMMvDmZ5g8FjvTcv7k3Z1bJ66hoTXpKNVuY5/543GIL3K9htvaEx1Xc9RSLvORPbvezuCZmWN4yxxx1M0/TvuZAXx3GWWukX6C6cuaY4wts+cwcuCfzTP48W1D+j10lqdch9xJSV577IH4AUJSzLu2VnGVwjnOg+Oijj+RwODRixIhCoVmSJkyYoC+++ELff/+9br/9djVtanY9PHjwYJHry5verFmzYrcZFhZWZOv1ob9OVlrjxtY6Jze3q6nhMpR+IlQ5mcGyhzsV6ToiWwV2NXWlpclx7Lhyjh9XzvFjyjl+3Hx8LPf+8WPKOZEoOUrfinl4yhQdlhRco4aCa9eWvXZt2evUVnCt3L9502rXVnDtOrLXqa2g8DJcIqcUzPOwH3c/rtDzsJOPSPtXSftWm0H52G8q1NIQ01Bq2sf8xbRpH6lOW+mFLuc2aqTNZo6sGBQsyTe9M4rlcpqDUJVU501fe/8l2OXMD9E5GUWH67xpOZkF5uX9PWtawWXST3pey7Q4ed2YSsseUUSwzr2FFTHt7Js9vHAXy2K7lB8uMBBKrtgm5kBAXa+Warcqfd0AKl6wPX9U3JHPSXu+y2+Jzsko+fntx0gX3is16EJXbAAVo7SX9rKgah+c84JuXFxckfPzpp8+bX4B7tq1qyRpy5YtcjgcCjmr9TLv/OguXbqcUz2WOic3dwCm5ANhOrYhTjkZ+QHEHuFU/e7Jii1jV1PD4VBOYmJuED6mnOMncsPwMTmOH1dOblh2pZayy7DNpqCYGLlKc564zSYZhpxnzsh55oyyd+8u8SlBkZEKrlMnN0zXkr32WfcLBO+gmBjZvHzRqNDzsPMuGZDXmrxvlXR6b+HlarWSmvWRmvUzg3LN5oW/HJXlGoD+UtZrFXpbT1h0xbSW7l0pvTuq5OX63C3F1Ddbct23ZM/HWcnmTTK//KZmFD2KZWkEhZwVtmNyz2MsofNRp/FSt+ukFhf6f/8DKJk9VGoz3Ly1HCjNu73k53QYa44LAQAVpcRLe1lXtQ/ODRo0kCStW7euyPlr15otQc2bN5cktWjRQu3bt9e2bdv05Zdfaty4cR7Lz5kzR5LZxfuc5Iaqo49Mk4KCFBQWLltoqGyhIbKFhiooNFS2sLDcaQVuISFeQ1uZ5WRLmz5S8m+ndOjHmoVnZwTp0I81JJ1U7LePy2hxoZxGjHIyg5VzOjU/GOe1Gh87JseJ43Imniz6XOQiBEVFyV6vnuz168ter65C6teXvV592evVU0j93Ol16khBQdp10VDlHDtW9LptNtnr11erb76WKzVVOYmJcp48qZyTp+Q8maicxJPKOXVSzsSTyjmZf9/IzpYrPV2u/fvl2L+/8HrP3kxIiBmya9VScJ3auSG7loJr11ZwzVo6/tRTvjsP2+U0W5D3rTJblff/JKUeO6ugIKl+p/zW5KZ9zIBWkkAZNdLqdZb2mo/DHi1dEHU5zfDsLVx7hO+kwvMNl9k9Pz3RvJVFj0mFu+cDCAxx8aVbLoBbggAECK+NH9ZW7c9x3rBhg3r0MAdqyjuPOc9PP/2koUOHKi0tTUuWLNHQoUMlSW+++aZuvvlmtW7dWj/88IPq1asnSfr88891xRVX6LzzztO2bdtkt5fud4m86zj/fF5rRZfjXNdCYTo0VEFhobKFnDU9LEy20BAzhIeEyGZkyeZIkc1xWrasU7JlJioo84Qkp05siZUr2yZ3K54HQwqS7OFOObOCZThLGdyDg2WvmxeEcwNw/XoKcYfkerLXq6/g6NJfQzb5m2906K+T5R506SyNX5hZptZcwzDkSk3NDdgnzXB9MlHOk6dy/5rBO+dkopyJJ+VKK8V5waXQ9N13FdXrgsIzcrLMc5Lzul4fWJPfApknOHcwhryu1/EXFL5mXlkEyrWHrVxnbhdol0vKOBGSf6pDXYdsQarcUbUNwxz87eyW7Z1LpHVvmou45HlKRt1ss05JuuItqfP4yqkVgG+5nNKMTiX/kDdls3X+/QRQtXEd58DTvXt33XvvvXrmmWd0xx136OWXX1aHDh10+PBhrV69Wi6XS7fccos7NEvSjTfeqK+++kpz585Vu3btdNFFFykxMVErVqxQRESEZs+eXerQ7E1I03gFRUXLyM7Ov2VlmS2hDkeh83vzlvGNmFIsY5NcUk56/msNDnXKHuGSPdIpe4RTIREu2SOcuTeXQiKdCg5zyWY7KEXXM8+xjW1k/o05I4WlSK40KT1LCm5gDopUipb02CaZUr/TOrYh1rNLeaRT9bslm/PLwGazKTgmRsExMQrN7W3gjSszMz9knzxp3i/Qkp25Y0epuoYnvvaqnGfOKCqhvYJTduSOeL3aHP367IGsQmOkpr3yg3Kj7uZAWL4SKKNGWrnODmOU3PR+HXv5f8pJy/8c26Ok+nder9jKbBW32cxu2WEx5mBfeUKjpHVvKvlAeDGnZCQpNj6TliggkPnq9BYA8JW8S3s92qTERa2i2rc455k7d65ee+01rV+/XklJSYqJiVFCQoJuvvlmXX311YWWdzqdmjlzpt5++23t3r1bUVFRGjx4sB599FF16NChTNsursW52NbHXIbLJcPhcIfpvJsrLUnG0d9lHN8p48QeGYl7ZZzcL1d6sgyXTYbTZv51SYbTJpdhlxFWS0ZoLRmhcTKCY2QERyr7yAllbvmtxPrr3HG74i67TPZ69RQkh5RyVEo5bP6ynZJ7Sz6ce/+o+deVU7o3xx5hjugZ00iKaZB/P7ZhbthuKEXVk17qLiUfLqbFzP+/pKet+Vn7J04swzMMhdd0KKpBlqLqZymiTraC4up6DuTVoDNfciQpO016opF5/6HDZhC0iOLOa8/7MaixRcYySJ7cSYeW5NVY8Icqc1rjYTbFztzC5w0IdEWOnt/YGqe3AKh+stOUPK1hwLQ4E5wtoKjgbG/QQOctW+r9fFdnjnRyl3T8N+n4NunYVvP+6X0qtitWzebmZSbqd8j921Gq1VIKLnyJptKGvZICfiEul3l+ZfLhs0J27uO8+6UZkbgsOl4u1Yg3z/3Nu8nm+dimEubbzvrrbZnc+7LJcLq064Z/KCddKq7be3CYSzHxGUo/HqbsZM/9YQsNUUT37orq01dRfXorvGNH6122DB4Mp9M89/5oMYN4FTj33ma3Szabb8cpKCXD6dSuC/sp5+QZFffZtNeuofO+/5HPHFAVWPn0FgDVTl4OCoTgXO27aluPGXjr/3lY/pdUw5CSDprh+PhvuQF5m5S4Q3IW0zU7qp5Ur70ZjOvlhuR67crUGhd5fg/ZGzQo/ou/zIAfeX6PUq9TkhQUZHbTjq7nfTlHRn4LdcEW67z7ecG7tNfi/a3wdbcri01S/W7huQOtnX0etrnPG5yfpNihQ6ROV8gR2UbpW/YqbfVPSlu9WjnHjin9pzVK/2mNTjwvBcXGKvKCnorq00dRffoqtEVzv4QuFM1wuZS8aJHXY0eGoZyjR7WjSxEj2NpshW62vOlFzLeV9JxCN8km877L4ZDrdJKKDs2SZFPOySSlr1tfth/IAFiTlU9vAQALIzhbjPuc3OOvSwuOSid2mCE5K6noJ4RE5QbkDvkBuX5HKapOuWuxBQer/kMPeu1qWv+hByuuFSokQqrVwrwVxzCkHYukjwp3py+k42VmlzTDMEdByrvprMeG4blMkfML3Pc632VuO/WYYvV77nnYcUWfhx2fKXW6Quo8XiGS4lp1UdzYsTIMQ9l79ypt9WqlrV6t9DU/y5WcrNSly5S6dJm5jvr1zRDdt48ie/dWSL0SfpSAz7iys5W1c6eytm9X5rbtyty+TVnbd5T+kmpFyfsMFpzkbfFz31KpZe3aRXAuA1d6unZ0N39UbLthvYIiI/1cUdECpU4AAPyNrtoWkNdF4fCNtdSgcU7+KLYFBdml2q1zA3J7qV5H835cU7MFtyLr++YbHXv8ceUcP+GeZm/QQPUfetD/52dKgTFaaIFr+noduXjiFyVe8sfIyVHm1q1KW2UG6YwNG2ScNVBc6Hmt3N26I3v2VHBMaQZ788QX6sKcZ84oMzcgZ23fZv7ds0fKKeKcfbu96OlnafzyS4rs3r3ADzbmzTCM3I+z4Rmk3fPPmufteV6mp2/erGPTppfq9Ueef75iLhmh2IsvNi8Fh2I5U1L0e0/zh4b4/76uqH79LNnVPVDqBABUTYHUVZvgbAHuD8wDMYoNK9Bdsu0lUscrzKBcp7VkD/Nbjc6Tx/R7v0GSpPiXX1DUoCHW+nKVe9kfUxGjhVbmZX+KUoHh3pWRofQNG5S+erXSVv+kzK1bPVsrg4MV0bmzuzU6IiFBQaGhJa63On+hNgxDjkOHlLltm7K2bTfD8vZtyjl8pMjlg+PiFNahvcLbtVd4+3YKa9dOoU2baveIS0q8vniJYxlUMPe52MXVKUkhIZ6j+AcFKbJnT8VeMkIxw4fLXqtW5RQbIMwfG/+lnOPH3dMs9WNjrkCpE7AqfmAGyo/gjDIpNjiXovURBVh9tNBKCvc5p08r/ee1Slu9SmmrV8uxb7/HfFtEhCJ79HB37Q5r21a2s3otBNIX6vIGfFd2trJ37crtZr1dWdu2KXPHDrlSUopcPiQ+XuHt2im8Q3uFtWun8PbtZa9fv8hzzN2jass4a5dbaFRtla7OiE6dlPz1N0pevEiZm37NXyYoSFG9eylmxAjFDBsme82alVm65QTESOoKnDrhe4Q93+G9BMqP4IwyKRycLdC1OFBZfbRQP4R7x6FDSvvpJ7Nr908/yXnypMf84Jo1Fdm7V26Q7qvMrVsD5gt1WQO+MylJmdt3uLtZZ27frqzduwtdE12SbCEhCmvdWmHt2+W3JLdtW+Zu74HyI0RZ6sw+eEgpXy9W8qLFytyyJX9GcLCievdW7KWXKOaiixRco0YlVW8NpR1JvTy9DAzDkHJyzEsRevzNkeHILjzPkZN7P1tGTo7kcMiVna1jTzwpV3JyhdUJ66rOvYl8jfcSKD+CM8rEMzjntvz5u2txoLLwNX3d/BjuDcNQ1u87lbZ6ldJX/6S0tWtlpKd7LhQcLDmdRa/AQl+oS2oxq//IIwqpW8ejJdlx+HCR6wqKizNbkd0tye0V1rKFbCGFL9N2LgLly9W51Jl94ICSFy1W8uJFytq6LX+G3a6ovn0Ue8mlirloiIIt/p+hL5T2En7h3bvLHhNzVvB1uAOu3GH3rHm5wbeyhHVor4hOnRXaNF4hTeLNv02bKjg6utJqyBMILXuBUGOg/JAXCHgvgfJzpadrfdcEXbBrJ8EZpeMRnOs2sU7X4kAUCMHZQjUa2dnK2LzZfdmrjF9+Ma+zXYKQ+HgzCNmDZbOHyBYcLJs9WLLbZQu2e94PDpZC8u/bQuySx/1gc15R9+0hstmDzXXY7bLZzecZQUE6cv//yXm67Nf6DmnSxH0ecnj79gpv1072hg25nJcPZP/xh5IXmy3RWTt25M8ICVF0v36KvWSEoi+6yC/BqyIYhqGcw4dzz4HfrtTl33m2wFeWvGMjJMT8m3tfIXnTQjzmO8+cUdbOnee8ueAaNRTStKlCmzRRSNN4hcY3VUh8E4U2bSp7vXqFTv3whUD48cnqNdI933d4L6u3QPiRLFA4U1K0oXsPgjNKzx2cN8xVbNfR1upajGrl9JzPdPQf//B3GT4T0rSpInv0UHh7MySHtW1bLVo+rSBrzx4lL16slEWLPUKaLTRUUQMGKHbECEUPHqzgaAv+uFWE/MuO7TB7MGzfbp4LX1x3Zy9qTpyo8Dat8wOu3W6G25AQ2ULyA67sBYJvaEjR4dhuL3NQLW3LeK2//EVBoSHK3n9A2Qf2y3HgoJynTnl9ji00VCFNmig0Pl4h8bmt1PHx5uMmTRQUHl6mWqXAaNmzeo2unBztzhsEsCgW6k1kdYbTqV2Dh3jsaw+8l1We1X8kkwIj3Of9u3nmyBGCM0rPHZxPHFZsnYb+LgfVWGm/UNe9716Ft26d333U6cy975ScOUXeN3Icucs5c5+T475vOHMkj/u58zzu564jx6mcU6eUc6ToEa4LavTMM4obNdIXbw3KIWvnTiUv/lrJixYpe88e93RbWJiiL7zQbIkeOFBBUaUL0RX9hSDn1Kn863Lv2K6sbduVtXdv0ZcXCwlRWKtWCm/XTqFtWuvUG28W3xPCIl+oSxxJ3UudztRUOQ4eVPb+/XIcOKDsAwfk2J/79/Dh4k/zyGWvX99snY5v6tkFPD5ewTVrFur5EQgtexVRo+FwyJWeLldamvm34H3332LmF7lsWql6E0UPHarIbgkKadhQIY0ayd6wkex161RIL4KS+POLvysjQ47Dh+U4dEjZBw/KceiQHIcOm5/9P/6QKzW1xHXEv/GGogf0r4RqUZms/iNZHquH+4L/bqY6nQRnlF4gnRSPqq08X6grU2kDftN331VUrwsqoSKUhmEYZohetEgpXy1S9r597nm28HBFDxyo2EsuUfTACxUUEVHsenz1hcBwOpW9b7+ydniG5OJakoLj4hTWvr3C27bNHTSuncJatpStwOXdqtJI6mUOezk5chw5YgbqAq3UZrjeb4Y3L4Kiojy6gIc0bqLEF1+s0B8iDMMww77L5b5vuAzJcJn3DcMMnC6XjNy/Be+7HA7tn3SDnImJxb+uuFjVvvVWGZmZMtLT5UxLk1Ew4Kaly5We99ecZlTiuewlCglRSIMGZphu2FAhjRvJ3rChQho2Ukgjc5q34/VcVeQXf1dWlhmMDx7KDcWH5Dh0UNm5Adnb/iy1kBDFDBqkmGFDFT1oEL2dqoBA+CFPsna4N5xOOVNTtWfUaDlPnJAkgjPKhuAMKwmEL/6BEvBRPMMwlLVjhzmw2KJFcuzPv2yaLSJCMYMHKeaSSxQ9YIBH995z/ULgSktT5u+/my3J23coc/s2Zf2+U0ZGRpHLhzRrao6m3q6teT58u3ayN2hQqnPhrfylpaDKrNMwDDnPnJFj/35lHzgox4Hcv/v3K/vAgeK7EJdCcJ06soWGSC7DM+wWuO8ReguEZauzhYQoKDJSQVFRCoqKlC0yUsFRUR5/3fPPvh8VpaBI837m77/r8JQpJW4vdtQoyWYzQ+WRw8o5drzEXgSSeXWGwqE6P1gH165dpnEkyvvZNLKz5Th61GwhzgvGeSH54EHl5H5h9yYoKkohTZoopHFjhTRprNDGjRXSuLFyTp/R0YcfLvVrMYu3K6pXLzNEDxmikHr1yvZ8+F1lXDXBF3wV7g3DMHu+FPyh7+xb2tnT0mRkZBQzz7wZmZmFtkVwRpkQnGE1gfDFPxACPkrHMAxlbt2qlNyBxRwHD7rnBUVGKnrIEMVeMkKuzCwdvvder18IYoYNU86xY/nnIW/foaxt25S9f3+RP7LYwsMV1qaNOaJ6+3YKa9tOYW3alPvca6t3k8tjOJ1KX7deOSdOyF63riLP7+GXOl1ZWQW6gJut1Onr1ilr27aSn1wZbDYpKMjsspx7swUFmaeQFPFF8GwRCQkKa93aIwQXGXbPul+wN0N5nOuPjUZOjnJOnDCD9OEjZo+Cw4fkOHJEOYePyHH4cIk9CaTcc98bNpS9UYFQ3bChO1jbGzZUUFiYpNJ98Y8ZMiQ3GOe3FjsOHTJD8sFDxb/OgquLjFRo40YKaZwbjnMDckhjMyQHxcUVGfZL+142fmGmUpcvV+rSpcrauctjfkTXrooZNkwxw4YqtGnTEt8/+F/qqlU6cONNJS8YGqqg0FBznIq8gVHtBQZKdQ+oas8f2NFuP2uwVXv+QKpFzQ+xFxiMNX9AVgUF68Tzz3sdeyMoKkqxl41z94ApMuDmht8iT0+qAARnlAnBGVYUCF/8AyHgo2wMw1Dmli3uS1zlHC5wLrvN5vXLsC00VLaICLmSkoqcb69b1+xi3bade2T10GbNKuRzHQgDs1hdaU/JqP/Iw4ro3FmyBckWZDMvqWez5YbcYHPa2fdtQbIF54Zgm838DAQFmUHp7Ps2W7GtpYF02khF/djoTE7ODdSH5Th8WDlHjpgh+/BhM2AfP15iiJXMngP2Bg2UvXOnjKys4hfMO9+6hB4DtvDw3EDcyOz+nxeOGzdWSJMmCq5R45yvplDW9zJr716lLF2qlKVLlbnpV491hbVpo5ihQxUzbKjC2rXjCg8W4jh6VKnff6+0lSuV+v1K75/LKswWFpb/w577B77I/B4vkZHu3i0F5xd6TqT5nMytW3Xgpr+4109wRpkQnIFzZ5UWM/ieYRjK3LRJyYsW68yCBXKV9vJjwcEKa9lCYe3My42FtWtrdrWuXbtiC4ZPBcIpGYFQY0H++LHRcDjkOHZcjsOHzFB95Ig50Fbe/cOHiz1lwhtbaKjZcl0gFIc2yQ/HZe0eXlbn+l46jh1zh+j0n9d6dIUPadLEHaIjEhIs8ZmpTozsbKVv+EWpK79X2vcrz+myfY2eeUYRnTq6B081cpxSjqPAQKk5+QOnOgoOiHrWQKqOnMIDpubkuAdJzR+cNW89TmUfPKis334rscboIUMU0aVLkQHXFhmpoIgC0yMizBZvHzr7302CM8qE4AwA3iUtXKjD991f4nJ1pkxR7Rsmubt9IrAFwikZgVBjQVbrTZR3/nvOkSNKWrhQp96ZVeJz6j/ysGpedZVfRvsuqLzvpfPMGaV8951SlixV2g8/eLRoBteurZiLLlLMsKGK6tXLZ9324clx+LBSv1+p1JUrlb56tdlFOU9QkCK6dFHUgP6K6tdfhyZPLr4HhQV+JAvUHjCpOQRnlAHBGQC8C6QvBPCtQDglIxBqDATV+Th3pacr9YcflLJ0qVKXfydXSop7XlB0tKIHDjQHFxswoNSX7kNhruxsZaxfnxuWv1f2rt0e84Nr11Z0//6KunCAovr2lb1mTfc8q/9IFqg9YLiOM8qE4AwA3gXaFwL4ltVaSYsSCDVaHce5ycjOVtrPa5WydIlSli2T80T+5bFsoaGK6tdPMUOHKnrIYI9gV5zqPuaC49Ahpeaep5z2008yzm5V7tpV0RcOUNSACxXeob3XngxW/5HM6uH+bIbTqaMrVqjRRRcFRA4iOFsAwRkAShZoXwgAlB3HuSfD5VLGpk1KWWKeF13w0n0KClJkz57medFDL1JIw4ZFrqO6/ajjys5Wxrp17i7Y2bvPalWuU0fR/fubYblvXwXXqFGm9Vv9/bR6uD9bIOUggrMFBNIHBgD8KdC+EAAoO47zohmGoazfd5ot0UuWKmv7do/54Z06uS9zFdaypaTAei/L0zKeffCge/TrtDVrCrcqJyTktioPUHh7763KVYHVw31BgZSDCM4WEEgfGADwt0D6QgDg3HCclyz7wAGlLF2mlKVLlbFhg0f39tCWLRXaqpVSlywp/ESLtt6XZZ+7srKUvnad0lZ+r9TvVyp7716P+cF16yh6wIWKHtDfbFWOi6vw+nFuAikHEZwtIJA+MAAAALCWnMREpSz7VilLlyrtp58kh6PE59jr1VPLL79QUHS0368fXZqW8ewDB8zrKn+/Umk//+x5GbPgYEV0SzDD8oUDuCZ2AAmkHERwtoBA+sAAAADAupwpKTr51ts6+dprpXtCUFDuNX2jiriZ04OjohQUHa2gyKKWyV82OCqqzJfOcp/XfnYksdkkw1DUwIFy7Nun7D/+8Jhtr1dPUQP6K3rAhYrq20fBfIcOSIGUg3x7RWsAAAAAfhMcE6Ow884r/RNcLrlSU+VKTfXJ9m0hIV7CtWcgt0VEKPHFl4oeRT13WtqKFebj4GBFduumqAtzW5XbtqVVGZWK4AwAAABUIfa6dUu1XPzrrymsfXu50tLkSkvP/ZsmV1pqgfvmzenxOL3QfCMrS5JkOBxynjkj55kzPns9de66S7Um/lnBMTE+WydQVgRnAAAAoAqJPL+H7A0alHhN7Kj+/X026JqRk+MlbBcO2q60NGXt2qXMLVtKXHdo8+aEZvgdwRkAAACoQmzBwar/0IPmucM2FXlN7PoPPejTkcptdruC4+LKNIJ12pqftX/ixBKXK20LOlCRqvZFzAAAAIBqKHb4cDWeOUP2uvU8ptvr17fMpajyWsZV3LnKNpvsDRoo8vwelVsYUARanAEAAIAqKHb4cMVcdJHS161XzokTstetq8jze1jmmtj+aBkHzhXBGQAAAKiibMHBiup1gb/LKFbs8OHSzBmFr+Ncv77HdZwBfyM4AwAAAPCb2OHDFdWnj37vaQb8+P++rqh+/WhphqUQnAEAAAD4VXBMjNpv3+bvMoBiMTgYAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBGcAAAAAALwgOAMAAAAA4AXBGQAAAAAALwjOAAAAAAB4QXAGAAAAAMALgjMAAAAAAF4QnAEAAAAA8ILgDAAAAACAFwRnAAAAAAC8IDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXtj9XYBVnDhxQv/+97+1cOFC7d+/XxEREWrevLkuuugi/ec//ym0/MKFC/XMM8/ol19+kSR1795d9913n0aOHFnZpQMAAACVwuFwyOl0+rsMWFBwcLBCQkL8XUaFsRmGYfi7CH9bv369Lr74Yp08eVIdO3ZUp06dlJycrK1bt+rgwYPKycnxWH7GjBm65557ZLfbNXToUIWFhembb75RRkaGXnzxRd11111l2n5ycrLi4uKUlJSk2NhYX740AAAAoNySk5OVmJiorKwsf5cCCwsLC1OdOnVKnWkCKQdV++B84sQJdejQQenp6frwww81ZswYj/k///yzLrjgAvfjHTt2qGPHjrLb7Vq+fLn69OkjSfr999/Vt29fJSUladu2bTrvvPNKXUMgfWAAAABQvSQnJ+vQoUOKjo5WXFycQkJCZLPZ/F0WLMQwDDkcDiUlJSk1NVWNGzcuVa4JpBxU7btqT5s2TYmJiXr55ZcLhWZJHqFZkmbOnCmn06m77rrLHZolqU2bNvr73/+uqVOnaubMmXrxxRcrvHYAAACgoiUmJio6OlpNmjQhMKNYERERiomJ0cGDB5WYmGj5IFxW1XpwsIyMDM2ePVtRUVG64YYbSvWcL7/8UpI0fvz4QvPypi1cuNB3RQIAAAB+4nA4lJWVpbi4OEIzSmSz2RQXF6esrCw5HA5/l+NT1brFed26dUpJSVH//v0VERGhRYsWacmSJcrMzFSbNm00YcIENWrUyL38mTNntH//fklSt27dCq0vPj5ederU0b59+5ScnFzlfmUBAABA9ZI3EFhVHvQJvpX3WXE6nVXqc1Otg/PWrVslSfXq1dO4ceM0f/58j/kPPfSQ3nrrLV199dWS5A7NNWvWVFRUVJHrbNKkiRITE7Vv3z517ty5AqsHAAAAKgetzSitqvpZqdZdtU+fPi1JWrBggRYvXqyXX35Zx48f1x9//KF7771XGRkZmjhxojZu3ChJSk1NlSRFRkYWu868QJ2SklLsMllZWUpOTva4AQAAAACsqVoHZ5fLJUnKycnRP//5T91xxx2qW7eumjVrpv/85z+68sor5XA4iryOc3k8+eSTiouLc9/i4+N9un4AAAAAgO9U6+AcHR3tvl/U4GB501asWOGxfHp6erHrTEtLkyTFxMQUu8yDDz6opKQk9+3AgQNlLx4AAACoYtKzc9T8gS/V/IEvlZ6d4+9yADdLn+Ocnp6uxMRE1a5d2+Oc4tOnT+vf//63tmzZoqZNm+pvf/ubWrVqVeb1N2vWTJLZ9bpu3bqF5jdv3lySdPz4cUlS06ZN3dtPS0sr8jzngwcPeqy7KGFhYQoLCytzvQAAAACAymfpFufHHntMLVq00Pbt293TsrKy1Lt3b/3nP//RV199pddee019+vTRkSNHyrz+vJGxMzIylJWVVWj+qVOnJOW3NNeoUcMdnn/55ZdCyx84cECJiYlq1qwZI2oDAAAAVdisWbNks9k0a9asc3q+YRj69NNPtXPnTt8W5sNtbN68WTabTffcc4+PKws8lg7O3377rVq1aqUePXq4p82ePVs7d+7U4MGD9fXXX+uvf/2rEhMT9fzzz5d5/U2bNlXXrl1lGIa7O3ZBedMKXnpq5MiRkqQ5c+YUWj5v2ujRo8tcCwAAAIDqwTAM3XnnnZowYYIGDBigbdu2WXIbnTt3VosWLbRgwQKf1xdoLB2c9+/fr9atW3tMW7BggWw2m9555x0NGzZMM2bMUJs2bbRo0aJz2sb9998vSbr33ns9Wq03btyoZ599VpJ02223uadPnjxZwcHBeu211/TTTz+5p+/cuVP/+te/ZLfbNXny5HOqBQAAAEDVd9ddd+nVV1+VJB07dkxDhgzx6GVrpW2MHTtWe/bs0ZYtW3xaX6CxdHA+ffq0atSo4X5sGIZ++OEHdenSxWMk6q5du57zAFvXXHONJk6cqM2bN6tDhw4aOXKkhgwZot69e+vUqVO6+eabdeWVV7qXb9u2rf7zn/8oKytLAwYM0KWXXqpx48apa9euOnnypJ577jmdd9555/yaAQAAAFRdd911l1555RX3mEhNmzZ1B9sdO3ZYbhtjx46VpGrf6mzp4NygQQPt3bvX/Xj9+vU6ffq0Bg4c6LFceS+y/c477+i///2vWrVqpe+++04///yzunfvrlmzZum///1voeXvueceLViwQH369NHKlSu1bNkynX/++Vq4cKHuvvvuctUCAAAAVFdOl+G+//PeUx6Pq4Knn35aL7/8srp166bPP/9ckjRs2DC9/PLLOnLkiIYOHer1Cj7+2Eb//v1Vs2ZNzZ8/v1x1BTpLj6qdkJCgL774QvPmzdNFF12kxx57TDabTaNGjfJYbufOnWrUqNE5b8dms+nmm2/WzTffXOrnjB49mnOZAQAAAB9ZvOWIpi34zf140jtr1TAuXNNGd9CITg39WJnv3Hjjjdq8ebNmzpyp5ORk9/Tbb79dLpdLMTExioyMtNQ27Ha7Ro4cqffff19HjhxRw4ZVY1+UlaVbnPPOP77iiitUo0YNLVy4UF27dtWQIUPcyxw7dkybNm3yGEAMAAAAQOBYvOWIbp+9QceSPa90czQpU7fP3qDFW8p+BR0rqlOnjt577z3VqlWr0Lw777xTf/7zny25jbFjx8owDC1cuLDc9QUqS7c49+3bV3PnztUzzzyjxMRE9ejRQ0888YSCgvLz/ocffqiYmBiNGDHCj5UCAAAA1YthGMpwOMu9HqfL0LQFv6moTtmGJJuk6Qu2qt95dRQcVL5TNCNCgst9mmd1NGLECIWFhWn+/Pm65ZZb/F2OX1g6OEsld4meMmWKpkyZUnkFAQAAAFCGw6kOj3xd4dsxJB1NzlTn6d+Ue11b/3mxIkMtH4EsJzo6WoMHD9ayZcuUlpamqKgof5dU6SzdVft///ufVq1aVeJyP/30k/73v/9VQkUAAAAAUP106dJFWVlZPhv5O9BY+ueWSZMmadKkSerbt6/X5d566y29/fbbPjknAAAAAEDJIkKCtfWfF5d7PT/vPaVJ76wtcblZN/TUBS0Kn7dbFhEhweV6fnX2zTffqG7dukpISPB3KX5h6eBcWi6Xi3MVAAAAgEpks9l80u15QOu6ahgXrqNJmUWe52yT1CAuXANa1y33Oc44N/v379fGjRs1adIkj/GmqpMq8ar37Nmj2NhYf5cBAAAAoIyCg2yaNrqDJDMkF5T3eNroDoRmP1qwYIEkc3Tt6spyLc7//Oc/PR5v3Lix0LQ8OTk52rFjh77//nsNGzasMsoDAAAA4GMjOjXUq9d117QFv3lckqpBFbuOc6CaP3++IiIiNHz4cH+X4jeWC87Tp0+XzWaTYRiy2WzauHGjNm7c6PU59erV0xNPPFE5BQIAAADwuRGdGqrfeXXco2fPuqEn3bMtICkpSStWrNCIESMUGRnp73L8xnLB+Z133pFkXhfuxhtvVP/+/XXTTTcVuWxoaKgaNWqk3r17KywsrDLLBAAAAOBjBUPyBS1qEZotYNGiRXI4HNW6m7ZkweA8ceJE9/13331Xl1xyicc0AAAAAAh0zZs3l2EUNRyatbYxf/58BQUFadSoUT6qKjBZLjgXtHz5cn+XAAAAAADVksPh0KJFi9SrVy/Vr1/f3+X4laWDMwAAAADAP/bs2aP+/fvrqquu8ncpfmf54Lx161Y9/fTT+v7773XkyBFlZ2cXuZzNZlNOTk4lVwcAAADAVyJD7frjqZH+LgO52rZtqy+++MLfZViCpYPz6tWrNXToUGVkZEiSatWqpQYNGvi5KgAAAABAdWLp4Pzggw8qIyNDU6ZM0T/+8Q/VqlXL3yUBAAAAgBISEjRt2jQlJCT4uxRUAksH53Xr1ikhIUHPPfecv0sBAAAAALeEhARCczUS5O8CvAkNDVW7du38XQYAAAAAoBqzdHDu37+/tm7d6u8yAAAAAADVmKWD8xNPPKFdu3bp5Zdf9ncpAAAAAIBqytLnOG/YsEE33HCD/vrXv+qTTz7RsGHD1KRJEwUFFZ33//znP1dyhQAAAACAqs5mGIbh7yKKExQUJJvNpoIl2my2QssZhiGbzSan01mZ5flMcnKy4uLilJSUpNjYWH+XAwAAAEiSMjMztXfvXrVo0ULh4eH+LgcBoCyfmUDKQZZucX7kkUeKDMoAAAAAAFQWSwfn6dOn+7sEAAAAAEA1Z+nBwQAAAAAA8DeCMwAAAAAAXli6q/aQIUNKvazNZtOyZcsqsBoAAAAAQHVk6eD83XfflbhM3qjbDCIGAAAAAKgIlg7Oe/fuLXK6y+XSgQMH9M0332jmzJm64447dMcdd1RydQAAAAB8KjtNeqKRef+hw1JolH/rAXJZOjg3a9as2HktWrTQhRdeqCFDhujiiy9W7969vS4PAAAAAMC5CPjBwYYMGaLzzz9fTz31lL9LAQAAAFBNzJo1SzabTbNmzTqn5xuGoU8//VQ7d+70bWEFpKena968edq4ceM5PX/z5s2y2Wy65557fFtYAAr44CxJTZo00W+//ebvMgAAAACgRIZh6M4779SECRM0YMAAbdu2rUK2c/z4cV122WV66aWXzun5nTt3VosWLbRgwQIfVxZ4Aj44Z2RkaO3atQoPD/d3KQAAAABQorvuukuvvvqqJOnYsWMaMmSItm/f7ueqijZ27Fjt2bNHW7Zs8XcpfmXp4Lx///5ib1u3btW8efM0fPhwHThwQCNGjPB3uQAAAADKw+XMv79vlefjKuKuu+7SK6+84h6fqWnTpu7wvGPHDj9XV9jYsWMlqdq3Olt6cLDmzZuXeJkpwzDUtm1b/ec//6mkqgAAAAD43NYF0qL78x+/P16KbSSN+LfUYYz/6vKhp59+Wi+//LK6deumN998Uz169NCwYcPUo0cP3XHHHRo6dKh27NihyMhIf5fq1r9/f9WsWVPz58/XQw895O9y/MbSwfnCCy8sNjiHhoaqYcOGGjhwoK6++mq6agMAAACBausC6ZM/SzI8pycfMadP+F+VCM833nijNm/erJkzZyo5Odk9/fbbb5fL5VJMTIylQrMk2e12jRw5Uu+//76OHDmihg0b+rskv7B0cP7uu+/8XQIAAACAohiG5Egv/3pcztyWZqOImYYkm7T4/6SWg6Sg4PJtKyRSKqFHa0WqU6eO3nvvPUnyCM6SdOedd/qjpFIZO3asZs+erYULF+qWW27xdzl+YengDAAAAMCiHOnSE40qYUOGlHxYeiq+/Kt66LAUGlX+9VQzI0aMUFhYmObPn09wtrrs7Gxt3LhRhw4dkiQ1btxYCQkJCg0N9XNlAAAAAFB1RUdHa/DgwVq2bJnS0tIUFVX9fnywfHDOzMzUI488otdff12pqake86Kjo3Xbbbfp0Ucf5RxnAAAAoDKFRJotuOW1b5U5EFhJrp0jNetbvm2FWOv84UDSpUsXLV68WDt27FD37t39XU6ls3RwzsrK0tChQ7V69WpJ5s7KG2n7jz/+0KZNm/TMM8/oxx9/1LJlyxQWFubnigEAAIBqwmbzTbfnVkPM0bOTj6jo85xt5vxWQ8p/jnM1kZ6eXuQgY9nZ2QoODlZwcNnfx2+++UZ169ZVQkKCDyoMPJa+jvPzzz+vVatWqV+/ftq4caN++eUXzZ07V59//rk2bNigTZs2acCAAVq9erVmzJjh73IBAAAAlFVQsHnJKUnS2QN35T4e8RShuZS+++47tWzZUj/++KPHdIfDofHjx+vaa6+V01m262Pv379fGzdu1MiRIxUUZOkIWWEs/ao//PBD1a1bV19++aU6d+5caH6nTp30xRdfqE6dOnr//ff9UCEAAACAcuswxrzkVEwDz+mxjarMpagqS1pamk6fPq1LLrlEq1atkiTl5OToT3/6kxYuXKjk5OQyB+cFCxZIMkfXrq4sHZx37dqlQYMGKSYmpthloqOjNWjQIO3evbsSKwMAAADgUx3GSHf+nP/42jnSlM2E5jIaOXKk5syZo6ysLE2aNEmS9MEHH2ju3Lm6+OKLNXfu3DIPsDx//nxFRERo+PDhFVBxYLB0cLbb7UpPL/nacOnp6bLbLX26NgAAAICSFOyO3awv3bPP0ejRo/Xpp5+6HzscDg0bNkzz5s0r87hQSUlJWrFihYYOHVrkedPVhaWDc+fOnfXtt99qz549xS6zd+9effvtt+rSpUslVgYAAAAA1jVmzBh98sknCgkJ0UUXXaT58+ef05WIFi1aJIfDUa27aUsWD8633nqrMjIyNGjQIL311lvKyMhwz8vIyNA777yjQYMGKTMzU7fddpsfKwUAAACA0mvevLkMw9Cbb75ZYdsYN26cTpw4ocWLFysiIuKc1jF//nwFBQVp1KhRPq4usFi6f/P111+vH374QW+88YZuueUW3XLLLapTp44kKTExUZJkGIZuvfVWXXvttf4sFQAAAAAsJy4u7pyf63A4tGjRIvXq1Uv169f3YVWBx9LBWZJef/11DR8+XC+88ILWrFmjEydOSJJCQ0PVu3dv3X333briiiv8XCUAAACAcguNkqYn+bsK5NqzZ4/69++vq666yt+l+J3lg7MkXXHFFbriiiuUk5OjkydPSpJq167NgGAAAAAAUEHatm2rL774wt9lWEJAJU+73V7tuwgAAAAAACqXpQcH27Vrl1544QVt2bKl2GW2bNmiF154wevI2wAAAADgSwkJCZo2bZoSEhL8XQoqgaVbnGfMmKHXX39du3fvLnaZmJgYTZ06VXv27NGMGTMqrzgAAAAA1VZCQgKhuRqxdIvzsmXLlJCQoKZNmxa7TLNmzZSQkKAlS5ZUYmUAAAAAgOrC0sH5wIEDatmyZYnLtWrVSgcPHqyEigAAAAAA1Y2lg3NwcLCysrJKXC4rK0tOp7MSKgIAAAAAVDeWDs5t2rTRDz/8oPT09GKXSU9P1w8//KDWrVtXYmUAAAAAgOrC0sF5/PjxOnXqlP7yl78oLS2t0Pz09HTdfPPNOn36tMaPH++HCgEAAAAAVZ2lR9W+++679d577+njjz/W8uXLdfXVV6tVq1aSpN27d+vDDz/U8ePH1bZtW02ZMsW/xQIAAAAAqiRLB+fIyEgtXbpU1113nb799lvNmDFDNptNkmQYhiRp8ODBeu+99xQVFeXPUgEAAAAAVZSlg7MkNWjQQEuXLtXatWu1dOlSHThwQJIUHx+voUOHqmfPnn6uEAAAAABQlVk+OOfp2bMnIRkAAAAAUOksPTgYAAAAAAD+RnAGAAAAYAnpjnR1frezOr/bWemO4i9JC1Q2gjMAAAAAAF4QnM9y8uRJ1atXTzabTeedd57XZWfNmqULLrhA0dHRqlWrli699FKtWrWqkioFAAAA4C+zZs2SzWbTrFmzzun56enpmjdvnjZu3OjTugoyDEOffvqpdu7ceU7P37x5s2w2m+655x4fVxZ4CM5n+dvf/qbExMQSl5syZYpuuOEGbdmyRUOHDtUFF1ygJUuW6MILL9S8efMqvlAAAAAAAev48eO67LLL9NJLL1XI+g3D0J133qkJEyZowIAB2rZtW5nX0blzZ7Vo0UILFiyogAoDC8G5gGXLlundd9/VzTff7HW5pUuXaubMmapdu7Y2bdqkefPmafHixfr+++8VHBysG264QWfOnKmcogEAAADgLHfddZdeffVVSdKxY8c0ZMgQbd++vczrGTt2rPbs2aMtW7b4usSAQnDOlZGRoVtvvVUdOnTQvffe63XZ5557TpL0j3/8Q61bt3ZP79Onj2677TadOXNGb731VoXWCwAAAFQ1TpfTfX/9sfUej1F6d911l1555RU1a9ZMktS0aVN3eN6xY0eZ1jV27FhJqvatzgTnXI8++qj27Nmj1157TSEhIcUul5GRoW+//VaSNH78+ELz86YtXLiwYgoFAAAAqqCl+5Zq3IJx7sd3LLtDF392sZbuW+q/ogLQ008/rZdfflndunXT559/LkkaNmyYXn75ZR05ckRDhw5VenrpRyzv37+/atasqfnz51dUyQHB7u8CSnL8+HG98sor+v7773XkyBFlZWUVuZzNZtPu3bvPaRu//vqrnn32Wd1www0aMGCA/vjjj2KX3bFjh7KyslS3bl01adKk0Pzu3bu71wkAAACgZEv3LdXU76bKkOEx/Xj6cU39bqqeG/SchjYb6qfqAsuNN96ozZs3a+bMmUpOTnZPv/322+VyuRQTE6PIyMhSr89ut2vkyJF6//33deTIETVs2LAiyrY8Swfnbdu2aeDAgTp58qQMwyj5CefA5XLpL3/5i2rUqKGnn366xOX3798vSUWGZkmKiopSjRo1dPr0aaWkpCgmJsan9QIAAABWYBiGMnIyyr0ep8upJ39+slBoluSe9tTPT6lXg14KDgou17Yi7BGy2WzlWofV1alTR++9954keQRnSbrzzjvPaZ1jx47V7NmztXDhQt1yyy3lrjEQWTo433fffUpMTNQVV1yhBx98UG3atFF0dLRPt/Hiiy9q7dq1euedd1S7du0Sl09NTZUkr7/SREVF6cyZM8UG56ysLI+W87M/0AAAAIDVZeRkqNcHvSplW8fSj6nvR33LvZ4116xRZEjpW1thGjFihMLCwjR//vxqG5wtfY7zypUr1bZtW33yySfq3r27z0Pz/v379Y9//EMDBw7UpEmTfLpub5588knFxcW5b/Hx8ZW2bQAAAAAoi+joaA0ePFjLli1TWlqav8vxC0u3OBuGoYSEhArrTnHnnXcqOztbr732WqmfkxfevZ1Qn/dhKq6b9oMPPqipU6e6HycnJxOeAQAAEFAi7BFac82acq9n/bH1umPZHSUu98pFr6hH/R7l2laEPaJcz6/OunTposWLF2vHjh3ucZ2qE0sH5/PPP1/79u2rsPV/8cUXqlGjhm677TaP6ZmZmZKkQ4cOadCgQZKkjz76SA0aNFDTpk0lSQcPHixynWlpaTpz5oxq1qxZbHAOCwtTWFiYj14FAAAAUPlsNptPuj33bdRX9SPr63j68SLPc7bJpvqR9dW3Ud9yn+NsBenp6UWe9pmdna3g4GAFB1vzNX7zzTeqW7euEhIS/F2KX1i6q/b06dO1du3aCr2005kzZ7RixQqP25o15i9nmZmZ7ml5Ybpt27YKCwvTiRMndOjQoULr27BhgyTzFxkAAAAA3gUHBeuBCx4ocp5NZs/T/7vg/6pEaP7uu+/UsmVL/fjjjx7THQ6Hxo8fr2uvvVZOp/WuXb1//35t3LhRI0eOVFCQpSNkhbH8q548ebIuv/xyTZw4UbNnz9Z3332n77//vshbWRmGUeRt7969kqRWrVq5pzVv3lySFBERoSFDhkiSPv3000LrnDNnjiRp9OjR5/iKAQAAgOplaLOhem7Qc6oXWc9jev3I+lXqUlRpaWk6ffq0LrnkEq1atUqSlJOToz/96U9auHChkpOTLRmcFyxYIMkcXbu6snRX7UGDBslms8kwDL333nuaPXu21+Ur60M2depULVq0SI8//rhGjhyp1q1bS5JWr16t119/XTVq1NBNN91UKbUAAAAAVcHQZkPVq0Ev9+jZr1z0SpXpnp1n5MiRmjNnjsaPH+8enPiDDz6Qw+HQxRdfrLlz5yo0NNS/RRZh/vz5ioiI0PDhw/1dit9YOjj/+c9/tuR11oYOHarJkydr5syZSkhI0LBhw5Sdna0lS5bIMAy98847qlGjhr/LBAAAAAJKwZDco36PKhWa84wePVqffvqpxo8fL8nspj1s2DDNmzfPkuMgJSUlacWKFRoxYoTXS/JWdZYOzrNmzfJ3CcWaMWOGEhIS9NJLL2nJkiUKDQ3V0KFD9fDDD6tv3/JfYw4AAABA1TRmzBh98sknmjBhgi688ELNnz9f4eHh/i6rSIsWLZLD4ajW3bQliwdnf2nevLkMo/CIfmebNGlSpV7/GQAAAEDVMG7cOJ04cUJRUVGy2ysmlpU213gzf/58BQUFadSoUT6qKjARnAEAAADAD+Li4vxdglcOh0OLFi1Sr169VL9+fX+X41cBEZz379+vhQsXaufOnUpJSSnyVxObzaa33nrLD9UBAAAAQNWzZ88e9e/fX1dddZW/S/E7ywfnf/7zn3rsscfkcrnc0/KCc97AYYZhEJwBAACAABcZEqnNEzf7uwzkatu2rb744gt/l2EJlr6O88cff6zp06crPj5e//3vfzVs2DBJ0tdff61XX31VAwcOlGEYmjp1qr799ls/VwsAAAAAqIos3eL8yiuvKDQ0VMuXL1ezZs30ww8/SJI7QN966616/vnndf/992vcuHF+rBQAAABAdZKQkKBp06YpISHB36WgEli6xfnXX39V37591axZM0meXbPz3HPPPWrbtq0ef/xxv9QIAAAAoPpJSEjQ9OnTCc7VhKWDc1ZWlho0aOB+nHdtszNnzngs17VrV61du7YySwMAAAAAVBOWDs4NGzbU8ePH3Y8bN24sSfrtt988ljt48KCcTmel1gYAAAAAqB4sHZw7d+6sHTt2uB8PGjRIhmFo2rRpSktLkyR98sknWrlypTp27OivMgEAAAAAVZilg/Po0aN16NAh94jZ/fr10+DBg7V8+XLVrFlTderU0dVXXy2bzaaHH37Yz9UCAAAAAKoiSwfn6667Ttu2bfM44X7u3Lm65ZZbVKtWLaWkpKhDhw567733NGLECP8VCgAAAACosix9OaqwsDC1bdvWY1psbKxee+01vfbaa36qCgAAAABQnVi6xRkAAAAAAH+zdItznpycHH355Zf6+eeflZiYqF69eunGG2+UJB0+fFiJiYnq0KGD7PaAeDkAAAAAgABi+aT5ww8/6LrrrtOBAwdkGIZsNpscDoc7OK9evVoTJkzQp59+qssvv9zP1QIAAAAAqhpLd9XeunWrRowYoSNHjujuu+/WJ598IsMwPJYZPXq0IiMj9dlnn/mpSgAAAABAVWbpFufHHntMmZmZ+uqrrzR8+PAilwkNDVX37t31yy+/VHJ1AAAAAHzJlZ6uHd17SJLablivoMhIP1cEmCzd4rx8+XJdcMEFxYbmPI0bN9bhw4crqSoAAAAAQHVi6eB85swZxcfHl7hcWlqaHA5HJVQEAAAAAKhuLB2c69Wrp127dpW43LZt20oVsAEAAADAF2bNmiWbzaZZs2ad0/PT09M1b948bdy40ad1+XIbmzdvls1m0z333OPbwgKQpYPzkCFDtHHjRi1fvrzYZebOnatdu3Zp2LBhlVgZAAAAAJy748eP67LLLtNLL71k2W107txZLVq00IIFC3xcWeCxdHB+4IEHFBoaqnHjxunVV1/V0aNH3fNOnz6tt99+WzfddJOioqI0depUP1YKAAAAAFXP2LFjtWfPHm3ZssXfpfiVpYNzu3bt9OGHH8rlcumuu+5S48aNZbPZ9O6776pOnTq6+eablZWVpffff18tWrTwd7kAAAAAysFwOt3309et83gM/xg7dqwkVftWZ0sHZ0kaN26ctmzZorvvvlvt2rVTeHi4QkND1bJlS91666369ddfNWbMGH+XCQAAAKAckr/5RntGjnI/PnDLrdp10VAlf/ONH6tC//79VbNmTc2fP9/fpfiVpa/jnKdZs2aaMWOGv8sAAAAAUAGSv/lGhyZPkQzDY3rOsWPm9JkzFFvCJWpRMex2u0aOHKn3339fR44cUcOGDf1dkl8ERHAGAAAAYC2GYcjIyCj/epxOHXv8X4VCc+5GJJt07F9PKKpPH9mCg8u1LVtEhGw2W7nWUR2NHTtWs2fP1sKFC3XLLbf4uxy/IDgDAAAAKDMjI0M7uveohA2ZLc+/97yg3Ktqu2G9bJGRPiiqehkxYoTCwsI0f/58grMVtGzZ8pyfa7PZtHv3bh9WAwAAAACIjo7W4MGDtWzZMqWlpSkqKsrfJVU6SwXnP/74QzabTUZR3TRKQJcLAAAAoPLYIiLUdsP6cq8nfd06Hbjl1hKXi//v64o8//xybcsWEVGu51dnXbp00eLFi7Vjxw51797d3+VUOksF5zw9evTQddddp7FjxyqCDzcAAABgOTabzSfdnqP69ZO9QQPlHDtW9HnONpvs9esrql+/cp/jbAXp6emKLOJ9y87OVnBwsIJ98BorYhvffPON6tatq4SEhHLXF4gsdTmqjz76SKNGjdKvv/6qqVOnqmvXrnrggQe0ZcsW1atXT/Xr1/d6AwAAABBYbMHBqv/Qg7kPzp5pTqj/0INVIjR/9913atmypX788UeP6Q6HQ+PHj9e1114rZzmvXV0R29i/f782btyokSNHKijIUhGy0ljqVU+YMEHz58/XkSNH9OKLL6pjx4569913NXz4cMXHx+u+++7Txo0b/V0mAAAAAB+KHT5cjWfOkL1uPY/p9vr11bgKXYoqLS1Np0+f1iWXXKJVq1ZJknJycvSnP/1JCxcuVHJycrmDc0VsY8GCBZLM0bWrK5txLicUV6K9e/dq9uzZ+uCDD7Rjxw7ZbDa1b99e119/va655hrFx8f7u8RyS05OVlxcnJKSkhQbG+vvcgAAAABJUmZmpvbu3asWLVooPDy8wrfnTElxj54d/9/XLd09e9asWbrhhhv0zjvvaNKkSaV+3sKFCzV+/HgZhiGHw6GQkBA5HA5dfPHFmj9/vsLCwspdm6+3MWzYMP34449KTEwssgt4QWX5zARSDrJUi3NRWrRooYcffljbtm3TmjVrdPfdd+vkyZN66KGH1LNnT3+XBwAAAMBHCobkyPPPt2xoLo/Ro0fr008/dT92OBwaNmyY5s2b55PQ7OttJCUlacWKFRo6dGiJobkqs3xwLqhZs2Zq2bKlGjVqJMMw5HK5/F0SAAAAAJTJmDFj9MknnygkJEQXXXSR5s+f7/MWfV9tY9GiRXI4HNW6m7Zk0VG1C0pPT9fnn3+u999/X8uWLZPT6VRcXJxuvvlmXX/99f4uDwAAAADKbNy4cTpx4oSioqJkt1dMLPPFNubPn6+goCCNGjXKx9UFFksGZ5fLpa+//lqzZ8/WggULlJ6ertDQUI0ZM0bXXXedLr30UoWGhvq7TAAAAAA4Z3FxcZbehsPh0KJFi9SrV69qfxUjSwXnNWvW6P3339fHH3+sEydOyGaz6cILL9R1112n8ePHV8oHCwAAAIB/BEVGqv32bf4uA7n27Nmj/v3766qrrvJ3KX5nqeDcp08f2Ww2de7cWffee6+uueYaNW7c2N9lAQAAAEC107ZtW33xxRf+LsMSLDk42NatW/X3v/9dLVq0UGhoaKluvhqBDgAAAACAgizV4ixJhmEoJyfH32UAAAAAQLESEhI0bdo0JSQk+LsUVAJLBWcuLwUAAAAgECQkJBCaqxFLdtUGAAAAAMAqCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAHhlGIa/S0CAqKqfFYIzAAAAgCIFBwdLkhwOh58rQaDI+6zkfXaqCoIzAAAAgCKFhIQoLCxMSUlJVbYlEb5jGIaSkpIUFhamkJAQf5fjU5a6jjMAAAAAa6lTp44OHTqkgwcPKi4uTiEhIbLZbP4uCxZiGIYcDoeSkpKUmpqqxo0b+7sknyM4AwAAAChWbGysJCkxMVGHDh3yczWwsrCwMDVu3Nj9malKCM4AAAAAvIqNjVVsbKwcDoecTqe/y4EFBQcHV7nu2QURnAEAAACUSkhISJUOR0BxGBwMAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBGcAAAAAALwgOAMAAAAA4AXBGQAAAAAALwjOAAAAAAB4QXAGAAAAAMALgjMAAAAAAF4QnAEAAAAA8KLaB+f09HTNmzdPN910k9q2bavw8HBFRUWpa9eu+uc//6nU1NRinztr1ixdcMEFio6OVq1atXTppZdq1apVlVg9AAAAAKCi2QzDMPxdhD+9+eabuvnmmyVJ7du3V6dOnZScnKxVq1YpJSVF7dq104oVK1SvXj2P502ZMkUzZ85URESEhg8frszMTC1btkyGYWjOnDkaN25cqWtITk5WXFyckpKSFBsb68uXBwAAAACWFEg5qNq3OIeEhOiWW27R1q1btXXrVn3yySdavHixduzYoW7dumn79u2aMmWKx3OWLl2qmTNnqnbt2tq0aZPmzZunxYsX6/vvv1dwcLBuuOEGnTlzxi+vBwAAAADgW9U+OE+cOFGvv/662rdv7zG9YcOGevnllyVJn3/+ubKzs93znnvuOUnSP/7xD7Vu3do9vU+fPrrtttt05swZvfXWW5VQPQAAAACgolX74OxN165dJUlZWVk6efKkJCkjI0PffvutJGn8+PGFnpM3beHChZVUJQAAAACgIhGcvdizZ48kszt3rVq1JEk7duxQVlaW6tatqyZNmhR6Tvfu3SVJv/76a+UVCgAAAACoMARnL2bOnClJGjFihMLCwiRJ+/fvl6QiQ7MkRUVFqUaNGjp9+rRSUlIqp1AAAAAAQIWx+7sAq/rqq6/01ltvKSQkRI899ph7et7lqSIjI4t9blRUlM6cOaOUlBTFxMQUmp+VlaWsrCz34+TkZB9WDgAAAADwJVqci7B9+3Zdd911MgxD//nPf9znOvvKk08+qbi4OPctPj7ep+sHAAAAAPgOwfkshw4d0ogRI3T69GlNnTpVkydP9pgfHR0tSUpPTy92HWlpaZJUZGuzJD344INKSkpy3w4cOOCj6gEAAAAAvkZX7QJOnTql4cOHa9++fbrhhhv0zDPPFFqmadOmkqSDBw8WuY60tDSdOXNGNWvWLDY4h4WFuc+ZBgAAAABYGy3OuVJTU3XJJZdo69atuvzyy/XGG2/IZrMVWq5t27YKCwvTiRMndOjQoULzN2zYIEnq0qVLhdcMAAAAAKh4BGeZg3WNHTtWP//8sy6++GJ9+OGHCg4OLnLZiIgIDRkyRJL06aefFpo/Z84cSdLo0aMrrmAAAAAAQKWp9sHZ6XTq6quv1rfffqsBAwbo888/V2hoqNfnTJ06VZL0+OOPa+fOne7pq1ev1uuvv64aNWropptuqtC6AQAAAACVo9qf4/zSSy9p7ty5kqQ6derojjvuKHK5Z555RnXq1JEkDR06VJMnT9bMmTOVkJCgYcOGKTs7W0uWLJFhGHrnnXdUo0aNynoJAAAAAIAKVO2D8+nTp9338wJ0UaZPn+4OzpI0Y8YMJSQk6KWXXtKSJUsUGhqqoUOH6uGHH1bfvn0rtGYAAAAAQOWxGYZh+LuI6i45OVlxcXFKSkpSbGysv8sBAAAAgAoXSDmo2p/jDAAAAACANwRnAAAAAAC8IDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBGcAAAAAALwgOAMAAAAA4AXBGQAAAAAALwjOAAAAAAB4QXAGAAAAAMALgjMAAAAAAF4QnAEAAAAA8ILgDAAAAACAFwRnAAAAAAC8IDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBGcAAAAAALwgOAMAAAAA4AXBGQAAAAAALwjOAAAAAAB4QXAGAAAAAMALgjMAAAAAAF4QnAEAAAAA8ILgDAAAAACAFwRnAAAAAAC8IDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBOdyyMjI0COPPKI2bdooPDxcjRo10o033qhDhw75uzQAAAAAgI8QnM9RZmamhgwZoscee0ypqakaO3as4uPj9c4776hbt27as2ePv0sEAAAAAPgAwfkcPf744/rpp5/Up08f/f777/r444+1Zs0aPfvsszpx4oRuvPFGf5cIAAAAAPABm2EYhr+LCDTZ2dmqV6+ekpKStGHDBnXr1s1jfteuXfXrr79q3bp16tGjR4nrS05OVlxcnF75bp5u6jdSoXZ7RZV+zrJzcvTBpu+0P/momsY20DVdB1HnOQqEGiXq9KVAqFGiTl8KhBol6vSlQKhRok5fCoQaJer0tUCoMxBqlMw63/rxS90xaJySkpIUGxvr75K8Ijifg+XLl2vIkCFq1aqVdu3aVWj+Y489pkceeUTTpk3T9OnTS1xfXnBu/2p72UNr6/rWf9V9A66sgMrPzX9Wfqr3dr4gI/iMe5rNWYM6z0Eg1ChRpy8FQo0SdfpSINQoUacvBUKNEnX6UiDUKFGnrwVCnYFQo5RfZ072SW27fVtABGfr/fQQADZt2iRJ6t69e5Hz86b/+uuvZV63K+iM3t39T0myxIf7Pys/NesJkmwFplNn2QVCjRJ1+lIg1ChRpy8FQo0SdfpSINQoUacvBUKNEnX6WiDUGQg1Sp51BhJanM/B1KlT9fzzz+uee+7Rc889V2j+pk2blJCQoO7du2v9+vUlrq9gi3NwRLAMQwpyxunrCQsVGuy/3zaynTka/uloGUFJstkKz6fO0guEGiXq9KVAqFGiTl8KhBol6vSlQKhRok5fCoQaJer0tUCoMxBqlArX6cxwBkyLM8H5HNxyyy1644039Pe//12PP/54ofm7du1S69at1bp1a/3++++F5mdlZSkrK8v9ODk5WfHx8e7gDAAAAABVXSAF5wBrIK8annzyScXFxblv8fHx/i4JAAAAAFAMznE+B9HR0ZKk9PT0IuenpaVJkmJiYoqc/+CDD2rq1Knux3ktzme7re0TuqrLoHJWe+4++vU7vbbjoRKXo86SBUKNEnX6UiDUKFGnLwVCjRJ1+lIg1ChRpy8FQo0SdfpaINQZCDVKpa/TiuiqfQ5mzJihe+65R1deeaU++eSTQvO//PJLjRo1Spdddpk+//zzEtdX9DnONbRu4nK/Dh2fnZOj8/83WK6gM17OlaDO0giEGiXq9KVAqFGiTl8KhBol6vSlQKhRok5fCoQaJer0tUCoMxBqlArXSVftKq5r166SpA0bNhQ5P296ly5dyrzuvJ8xrm/zV79fby3Ubtf1rf8qKb+uPNRZNoFQo0SdvhQINUrU6UuBUKNEnb4UCDVK1OlLgVCjRJ2+Fgh1BkKNkvc6rY6u2uegX79+iouL0+7du7Vx40YlJCR4zJ8zZ44kafTo0WVed5Czhq5vY53rrOXVcfb14Kiz7AKhRok6fSkQapSo05cCoUaJOn0pEGqUqNOXAqFGiTp9LRDqDIQaJc86XTrp52pKj67a5+gf//iH/vWvf6lv37765ptvFBUVJUl67rnn9Le//U0DBw7Ud999V6p15XXVfuW7ebqp30i//xJUlOycHH2w6TvtTz6qprENdE3XQdR5jgKhRok6fSkQapSo05cCoUaJOn0pEGqUqNOXAqFGiTp9LRDqDIQaJbPOt378UncMGhcQXbUJzucoMzNTgwYN0po1a9SwYUMNGDBA+/bt05o1a1S3bl399NNPatmyZanWlRecA+EDAwAAAAC+EEg5iHOcz1F4eLiWL1+uhx9+WJGRkZo3b5727dunSZMmacOGDaUOzQAAAAAAa6PF2QIC6ZcWAAAAAPCFQMpBtDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvLD7uwBIhmFIMi8ADgAAAADVQV7+yctDVkZwtoCTJ09KkuLj4/1cCQAAAABUrpMnTyouLs7fZXhFcLaAWrVqSZL2799v+Q9MdZScnKz4+HgdOHBAsbGx/i4HRWAfWRv7x9rYP9bG/rE29o/1sY+sLSkpSU2bNnXnISsjOFtAUJB5qnlcXBwHtIXFxsayfyyOfWRt7B9rY/9YG/vH2tg/1sc+sra8PGRl1q8QAAAAAAA/IjgDAAAAAOAFwdkCwsLCNG3aNIWFhfm7FBSB/WN97CNrY/9YG/vH2tg/1sb+sT72kbUF0v6xGYEw9jcAAAAAAH5CizMAAAAAAF4QnAEAAAAA8ILgDAAAAACAFwTnCvTjjz/q0ksvVa1atRQdHa0LLrhA//vf/8q8nsTERL311lu65ZZblJCQILvdLpvNplmzZhX7nD/++EM2m63YW4MGDcrxyqoGf+6fPL/99puuvPJK1a1bVxEREercubNmzJghl8t1Dq+oavHV/smzcOFCDRw40H0dx0GDBunLL78sclmOHykjI0OPPPKI2rRpo/DwcDVq1Eg33nijDh06VOZ1nT59WpMnT1azZs0UFhamZs2aacqUKTpz5kyxz3E6nXr++efVuXNnRUREqG7dupowYYK2bdtWjldVdfhz/0yaNMnr8fHaa6+V89UFPl/tnxUrVujRRx/VyJEjVbduXdlsNjVv3rzE53H8lMyf+4hjqGS+2D9nzpzRBx98oKuvvlotWrRQaGioYmJi1KtXL82cOVMOh6PY53IMeefP/ePX48dAhZgzZ44RHBxs2Gw2Y+DAgcYVV1xh1KhRw5Bk/O1vfyvTuubOnWtIKnR75513in3O3r17DUlG/fr1jYkTJxa6/fWvfy3nKwxs/t4/hmEYq1atMiIiIgxJxgUXXGBMmDDBaNCggSHJuPLKKw2Xy1WOVxjYfLl/DMMwnn/+eUOSYbfbjREjRhhjx451v/cvvvhioeWr+/GTkZFh9O7d25BkNGzY0JgwYYJxwQUXGJKMunXrGrt37y71uk6cOGGcd955hiSjZcuWxoQJE4yOHTsakow2bdoYJ0+eLPQcp9NpXHbZZYYko0aNGsYVV1xhDBw40LDZbEZkZKSxZs0aX77cgOPv/TNx4kRDknHxxRcXeXx8++23vny5AceX+6dr166F/m9p1qyZ1+dw/JTM3/uIY8g7X+2fv//974Ykw2azGd26dTP+9Kc/GUOGDDHCwsIMSUb//v2NtLS0Qs/jGPLO3/vHn8cPwbkCnDx50oiNjTUkGZ999pl7+tGjR91fUJYvX17q9a1atcq44447jLffftvYvHmzcfPNN5c6OA8cOPDcX0gVZYX9k52dbbRo0cKQZDz33HPu6SkpKUafPn1KFbyrKl/vn+3btxvBwcFGWFiYsWrVKvf0HTt2GLVr1zbsdruxc+dOj+dU9+Mn7z+zPn36GCkpKe7pzz77bJnfl2uvvdaQZFx++eWGw+FwT7/77rsNScbEiRMLPeeNN94wJBmtW7c2jh496p4+Z84cQ5Jx3nnneayruvH3/sn70lKW47A68eX+ue+++4zHH3/c+Prrr43ffvutVKGM46dk/t5HHEPe+Wr/PPHEE8b9999v7Nu3z2P677//bjRt2tSQZDz44IOFnscx5J2/948/jx+CcwX497//bUgyxo4dW2je559/bkgyRo0adc7rv/XWWwnO5WCF/fPxxx8bkoyuXbsWmrd+/XpDktGpU6dzriGQ+Xr/3H777YYkY/LkyYXmPffcc4Yk46677vKYXp2Pn6ysLCMuLs6QZGzYsKHQ/C5duhiSjHXr1pW4rsOHDxtBQUFGaGiox5cPwzCMzMxMo27dukZwcLBx7Ngxj3nt27c3JBlz584ttM4xY8YYkow5c+aU7YVVEVbYP3zpL54v98/Zjhw5UqpQxvHjnRX2EcdQ8Spy/xT0wQcfGJKM5s2bF5rHMVQ8K+wffx4/nONcAfLOmxw/fnyheSNHjlR4eLiWLl2qzMzMyi4Nssb+8VZD9+7d1bJlS23ZskV//PFHhdVgVb7eP97Wlzdt4cKF51pulfPjjz8qKSlJrVq1Urdu3QrNL8t7tnjxYrlcLg0YMED169f3mBcWFqbRo0fL6XTqq6++ck/fu3evtm3bpoiICI0cObJc26+K/L1/4J0v98+54Pgpmb/3EbyrrP3TtWtXSdLhw4c9pnMMeefv/eNvdn8XUBVt2rRJkhmAzhYaGqpOnTpp3bp1+v3339WlS5cKreXYsWOaNm2ajhw5ori4OPXq1UtjxoxRaGhohW7Xyqywf7zVkDd9z549+vXXX0s1EExV4sv9c+bMGe3fv1+SivwHPj4+XnXq1NG+ffuUnJys2NhYj/nV8fgpzWdTkn799VefrOvtt9/2WFfeczp16qSQkJBybb8q8vf+Kejzzz/XZ599JqfTqRYtWmj06NFq165didutyny5f8qzfY6f4vl7HxXEMVRYZe2fPXv2SFKhwT45hrzz9/4pyB/HD8HZx5KTk5WUlCRJatKkSZHLNGnSROvWrdO+ffsqPDhv375d//znPz2mNW3aVJ9++qkuuOCCCt22FVll/+SFOW81SNK+ffsqZPtW5ev9k/c+16xZU1FRUcWuLzExUfv27VPnzp095lXH48eXn81zWRfHhnf+3j8Fvfjiix6P/+///k+33367Zs6cKbu9en698Pfn19/bDwRWeo84hgqrrP0zc+ZMSdLYsWP9sv1A5e/9U5A/jh+6avtYamqq+35kZGSRy+R9gU9JSamwOsLCwnT77bfru+++07Fjx5ScnKzVq1fr0ksv1f79+3XxxRdXy4PeKvsnrw5/1mBFvt4/Jb3Pxa2vOh8/vvxsnsu6ODa88/f+kczeG6+99pp+//13paena8+ePXr55ZdVo0YNvfLKK7rvvvtK92KqIH9/fv29/UBghfeIY6h4lbF/XnvtNS1dulQ1atTQAw88UOnbD2T+3j+Sf4+f6vlzVgkuu+yyMl+n7X//+5+lWqAaNmyoV155xWNa79699eWXX+raa6/VBx98oCeeeEKvv/66nyo8d1Vh/1RlVWH/VOXjByivyZMnezxu0aKF7rjjDg0cOFDdu3fXSy+9pKlTpyo+Pt5PFQLWxjHkPytXrtTkyZNls9n09ttvq1GjRv4uCQWUZv/48/ghOBdh79692rFjR5mek56eLkmKjo72mHb2OZOSlJaWJkmKiYkpR5Xn7qGHHtIHH3ygr7/+2i/bL6+qsH+io6N1+vRpd13+qKGiWGn/5K2vuPe5rOuTAv/4KUlJ75kv3/+i1uXL7VdF/t4/3nTs2FFjxozRnDlztGzZMk2aNKlUz6tK/P359ff2A4GV3yOOoYrdP1u2bNHYsWOVnZ2tF154QZdddlmlbr8q8Pf+8aYyjh+6ahdh48aNMsxLdZX6NmjQIElSbGys4uLiJEkHDx4scv1505s1a1Ypr+dsrVu3liQdOXLEL9svr6qwf5o2ber3GiqKlfZP3vt8+vRp9z/m5VmfFPjHT0l8+dk8l3VV5WPDF/y9f0pS1Y+Pkvj78+vv7QcCq79HHEMVs3/27t2r4cOH6/Tp05o+fbruvvvuSt1+VeHv/VOSij5+CM4VIG8I9Q0bNhSa53A4tGXLFoWHh6tNmzaVXZokM0RIKnawpKrOCvvHWw0Fp1f04HFW5Mv9U6NGDfc/8r/88kuh+QcOHFBiYqKaNWtWZOt2Uar68ePLz+a5rCvvOVu2bJHD4SjX9qsif++fklT146Mk/v63neOnZP7eRyXhGPL9/jly5IiGDRumI0eOaPLkyZo2bVqJ2+cYKpq/909JKvr4IThXgLzrvs2ZM6fQvC+++EKZmZkaOnSowsPDK7s0SdJnn30mqfih5Ks6K+wfbzX88ssv2rNnjzp16lTtLkUl+X7/eFtf3rTRo0eXur6qfvz069dPcXFx2r17tzZu3FhoflnesxEjRigoKEgrV67U8ePHPeZlZWVp4cKFCg4O1qWXXuqe3qJFC7Vv314ZGRnua3Cf6/arIn/vH2+ysrLc+6yqHh8l8eX+ORccPyXz9z7yhmPI9/vn9OnTuvjii7V7927dcMMNev75570uzzHknb/3jzeVcvwY8LmTJ08asbGxhiTjs88+c08/duyYcd555xmSjOXLlxd6Xtu2bY22bdsaBw8e9Lr+W2+91ZBkvPPOO8Uu89///tfYtm1boemfffaZERMTY0gyPv/881K/pqrECvsnOzvbaNGihSHJeO6559zTU1NTjT59+pT4/KrM1/tn+/btRnBwsBEWFmasXr3aPf333383ateubdjtdmPnzp0ez6nux8/f//53Q5LRt29fIzU11T392WefNSQZAwcO9Fj+xRdfNNq2bWs88MADhdZ17bXXGpKMK664wnA4HO7pf/3rXw1JxsSJEws954033jAkGa1btzaOHTvmnv7ZZ58ZkozzzjvPY13VjT/3z7Zt24z//e9/RmZmpsf048ePG+PGjTMkGV27djVcLlf5X2iA8uX+KejIkSOGJKNZs2Zel+P4KZk/9xHHUMl8tX/S0tLc36kmTJhg5OTklGr7HEPe+XP/+Pv4IThXkDlz5hhBQUGGzWYzBg8ebIwfP96oUaOGIcmYOnVqkc+RZEgy9u7dW2her1693Ld69eoZkoyWLVu6p91+++0eyw8cONCQZHTp0sUYP368cfnllxvt2rVzb+O+++6riJcdMPy9fwzDMH788UcjIiLCkGT06tXLmDBhgtGwYUNDkjF+/Phq/Z+mr/fPc889Z0gy7Ha7cckllxhjx451v/cvvPBCoeWr+/GTkZFh9OrVy5BkNGzY0JgwYYL7cd26dY3du3d7LD9t2rRiQ/CJEyeMVq1aGZKMVq1aGX/605+MTp06ub+UnDx5stBznE6ncdlllxmSjJo1axrjx483Bg0aZNhsNiMiIsL46aefKuqlBwR/7p/ly5e798uwYcOMa665xhg0aJD7B6UmTZoYO3bsqMiXb3m+3D9vvPGG+/+R7t27G5KM0NBQj/9z1q9f7/Ecjp+S+XMfcQyVzFf7Z8qUKYYkIzg42LjmmmuMiRMnFnk7G8eQd/7cP/4+fgjOFeiHH34wRowYYdSoUcOIjIw0zj//fGPWrFnFLu/ti3/evOJuZ/+6M3v2bGP8+PHGeeedZ8TGxhohISFGo0aNjMsvv9xYsmSJj19pYPLn/smzZcsW44orrjBq165thIeHGx07djSee+45w+l0+uhVBi5f7h/DMIwFCxYYAwYMMKKjo43o6GhjwIABxsKFC4tcluPHMNLT042HH37YaNWqlREaGmo0aNDAmDRpknHgwIFCy3r7UmkYZi+Cu+++24iPjzdCQ0ON+Ph4469//atx+vTpYrefk5NjPPvss0bHjh2N8PBwo3bt2sb48eON3377zUevMLD5a/8cOnTImDJlitG7d2+jQYMGRkhIiBEdHW10797dmDZtmnHq1Ckfv9LA5Kv9kzfP262oHjgcPyXz1z7iGCodX+yfiRMnlrhvpKKjEMeQd/7aP/4+fmyGYRgCAAAAAABFYnAwAAAAAAC8IDgDAAAAAOAFwRkAAAAAAC8IzgAAAAAAeEFwBgAAAADAC4IzAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwBApbLZbB63oKAg1ahRQwMGDNCbb74pwzDKvY1BgwbJZrPpjz/+KH/BPjB9+vRCrzsmJkbx8fEaPny4pk+fbplay6t58+ay2Wz+LqNYZ86cUe3atXXllVf6uxS3efPmyWaz6ZNPPvF3KQCAYtj9XQAAoHqaOHGiJMnpdGr37t368ccf9cMPP2jZsmX68MMP/VxdxejatasSEhIkSZmZmTp27JjWrFmjJUuW6LHHHtPkyZP11FNPKTQ01L+FFuOPP/5QixYtNHDgQH333Xf+Luec/Otf/9Lp06c1bdo0f5fiNnbsWHXt2lUPPfSQLrvsMoWEhPi7JADAWQjOAAC/mDVrlsfjJUuW6NJLL9VHH32ka6+9VqNGjfJPYRVo3Lhxmj59use0nJwcffTRR5oyZYqef/55HTt2TO+//75/CvSBZcuWyeFw+LuMIh05ckQvvviiRo8erU6dOvm7HDebzaYHHnhAV199td58803dfvvt/i4JAHAWumoDACxh2LBhuv766yWZXVerC7vdruuuu04//PCDoqOj9cEHH2jBggX+LuuctWrVSu3atfN3GUV6++23lZWVpT//+c/+LqWQsWPHKiYmRq+99pq/SwEAFIHgDACwjG7dukmSDhw4IMnsGmyz2TRo0KAil887d/js1uvi7Nu3T7fffrvatGmjyMhI1apVSx07dtStt96qHTt2FFr+wIEDuuuuu9SqVSuFh4erVq1aGjVqlFatWnVOr8+bdu3aacqUKZKkF154wWOet3O2i3uPCr43P//8s0aNGqXatWvLZrNp48aNkqSNGzfq/vvvV48ePVS3bl2FhYWpZcuWuuOOO3T48OFC62vRooUkacWKFR7na0+aNMm9nLdznFevXq2xY8e6t9W8efMityWZPRJsNpumT5+u/fv365prrlHdunUVERGh888/XwsXLvTybhZmGIbeeustxcTEaOTIkYXmF3y/1q9fr0suuUQ1atRQrVq1NGHCBB08eFCSlJaWpvvvv1/NmzdXeHi4OnXqpDlz5hS5zVWrVmncuHFq1qyZwsLC1KBBA11wwQV64IEHlJqa6rFsRESExo0bp19//VVr1qwp02sDAFQ8gjMAwDJSUlIkSWFhYT5f94EDB9S9e3d3i96ll16qgQMHKiwsTG+88YZWr17tsfzq1avVtWtXvfzyywoJCdHIkSPVqVMnff3117rwwgv18ccf+7zGq666SpIZuLKzs32yzu+//179+/fXH3/8oeHDh+vCCy9UUJD53/9TTz2l559/XpLUv39/XXrppTIMQ6+++qrOP/98j0CbkJCgK664QpJUv359TZw40X3r379/iXXMnj1bAwYM0IIFC9S2bVtdfvnlCgsL06uvvqru3btr+/btRT7vjz/+UM+ePfXzzz/roosuUrdu3bR+/XqNGzdO33zzTanfh61bt2rv3r3q3bu3wsPDi11uzZo16tevn06cOKGLL75YtWvX1qeffqqLLrpISUlJGjx4sN5991317NlTffr00datWzVhwgR9/fXXHutZuHCh+/U2bNhQl19+ubp166ZTp07p3//+txITEwttO+/Hjy+//LLUrwsAUEkMAAAqkSSjqP9+XC6X0adPH0OS8fe//90wDMPYu3evIckYOHBgkeuaNm2aIcl45513PKYPHDjQkGTs3bvXPe2RRx4xJBl33XVXofXs27fP2LVrl/txUlKS0bBhQyM4ONiYPXu2x7Jr1641atasaURHRxvHjx8v1WvOq3PatGlel3M6nUZYWJghydixY4fX15OnuPcob5uSjH//+99Fbu/bb781jh49WqiGRx991JBk3HDDDaXaVkHNmjUrtH/3799vREREGMHBwcb8+fM9tjVlyhRDknH++ed7POedd95x1/+3v/3NcDqd7nnPP/+8IckYMGBAsXWc7dVXXzUkGQ899FCR8wu+X6+++qp7enZ2tjF06FBDktGhQwdjyJAhRmpqqnv+m2++aUgyLrzwQo/1XXjhhYYkY86cOYW29fPPPxvJycmFpm/evLnIdQEA/I8WZwCAXzmdTu3cuVM33nijVq9erbCwMN1www0+386JEyckSUOHDi00r2nTpmrVqpX78dtvv60jR45oypQpuvbaaz2WPf/88/Xwww8rNTVVs2fP9mmNQUFBqlmzpiTp9OnTPlln586ddd999xU5b/Dgwapfv36hGh555BE1btzYZ+dav/nmm8rIyNCECRM0ZswYj2099dRTatSokdatW6cff/yx0HNbtGihJ554wt1KLkl33XWXatasqZ9++qnULfO//vqrJKlt27Zel+vfv79uu+029+OQkBDdfffdkqTt27fr1VdfVVRUlHv+pEmTVKdOHa1evdpjUDRvn7eePXsqJiam0PS8c8PzutIDAKyDUbUBAH5R1HmwMTExevfddz1CrK/06NFDkvTQQw8pODhYQ4cOLbbLbl4X4Msvv7zI+QMGDJAk/fzzzz6v08i9jrWvroU8atQor+s6efKkFixYoC1btujMmTNyOp2SJIfDoZMnT+rUqVOqVatWuWpYuXKlJBX6EUIyu+VfeeWVmjlzplauXKl+/fp5zB80aFChy3PZ7Xa1aNFCGzZs0MmTJ9WwYcMSazh+/LgkuX+YKM7w4cMLTWvZsqUk8/ztNm3aeMwLDg5Ws2bNtH79eiUmJrpr6dGjh7Zt26brr79eDz/8sHr06OER/otit9sVExOj5ORkZWdnW/ayZABQHRGcAQB+kXcd56CgIMXGxqpz5866/PLLSww252rSpEn65ptv9Mknn2j06NEKDw9Xz549NWLECN14441q0KCBe9m8QbjODnFnK+o81fJwuVzulubyhtU8TZs2LXbehx9+qFtuuaXQQFUFpaSklLuWvHOlmzdvXuT8vOmHDh0qNK9JkyZFPievxTYrK6tUNSQlJXk8rziNGzcuNC06OrrYeQXnF6zliSee0ObNm7Vw4UItXLhQNWvWVP/+/TVmzBhdd911xf5oExsbq5SUFJ05c0b16tUr+YUBACoFwRkA4BelHQnbG5fLVeplg4OD9fHHH+uBBx7Q/Pnz9e2332rNmjVauXKlnnrqKS1evFh9+/b1WO/48eM9uuWezdeXXfrtt9+UnZ2tyMjIYkPm2Up6D4oLaPv27XOPhj1jxgyNHDlSjRs3VkREhCSpb9++Wr16tbsFvCJ5axEvqZW2tOLi4iTlD0B3LtsrSy3x8fFat26dvv32W33xxRdasWKFO0Q//fTTWr16tWrXrl3oeXkBv0aNGqXeFgCg4hGcAQCWlddVtbgW0bzLVpVFt27d1K1bN02fPl3JycmaPn26nn/+eU2ZMsXd9bpJkybasWOHHnjgAXcX78qQN1J3//79Zbfn/xft7X04l/dAkr766itlZ2fr3nvv1eTJkwvN37NnzzmttyiNGjXSjh07tG/fPnXs2LHQ/LwW/uJadH0hr/X21KlTFbaNs9ntdg0fPtzd/Xvfvn268cYb9e233+rf//63nn76aY/lHQ6HUlNTFRsbSzdtALAYBgcDAFhWnTp1ZLfbtXfvXuXk5HjMczgcWrFiRbnWHxsbqyeffFI2m01btmxxTx82bJgkae7cueVaf1ls375dM2bMkKRCQTbvvNnff/+90POWLFlyTtvL6xJeVFfo77//XseOHSs0PS/Mnb0vSpJ3TviHH35YaF52drY+/fRTj+UqQteuXSWpyOt1V5ZmzZrp//7v/yTJ4/OWJ++SXAkJCZVZFgCgFAjOAADLCg0NVZ8+fXTq1Cm9/PLL7uk5OTn629/+pr1795Z6Xe+9916RYWXRokUyDEPx8fHuabfeeqvq1aunp59+Wv/9738LdYfOycnR119/XeT6yionJ0fvv/++BgwYoLS0NP35z3/WpZde6rHMwIEDJUnPPvus0tPT3dO//fZbd9guq7xBrmbPnq20tDT39EOHDnmMKl1QnTp1FBISot27d7sHESuNm266SREREfroo488rlHscrn00EMP6dChQ+rRo0eJ55SXR14oX7t2bYVto6Dnn39eR48eLTT9q6++kiSPz1uevB4PefsbAGAddNUGAFjaI488oosvvlhTpkzRxx9/rAYNGmj9+vVKT0/XxIkT9e6775ZqPZ999pn+/Oc/q1WrVurcubMiIiK0d+9erVmzRkFBQXr88cfdy9aoUUPz58/X6NGjdeutt+rxxx9Xp06dVLNmTR09elQbNmzQmTNnNHfuXHXq1KnUr2XevHnubsmZmZk6ceKE1q1bp+TkZAUFBelvf/ubnnzyyULPu/rqq/X0009r1apVat++vXr27KmDBw9q7dq1mjp1qp555plS15BnzJgx6tixo9atW6fzzjtP/fr1U2ZmppYvX66EhAT17dtXq1at8nhOaGioRowYoYULF6pr167q3r27QkND1a9fP6+XEGvatKlef/11TZo0SaNHj1a/fv0UHx+vDRs2aMeOHapfv77PL+11tvbt26tFixZas2aNMjMziz3321ceffRR3Xvvveratatat24twzC0adMm/f7776pVq5buvffeQs/57rvvJEkjR46s0NoAAGVHizMAwNKGDh2qBQsWqGfPntqwYYNWrFih3r17a+3ataUeQEuSpk6dqjvvvFMxMTFauXKl5s6dq+PHj+tPf/qT1qxZoyuvvNJj+d69e2vz5s26//77FRsbqxUrVmjevHnat2+fBg4cqFmzZhV5jV5vNm3apHfffVfvvvuuvvjiC+3YsUO9evXS9OnTtWfPHj3zzDMKCQkp9LyIiAgtW7ZMV199tVJSUvTVV1/J6XTq448/1p133lmmGvKEhoZq5cqVuv322xUeHq4vvvhC27Zt0913360lS5YUWYdkXpP5+uuv18mTJ/XBBx/orbfeKlWX+euvv14rV67UqFGjtG3bNs2ZM0cZGRm6/fbbtX79ep8PtHY2m82mv/zlL0pJSdHChQsrdFuS9OKLL+qqq65Senq6Fi1apMWLF8tut2vq1Kn69ddf1bp1a4/lMzIyNG/ePHXp0kW9evWq8PoAAGVjMypjuEwAAAA/O3r0qFq0aKGhQ4dWSnguiw8//FDXXHONXnnlFd1+++3+LgcAcBaCMwAAqDbuv/9+PfPMM9q0aZM6d+7s73IkSYZhqFu3bkpNTdXWrVsZURsALIiu2gAAoNp46KGHVLNmTT366KP+LsVt/vz52rRpk5544glCMwBYFC3OAAAAAAB4QYszAAAAAABeEJwBAAAAAPCC4AwAAAAAgBcEZwAAAAAAvCA4AwAAAADgBcEZAAAAAAAvCM4AAAAAAHhBcAYAAAAAwAuCMwAAAAAAXhCcAQAAAADwguAMAAAAAIAXBGcAAAAAALwgOAMAAAAA4AXBGQAAAAAALwjOAAAAAAB4QXAGAAAAAMALgjMAAAAAAF4QnAHg/9m78/Cmqvx/4O+bvW3aQveWrhQQF3ZHBEQ2QZRBxAV/fhVFxm3EhcFRB3QcNkUdFVDcRmXRkXHBwX0UcFgUhEEYKAPKTje6b+me7f7+uEnaNGmatmlz07xfz9Mnyb03N5/0EO0759xziIiIiIg8YHAmIiIiIiIi8oDBmYiIiIiIiMgDBmciIiIiIiIiDxiciYiIiIiIiDxgcCYiIiIiIiLygMGZiIiIiIiIyAMGZyIiIiIiIiIPGJyJiIiIiIiIPGBwJiIiIiIiIvKAwZmIiIiIiIjIAwZnIiLqEfbu3QtBEKBUKpGbm+u0r6ysDIIgePVz2223+ekdyIPFYsFzzz2Hfv36QavVol+/fnjuuedgsVjafG5NTQ0WL16M6dOnIzExEYIgYM6cOa0ef+7cuVbb4e677/ZpbURERJ2h8ncBREREvrBhwwZoNBoYjUa8//77WLRokdP+999/3+nxwoULUVtbi1deecVp++DBg7u8Vjl76KGH8MYbb+Cuu+7C6NGjsXv3bixcuBC5ubl47bXXPD63tLQUS5YsQWJiIi699FJ89dVXXr3mjBkzcNNNNzlt69evn09rIyIi6gxBFEXR30UQERF1RmNjIxITEzF58mScO3cOlZWVOH78eKvHWywW6PV6jB49Gt9//303VipvR44cwZAhQ/DQQw9h9erVju2PPPIIXn31VRw+fBiDBg1q9fmNjY0oLS1Fnz59YDaboVarceedd2L9+vVujz937hwyMjLw5JNPYvny5V1aGxERUWdwqDYREQW8L774AhUVFbjttttw22234cSJE9i7d2+rx//6669oaGjA0KFDu6/IAPDhhx9CFEXMnz/fafv8+fMhiiI++ugjj8/XarXo06dPh167vr4e9fX1XVbb+vXrIQgCvv32Wzz99NNITk5GWFgYrr76auTk5AAAXn31VfTr1w86nQ4jR47E4cOHnc5RV1eHRYsWoX///ggJCUHv3r0xbNgw9nYTEQUBDtUmIqKAt2HDBkRFRWHq1KmoqKjAggULsGHDBlx++eVuj7cHIn8EZ6vVivLycq+OVavViIyM7OKKmvz888+Ij49HRkaG0/aMjAzExcXh559/7pLXXb16NZ555hkA0hDtP/zhD3jggQe6pLZFixZBp9Ph8ccfR15eHlauXImZM2di1qxZ2LhxI+bNmweDwYAXXngBN954I44fPw6lUgkAeOCBB7Bx40bcf//9GDx4MGpra3H06FH88MMPmDdvng9+E0REJFcMzkREFNCKiorw3XffYe7cudBoNIiPj8dVV12Fjz76CKtWrYJWq3V5zqFDhwAAQ4YM6eZqgZycHJfw15px48Zhx44dXVtQM+fPn2+1x7hPnz7Iz8/36espFApMmjQJM2fORGpqKs6fP4+//e1vmDdvHs6ePYu//vWvPq9NqVRi165dUKmkP4FMJhNWrVqFiooKHD16FCEhIQCA0NBQPP744/j+++8xZcoUAMDnn3+Oe+65x+W6eCIi6vkYnImIKKB98MEHMJvN+L//+z/Htttuuw3fffcdvvjiC9x8880uzzl06BC0Wi0uvPDC7iwVAJCQkICtW7d6dWzv3r29Ok4URTQ2Nnp1rEKhgEajcbuvrq4O4eHhbvfpdDoYDAavXsNbqamp2LZtm9O2u+++G+PGjcPLL7+M+++/H5mZmT6t7Z577nGEZgAYM2YMVq1ahdmzZztCMwCMGjUKAHD69GnHtl69emHv3r3Izs5GWlqad2+SiIh6BAZnIiIKaBs2bEBKSgquvPJKx7aZM2ciJCQEGzZscBucDx8+jIsuughqtbpDr1laWooLL7wQeXl5eOCBB7Bt2zaUl5ejb9++WLZsGa677rpWn6vT6XDVVVd16HVbk52d7ZNe7NDQ0FYDeENDg1Ow7CpKpRJPPPEErrvuOnz//feO4Oyr2loG3l69egGQQry77c2H1a9cuRKzZ89Geno6LrnkEkycOBE33XQTxo4d69VrExFR4GJwJiKigHXo0CFkZWXhsccegyAIju16vR7XXXcdPv30UxQVFSE+Pt6xr6CgAMXFxZg2bVqHX/df//oXJk+eDLPZjJSUFOzcuROpqan47rvvcPPNN+PgwYMYMGCA2+daLBaUlJR49ToajQZRUVFtHhcTE4N169Z5dc6EhIRW9yUlJblMiGWXn5+PYcOGefUanWUPt6WlpT6vzX69srfbmy8+cv311+Ps2bP46quvsGPHDnzyySd45ZVXcN999+HNN9/06vWJiCgwMTgTEVHA2rBhAwBpaHZLt912Gz766CN88MEHWLBggWN7W9c3G41GxMXFwWAwIDQ0FIDUo6lSqfD+++/j5ptvxtdff43p06cjLCwMixcvdjz3mmuuwYABA7B///5Wg3Nubq7Pr3HW6/WYM2eOV+f0ZMSIEdiyZQvOnj3rVOPZs2dRXFyMESNGdPo1vHHq1CkAcPrCQy61xcTEYM6cOZgzZw7MZjNuu+02vPXWW3j88cfRt2/fbqmBiIi6H5ejIiKigGQ2m7Fx40ZcdNFFbkPw1KlTER0d7QjXdvbg3NqM2hqNBq+++iquuOIK1NTUoKamBoMHD8bHH3+Mm2++GWazGdu2bcM111zj8tySkhL88ssvuPjii1ut236Nszc/L730kve/EB+45ZZbIAgCVq1a5bR91apVEAQBt9xyi2ObyWTCr7/+ioKCgg6/XnFxscu2+vp6LF++HGq12jEpV3tr6woWiwWVlZVO21QqlWPt6IqKii59fSIi8i/2OBMRUUD617/+heLiYvTv3x/PPfec22MSExORlZWFQ4cOOYJyW8EZAI4cOeIIRGazGceOHXM83rNnDwYOHOgyhNpsNuP222/HLbfc4vHcXXGNs68MGTIE9957L1555RVUV1djzJgx2L17N9atW4f77rsPgwcPdhybn5+PCy+8EHfeeSfWr1/v2L5mzRpUVlbCarUCALKysrB8+XIAwHXXXed0jvvuuw9lZWWYOHEikpOTcf78eWzYsAFnzpzBihUrkJKS0qHaukJ1dTWSkpJw/fXXY+jQoYiOjsavv/6K1157DRdffDHXBCci6uEYnImIKCDZe5J3796N3bt3ezx2/fr1jp7Kw4cPIz093eP6yEeOHMH1118PAPj111+hVquRnp4OAPj666/x29/+1ul4q9WK2bNnAwD+9re/deDdyMeaNWuQmpqKd955Bx988AH69OmDZ555Bo8//rhXz3/xxReRnZ3tePzf//4X//3vfwEAycnJTgH3t7/9LTZs2IA33ngD5eXl0Ov1GD58OFauXOl2grXO1tYZoaGhePjhh7Ft2zZ8++23qKurQ3JyMu6//34sXLiw1WukiYioZxDE5rNeEBEREZKTk/HJJ59g1KhR+OCDD7BmzRr89NNPAIBLLrkE//jHPxw90KIoYu7cuThz5gz+9a9/Oa6LJiIiop6DPc5ERETNVFRU4Pz587jkkksASD3OF1xwAQBp2afq6mpHaAaA3//+9/jll1+wdetWhmYiIqIeipODERERNXPkyBGkp6cjPDwcgLQM0tdff43vv/8eX3/9tdMyVtnZ2Xjrrbdw6NAhJCYmQq/XQ6/X49lnn/VX+URERNQFOFSbiIjIS9OmTcMDDzzQqTWgiYiIKPCwx5mIiMhL48aNw8SJE/1dBhEREXUz9jgTERERERERecAeZyIiIiIiIiIPGJyJiIiIiIiIPOByVDJgtVpx/vx5hIeHQxAEf5dDRERERETU5URRRHV1NZKSkqBQyLtPl8FZBs6fP4+UlBR/l0FERERERNTtcnNzkZyc7O8yPGJwlgH7WqFnz55FVFSUn6uhlkwmE7Zs2YIpU6ZArVb7uxxyg20kb2wfeWP7yBvbR97YPvLHNpK38vJyZGRkOPKQnDE4y4B9eHZ4eDgiIiL8XA21ZDKZEBoaioiICP4HV6bYRvLG9pE3to+8sX3kje0jf2wjeTOZTAAQEJerynsgOREREREREZGfMTgTERERERERecDgTEREREREROQBgzMRERERERGRBwzORERERERERB5wVm0iIiIiIpIFi8XimGnZF0wmE1QqFRoaGmCxWHx2XvJMrVZDqVT6uwyfYnAmIiIiIiK/EkURhYWFqKys9Pl5ExISkJubGxBLHvUkvXr1QkJCQo/5vTM4ExERERGRX9lDc1xcHEJDQ30WtqxWK2pqaqDX66FQ8CrV7iCKIurq6lBcXAwASExM9HNFvsHgTEREREREfmOxWByhOTo62qfntlqtMBqN0Ol0DM7dKCQkBABQXFyMuLi4HjFsm/96iIiIiIjIb+zXNIeGhvq5EvIle3v68pp1f2KPMxERERER+ZcoQjDVAWI9oFQDGj3QQ66NDVY95dpmu6Dqca6vr8fTTz+NAQMGQKfTISkpCXPnzkV+fn6Hznfu3Dncf//9yMjIgFarRUxMDEaNGoW//vWvPq6ciIiIiKiHOvVvoLoAqMyWfspOAUVHgfpKf1dG5BA0wbmhoQETJ07EsmXLUFNTgxkzZiAlJQXr1q3DsGHDcObMmXad71//+hcuvvhi/O1vf0N0dDRuuOEGDB8+HOfOncNbb73VRe+CiIiIiKgHOfYF8O0TgNXsvN1qAirOMjyTbATNUO3ly5dj7969GDVqFLZs2QK9Xg8AePnll/Hoo49i7ty52LFjh1fn+vXXX3HDDTcgPDwcW7duxejRox37rFYrDh482BVvgYiIiIio57BapNAMDxNHVeUBukgO2ya/C4oeZ6PRiDVr1gAAXnvtNUdoBoAFCxZg8ODB2LlzJw4cOODV+RYsWICGhgasX7/eKTQDgEKhwKWXXuq74omIiIiIeqLsPYDhvOdjrCbAWNM99RB5EBTBeffu3aiqqkJmZiaGDRvmsv+mm24CAHz55Zdtnis3Nxffffcd+vbti2uvvdbntRIRERERBYWaIu+Os/SMWZntxo8fj/Hjx/u7DGqnoAjOhw8fBgAMHz7c7X779qysrDbPtWPHDlitVowePRpmsxkff/wxHnnkETz44IN48803UVFR4bvCiYiIiIh6Kn28d8cp1V1bRwA4efIk3n77bYii6NdzvvjiiwgPD0dDQ4PP6ggUQXGNc05ODgAgOTnZ7X779uzs7DbPdezYMQCAXq/H2LFjsXfvXqf9Tz75JDZt2oQJEyZ0pmQiIiIiop4tbTQQkQRYPVy/rLAtTRXEjh07hkmTJqGwsBA//fQT3nnnHSgUnev/7Og5Z86cicceewxbt27F9OnTO1VDoAmK4FxTI10X0dqi6mFhYQCA6urqNs9l71F+5513oNfrsXHjRkydOhUlJSVYtmwZ/v73v2PmzJk4evQo+vTp4/YcjY2NaGxsdDw2GAwApMXBe8oC4T2JvU3YNvLFNpI3to+8sX3kje0jb2yfzhMmPwvxu6ddtjv6QCP6SD2iHexpNVus2J9dhRpLNeIjdPhNehSUCnlMNGa1Wts8JisrC1OmTEFZWRkAYP369TCZTFi7di2USg+TqnXROTMyMjBo0CD885//xLRp0zwea7VaIYoiTCZTq+cNpM9OUARnX7L/AzebzXjrrbcwa9YsAEDv3r3x/vvv4/jx49i/fz9ef/11PPPMM27PsWLFCixZssRl+/bt21sN9+R/W7du9XcJ1Aa2kbyxfeSN7SNvbB95Y/t0hgKJfW6DXnAOVlZBiQZ1NExGATAaOnTm74+X4YVtZ1BUbXRsiw/X4PGr+mLSBdGdqrozzGZp6S1751lrTpw4gWuuuQZxcXG47bbbsGrVKqxZswYPPfQQzGYz3njjjXa/ti/Oec011+Cdd95BRUWFx6BtNBpRX1+PXbt2Od5zS3V1de1+D/4SFMHZPot2aw1TW1sLAAgPD/f6XHq9HjfffLPL/rvuugv79+/Hzp07Wz3HwoULsWDBAsdjg8GAlJQUTJgwAdHR/vsQk3smkwlbt27F5MmToVbzGhs5YhvJG9tH3tg+8sb2kTe2j280NExEbm4uxMg4iGoFoFBB0OgRAiCkg+f89n+F+OPmX9Gyn7q42og/bv4Vr/3fMEy9JKGTlXeMSiVFsIiICI/HDRo0CLNnz8bChQuxceNGAMD9998PvV6P8PDwNp/fVee85ZZb8MILL+DQoUMeL09taGhASEgIrrzySuh0OrfH2Hu9A0FQBOfU1FQAQF5entv99u1paWltnst+TGpqKgQ368mlp6cDAIqLi1s9h1arhVarddmuVqv5H10ZY/vIH9tI3tg+8sb2kTe2j7yxfTrHYrFAEAQIWj0EW8DqzGBqi1XEsq9/cQnNgDQEXACw7OtfcPUliX4dtt3WNcUhISFYtWoVADhyh0KhwB133NHh1/TFOYcPH4709HR8/vnnmDRpUqvHKRQKCILg8fMRSJ+boJhVe8iQIQCAgwcPut1v3z548OA2z2Vfzqq12bPLy8sBwGmtaCIiIiIi6h7/OVuOgqrWZ30WARRUNeA/Z8u7r6geZubMmfjss898Osu33AVFcB4zZgwiIyNx+vRpHDp0yGX/pk2bAMCrmeFGjx6N6OhoFBYW4vjx4y777UO03a0XTUREPmC1AGd/AI5skm6tFn9XREREMlJc7d1SSd4eR64mTpSG1584ccLfpXSboAjOGo0GDz74IABg3rx5jmuaAeDll19GVlYWxo0bhxEjRji2r1mzBgMHDsTChQudzqVSqbBgwQKIooh58+Y5XdS/bds2rF+/HoIg4L777uvid0VEFISOfQGsugTY8Fvg099Jt6sukbYTEREBiAt3fz1tR48jV3v37kV0dDT69evn71K6TVBc4wwATz31FLZt24Y9e/agf//+GDt2LLKzs7Fv3z7ExsZi7dq1TseXlpbi+PHjKCgocDnXY489hu3bt2Pbtm0YMGAALr/8cpSWlmLv3r2wWCx45plncNlll3XXWyMiCg7HvgA+vgNoedWaoUDaPus94KLr/FIaERHJx2UZUUiM1KGwqsHtdc4CgIRIHS7LiOru0nqMzZs3Y/r06R1eEisQBUWPMwDodDps374df/7znxEaGorPPvsM2dnZmDNnDg4ePIi+fft6fS61Wo1vvvkGzz//PGJiYvDdd9/hyJEjGDduHL788kssWrSoC98JEVEQslqAb5+AS2gGmrZ9+ycO2yYiIigVAv4y/SIArpOM2R//ZfpFslnPOdCcPHkSx44dww033ODvUrpV0PQ4A9IsckuXLsXSpUvbPHbx4sVYvHhxq/vVajUef/xxPP744z6skIiI3MreAxjOezhABAz50nEZY7utLCIikqeplyTijduHY/EXx1BoaLqWOSFSh79MvwhTL0n0Y3WBbfPmzdDr9Zg8ebK/S+lWQRWciYgoQNUU+fY4IiLq8aZekohJA+Ow42geaq1KxEeE4LKMKPY0d9LmzZtxzTXXtLo2c08VNEO1iYgogOnjfXscEREFBaVCwG/SInHdkCSMyowO2NA8f/5875d+EkWgsRqoK5duW3leu85pU1BQgH379mHmzJntel5PwB5nIiKSv7TRQESSNBFYa1O9RCRJxxEREQWr+kqgKg+wmpq2KdRAZDIQ0qvTp//888+hVqsxbdq0Tp8r0LDHmYiI5E+hBKY+b3vQylQvU5+TjiMiIgpG9ZVAxVnn0AxIjyvOSvs76cYbb8TJkycRERHR6XMFGvY4ExFRYLjoOmnJqW+fcJ4oLCJJCs1ciqrnslqkid9qiqTh+Gmj+SVJT8c29x3+LoODKEo9zZ5U5QG6SEDo+HD12NjYDj830DE4ExFR4LjoOmDgNP4RGEyOfdHKlyXP88uSnopt7jv8XcrSjh07fH9SY41rT3NLVpN0nDbc968fBDhUm4iIAotCKS05Negm6Zahuec69gXw8R2uS5EZCqTtx77wT13UddjmvsPfZXCxtBGa23scuWBwJiIiIvmxWqSeMreTwdm2ffsn6TjqGdjmvsPfZfBRqn17HLlgcCYiIiL5yd7j2lPmRAQM+dJx1DOwzX2Hv8vgo9FLs2d7olBLx1GHMDgTERGR/NQU+fY4kj+2ue/wdxl8BEFacsqTyOROTQwW7Dg5GBEREcmPPt63x5H8sc2biKLzLVo+hhSABIX7IMTfZXAK6QUgo0vXcQ5mDM7kFdFiQd3PB2AuKYEqNhahl46AoOSEPERE1EXSRkuz/xoK4P46TUHanza6uyujrtKRNreYAXM9YKoHTHW223ppAiRLI2A2Ahajh/tGwNzY4r79ubb7VhNgNUvXA1st0n3RAlhMUDUYMLm2CqrTCwGlBhCt0jGixfk5zR+LVrgGYXfv11u2AK1Q2oK0AtKgUsHzeQUl8MXD0jWvSg2gVEm3CnWL++qmYxSqFvc1TfsVtu0qLaDSAWqddGv/8fSYfCekl7TklLFG+vertA3PZk9zpzE4U5sMW7ag6NkVMBcWOrapEhIQv2ghIqZM8WNlRETUYymU0pI5H98B1wBg+wNw6nOcVV3uRFEKtI010h/yjdXSrbHW+b498CYM9nBtrigFgZcvagrKbS2/08UEAKEAYKrwYxWiLci3c6Iv0QJUnOmaktorsh8w6jmgxARolM160hWAwvZlgNDsiwHHtpbbm913fIkQhIFRELjkVBdgcCaPDFu2IP+R+c5DgwCYi4qk7atXMTwTEVHXuOg6YNZ7raxD+xzXoe1KFjPQaAAaqpx/7NsaawBjtS0AtwjF9sfGWulWtPqurtri1vepQwF1iNSDae/5VKoBpdZ2X2PbrpG2Od1X245pft92DoXK9qOUQtr5/wK7V7Vex1VLgMyJ0vEKlfQchbLp+U5f9thCnaM3sOXj1rbZ2HuwRasUhO33rRbpb7dT24AfXnL+vYXFApfPA9LHNPWgW4y2W1Oz+0bbY1uvu9P9VvbZe+3NDU0/pgY3j+ud/11YGm21m9v/BUAbBACREIAGZVPQbt4WCnv4VjYF7+b7hebHBGEIJwcGZ2qVaLGg6NkVLqFZ2ikCgoCiZ1cgfNIkDttuBw57JyJqh4uuAwZOk2b/rSmSrslMGy2/nmarRV41Wq1AYxVQVw7UV0r3W4bgBoP7UNxQJQVenxKk4aJavXSrCZN6xOz31SFNwVcdKgXW6gIphIXFAolDAW0YoAppcWyz47sj1FgtwHcLPb/P//wNGP2QPP6NxvQDLrtHXv82AenvSKtZGjlgbgTqqoHiGqBXCqCzDXkXRTdfCLj7ksDNNvsP7F872F6vs9wGb/tjVbPHzb8wUTUFcgpoDM7UqrqfDzgNz3YhijAXFqLu5wMIG3lZ9xUWwDjsnYioAxRKIGOsv6to3bEvWukVf77zveKiCKWlUVo6yGgA6suB+gpbIC4H6iqkx/XlTdvqbdt80dOrDpOul9RFAroI6VYb0RSA7eHXEYj1rvs0YVK4VfSA4NCeZZ7k8m9Wjp8fQWi6NhoAVOFA2VlAEwJofHTNsy14W60W1BiqoNeHQWEP2I6e+ubXoDe7Pr35MVYLmq5Jt4f1jlwioHAO1o7w3SJwNx/hoFAxcMsIgzO1ylxS4tPjgh2HvRMR9UDHvrBdh91idJahQNo+6z3n8Gy1SqG2tgSoK5Vua0uBujLn+7YQrKorx28tjUBWB+tThwIhvQFdr2YBuFkIdvejjbAdH9EUbEjCZZ4ChyBIwRQCrAq1NIS/o1/eWK3uA7X9sdNEcG4mkpNOYgvn7QzdgrtQ3SJct9zGIeVdgsFZRirefRdCnz5Q9e4NZYsfhUbT7fWoYmN9elx3kOswaA57Jwoychu2S13DapF6mt3OXGzb9s97gX1vNQXj+vJ29QTb//wVFWoIoVFSCA6JAkKjpNlzHfd7u94P6S3NXEy+w2WegpPCPlO5Wvq7zX7tvlIDhLQxY7UoNgvYUpgeP+VaQBSx45tP3czCbm76AZomfrMY21FvizCtVLu5b5s9nT3aXmNwlpGKt9+BqZXQpAgLaxake0nhupftcZR06xS4IyM7HcBCLx0BVUICzEVF7gOfIEAVH4/QS0d06nV8Rc7DoDnsnSiIdOWwXeo+xjrpi4+aYttty/tFQGWu1GvsibkeyP7RdXtIbyA0Bgiz/YTGSNfyhsUAodG2ABwFkyYCW3b9B1N+ewPUfvgSnVrgMmnBrb6y/WskC4Lt+udmsct+Pzyh9dcSRacgffLEcezYuQt333ErBLFFwHYsdWbr3W4evD04evw0fjrwP9w9e1ZToFbaQnXzcG2/r1DgxRdfxJIlS1BSUgKdLri+mGNwlpHwmTOhb2iApaIClsoKmCsqYamoACwWWGtrYa2thSkvz7uTCYIUpqOjoYyJhio6RrofHQ1VdDRUMdFQRsdAFRMNVVQUBDf/MxaUSsQvWigNIxYE5/Bs+2YtftFCWfSQyn0YNIe9EwWJ9g7bpe5ltUhDod2FYKdtxdJEWb5y2b3SBGf2cBwa5f0QaJMJZuURDr2UCy6TFrzqK4GKs67brSbb9ozWw3NHCIJtPW0Vjh07hklTfovCwkL8dPAI3nnnHSjcDTsXrc491xZTU4h23DdJs+Zbzdi6ay/+sPgl3P1/M6SZzdusSYmZYy/BYzU12PrPv2P6tKnN1vBuftsz//0zOMtI7BOPIzo62mmbaLXCWlMDS3k5zBUVsNjCtKWyApaKCudt5eUwV1bCWlUFiCIs5eWwlJcDJ0+2+dqKiAgpUEdHQxljD9lRUEXHIPru36Hy039K57JRxcfLoicXCIxh0IE47J2I2qnNYbsC8O2fpADVQ/+o8KsGgzQLs+E8UF0IVJ+XvrBwbCuQQnF7JsxS6aQht/p4QB8n3YYnNN03nAe+XtD2eS68Tn6TM1HHcZm0wGK1QJX7EyBWA+GJHbt0RhSlnmZPqvKkOQJ8/CVXVlYWrrrqKpSVlQEA1q9fD7PZjHXr1kHZ8u9aQWFbds2LE4siEPG9dD+6X+tB235rWy88MyUegy7sh81ffInp41sZdSoopBBtVUiXqfz4GRCql37/4QmAPkG61eo7+mvxCwZnmRMUCigjIqCMiIAmPd2r54hmsxSqy8thLi2FpawM5tIymMtKYSkrh7nMdr+0DObycsBshtVggNFggPGsm2/SWlDo9VBERKDy409Qs30HVHFxUMXGQhUXC1VsLNRxcVDGxnbbddmBMAw60Ia9E1EHBOJsu4HAYpYCb/MA7C4ce718kiD1+jqF4Rbh2P6jDff8R7DVAvzwIoftBiPbMmnmM7tw6IfvMHTs1VD1vZJfisnNsS8gfPsE9J29dMZY0/akXlaTdJw2vGO1uvHrr79i4sSJSEhIwO9+9zs899xzWLt2LX73u99BEARs2LCh4yd3TJ6Gtmu2X6dt66meef1MvPbWu7DooqEUrM3W8zY3LQtmaQTMorSe+6EPgJpc1/NqwqFSxXT8PXQzBuceSFCppCAbGwtccIHHY0WrFZaqKqm3urQMlrJSW8huEbRLS2AuKQVMJlhramA8cQLGEyc8nlsZGdksVLu5jZNuOxuwA2EYdCANeyeiDuJsu+1nMQM1hUBVPlCVK32xUJUnPTbkS+G4ttj7XmJthNSjEZEIhCfZbhOdt4XFSsMffYHDdoObQgkx7QrkHzVgSNoVbGe58eWlMxYvZ8L29jgvZWRkYPbs2Vi0aBE++OADAMCcOXOgUqkQHu67gN6mFtdpz5z1f1i64q/Ydfg0JkyY4Hys1dIUoutqgBAzMPQ2oOq07QvPAunWWAMYqyFU+/CymC7G4CwjdQcPImrixG4NT4JCAZVtYjFtZqbHY+0h21xcDHNxiXRb0uy22X3RZJICeVUVGtsYKq6MioIqIR7q+ATn24QEqOKlW0VISKvPD5Rh0BFTpgCrV7lOYCajYe9E1AmcbdeZKEpD9KrymgXiPOdwXF3QbKkWDxQqaWifuyBsv/XXsD8O2yWSn2aXzriOGenApTPezkng4+XbtFotVq5c6bL99ttv9+nrtNfQoUORnp6OzZs3uwZn+5rUKgCiCtBWAFf8AWg5kVhjNVBdBHPur8Bz07ut9s5gcJaRwgfmoaFPH9mGqOYh21NPtiiKsFRW2oK0c6BueSsajY5rsRuP/dLqORWRkVDHx7cI1olQJcQ7erDNJSWyHwYdMWUKwidNkuWSWUTUScE2225jjS0E59p6jN0EZHND2+dRqKTfS0SyNDNtZB8goo90PzyhqZe4o+uvdgfbsF0uQUYkE76+dEajt12z66FHWaGWjgsSM2fOxKZNm7B69WoIHbmuWxsOaMMhCr19X1wXYXCWGbnMAt0ZgiA0BewBA1o9zhGwi4pgKiyEubAIpiLp1lxUCFOhtF2sq4O1qgqNVVVobGN4eCsvhOh775XWwJNBQBWUSi45RdQT9bRhuw0GoDKnxU+2FJQrc4D6Cu/Oo4+3BeE+QGRKUyiOTJbu6+MC53fiiULJa9eJ5MLXl84IgvTfLHezattFJgfV7PcTJ07EypUrceLECVzQxqWhPQWDs9zIZBbo7tA8YOsGDnR7jCiKsNbUwFwoBWl7oHbcFhbCVFQEq8Hz9RFFS5eiaNkyafKyxESokhKhTkqCOjFJurU9VoSHd+xbMyIiILCG7TZUAZU5EErPoG/xd1Bs3S31wNhDckNl2+fQRrr2EjcPxRFJgErb5W+FiMhJV1w6E9ILQEb713GWobq6OoSGhjptMxqNUCqVrjN1t2Lv3r2Ijo5Gv379uqJEWWJwliMZzAItF4IgQBkeDmV4OLT9+7d6nLW2FqaiYpgKzqP2p70w5ebAWt8A0WSCuaAApoICiI2Ntuuzi4HDh92eRxEWBnVSIlRJSVAnJkKd1AeKuFjosrNhKiyEKimpR3+ZQUQ+IJdhu/WVrj3GVblSr3FljhScIf0hMAgA8t2cIzQa6JXa9BPZ/H4yoIvovvdDROStrrp0JqSXtOSUsUaaCExpG54dQJ0uv/zyCyZMmIA33njDsa2hoQE33HADoqOj8f7773t1ns2bN2P69OleB+2egMFZxvw5C3SgUYSFQds3A9q+GdCPGeOyX7Sta206XwDT+fMwFZyH6fx5KVTnn4epoACWigpYa2vRePIUGk+ecnp+KoDsN94ElEqo4+Oh7tMH6uRkqJP7QN2nDzTJyVAnJ0MVFwdBztfhEVH36I5hu+ZGoDJXGjpYca7Zjy0YN1a1fY7QGFgjU1BQr0LCwMugjMpoFoxTAm6NTSIiAE6XzogQIPjy0hlB8OmSU90tMzMTI0eOxKxZs3DllVcCAGbMmIGdO3di06ZNXp3j5MmTOHbsGJ577rmuLFV2GJxlzN+zQPckgiBAFR0NVXQ0QgZd4vYYa10dTIWFtiAthWnz+fNozD+P6tOnoa6uBsxmKXifPw/s3+/6Omq1NPTbFqSlUN3H8VjZuzeHghORd+yzUjsC8Vmg/FzTY0M+3PekNBMa49xj3CsV6JVmu00BNGGwmEz4+ZtvcO2ka6FU+3ZGWCIivwmkS2e6kUajwaZNm3Drrbfi008/BQDs2rULn332GaZOnerVOTZv3gy9Xo/Jkyd3Zamyw+AsRzKaBTqYKEJDoe3bF9q+fZ22m0wmfPPNN7jm6qshVFZKwTk/H6a8PBjz8qSgnZcnDQc3mWDMzoYxO9vtawihodD06ePUY23vrVYnJ0OpZ+8OUVAxG6Xh0+VnW/QcZ0u3xmrPz1eHAb3TgagM6bZ3uhSMe6dJQ6k1YV39DoiI5Oui6yAOuAa1v2xDqFgNRXgiZ7wHoFar8eGHH2L27Nn4/PPP8cUXX+Cqq67y+vmbN2/GNddcA13LJaZ6OAZnubH1RsYvWshraWVGUCqhTkiAOiEBGD7cZb9oNkszgeflScE63xas86SQbS4uhlhXh8aTJ1td21oZGdksVCdDk5oCdUoKNKmpUCcmQlDxI0sUUEQRqCtv6jF2GlJ9Tppkpq1e44g+TaG4dzrQu1lIDosJqGvriIi6nUIJc8ooICJC3svatWH+/PmYP3++z86nUqnwwQcfoL6+HmFh3n/JWlBQgH379uHhhx/2WS2Bgn+Fy4wqPl626ziTZ4JKBU2yNDTbHWtjo9RbnSeFalNeHoz5+Y5gbamogKWqCpaqKjQcO+Z6ApUK6qQkaFJSoE5NgSYl1RasU6FJSYaixeyIRNRNRBGoLQXKTwPlZ6SfMvv9s21fa6wOdQ3Ejt7jVEAdXN/oExFR91AoFO0KzQDw+eefQ61WY9q0aV1UlXwxOMtIwuuvIXniRPY091AKrRbajAxoMzLc7rfU1Np6qu3DwHNhys2DMTcHptw8iI2NMOXkwJSTA+x2fb4yNkYK0/ZgnWq/n8prq/1MtFhQ9/MBmEtKoIqNReilI/g5DzSiCNQUO4djR0A+2/aQ6vAk50DcfGh1WCx7jYmIKCDceOONuPbaaxER4YNVFawWCDk/df483YTBWUZChw/nH9NBTKkPg/KCAdBdMMBln2i1wlxSAlNODow5uVKYzsmFMTcXppwcqae6pBT1JaWoP3jQ5fmKsDCobUHa0Uttu1UnJvDfXRcybNmComdXwFxY6NimSkjgyBI5EkWgutAWipv3HttuTbUenixIs1BHZQBRfYHoTOk2qq8UjtUh3fUuiIiIukysryYvPvYF8O0TUJW4WwtRnhiciQKAoFBIy2DFxyP0N79x2W8xGGDMyYUp1xasc7IdwdpcVCQts/XLL2j85RfXk6vV0CQlScE6NRWatDRo0tOgSUuDuk8fXlfdCYYtW5D/yHwpkDVjLiqStq9exfDc3axWoLrANRyXn7WF47rWnysobOG4r/twrNJ229sgIqLAtWPHDn+X4F/HvgA+vgNtzvEhM/yLmKgHUEZEIOSSixFyycUu+6yNjdLQ75wcmHJznXqsTXl5TjOBu/SnqVTSLOC2IC39pEOTlgp1UhJ7qj0QLRYUPbvCJTRLO0VAEFD07AqET5okm99jjxlSLopAbQlQehIoOwmUnWoKxuVnAXN9688VFNJ1xVGZrgG5VyrDMRERUWdYLdISYQEWmgEGZ6IeT6HVQpuZCW1mpss+0WKBuaioKUzbArTxXDaMOTkQGxtbD9VqNTTJyU2Bulm4ViUmQgjgmSt9oe7nA07Ds12IIsyFhaj7+QDCRl7WfYW1IiCHlJvqpWuMy04CpaeaQnLpKc8TcglKabmmqL6uATkyBVBpuu89EBERBZPsPc7ragcQBmeiICYolVAnJUGdlISwy0c67ROtVilUNwvSUog+B1NOLkSjEcazZ2E8e9b1vBqNbYKyNJdgrYqPD4pQbS4p8elxXUnWQ8qtVsCQ7xyOS09KgbkqF61/Yy0AvVKA6P5AdD/pJ6qvdA1yr1RAqe7Od0FEREQAUFPk7wo6jMGZiNwSFAqoExOhTkxE2OWXO+0TLRaYCwsdvdHGc9lN9/PypFB96jSMp067nlenkyYps19HnZYmBez0dKjiYts9+7dosaBu/36EHzqEuthYRIwcKYvhxSovJ8/w9riuIpsh5Q1VtmDcPByfkgKyp6HVukgpHMfYAnJMf+lxVAYn5CIiIpIbfby/K+gwBmciajdBqYS6Tx+o+/RB2OjRTvtEsxmmggIYs3NgzD7nCNSmc9kw5udDbGhA48mTaDx50uW8itBQaNLTpZ+MjGa3aVDq9S7HNx9enAjg/D8+RLFMhheHXjoCqoQEmIuK3IdSQYAqPh6hl47o/uKa6dYh5RYTUJXdbEh1s9va4tafp1BLQTi6vzSc2h6OY/oDodFcyomIiChQpI0GIpIAQwEC7TpnBmci8ilBpZJ6lFNSgCvGOO0TTSaYzp937aXOzoYpPx/Wujo0HDuGhmPHXM6rio11CtSWigqUvf22y3GyGF4M6cuF+EULpVoEwTk824Je/KKFfu8d9/mQclEEakub9RqfhLLkJCbmHIbq8FzAam79ufp4WyDu1zTEOqY/0CsNUPJ/V0RERAFPoQSmPm+bVVtAIIVn/iVCRN1GUKsd1zzjSud9otEIY16edN30uXNotN0az56DpawM5pISmEtKULd/v+cXsQXUouXPQD9xIhR+XE4rYsoUYPUq10m34uNl0SsOdGJIualemqXaPnN188m5Gpwn5lIACHecKMQWiPs5D7GOzpSGXVP3slogZP+IPuU/QciOAPpeKf1RQz1Wj5k9n4gC10XXAbPek2bX5jrO8lRfX48VK1bgww8/RE5ODqKiojB16lQsW7YMffr08fo86enpyM7ObnX/L7/8goEDB/qiZKKgIWg00PbtC23fvi77LAaDFKJtgbruwAHU/8dzgDYXF+PEpb+BNjOz2bBvaRi4Nj0dirCwrnorTiKmTEH4pEmy/UO1zSHlAFQxvREq/hf4ZlNTSG7HxFyW3n2x71QZfnPtbVD3TgWCYHK4gHDsC+DbJ6AynMelAJD9hjR8burz0h811OME5Oz5RNQzXXQdMHAazIf/BTw33d/VeCVognNDQwMmTpyIvXv3IjExETNmzMC5c+ewbt06fPXVV9i7dy/6uvmD3ZM777zT7fbISPaaEPmSMiICIYMHI2TwYABA1VdftxmcAUBsaEDD0aNoOHrUZZ8qLs5toFYnJ0PwcS+1oFTKYskpdwSlEvGPzUf+o39ys1cKxvEXnIbw7eOuu1tOzGUfWh3V12liLqvJhJLib4CIPgzNcnHsC9swuRZffhgKpO2z3mN47mFkPXs+EQUnhRJi6ih/V+G1oAnOy5cvx969ezFq1Chs2bIFettEQy+//DIeffRRzJ07Fzt27GjXOdevX+/7QomoTd4OL0589lkoI8Kdhn0bz52Dpbwc5uJimIuLUbdvX4uT267RbhaqtbZrq5XR0e2e9Vs2LGagMrvZ0OqTjjWQI2qKgDE6FB2MhLm+qSdcFWpB/IhaRAxOcZ21OrofEBbDibkCkdUiDY9zO2JABCAA3/4JGDiNw7Z7CNnMnk9EFMCCIjgbjUasWbMGAPDaa685QjMALFiwABs2bMDOnTtx4MABjBjh3xluieRCNBlR9817MJ/PgSopFaHX3gFBrfF3WQC8n7E6csZ1EJTKputrbSyVlTBmZ7sEauO5cxAbG1tdn1oRHt4UpjMypHCdkQFNaioUITJY+sgxMVeLJZ1KTwIVZz1OzBVxYSTCR2eizhANsxgFVeoAhF45BUJ0Btc87mmy9wCG8x4OEKW1s7P3ABlju60s6jrdOns+EVEPFRTBeffu3aiqqkJmZiaGDRvmsv+mm25CVlYWvvzySwZnIgCGtc+i6LX3Ya5t2qZa+hLi581GxNxF/ivMprMzVit79UJIr14IGTLEabtotcJcWOgaqM+ehen8eVirq9Fw5AgajhxxOacqKRHa9GZhOj0d2ox0qBITIfh6eLKpASg/3Wyt41NNPcktJuZyLrL5xFz9ms1g3Q/QRUIA0D1XfpNf1RT59jiSPZ/Pnk9EFISCIjgfPnwYADB8+HC3++3bs7Ky2nXev/71rzh9+jS0Wi0uvvhizJw5E7FeDiElkivD2meR/8J7tkdNw3DNtaJjuxzCc1fMWC0oFFAnJUGdlASMcV5Ky9rQAGNOjhSmbT3SjefOwnj2HKwGA8znC2A+X4DaPXucz6nTSTOJZ2Q4DfvWZGRAGd6yL7z5C1qB6vPuw3FlGxNzRaa0mLU6U7rPa4wJkJb98uVxJHsdnj2fiLrE+PHjAaDdl4mSfwVFcM7JyQEAJCcnu91v3+5ppmx3Hn/cebKcP/zhD3j11Vcxd+7cDlRJ5H+iyYii1963PWp57aq01l7Ra+8jfPYfZTFs2z5jtWHfPhzYuhUjJk9GxMiRXXKNnkKng27AAOgGDHDaLooiLBUVTWH67FlpjeqzZ2HMzYXY0IDG48fRePy4yzmVMTHQpqVAEx8JTZQGGr0RGm05NNY8CJVnAFNd6wVpI5uF42YhucXEXEQu0kZLs2cbCiBaRdSVaGBuUEKlsyA01ghBIUj700b7u1LyEW8vbwm9lKPuiOTq6NGj+Omnn3D33Xf77JwnT57Ejh07cPfdd3s9h8uLL76IJUuWoKSkBDqdzme1BIKgCM41NTUAgNDQULf7w2zL0lRXV3t1vuuuuw4TJkzAiBEjEBsbizNnzmDt2rVYvXo17r77bkRHR2PGjBmtPr+xsRGNjY2OxwaDAQBgMplgMpm8qoG6j71NgqFt6r9cZxue3dp/PAWYa4HqL9chZLp8viBSDx2K6pISqIcOhdlqlXpru1N4ONSDB0M9eLDTUGfRbIYpPx+mM6dh/PUwTCd/gSk7G8aCMliqjbCUlqKutBQu8VgQodHroYkIgSY2DOrkeKgz+kJ9wSAo0wdLATnUw8RcMvu3GkyfoUAhTH4Wdat+j6KDEc4TwoVYED/cgND5z0C0WAFLN3+WyIWvPj8xTzyOwj8scL9TFBHzxOP++e9ngON/33zDZDJBFEVYrVZYffxvULR9WWQ/v5y0p54tW7ZgwYIFPuugO3bsGCZPnozCwkLs2bMHb7/9NhRejEqbMWMGHnvsMXz33XeYPt3zMlJWqxWiKMJkMkHZSqdGIH12giI4+9orr7zi9Pjiiy/GSy+9hIEDB+Lee+/FE0884TE4r1ixAkuWLHHZvn379lbDPfnf1q1b/V1Cl0v96Qd4893h6Z9+QI4yocvraS+/tpEoQmOuhr6xAPrGQugbCqX7DQWIMBZDIVqAaEg/ACxGAcYaFYwGFepqw1BfEwqjQQlrlQmC2QpjtQrGahWQbwYO5QPIB/ADLCE6GGNiYYqNgTE2FsaYWBhjY2CKiYGolvckXsHwGQoU+v8dQ+Lu3hBaDPk31yuQt7s3CvoeQ80ZDuuXk85+fhIr92PgmHI3s+ebET/MgF+Ld6PgG2Nnywxa/O9b56hUKiQkJKCmpgZGo2//HVpEC7LKslCWV4ZoXTQGRw+GUvDv7PFmszRZp73zzBsNDQ3tfk5r/ve//2HmzJkoLy8HAGzYsAH19fV47bXXWg24drGxsbjooovwySefYNy4cR6PNRqNqK+vx65duxzvuaW6Og+j62QmKIKzfRbt1hqmtlaaASnc0/WGXvjd736Hp556CsePH8e5c+eQnp7u9riFCxdiwYKmb30NBgNSUlIwYcIEREdHd6oG8j2TyYStW7di8uTJUMs8mHRWvaUQ+V8eaPO4zFFjccm113ZDRV6wWmA5+yP+99M2XDLqKigzrujaJXTMDUD5WQjlpyCUnYJQfhoos91vqGz1aaIqBIjqCzG6H8SofhCjM6GO7gdVVD+E6iKajrNaYS4uhsk2MZnJNtu3KfsczOcLoKxvQEhuLkJyc51fQBCgSkyEOl1ak1qdlibdZqRDFR/v+wnK2iGYPkOBQLRYcO7llbAAcHdJhiAISN26DWmPPsqliWTAJ58fqwWqNX8CUhoQ3qfBZXg+FAJ+U/ZPmP/fU1yCrJ343zffaGhoQG5uLvR6vU+H/27L2YYX9r+AorqmyQ7jQ+Px+G8ex1WpV/nsddpLpZIiWERERBtHNrH/XtrzHHd+/fVXXH/99UhISMDvfvc7PP/883jnnXdwzz33QKPReLXc7o033ojXX38dYWFhHoN2Q0MDQkJCcOWVV7barmVlZR19K90uKIJzamoqACAvL8/tfvv2tLS0Tr2OQqFAZmYmiouLUVBQ0Gpw1mq10Gq1LtvVajX/oytjwdA+qul3QfXMKphrbWu5uhChChMQPv0uCHL4XRz7Avj2CagN53EpAGS/IV2bOfV54KLrOn5edxNz2SfnqsyBdxNzOV9/LNgm5vLmCiJNSgqQkgJc6bwUkLWhAcbsHOn6advEZM4TlJ2H+fx51LuboMy2jJY023fT7N/KZsvzdbVg+AwFgtqD/4WlyMOM2baliUyHs7g0kYx06vNzdq/03zQAUABh8S179KQlyNTn93MJsg7if986x2KxQBAEKBQKr4YLe2Nb9jb8cecfIbb4f3ZxXTH+uPOPeHn8y7gqzX/hGUC73qv9GuTO/n4yMzMxe/ZsLFq0CB988AEAYO7cudBoNAgPD/fq/DfccAOWLVuGH3/8ERMmTGj1OIVCAUEQPH4+AulzExTBeYhtyZmDBw+63W/fPnjw4E6/VkVFBYCm66aJAomg1iB+3mzb7Nktw7P0P574ebNlMTEYjn0BfHwHXEKsoUDaPuu9tsNzfSVQdlqaqdoxa/Vpaamndk3MZbsfndmlE3MpdDroLhgA3QVuJigrL7cF6nPSBGX22b/tE5T9+isaf/3V5ZzK2Bho05pm+ravUa1OToagCor/RQQdLk0UhLgEGQUZi9WC5/7znEtoBgARIgQIeP4/z2NCygQog2yUhVarxcqVK12233777V6fY+jQoUhPT8fmzZs9BueeJij+KhozZgwiIyNx+vRpHDp0CEOHDnXav2nTJgBo8wL3thw9ehTHjx9HaGgoBg4c2KlzEfmLfakpl3WcwwTZrOMMqwX49gm47/m1Bf5v/wQMnAZYzUDFOdfe47JTQK2HYKBQAb0zmi3n1Gzm6rDY1ifm8gNBEKCKjoYqOhqhl17qtE80mWDMy2tal9qxlNY5WEpLYSkpRV1JKep+/tn5pEoFNKlpTmHavj61MirK69k3SX64NFEQ4hJkFGQOFh90Gp7dkggRhXWFOFh8EL9J+E03VtZzzJw5E5s2bcLq1auD5m+CoAjOGo0GDz74IJ555hnMmzcPW7ZscfQIv/zyy8jKysK4ceMwYkTTMgxr1qzBmjVrMHPmTKxYscKx/ZtvvoFOp8PEiROdXiMrKwv/7//9P4iiiLvvvhsajQx65Ig6KGLuIoTP/iPqvnkP5vM5UCWlIvTaO+TR0wwA2XsAw3kPB0jDDvHyRUBtMSB6mLUyPNEWim0/Mf2l215pgDLw/xMpqNXQZmRAm5EBtPhS2FJdbQvUZ9H4n+9gPPhvGMtNMFYrIVrgCNktKSIibGtSN4Vp6TYNCjeXoZC8cGmiINRsCTL3XzhyCTLqWUrqvBsx4+1x5GrixIlYuXIlTpw4gQsuuMDf5XSLwP+r0EtPPfUUtm3bhj179qB///4YO3YssrOzsW/fPsTGxmLt2rVOx5eWluL48eMoKChw2v6f//wHS5YsQVpaGoYMGYLQ0FCcOXMGBw8ehNlsxvjx4/Hcc89151sj6hKCWoOwGb5bK9AnGqqknuJjn3l3fE2hdKvRuwbj6H5ST7K2c5MCBjJleDhCBg1CiPIscPAD4DL7kh2AuU6Jxmpp1m9jwjUwVgkwnj0LU0EBrAYDGg5noeFwlvMJBQHqpKSmYd/padBmZEBISeESNzIiKJWIX7QQ+Y/Ml0ZONA/Ptl6D+EULOTFYT6JQSnM/fHwHpEtwmodnW0/R1Oc4MRj1GLGh3o2Y8fY4f6qrq3NZdcdoNEKpVLY5A3ZX2rt3L6Kjo9GvXz+/1dDdgiY463Q6bN++HStWrMDGjRvx2WefISoqCnPmzMGyZcuQnJzs1Xmuvvpq5ObmYv/+/di9ezeqqqoQERGBK664Arfddhvuuusuv/4jJgp4ZiNQmd1saLXtuuPSk1LvcXtc/SxwyY3S8EN/DiOyWqRe8poiqZa00fL5A9XNsHdBANRhFqjDLECCEYj4CXjxCKBQ2iYoy3ZcT208exaNtiHg1upqad3q/HzU/vij08v0U6uRs+E9aJ2GfUvDwLtzgjKSREyZAqxehaJnV8BcWOjYroqPR/yihdJ+6lkuuk6a++HbJ5xH7EQkSaG5MxMqEsnM8LjhiA+NR3FdsdvrnAUIiA+Nx/C44X6oznu//PILJkyYgDfeeMOxraGhATfccAOio6Px/vvv+622zZs3Y/r06Z3KPaLFgrpW5qCSo6AJzgAQEhKCpUuXYunSpW0eu3jxYixevNhl+6hRozBq1KguqI4oiFgtQFWeNAlX2Wmg/Ixtkq5T0vXIoqX15+rjgahMoOCQhwm8bMMOR97v/4Bqm/nb9Q/VTs787SveDnvP3gNkjLVNUHYBdC2GZYmiCEtZmevkZGfPwpiXB4XJBOOvv8L466+obvEKytiYpmHf9muq09M5QVkXi5gyBeGTJsGwbx8ObN2KEZMnI2LkSPY092QXXSfN/SDXL/KIfESpUOJPl/0JC3YsgADBKTwLtlEWT1z2hOwnBsvMzMTIkSMxa9YsXHnllQCAGTNmYOfOnY45mvzh5MmTOHbsWKdG2Rq2bEHRsytQmZ/vw8q6Fv8iIaKuYbVKgav8jGtArjgLWFouh9KMOqxpQq6WQ6t1kdIxjlm1AdkOO/TFzN9dzUez7QqCAFVMDFQxMS4TlBnr6rBt40aMTkuDJScXxnNnpXB9Ltt5grL9+51PqlZDk5ICTUYGtBnpTtdTK3v3DprJSLqSoFQi9De/QXVJCUJ/8xuG5mCgUHLJKQoKV6VdhZfHv4zn/vOcyzrOT1z2hN+XovKGRqPBpk2bcOutt+LTTz8FAOzatQufffYZpk6d6re6Nm/eDL1ej8mTJ3fo+YYtW6TLhdzNsyFjDM5E1HGiCFQXNC3h1DIcmxtaf65CDURlSL3HUX2B6L5Ns1aHJ7Y9tFruww7bM/O3PwN+N8y2K6jVMMXGImz8eJf1Gi0GQ7Mh31KYtg8DFxsbYTxzBsYzZ1DT4pyKyEho053DtCYjHZo0TlBGRESSq9Kuwrg+4/DjuR9RJ9QhLiwOw+OGy76nuTm1Wo0PP/wQs2fPxueff44vvvgCV13l39C/efNmXHPNNdDpdO1+rmixoOjZFQEXmgEGZyJqiyhKvY32UNw8IJef8bzesUIlzU4dnSkF5Gh7SM4EIlM6Hxhtww7NZ3bh0A/fYejYq6Hqe6X/e5qBdg+B9hs/z7arjIhAyODBCBk82Gm7aLXCXFAgXT99rvkyWmdhPl8Aa1UV6g8fRv3hwy3KFaDu08cpTGszMqBJS4MqIQGCQtEl74OIiORJqVBiWMwwREREQBGg/w9QqVT44IMPUF9f71gZyBfmz5+P+fPnt+s5BQUF2LdvHx5++OEOvWbdzwec5tYIJAzORCSF49rSZqG42W35WcDYsr+vGUEJ9EptEY4zpR7kyNSuX9JJoYSYdgXyjxowJO0KeYRmwGdDoLucTGfbFRQKqPv0gbpPH+CKMU77rPX1MObkNIXpZtdUW2tqYMrLgykvz2WCMkGjgTolBZq0NGhSU6FJS4U6NRWatHSoExM4TJmIiGRLoVD4NDR31Oeffw61Wo1p06Z16PnmksBdAozBmShYWMyAIU8KwhVnm92ekybkMracsqkZQSH1ENtDsb3XOCoT6J0GKNWtPzdYdcMQaJ+R+7D3FhQhIZ4nKGsRpo3nzsGYnw/RaITx9GkYT592PalaDU1yshSqmwVqTVoq1ImJnKSMiIgIwI033ohrr70WERERHXq+Klb+S4C1hn8JEPUkxjopBDsFY9ttZQ5gNXt4sgBEJjuH4ubhWMXrRtvFz0Og260HzLbrNEHZb37jtE80m2EqLJSuoc7Jhik7R+q1zs6GKTcXosnk6MF2oVJB06cP1Olp0KTaeqvTpVt1UhIENb84IiKi4BDbyeAbeukIqBISYC4qCrjrnBmciQKJKAJ15e6DcflZoKaNa0aUGqB3OtA7Q5qYq/ltr1RA3f5JHqgVMh0C7VEPnm1XUKmkHuXkZADOQ79FiwXmwkJpfeqcHBizc2z3s2HKyZV6qrOzYczORm3LEyuV0jXVzYZ/a9LSpB7rPn0gaDTd9RaJiChA7Nixw98l+I2gVCJ+0UJpVu0AWx2DwZlIbuxDqiuy3QTkc0CjwfPzdZHug3FUBhCeBAToxBgBKcCGQAcrwRZ+1X36IGy08wgA0WqVQnXLQG3rsRYbG2HKyYEpJ8c1VNuu09akpECdmiLdJqdAk5oCdUoKlHp9t71HIiIiuYiYMgVYvUqaXZvrOFNHCDk/Ab2vkVcPFPme1SoNha3MlsJxy1tDPiBaPJ8jPKlZIE53DsihUd3yNshLPWAIdDATFAqok5KgTkpC2OWXO+0TrVaYi4ulQJ2TDVN2tu2+LVTX18OUmwtTbi6wx/Xcyl69pMnKUlJst8lSsE5JlmYA52RlRETUQ0VMmYLwSZOQ9+9/A1Om+LscrzA4y4jq4/8DtveRhneyJypw2YdTV56TrituGY4rcwFLo+dzKDXS0Gl3Pce90wB1SLe8FfKRHjwEOpgJCgXUCQlQJyQgbORlTvtEUYS5uASmHNvw79xcmHLzYMyTbi3l5bBUVsJSWYmGI0dcT65WQ5OUBHVKCtQpydCkpNpupV5rpd7/M6sSERF1hqBUInT4cH+X4TUGZ7kxFEjXRM56j+FZzhqrWwTiHOdw7Gn5JkBawimyj7TGca80KQw3v9XHc0g1UQATBAHq+Dio4+NcJioDAItt2SxHoM7NgSk3D6bcXBjPnwdMJsd11e4oe/eWhn8npzQFalvvtSoujr3VREREPsbgLDsiAAH49k/S8E4O5+x+ogjUlgBVuUBlLhQV2bgk70coP/lQuva4Kg+oL2/7PPqEFoE4tel+RB8u4UQUxJR6PZQDB0I3cKDLPtFigbmoCMacXJjycmG0B+q8PJhycqSe6ooKWCoq0HA4y/XkajXUiYlQ90mSrrG2Xb9t/1HFxjJYExERtRODsyyJ0nWu2Xs4vLMrmBul329lrhSCq3IdIVl6nOc0lFoJIBMAWq7XHhJlG07dPByn2wJyCodTE1GHCEql47pqYKTLfkt1tdveamNeLkz5Um+1fcIyt9RqaYh5nz5Q90mCMiER4aUlqI+PB9LS2GNNRETkBoOznNUU+buCwCOKQH1FUwC2h+KqvKZg3NaSTQAAAQhPBHqlwBqehNOlRvQdNg7KqDRpreNeqYCuYwu/ExF1hjI8HMoLL4Tuwgtd9olmM8xFRTCdPw9jfj5M+fkw5Z+33ebDVFgoBWv7pGU2iQDyP/pYeqBSNQvWfVx6rlVxcRBU/POBiIiCC//PJ2f6eH9XIC+iCNSVSb3FhvPNbpvdr8oHzPVtn0sVYgvAKdJtZKrz42ZDqS0mE4598w3SL70WSjWHVxNR20SLBXU/H4C5pASq2FiEXjqiW3pxBZXKEXjdXVstWiwwFxc7grQxPx+NuXkoyspCRGMjzIWFgNkMU14eTHl57l9EqZSCdVISVIkJUCcmQZ2YAHViIlQJiVAnJUIZHt7F75SIyD9EUYS1tg6i2QRBpYYiLBRCgK1HTB3D4CxLgrTOa9rotg/tKawW6bpid2G4+X2LsX3nVemAvuOAjPHNgnEKEBodcIuuE1FgMGzZgqJnV0gh1EaVkID4RQultSv9SFAqpeufExOBSy8FAJhMJhz45htce+21UCkUMJeUNPVQ28K1o+e6oEDqsbZta40iLKwpVCckQJ1kC9WJiVAnJkCVkACFVttdb5uIyCcsVVUwFRZCNJkc2wTb5S/KyEg/VkbdgcFZdmxhbupzPWNiMFEEGiqB6sKmn5pm96sLpEBcXQBYzd6dMyxOmpE6oo/0BUNEElBdBOx7w/VYcyNwYgsw9HbOUk5EXc6wZQvyH5kv/bevGXNRkbR99Sq/h2dPBHtvckICMGKEy37RYmkK1ucLYCosgLmgAKaCQpgKpPuWqipYa2thPHUaxlOnW30tZXS0c6h23Lf1ZsfE8FprIpINS1UVjM0ucbETTSYYc3OhARieezgGZ7mJSJJCs9xDnv1aYnv4rSmSbquLWgTjwrbXLLYTFNJM1PYw3DwY2++HJwIqjfPzrBZg1SWtFQrOUk5E3UG0WFD07AqX0CztFAFBQNGzKxA+aVLABkLnYO3+GGtdHUyFTUHaVFDoHLALCyHW18NSVgZLWRkajh51fyKFAqqYGKji46GKj4M6Lr7pfnw8VHFxUMXHQ6nXd90bJiKCNDzbVOh5jhxTYSEUEREctt2DMTjLiHnWRmDINf4Nd6Z6oKZYGjZtv60tlu7bg7G917g9w6Z1vYDwBOlHn9B0PzwBiEiWQrE+HlB24J9k9h6p17pVMpul3GqRaqkpkt5z2mgGeqIeoO7nA07Ds12IIsyFhaj7+QDCRl7WfYV1M0VoKLR9+0Lbt6/b/aIowlJZCbMtXJsKCqT75wtsgfs8zEXFgO16bHNxMXDE8+tJgToe6vg4KVDHNQvY8fFS73UXT2jmr+vaiajrWWvrnIZnuyOaTLDW1kGpD+umquRPtFggms2oy8pCY0kJzMUljv+um0uk+xUFBf4u02sMzjIipo7yfYASRaDRANSW2oJwcbNAXOIako017Tt/SG+pF1gfL92Gx7s+1sd37dJM3s4+LodZyo99AXz7hHPQj0gCpj4v/1EGROSRuaTlmnWdO66nEgQBqt69oerd2+3M4IBtSHhpme0PrCKYiopgLiqGuajI9lj6w8taXQ1rXR2MZ8/CePaspxeFMiZa6rWOi5N6smNjoIyJke7HxEIVFwtVdDQUIe3//5Wcr2snos4TzZ5Dc3uPGz9+PABgx44dHazIv0SrFaLJBNFsBsxmx33RZIZoNkE0mQGzCY1mM8wlJShY/gwUrQRk0WLp5uo7jsE50Fit0jXDtaXSDNOt/dSWNgVib4dK2ym1gD4OCIuRrifWx0q34YnOPcX6eEAlg8ldvJ193N+zlB/7Avj4DkjDx5sxFEjbZ73H8EwUwFSxsT49LpgJSiXU8XFQx8cBaO1SHMBaWwtTcbEUqpsH7GIpZJtsvRowm2EpKYWlpBRobWi4jUKvt4XpGChjbaE6Jgaq2FioYmOa9kVFQVAqA/66diJqm6DyblUVb4/riKNHj+Knn37C3Xff3SXnFK3WpiDc7Kfl45Vvv41nX38dObt2QefNJI+CAHWfJGiTk20jguKk/57GxUEVFwuDWg0MH+6z99SVGJxlRDizHcg1eQ7E9RWAaG3/yTV6ICzWFohjne87ttlCsjbCdcbp5sOLzY3SNcdyGV6cNlrqtTUUwCWUApDFLOVWi9TT7LY+XodN1BOEXjoCqoQEmIuK3F/nLAhQxccj9NJWLg6mdlOEhUGbkQFtRkarx4hWKyxlZbZe6iKYS0phLi2BubQUltJS6XGJ9FhsbIS1pgbGmhoYz51r48UVUPTuDWtVVevXtQMoXLYcYZdfDkV4OK997GFEiwV1+/cj/NAh1MXGImLkSA7P76EUYaEQ1GqPw7UFtbQ0VVfZunUr/vCHP7QrOIsWi/RjMgMW10D8rw8/xGPLl+P20aMhWr3LF9dNnIhFL76I7/fuxW8nT4agUkFQq6VblRpQqxzbYDZDrVYj9Z13oNPp3J6voazM6/fjbwzOMqL651xA6+X/VLWRQGiU1CscGm37iWp2P9q5t1jTiQ+y3IcXK5RSLR/fAWlW8uZ/wMhklvJAuw6biNpNUCoRv2ih1MsoCM5hyhaY4hct5B/W3UxQKGy9xbEALm71OFEUYa2pcQRrKVRLgVra1vRjKSsDrFZYvfiDz1JSghOXjYSg00EZ1RuqqOhmt1FQRfWG0r4tOhrK3tI2RWjX/QFOndd8eH4igPP/+BDFHJ4vS6LFgsYDB2Goq4U6Lr5D8w8IggB1QoLbWbXt1AkJXfrlmGj7f4q1oUEa3mw2O64hhsUC0WyBaDE3bbdYpJGqns5plOYrcoRmQbAFYOkHze7bH1/Yvz8GDRqEr3/+GTfNm+fx/IK7LxUDGIOzjIhxFwMxCc7hNzQKCI1x3hbS23Vm6a4SKMOLL7pOqsVtwJfBLOWBdB02EXVYxJQpwOpVrte7xsfzD2qZEwQByvBwKMPDoe3beg82AIhmM8zl5ajavBklK1d5dX6xoQHm8wUwn/duIhxBq4UyMhKKyEgkWywo2LoV6t5RUPbqBWWvSNut7Scy0nErqLtuqChJODw/cBi2bEHRM89KI4FsOjr/gDIyEhrAd+s4i2JTCLYHXVsQdrpvtgAWs+P/KY2nTrXvdQQFBJXSbRhW9uoFAND27y/tUyi8Cv8zZ87Ea6+9BovFAmUQfRnM4Cwj5ju+AqKj/V1Gk0AbXnzRdVItcpyxOlCuwyaiTouYMgXhkyZxhuUeTFCpoI6LQ8jQYV4dn/zmm9Bm9oWlrAzm8gpYKsphLiuHpbwc5vIyWMorbPelbaLRCLGxUZpRvLgYoQBqz5zx6rUUYWFuQ7UiIhzK8Iim23A9lBERUIaHQxERAaVeD0HTTV/KB7BgWHaup+iKLziUkZFQRERIs2ybTRAUCghaLWCxwFJd7RqCLRbAYpV6gu3Dpi1WWGtrAXQgBEMa3QSlLQgrlVIQViohKFWAqtl22zGewrB9MkSFN9cqNzNz5kwsXboUu3btwoQJE9r9HgIVgzO1LhCHFyuU8qmluUC4DpuIfEZQKnv0klMk8fa6dv3YK6Q/ZFNS2jynKIqw1tbCUlkFS2UljGWlOLBjJwZlZADVBliqpO2Wyspm96tgNRikHqzaWmnStPz8dr8fISRECtK2nne3Ydt2qwgLgzIsDIrmP6GhEEJCevS13Fx2LjB4+wVH2BVXSBf5Wa1SsG3PrZfXBHviCLjNbgWVym0wVsXFAUCrqxF0p6FDhyI9PR2bN29mcCYCwOHFvhQI12ETEVG7dMV17YIgQKnXQ6nXA8l9oDKZUF1ZiV7XXgu1h2HYosUCi8HgJlRL963VNbBWG2AxVMNSbYDVUA1LdbW0pFeNtBSlWF8Pc309UFzcod8HAGnStNBQl0Dd9DjUdVtoKBQhIVDodBB0IVCE6CDodLbHOihCQiBotbII5Fx2rnuJoghYrU1BtZX7LbfV//e/Xn3BUfXFlwi5pPW5D7whKBQu4dclDLfYpggNBQShXSFYbiMYZs6ciU2bNmH16tWy+Gx2BwZnah2HF/uW3K/DJiKSKdFike3Qd7lc1y4olY71sdtLtFhgramRgrRBCtfWmmrptnnYrq5xhG57z7a1rs5xC1vIsdbUOMK4T99jSAgUWq10q9NBCNFBoQtpCtj2bVotBLUGgkYjzfar0TT9OB6rHfcVLY9r+RyVClAoIaiUUPbu5VWtclp2rj2fH2kGZvuavCbpWl77TMz27UaTtF6x0/q9JtsavmaIjQ2wNjZCbGiEaGyUruNtNLrZ3gixoQFWYyPMYXqY7rgD0gKqAiBapdDcwcmljHl5Xh1nNVRJ7a1QAkqF9HuxB2GFQmp3ZbPHLW8VCum2vXpA0Jw4cSJWrlyJEydO4IILLvB3Od2CwZlax+HFvifn67CJiGSo+ezFdh2d3KerBPp17YJSKV0L3d7JjZoRrVaI9fWw2AN1bR2sdc3uO7a3CNz2+40NEOsbYG1ogKWyEtbqapfQJNbXw1JfD1RWdvIddzGFAvmPPw6FfZitQiFde6pQSreCLWjZw5PLrf2mtf22W1GEaLUAVlEaPmz74gJWC0TbNkttDSzlFYDF4lSfIjQUgkolPadZ+O1oUO0sa2KitFySyQTRbRAVpAArNAurCqEpwAoKR5CFQoC2b6ZXrxsyZCh0Mg99dXV1CG0xy77RaIRSqezwxFy+OOfevXsRHR2Nfv36daiGQMTgTK3j8OKuIdfrsImIZCaQZi8O9uvaBYUCgm3odWe01uZ28YsWImTYcIgN9bA2NMBaXy+tv11fL/Ve1jdAbGyQApjRCKvR6LgvGk3N7hvbvG81mQAP6/a2ymqFpagIlraP9A/bqACvCELTGr1qNdDsfstbaf1etdTjr9VC0Glt93UQtBootDoIWi0UOtt+rQ4KrQaCTgejTofC0FBoUlKhDdFJXw7YJ7VSKKQ62tFLq5o00av5B0IvHeH1Of3hl19+wYQJE/DGG284tjU0NOCGG25AdHQ03n//fb+dc/PmzZg+fTpn1SZy4PBiIiLyA85eHHw8tjkACALK1q5Dv+9v67Y2F61WaUiy1SotC2SVZkau+fe/UbxqNSzNrmVWxsQg+u67ETbyMogWq9SD2uzWPrOy/f3Z1+V1vF/H2272/lsc0/w5TT2siqZeV6X0WBSB848+Ckt5eavvTRkTg9S179qGtqul3nG12jkUd9PvuaGhAYqzZ6EIkYbcd1ZXzD/gD5mZmRg5ciRmzZqFK6+8EgAwY8YM7Ny5E5s2bWr1edIkg7aZv1VqKMJCHV88dPSczZ08eRLHjh3Dc88918l3GFgYnKltHF5MRETdjLMXBx85trmgUAAaDVr2dfa68UZEXn89DPv24cDWrRgxeTIiRo6UTRCr3fcfj6EZACylpbBUVELXQz8/jvkHWq7j3M3zD3SGRqPBpk2bcOutt+LTTz8FAOzatQufffYZpk6d6vY5lqoqj2tNd+ScLW3evBl6vR6TJ0/u5DsMLAzO5B0OLyYiom7E2YuDT6C1uaBUIvQ3v0F1SQlCf/Mb2YRmIPB+l10lYsoUhE2YgNJdP0BbVwt1XHxAzT8AAGq1Gh9++CFmz56Nzz//HF988QWuuuoqt8daqqpgzM112S6aTDDm5kIDaS3q9pzTnc2bN+Oaa66BzgejAwIJgzMRERHJjrezEstp9mLqHLa57/B32URQKqEdMRwRERFQdGQGbBlQqVT44IMPUF9fj7BW5hEQRREmTyM2AJgKC6GIiIAgCF6d052CggLs27cPDz/8cLveQ08QmP96iIiIqEcLvXQEVAkJrS/bIghQJSTIfnIf8h7b3Hf4u+x5FAqFx4Brra1zGp7tjmgywVpb5/U53fn888+hVqsxbdq0dj2vJ2BwJiIiItmxT+4jPWjxx38ATe5D3mOb+w5/l8FHNHs3A7y3x7XmxhtvxMmTJxEREdGp8wQiBmciIiKSpYgpU9Bn9Sqo4uOdtqvi49FHRktRke+wzX2Hv8vgIqjUPj2uNbGxsUhNTe3UOQIVr3EmIqKAIlos0uy7JSVQxcYG3EQv1D4RU6YgfNIktnkQYZv7Dn+X8rRjxw6fn1MRFgpBrfY4XFtQS0tTUccwOBMRUcAwbNmComdXOC1Zo0pICJilRahjBKWSS04FGba57/B3GRwEQYA6IcHtrNp26oQEx3rO1H4cqk1ERAHBsGUL8h+Z77LOq7moCPmPzIdhyxY/VUZEROR/yshIaFJSIKidh2MLajU0KSlQRkb6qbKegT3OREQke6LFgqJnVwCi6GanCAgCip5dgfBJkzgEkYiIgpYyMhKKiAhplm2zCYJKGp7NnubOY48zERHJXt3PB1x6mp2IIsyFhaj7+UD3FUVERCRDgiBAqQ+DqlcvKPVhDM0+ElTBub6+Hk8//TQGDBgAnU6HpKQkzJ07F/n5+Z0678mTJxESEgJBEHDVVVf5qFoiIrIzl5T49DgiIpIf0d2oIgpYPa09gyY4NzQ0YOLEiVi2bBlqamowY8YMpKSkYN26dRg2bBjOnDnT4XPfe++9aGxs9GG1RETUnCo21qfHERGRfKhU0tWjZrPZz5WQL9nb096+gS5ogvPy5cuxd+9ejBo1CidOnMBHH32Effv24aWXXkJJSQnmzp3bofO+++672LFjB+655x4fV0xERHahl46AKiEBaG24mSBAlZCA0EtHdG9hRETUaUqlEkqlEgaDwd+lkA8ZDAZH2/YEPSP+t8FoNGLNmjUAgNdeew16vd6xb8GCBdiwYQN27tyJAwcOYMQI7//oKioqwmOPPYbJkyfj1ltvxd/+9jef105ERNJyKvGLFiL/kflSeG4+/MsWpuMXLeTEYEREAUgQBMTFxaGgoABarRZhYb67LtdqtcJoNKKhoQEKRdD0GfqVKIqora2FwWBAYmJij7nGOiiC8+7du1FVVYXMzEwMGzbMZf9NN92ErKwsfPnll+0Kzo888gjq6+vx+uuvIy8vz5clExFRCxFTpgCrV7mu4xwfz3WciYgCXGRkJOrr61FaWooSH85XIYoi6uvrHfMRUfcQBAG9evVCZA9aAisogvPhw4cBAMOHD3e73749KyvL63N+8803+Oijj7B06VL069ePwZmIqBtETJmC8EmTpFm2S0qgio1F6KUj2NNMRBTgBEFAYmIi4uLiYDKZfHZek8mEXbt24corr4S6xfrG1HXUanWPGaJtFxTBOScnBwCQnJzsdr99e3Z2tlfnq62txQMPPIALLrgATzzxhG+KJCIirwhKJcJGXubvMoiIqAv4+ppYpVIJs9kMnU7H4EydEhTBuaamBgAQGhrqdn9YWBgAoLq62qvzPfXUU8jOzsb27duh0WjaXU9jY6PTLNz2iRBMJpNPv2Ej37C3CdtGvthG8sb2kTe2j7yxfeSN7eM7osWC+oMHYSkpgTI2FiHDh/tkNBHbSN4CqV2CIjj70s8//4xXXnkFd9xxB8aPH9+hc6xYsQJLlixx2b59+/ZWwz3539atW/1dArWBbSRvbB95Y/vIG9tH3tg+naP/3/8Q+8WXUFdVObaZIiNRct101FxyiU9eg20kT3V1df4uwWtBEZzts2i31jC1tbUAgPDwcI/nMZvNuOeee9CrVy+8+OKLHa5n4cKFWLBggeOxwWBASkoKJkyYgOjo6A6fl7qGyWTC1q1bMXnyZA7xkSm2kbyxfeSN7SNvbB95Y/t0Xs22bSj8+wfOqyUAUBsMSPr7B0h4+SXor7qqw+dnG8lbWVmZv0vwWlAE59TUVABodQIv+/a0tDSP58nLy8OhQ4eQkJCAm2++2WlfZWUlAODAgQOOnugdO3a4PY9Wq4VWq3XZrlar+YGWMbaP/LGN5I3tI29sH3lj+8gb26djRIsFpc+/4BKapZ0iIAgoff4F9JoypdPDttlG8hRIbRIUwXnIkCEAgIMHD7rdb98+ePBgr85XWFiIwmZLoTRXWVmJnTt3dqBKIiIiIqLgUffzAaflBV2IIsyFhaj7+QAnhSS/C4pVwMeMGYPIyEicPn0ahw4dctm/adMmAMD06dM9nic9PR2iKLr92b59OwBg0qRJjm1EREREROSe2cv1mr09jqgrBUVw1mg0ePDBBwEA8+bNc1zTDAAvv/wysrKyMG7cOIwYMcKxfc2aNRg4cCAWLlzY7fUSEREREfV0qthYnx5H1JWCYqg2IC0htW3bNuzZswf9+/fH2LFjkZ2djX379iE2NhZr1651Or60tBTHjx9HQUGBnyomIiIiIuq5Qi8dAVVCAsxFRe6vcxYEqOLjEXrpCNd9RN1MlsF5x44d2LVrFwoKCpzWO25OEAS8++67Xp9Tp9Nh+/btWLFiBTZu3IjPPvsMUVFRmDNnDpYtW4bk5GRflU9ERERERG0QlErEL1qI/EfmA4LgHJ4FAQAQv2ihT9ZzJuosWQXnqqoqzJgxAz/88EOb1wi3NzgDQEhICJYuXYqlS5e2eezixYuxePFir889fvx4XtdMRERERNQOEVOmAKtXoejZFU4Thani4xG/aKG0n0gGZBWcn3jiCezatQv9+vXD/fffjwEDBrS5tjIREREREQWuiClTED5pkjTLdkkJVLGxCL10BHuaSVZkFZw///xzxMfHY+/evYiKivJ3OURERERE1A0EpZJLTpGsyWpW7aqqKowZM4ahmYiIiIiIiGRDVsG5f//+TktFEREREREREfmbrILzQw89hB07duDUqVP+LoWIiIiIiIgIgMyC8913342HH34Y48aNw7p165CXl+fvkoiIiIiIiCjIyWpyMKVt5jxRFHH33Xd7PFYQBJjN5u4oi4iIiIiIiIKYrIJzSkoKBNti50RERERERERyIKvgfO7cOX+XQEREREREROREVtc4ExEREREREcmN7INzRUUFKioq/F0GERERERERBSlZBudvvvkGV199NfR6PWJiYhATEwO9Xo+pU6fim2++8Xd5REREREREFERkF5z/8Ic/YPr06di6dSvq6uoQERGByMhI1NXVYcuWLZg+fToWLFjg7zKJiIiIiIgoSMgqOH/00UdYvXo1YmNj8corrziGaZeXl6OyshKvvvoq4uLisHr1anz88cf+LpeIiIiIiIiCgKyC8+uvvw6dToddu3bhwQcfRGRkpGNfREQE5s2bh507d0Kr1eL111/3Y6VEREREREQULGQVnA8fPoyJEydiwIABrR4zYMAATJw4EYcOHeq+woiIiIiIiChoySo4G41GhIWFtXlcWFgYjEZjN1REREREREREwU5WwTkzMxM7d+5EbW1tq8fU1dVh586dyMzM7MbKiIiIiIiIKFjJKjjPmjULxcXFuP7663Hy5EmX/adPn8YNN9yAkpIS3HLLLX6okIiIiIiIiIKNyt8FNPfHP/4Rn3/+Ob7//ntcdNFFGD58ONLT0wEA2dnZOHDgACwWCy699FI8+uij/i2WiIiIiIiIgoKsgnNISAh27NiBhQsXYu3atdi/fz/279/vtH/u3LlYsWIFQkJC/FgpERERERERBQtZBWcA0Ov1ePXVV/H888/jwIEDOH/+PAAgKSkJI0aMQGhoqJ8rJCIiIiIiomAiu+BsFxoairFjx/q7DCIiIiIiIgpyspocjIiIiIiIiEhu/NrjvHTpUgiCgHnz5iEqKgpLly71+rmCIODPf/5zF1ZHRERERERE5OfgvHjxYgiCgFtuuQVRUVGOx6IotvlcBmciIiIiIiLqDn4NzuvWrQMAJCYmOj0mIiIiIiIikgu/Buc777zT42MiIiIiIiIif5PV5GA5OTkoLy9v87iKigrk5OR0Q0VEREREREQU7GQVnDMyMvDYY4+1edzjjz+Ovn37dkNFREREREREFOxkFZxFUfRqYjD7sURERERERERdTVbB2VulpaUICQnxdxlEREREREQUBPw6ORgA7Nq1y+lxYWGhyzY7s9mM48eP47vvvsPFF1/cHeURERERERFRkPN7cB4/fjwEQXA8/u677/Ddd9+1erwoihAEAY8++mh3lEdERERERERBzu/B+Y477nAE5w0bNiAzMxNjxoxxe6xGo0FSUhKmT5+O4cOHd2eZREREREREFKT8HpzXr1/vuL9hwwZcccUVWLt2rf8KIiIiIiIiImrG78G5OavV6u8SiIiIiIiIiJwE5KzaRERERERERN1FVj3Odjk5Ofjyyy9x8uRJVFdXu12zWRAEvPvuu36ojoiIiIiIiIKJ7ILz0qVLsWzZMqdh2/bgbJ9EzD6zNoMzERERERERdTVZDdX+6KOPsHjxYqSkpOBvf/sbJk+eDEBaouqNN97AuHHjIIoiFixYgH//+99+rpaIiIiIiIiCgax6nF9//XVoNBps374daWlp+PHHHwHAEaDvu+8+rFy5Eo8//jiuv/56P1ZKREREREREwUJWPc5ZWVkYPXo00tLSADgPzbb7wx/+gAsuuADLly9v9/nr6+vx9NNPY8CAAdDpdEhKSsLcuXORn5/v9TnMZjMWL16MadOmoW/fvggPD4dOp0P//v3xwAMPIDs7u911ERERERERkXzJKjg3NjYiISHB8Vin0wEAKisrnY4bMmQI9u/f365zNzQ0YOLEiVi2bBlqamowY8YMpKSkYN26dRg2bBjOnDnj9XmWLFmCXbt2ITExEVOnTsXVV18No9GIN954A4MHD8bPP//crtqIiIiIiIhIvmQVnBMTE1FcXOx43KdPHwDA0aNHnY7Ly8uDxWJp17mXL1+OvXv3YtSoUThx4gQ++ugj7Nu3Dy+99BJKSkowd+5cr86j0+nw448/oqKiArt378Ynn3yCzz//HGfOnMGf/vQnGAwG3H///e2qjYiIiIiIiORLVsF50KBBOH78uOPx+PHjIYoi/vKXv6C2thYA8PHHH+OHH37AxRdf7PV5jUYj1qxZAwB47bXXoNfrHfsWLFiAwYMHY+fOnThw4ECb51KpVBgzZgxUKufLw5VKJZYtWwadTocDBw6gqqrK6/qIiIiIiIhIvmQVnKdPn478/HzHjNljxozBhAkTsH37dvTu3RsxMTG49dZbIQgC/vznP3t93t27d6OqqgqZmZkYNmyYy/6bbroJAPDll192qn5BEKBUKiEIAjQaTafORURERERERPIgq+B8++2345dffsHQoUMd2zZv3ox7770XUVFRqK6uxkUXXYT3338fU6dO9fq8hw8fBgAMHz7c7X779qysrA7XLooinn/+edTW1mLChAkICQnp8LmIiIiIiIhIPmS1HJVWq8UFF1zgtC0iIgJvvvkm3nzzzQ6fNycnBwCQnJzsdr99e3tnxH7iiSdQVFQEg8GArKwsnD59GhdeeCHeeeedDtdKRERERERE8iKr4NxVampqAAChoaFu94eFhQEAqqur23XeTz/9FKdPn3Y8Hjx4MP7+978jIyPD4/MaGxvR2NjoeGwwGAAAJpMJJpOpXTVQ17O3CdtGvthG8sb2kTe2j7yxfeSN7SN/bCN5C6R2CYrg3FVOnToFACgtLcWBAwfw5JNPYsSIEXj77bdx5513tvq8FStWYMmSJS7bt2/f3mq4J//bunWrv0ugNrCN5I3tI29sH3lj+8gb20f+2EbyVFdX5+8SvObX4Dxx4sQOP1cQBHz//fdeHWufRbu1hrHP2B0eHt6hWmJiYnD11Vfj8ssvx6BBg/D73/8eEydOREpKitvjFy5ciAULFjgeGwwGpKSkYMKECYiOju5QDdR1TCYTtm7dismTJ0OtVvu7HHKDbSRvbB95Y/vIG9tH3tg+8sc2kreysjJ/l+A1vwbnHTt2dPi5giB4fWxqaioAaf1nd+zb09LSOlwPAERGRmL69Ol4/fXXsXXr1lbXhtZqtdBqtS7b1Wo1P9AyxvaRP7aRvLF95I3tI29sH3lj+8gf20ieAqlN/Bqcz5492y2vM2TIEADAwYMH3e63bx88eHCnXysmJgYAUFJS0ulzERERERERkf/5NTh3tofXW2PGjEFkZCROnz6NQ4cOOS13BQCbNm0CIK0j3Vk7d+4EAGRmZnb6XEREREREROR/slrHuatoNBo8+OCDAIB58+Y5rmkGgJdffhlZWVkYN24cRowY4di+Zs0aDBw4EAsXLnQ619dff409e/a4vEZdXR2efPJJ7Ny5EwkJCe1aZ5qIiIiIiIjkK2hm1X7qqaewbds27NmzB/3798fYsWORnZ2Nffv2ITY2FmvXrnU6vrS0FMePH0dBQYHT9v3792PJkiXo06cPhg4disjISBQWFuLQoUMoLy9HZGQkPv74Y8eEZERERERERBTYZBWclUql18cKggCz2ez18TqdDtu3b8eKFSuwceNGfPbZZ4iKisKcOXOwbNkyJCcne3WeG264AdXV1fjhhx+wf/9+lJeXIyQkBP369cN9992Hhx56CImJiV7XRURERERERPImq+CckpLidrZsq9WKwsJCxwLZHb02OiQkBEuXLsXSpUvbPHbx4sVYvHixy/bBgwfjpZde6tDrExERERERUeCRVXA+d+5cq/usViv+/e9/45FHHsGgQYPwj3/8o/sKIyIiIiIioqAVMJODKRQKXHXVVfjqq6/w9ddf4/nnn/d3SURERERERBQEAiY422VkZODyyy93mcyLiIiIiIiIqCsEXHAGgNDQUOTm5vq7DCIiIiIiIgoCARecs7OzsWvXLsTHx/u7FCIiIiIiIgoCspoc7L333mt1X01NDU6cOIG///3vMBgMeOCBB7qxMiIiIiIiIgpWsgrOc+bMcbsclZ0oigCAO+64A0uWLOmusoiIiIiIiCiIySo4P/30060GZ41Gg8TERFx55ZXo27dvN1dGREREREREwUpWwXnx4sX+LoGIiIiIiIjIScBNDkZERERERETUnWTV49zcnj178MMPP+D8+fMAgKSkJFxxxRUYM2aMnysjIiIiIiKiYCK74HzkyBHMmTMHhw4dAtA0IZj92uchQ4Zg/fr1GDx4sL9KJCIiIiIioiAiq+B8/PhxjBs3DpWVlUhOTsZNN92E9PR0ANL6zZ9++ikOHTqE8ePHY8+ePRg4cKB/CyYiIiIiIqIeT1bBedGiRaisrMSf/vQnLF26FCqVc3kvvPACnn76aaxYsQJPPvkkPv30Uz9VSkRERERERMFCVpODbd++HRdffDGeffZZl9AMAEqlEs888wwuvvhibN++3Q8VEhERERERUbCRVXA2mUxeXbs8ePBgmEymbqiIiIiIiIiIgp2sgvOQIUNw+vTpNo87ffo0hgwZ0g0VERERERERUbCTVXB+8sknsX//fqxdu7bVY9atW4f9+/dj0aJF3VgZERERERERBStZTQ4WFhaG3//+97jnnnuwfv163HLLLUhLSwMgzar98ccf48cff8Tvf/976PV67Nq1y+n5V155pT/KJiIiIiIioh5MVsF5/PjxEAQBoijixx9/xO7du53229d0fuONN/DGG2+4PN9isXRLnURERERERBQ8ZBWc77jjDgiC4O8yiIiIiIiIiBxkFZzXr1/v7xKIiIiIiIiInMhqcjAiIiIiIiIiuZFVj3NzRqMRhw4dQn5+PgCgT58+GDp0KDQajZ8rIyIiIiIiomAiu+Dc0NCAp59+Gm+99RZqamqc9un1etx///1YsmQJdDqdnyokIiIiIiKiYCKr4NzY2IirrroKP/30EwBg8ODBSE9PhyAIOHfuHA4fPowXX3wRu3fvxvfffw+tVuvniomIiIiIiKink9U1zitXrsSePXswZswYHDp0CP/973+xefNm/POf/8TBgwdx+PBhjB07Fj/99BNWrVrl73KJiIiIiIgoCMgqOP/jH/9AbGwsvv76awwaNMhl/yWXXIKvvvoKMTEx+OCDD/xQIREREREREQUbWQXnU6dOYfz48QgPD2/1GL1ej/Hjx+P06dPdWBkREREREREFK1kFZ5VKhbq6ujaPq6urg0olq8uziYiIiIiIqIeSVXAeNGgQ/v3vf+PMmTOtHnP27Fn8+9//xuDBg7uxMiIiIiIiIgpWsgrO9913H+rr6zF+/Hi8++67qK+vd+yrr6/HunXrMH78eDQ0NOD+++/3Y6VEREREREQULGQ13nn27Nn48ccf8fbbb+Pee+/Fvffei5iYGABAaWkpAEAURdx333247bbb/FkqERERERERBQlZ9TgDwFtvvYVPPvkEV1xxBdRqNUpKSlBSUgK1Wo2xY8fik08+wRtvvOHvMomIiIiIiChIyKrH2e7GG2/EjTfeCLPZjLKyMgBAdHQ0JwQjIiIiIiKibieLJPrNN9/gs88+Q25uLrRaLYYMGYK77roL6enpiI+P93d5REREREREFMT8Hpxvu+02fPjhhwCk65cB4Msvv8Rf//pXfPjhh7juuuv8WR4REREREREFOb8G53fffRf/+Mc/oFKpMHv2bAwbNgzV1dX46quv8NNPP+GOO+5AdnY2IiMj/VkmERERERERBTG/BucNGzZAoVDgX//6FyZNmuTYvnDhQtx1111477338M9//hN33XWXH6skIiIiIiKiYObXWbWPHDmCyy+/3Ck02y1atAiiKOLIkSN+qIyIiIiIiIhI4tfgbDAYkJmZ6XaffbvBYOjOkoiIiIiIiIic+DU4i6IIpVLpdp9CIZVmtVq7syQiIiIiIiIiJ34Nzt2tvr4eTz/9NAYMGACdToekpCTMnTsX+fn5Xp+jsrISGzduxK233oqMjAxoNBqEh4dj5MiRWL16NUwmUxe+AyIiIiIiIupufg/OGzZsgFKpdPsjCEKr+1Wq9s1r1tDQgIkTJ2LZsmWoqanBjBkzkJKSgnXr1mHYsGE4c+aMV+d58cUXcdttt+Gjjz5C7969ccMNN+Cyyy7D4cOHMX/+fEycOBF1dXUd+VUQERERERGRDPk9OIui2KGf9g7hXr58Ofbu3YtRo0bhxIkT+Oijj7Bv3z689NJLKCkpwdy5c706T1hYGB5//HGcO3cOBw8exIcffojvv/8eR44cQWpqKn788UcsX768I78KIiIiIiIikiG/Bmer1dqpH28ZjUasWbMGAPDaa69Br9c79i1YsACDBw/Gzp07ceDAgTbPtXDhQjz//PNITU112t6/f38899xzAIB//OMfXtdGRERERERE8ub3HufusHv3blRVVSEzMxPDhg1z2X/TTTcBAL788stOvc6QIUMAAOfPn+/UeYiIiIiIiEg+giI4Hz58GAAwfPhwt/vt27Oysjr1OvbrpBMSEjp1HiIiIiIiIpKPoAjOOTk5AIDk5GS3++3bs7OzO/U6q1evBgDMmDGjU+chIiIiIiIi+Wjf1NQBqqamBgAQGhrqdn9YWBgAoLq6usOv8eabb2Lbtm3o1asX/vSnP3k8trGxEY2NjY7HBoMBAGAymbiclQzZ24RtI19sI3lj+8gb20fe2D7yxvaRP7aRvAVSuwRFcO5qP/zwAx555BEIgoC1a9ciKSnJ4/ErVqzAkiVLXLZv37691XBP/rd161Z/l0BtYBvJG9tH3tg+8sb2kTe2j/yxjeQpkJbxDYrgbJ9Fu7WGqa2tBQCEh4e3+9z/+9//MGPGDBiNRrzyyiuYOXNmm89ZuHAhFixY4HhsMBiQkpKCCRMmIDo6ut01UNcymUzYunUrJk+eDLVa7e9yyA22kbyxfeSN7SNvbB95Y/vIH9tI3srKyvxdgteCIjjbl47Ky8tzu9++PS0trV3nPXv2LKZMmYKKigosXrwYDz30kFfP02q10Gq1LtvVajU/0DLG9pE/tpG8sX3kje0jb2wfeWP7yB/bSJ4CqU2CYnIw+zJRBw8edLvfvn3w4MFen7OgoACTJ09GQUEBHnnkEfzlL3/pfKFEREREREQkO0ERnMeMGYPIyEicPn0ahw4dctm/adMmAMD06dO9Ol9FRQWuvvpqnD59GnfddRdWrlzpy3KJiIiIiIhIRoIiOGs0rNRDQgABAABJREFUGjz44IMAgHnz5jmuaQaAl19+GVlZWRg3bhxGjBjh2L5mzRoMHDgQCxcudDpXXV0dpk2bhiNHjmDWrFl4++23IQhC97wRIiIiIiIi6nZBcY0zADz11FPYtm0b9uzZg/79+2Ps2LHIzs7Gvn37EBsbi7Vr1zodX1paiuPHj6OgoMBp+5NPPomffvoJSqUSKpUKv/vd79y+3vr167vqrRAREREREVE3CprgrNPpsH37dqxYsQIbN27EZ599hqioKMyZMwfLli1DcnKyV+epqKgAAFgsFmzcuLHV4xiciYiIiIiIeoagGKptFxISgqVLl+LUqVNobGxEQUEB1q1b5zY0L168GKIougTg9evXQxTFNn+IiIiIiIioZwiq4ExERERERETUXgzORERERERERB4wOBMRERERERF5wOBMRERERERE5AGDMxEREREREZEHDM5EREREREREHjA4ExEREREREXnA4ExERERERETkAYMzERERERERkQcMzkREREREREQeMDgTERERERERecDgTEREREREROQBgzMRERERERGRBwzORERERERERB4wOBMRERERERF5wOBMRERERERE5AGDMxEREREREZEHDM5EREREREREHjA4ExEREREREXnA4ExERERERETkAYMzERERERERkQcMzkREREREREQeMDgTERERERERecDgTEREREREROQBgzMRERERERGRBwzORERERERERB4wOBMRERERERF5wOBMRERERERE5AGDMxEREREREZEHDM5EREREREREHjA4ExEREREREXnA4ExERERERETkAYMzERERERERkQcMzkREREREREQeMDgTERERERERecDgTEREREREROQBgzMRERERERGRBwzORERERERERB4wOBMRERERERF5wOBMRERERERE5AGDMxEREREREZEHDM5EREREREREHjA4ExEREREREXkQVMG5vr4eTz/9NAYMGACdToekpCTMnTsX+fn57TrPzp07sWTJEkybNg2xsbEQBAHp6eldUzQRERERERH5lcrfBXSXhoYGTJw4EXv37kViYiJmzJiBc+fOYd26dfjqq6+wd+9e9O3b16tzPfLIIzh8+HAXV0xERERERERyEDQ9zsuXL8fevXsxatQonDhxAh999BH27duHl156CSUlJZg7d67X55oyZQqWL1+O7777DkePHu3CqomIiIiIiMjfgqLH2Wg0Ys2aNQCA1157DXq93rFvwYIF2LBhA3bu3IkDBw5gxIgRbZ7vhRdecNwvLCz0fcFEREREREQkG0HR47x7925UVVUhMzMTw4YNc9l/0003AQC+/PLL7i6NiIiIiIiIZC4ogrP9euThw4e73W/fnpWV1W01ERERERERUWAIiuCck5MDAEhOTna73749Ozu722oiIiIiIiKiwBAU1zjX1NQAAEJDQ93uDwsLAwBUV1d3Sz2NjY1obGx0PDYYDAAAk8kEk8nULTWQ9+xtwraRL7aRvLF95I3tI29sH3lj+8gf20jeAqldgiI4y82KFSuwZMkSl+3bt29vNdyT/23dutXfJVAb2EbyxvaRN7aPvLF95I3tI39sI3mqq6vzdwleC4rgbJ9Fu7WGqa2tBQCEh4d3Sz0LFy7EggULHI8NBgNSUlIwYcIEREdHd0sN5D2TyYStW7di8uTJUKvV/i6H3GAbyRvbR97YPvLG9pE3to/8sY3krayszN8leC0ognNqaioAIC8vz+1++/a0tLRuqUer1UKr1bpsV6vV/EDLGNtH/thG8sb2kTe2j7yxfeSN7SN/bCN5CqQ2CYrJwYYMGQIAOHjwoNv99u2DBw/utpqIiIiIiIgoMARFcB4zZgwiIyNx+vRpHDp0yGX/pk2bAADTp0/v5sqIiIiIiIhI7oIiOGs0Gjz44IMAgHnz5jmuaQaAl19+GVlZWRg3bhxGjBjh2L5mzRoMHDgQCxcu7PZ6iYiIiIiISD6C4hpnAHjqqaewbds27NmzB/3798fYsWORnZ2Nffv2ITY2FmvXrnU6vrS0FMePH0dBQYHLud555x288847AJqmUC8oKMDll1/uOOb111/H8OHDu/AdERERERERUXcImuCs0+mwfft2rFixAhs3bsRnn32GqKgozJkzB8uWLUNycrLX58rLy8O+ffucthmNRqdt9rWZiYiIiIiIKLAFxVBtu5CQECxduhSnTp1CY2MjCgoKsG7dOrehefHixRBFEevXr291n6ef8ePHd/0bIiIiIiIioi4XVMGZiIiIiIiIqL0YnImIiIiIiIg8YHAmIiIiIiIi8oDBmYiIiIiIiMgDBmciIiIiIiIiDxiciYiIiIiIiDxgcCYiIiIiIiLygMGZiIiIiIiIyAMGZyIiIiIiIiIPGJyJiIiIiIiIPGBwJiIiIiIiIvKAwZmIiIiIiIjIAwZnIiIiIiIiIg8YnImIiIiIiIg8YHAmIiIiIiIi8oDBmYiIiIiIiMgDBmciIiIiIiIiDxiciYiIiIiIiDxgcCYiIiIiIiLygMGZiIiIiIiIyAMGZyIiIiIiIiIPGJyJiIiIiIiIPGBwJiIiIiIiIvKAwZmIiIiIiIjIAwZnIiIiIiIiIg8YnImIiIiIiIg8YHAmIiIiIiIi8oDBmYiIiIiIiMgDBmciIiIiIiIiDxiciYiIiIiIiDxgcCYiIiIiIiLygMGZiIiIiIiIyAMGZyIiIiIiIiIPGJyJiIiIiIiIPGBwJiIiIiIiIvKAwZmIiIiIiIjIAwZnIiIiIiIiIg8YnImIiIiIiIg8YHAmIiIiIiIi8oDBmYiIiIiIiMgDBmciIiIiIiIiDxiciYiIiIiIiDwIquBcX1+Pp59+GgMGDIBOp0NSUhLmzp2L/Pz8dp+roqICjzzyCNLS0qDVapGWlob58+ejsrLS94UTERERERGR3wRNcG5oaMDEiROxbNky1NTUYMaMGUhJScG6deswbNgwnDlzxutzlZaW4rLLLsMrr7wClUqF66+/HuHh4Vi9ejVGjhyJ8vLyLnwnRERERERE1J2CJjgvX74ce/fuxahRo3DixAl89NFH2LdvH1566SWUlJRg7ty5Xp9r/vz5OHXqFG644QYcP34cH330Ef73v//hoYcewokTJ7BgwYIufCdERERERETUnVT+LqA7GI1GrFmzBgDw2muvQa/XO/YtWLAAGzZswM6dO3HgwAGMGDHC47kKCgrwj3/8AxqNBq+//jpUqqZf4V//+ld8+OGH+Pvf/44XXngBcXFx7arzw6yduGfsddCo5NcsRrMZGw/vQI6hEKkRCfi/IeNZZwcFQo1AYNX590P/xg9lWSg7pMPtwybJrs5A+l2yTt8IhBqBwPj8AIHx+wyEGgHW6Uv8/PgW6/SdQKgRkOr8MGunv8vwmiCKoujvIrra9u3bMXHiRGRmZuLUqVMu+5ctW4ann34af/nLX7B48WKP51q3bh3mzp2LSZMmYdu2bS77f/e732Ht2rVYt24d5syZ41V9BoMBkZGRuPCNC6HSRGN2/4fx2NibvXpud/jrD5/g/ZOvQFRWOrYJll5BU6fJZMI333yDa6+9Fmq1WpY1+hrr9J1AqBHo2jqD7TMUCDUCrNOX+PnxrUCoMxBqBFgnEHyfoUCoEWiq02wswy+//wVVVVWIiIjwd1keye+rhy5w+PBhAMDw4cPd7rdvz8rK8sm51q5d69W53LEqKrHh9FIAkMU/7r/+8IlUjwIQmm1nne0XCDUCrNOXAqFGgHX6UiDUCLBOXwqEGgHW6UuBUCPAOn0tEOoMhBoB5zoDSVAE55ycHABAcnKy2/327dnZ2d16LncEARBF4P0Tq3H70KugUfqviYwWM947uVr68AnO+4KpTpPJhGqzEWV11R3+ppK/S98KhDoDoUage+oMls9QINQIsM5AqzFYPj9AYNQZCDUCrLO5YPkMBUKNgOc65S4ohmrfe++9ePvtt/Hkk09i+fLlLvtPnTqF/v37o3///jhx4oTHc02ZMgVbt27F22+/jbvvvttl/7Zt2zB58mRMnjwZW7ZscXuOxsZGNDY2Oh4bDAakpKTgwjcuhDJE2c53R0REREREFHgs9ZaAGaodYB3kPcOKFSsQGRnp+ElJSfF3SURERERERNSKoBiqbZ9Fu66uzu3+2tpaAEB4eHi3nGvhwoVOS1bZe5xbuqffcsy6ZGybNXWVj//3A94+9VSbx/X0Ok0mM3bu3Ilx48ZBre7YR4a/S98KhDoDoUage+oMls9QINQIsE5f4ufHtwKhzkCoEWCdzQXLZygQagS8r1OOgiI4p6amAgDy8vLc7rdvT0tL65ZzabVaaLXaVveLIqCw9ML9I6f5der4+0dOwzvHX4RVUen2GoRgqdNkMiFcpUFCZO8OXxvD36VvBUKdgVAj0D11BstnKBBqBFinL/Hz41uBUGcg1AiwzuaC5TMUCDUCbdcpZ0ExVHvIkCEAgIMHD7rdb98+ePDgbj2XO/YrzmcPeNjv661pVCrM7v8wgKa67Fhn+wRCjQDr9KVAqBFgnb4UCDUCrNOXAqFGgHX6UiDUCLBOXwuEOgOhRsBznXIXFD3OY8aMQWRkJE6fPo1Dhw5h6NChTvs3bdoEAJg+fXqb55o6dSoUCgV++OEHFBcXIy4uzrGvsbERX375JZRKJa699toO1aqw9MLsAfJZZ81eR8v14Fhn+wVCjQDr9KVAqBFgnb4UCDUCrNOXAqFGgHX6UiDUCLBOXwuEOgOhRsC5TivK/FyN94JiVm0AeOqpp/DMM89g9OjR2LJlC8LCwgAAL7/8Mh599FGMGzcOO3bscBy/Zs0arFmzBjNnzsSKFSucznX77bfjgw8+wI033ogPP/wQKts3N4888gheeeUV3HnnnVi/fr3XtRkMBkRGRmLNvz/FPWOv8/s3Qe4YzWZsPLwDOYZCpEYk4P+GjA+aOk0mE7755htce+21HR7i09U1doVAqvPv//0ePxz5D8YOugy3D5skuzoD6XfZFXUG42coEGoEAuPzAwTG75OfH98KhDr5+fEtfoZ8JxBqBKQ63/7hCzw48caAmFU7aIJzQ0MDxo8fj3379iExMRFjx45FdnY29u3bh9jYWOzduxd9+/Z1HL948WIsWbLEbQguLS3F5ZdfjtOnTyMzMxOXXnopjh49iv/973/o378/9u7di6ioKK9rswfn0tJSREdH++otk4/4+j+45HtsI3lj+8gb20fe2D7yxvaRP7aRvJWVlSEmJiYggnNQXOMMADqdDtu3b8ef//xnhIaG4rPPPkN2djbmzJmDgwcPOoXmtsTExOA///kPHnroIRiNRmzevBlVVVV4+OGH8Z///KddoZmIiIiIiIjkTX599l0oJCQES5cuxdKlS9s8dvHixVi8eHGr+6OiovDKK6/glVde8WGFREREREREJDdB0+NMRERERERE1BEMzkREREREREQeMDgTERERERERecDgTEREREREROQBgzMRERERERGRBwzORERERERERB4wOBMRERERERF5wOBMRERERERE5IHK3wUQIIoiAKC6uhpqtdrP1VBLJpMJdXV1MBgMbB+ZYhvJG9tH3tg+8sb2kTe2j/yxjeSturoaQFMekjMGZxkoKysDAGRkZPi5EiIiIiIiou5VVlaGyMhIf5fhEYOzDERFRQEAcnJyZP8PJhgZDAakpKQgNzcXERER/i6H3GAbyRvbR97YPvLG9pE3to/8sY3kraqqCqmpqY48JGcMzjKgUEiXmkdGRvIDLWMRERFsH5ljG8kb20fe2D7yxvaRN7aP/LGN5M2eh+RM/hUSERERERER+RGDMxEREREREZEHDM4yoNVq8Ze//AVardbfpZAbbB/5YxvJG9tH3tg+8sb2kTe2j/yxjeQtkNpHEANh7m8iIiIiIiIiP2GPMxEREREREZEHDM5EREREREREHjA4ExEREREREXnA4NyFdu/ejWuvvRZRUVHQ6/W47LLL8N5777X7PKWlpXj33Xdx7733YujQoVCpVBAEAevXr2/1OefOnYMgCK3+JCQkdOKd9Qz+bB+7o0eP4uabb0ZsbCxCQkIwaNAgrFq1ClartQPvqGfxVfvYffnllxg3bpxjHcfx48fj66+/dnssPz9AfX09nn76aQwYMAA6nQ5JSUmYO3cu8vPz232uiooKPPLII0hLS4NWq0VaWhrmz5+PysrKVp9jsViwcuVKDBo0CCEhIYiNjcWsWbPwyy+/dOJd9Rz+bJ85c+Z4/Hy8+eabnXx3gc9X7bNz504sWbIE06ZNQ2xsLARBQHp6epvP4+enbf5sI36G2uaL9qmsrMTGjRtx6623IiMjAxqNBuHh4Rg5ciRWr14Nk8nU6nP5GfLMn+3j18+PSF1i06ZNolKpFAVBEMeNGyfeeOONYq9evUQA4qOPPtquc23evFkE4PKzbt26Vp9z9uxZEYAYHx8v3nnnnS4/Dz/8cCffYWDzd/uIoiju2bNHDAkJEQGIl112mThr1iwxISFBBCDefPPNotVq7cQ7DGy+bB9RFMWVK1eKAESVSiVOnTpVnDFjhuN3/+qrr7ocH+yfn/r6evHyyy8XAYiJiYnirFmzxMsuu0wEIMbGxoqnT5/2+lwlJSViv379/j979x0eVZm/f/we0gkp9BaIEJqAVAEBgcCCIr1EFtQ1gF1ZQUR2rSC6q4g0KzZAV0XXgCIouCC9KkUQKaETioSWBEISUp7fH/xmvgxJTgKTZMLk/bquXJDnPM85nzPnnGTunDJGkqldu7YZNGiQadSokZFk6tWrZ86cOZNtTGZmpunfv7+RZEJDQ83AgQNNp06djM1mM6VLlzYbN24syNW94bh7+0RHRxtJ5s4778zx+Fi2bFlBru4NpyC3T9OmTbP9bgkPD7ccw/GTN3dvI44hawW1fZ5//nkjydhsNtO8eXPz17/+1XTp0sX4+fkZSeb22283ycnJ2cZxDFlz9/Zx5/FDcC4EZ86cMcHBwUaSmTt3rqP9zz//dLxBWb58eb7nt27dOvP444+bmTNnmt9//9089NBD+Q7OnTp1uv4V8VDFYftcunTJ1KpVy0gyU6ZMcbSfP3/etG3bNl/B21MV9PbZvXu38fLyMn5+fmbdunWO9j179pjy5csbb29vs3fvXqcxJf34sf8ya9u2rTl//ryjffLkydf8utx7771GkhkwYIBJT093tP/97383kkx0dHS2MR999JGRZOrWrWv+/PNPR3tMTIyRZOrUqeM0r5LG3dvH/qblWo7DkqQgt88zzzxjXn31VfPTTz+ZP/74I1+hjOMnb+7eRhxD1gpq+/z73/82Y8eONYcPH3Zqj42NNTVr1jSSzLPPPpttHMeQNXdvH3cePwTnQjBx4kQjyfTt2zfbtHnz5hlJplevXtc9/0ceeYTg7ILisH2+/vprI8k0bdo027TNmzcbSaZx48bXXcONrKC3z2OPPWYkmZEjR2abNmXKFCPJjBgxwqm9JB8/aWlpJiQkxEgyW7ZsyTa9SZMmRpLZtGlTnvM6fvy4KVWqlPH19XV682GMMampqaZixYrGy8vLnDx50mnazTffbCSZb7/9Nts8+/TpYySZmJiYa1sxD1Ectg9v+nNXkNvnaidOnMhXKOP4sVYcthHHUO4Kc/tc6csvvzSSzE033ZRtGsdQ7orD9nHn8cM9zoXAft9kVFRUtmk9e/aUv7+/li5dqtTU1KIuDSoe28eqhhYtWqh27drasWOHDh06VGg1FFcFvX2s5mdvW7BgwfWW63HWrl2rxMRERUREqHnz5tmmX8trtnjxYmVlZalDhw6qXLmy0zQ/Pz/17t1bmZmZ+vHHHx3tBw8e1K5duxQQEKCePXu6tHxP5O7tA2sFuX2uB8dP3ty9jWCtqLZP06ZNJUnHjx93aucYsubu7eNu3u4uwBNt27ZN0uUAdDVfX181btxYmzZtUmxsrJo0aVKotZw8eVLjxo3TiRMnFBISojZt2qhPnz7y9fUt1OUWZ8Vh+1jVYG8/cOCAtm/fnq8HwXiSgtw+CQkJOnLkiCTl+AO+Ro0aqlChgg4fPqykpCQFBwc7TS+Jx09+9k1J2r59e4HMa+bMmU7zso9p3LixfHx8XFq+J3L39rnSvHnzNHfuXGVmZqpWrVrq3bu3GjRokOdyPVlBbh9Xls/xkzt3b6MrcQxlV1Tb58CBA5KU7WGfHEPW3L19ruSO44fgXMCSkpKUmJgoSQoLC8uxT1hYmDZt2qTDhw8XenDevXu3JkyY4NRWs2ZNffPNN2rdunWhLrs4Ki7bxx7mrGqQpMOHDxfK8ourgt4+9te5bNmyCgwMzHV+p0+f1uHDh3XLLbc4TSuJx09B7pvXMy+ODWvu3j5Xevvtt52+/8c//qHHHntM06dPl7d3yXx74e79193LvxEUp9eIYyi7oto+06dPlyT17dvXLcu/Ubl7+1zJHccPl2oXsAsXLjj+X7p06Rz72N/Anz9/vtDq8PPz02OPPaYVK1bo5MmTSkpK0vr169WjRw8dOXJEd955Z4k86IvL9rHX4c4aiqOC3j55vc65za8kHz8FuW9ez7w4Nqy5e/tIl6/emDFjhmJjY3Xx4kUdOHBA7777rkJDQ/Xee+/pmWeeyd/KeCB377/uXv6NoDi8RhxDuSuK7TNjxgwtXbpUoaGh+uc//1nky7+RuXv7SO49fkrmn7Py0L9//2v+nLbPPvusWJ2Bqlq1qt577z2ntttuu00//PCD7r33Xn355Zf697//rQ8++MBNFV4/T9g+nswTto8nHz+Aq0aOHOn0fa1atfT444+rU6dOatGihd555x2NHj1aNWrUcFOFQPHGMeQ+q1ev1siRI2Wz2TRz5kxVq1bN3SXhCvnZPu48fgjOOTh48KD27NlzTWMuXrwoSSpTpoxT29X3TEpScnKyJCkoKMiFKq/fc889py+//FI//fSTW5bvKk/YPmXKlNG5c+ccdbmjhsJSnLaPfX65vc7XOj/pxj9+8pLXa1aQr39O8yrI5Xsid28fK40aNVKfPn0UExOjn3/+WUOHDs3XOE/i7v3X3cu/ERTn14hjqHC3z44dO9S3b19dunRJb731lvr371+ky/cE7t4+Vori+OFS7Rz89ttvMpc/qivfX5GRkZKk4OBghYSESJKOHj2a4/zt7eHh4UWyPlerW7euJOnEiRNuWb6rPGH71KxZ0+01FJbitH3sr/O5c+ccP8xdmZ904x8/eSnIffN65uXJx0ZBcPf2yYunHx95cff+6+7l3wiK+2vEMVQ42+fgwYO64447dO7cOY0fP15///vfi3T5nsLd2ycvhX38EJwLgf0R6lu2bMk2LT09XTt27JC/v7/q1atX1KVJuhwiJOX6sCRPVxy2j1UNV7YX9sPjiqOC3D6hoaGOH/Jbt27NNj0uLk6nT59WeHh4jme3c+Lpx09B7pvXMy/7mB07dig9Pd2l5Xsid2+fvHj68ZEXd/9s5/jJm7u3UV44hgp++5w4cULdunXTiRMnNHLkSI0bNy7P5XMM5czd2ycvhX38EJwLgf1z32JiYrJNW7hwoVJTU9W1a1f5+/sXdWmSpLlz50rK/VHynq44bB+rGrZu3aoDBw6ocePGJe6jqKSC3z5W87O39e7dO9/1efrx0759e4WEhGj//v367bffsk2/ltese/fuKlWqlFavXq34+HinaWlpaVqwYIG8vLzUo0cPR3utWrV08803KyUlxfEZ3Ne7fE/k7u1jJS0tzbHNPPX4yEtBbp/rwfGTN3dvIyscQwW/fc6dO6c777xT+/fv17BhwzR16lTL/hxD1ty9fawUyfFjUODOnDljgoODjSQzd+5cR/vJkydNnTp1jCSzfPnybOPq169v6tevb44ePWo5/0ceecRIMrNmzcq1z4cffmh27dqVrX3u3LkmKCjISDLz5s3L9zp5kuKwfS5dumRq1aplJJkpU6Y42i9cuGDatm2b53hPVtDbZ/fu3cbLy8v4+fmZ9evXO9pjY2NN+fLljbe3t9m7d6/TmJJ+/Dz//PNGkmnXrp25cOGCo33y5MlGkunUqZNT/7ffftvUr1/f/POf/8w2r3vvvddIMgMHDjTp6emO9ieffNJIMtHR0dnGfPTRR0aSqVu3rjl58qSjfe7cuUaSqVOnjtO8Shp3bp9du3aZzz77zKSmpjq1x8fHm379+hlJpmnTpiYrK8v1Fb1BFeT2udKJEyeMJBMeHm7Zj+Mnb+7cRhxDeSuo7ZOcnOx4TzVo0CCTkZGRr+VzDFlz5/Zx9/FDcC4kMTExplSpUsZms5nOnTubqKgoExoaaiSZ0aNH5zhGkpFkDh48mG1amzZtHF+VKlUykkzt2rUdbY899phT/06dOhlJpkmTJiYqKsoMGDDANGjQwLGMZ555pjBW+4bh7u1jjDFr1641AQEBRpJp06aNGTRokKlataqRZKKiokr0L82C3j5Tpkwxkoy3t7e56667TN++fR2v/VtvvZWtf0k/flJSUkybNm2MJFO1alUzaNAgx/cVK1Y0+/fvd+o/bty4XEPwqVOnTEREhJFkIiIizF//+lfTuHFjx5uSM2fOZBuTmZlp+vfvbySZsmXLmqioKBMZGWlsNpsJCAgwGzZsKKxVvyG4c/ssX77csV26detm7rnnHhMZGen4g1JYWJjZs2dPYa5+sVeQ2+ejjz5y/B5p0aKFkWR8fX2dfuds3rzZaQzHT97cuY04hvJWUNtn1KhRRpLx8vIy99xzj4mOjs7x62ocQ9bcuX3cffwQnAvRmjVrTPfu3U1oaKgpXbq0ufXWW83s2bNz7W/1xt8+Lbevq/+68/nnn5uoqChTp04dExwcbHx8fEy1atXMgAEDzJIlSwp4TW9M7tw+djt27DADBw405cuXN/7+/qZRo0ZmypQpJjMzs4DW8sZVkNvHGGO+//5706FDB1OmTBlTpkwZ06FDB7NgwYIc+3L8GHPx4kXz4osvmoiICOPr62uqVKlihg4dauLi4rL1tXpTaczlqwj+/ve/mxo1ahhfX19To0YN8+STT5pz587luvyMjAwzefJk06hRI+Pv72/Kly9voqKizB9//FFAa3hjc9f2OXbsmBk1apS57bbbTJUqVYyPj48pU6aMadGihRk3bpw5e/ZsAa/pjamgto99mtVXTlfgcPzkzV3biGMofwpi+0RHR+e5baScoxDHkDV3bR93Hz82Y4wRAAAAAADIEQ8HAwAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAUCzabzemrVKlSCgkJ0W233aZp06YpPT29QJYzfvx42Ww2zZ49O99jDh06JJvNpsjIyAKpoaDn5w6RkZGy2Ww6dOiQu0vJUUZGhurXr6/WrVu7uxSHrVu3ymaz6Y033nB3KQCAa+Tt7gIAALhSdHS0JCkzM1OHDh3SunXrtHHjRi1cuFCLFy+Wtze/uoqCzWZTeHh4sQ3Gefnggw8UGxurH374wd2lODRv3lx9+vTRa6+9pgcffFDlypVzd0kAgHzi3QcAoFi5+kzwxo0bFRkZqZ9//llfffWV7rvvviKvqXr16tq1a5dKly5d5Msurj777DNdvHhR1atXd3cp2aSlpWnChAlq0qSJevTo4e5ynDz77LP6/vvvNXHiRE2cONHd5QAA8olLtQEAxVqbNm00dOhQSdJPP/3klhp8fHzUoEED1axZ0y3LL45q1qypBg0ayMfHx92lZBMTE6P4+Hjdf//97i4lm9tuu0116tTRzJkzdenSJXeXAwDIJ4IzAKDYa9SokSQpPj7eqd0Yozlz5mjw4MGqV6+eAgMDFRQUpNatW+u9995TVlaW5Xw3btyoO++8U6GhoQoODla3bt20YcOGbP2u957kuLg4/e1vf1PFihVVunRptWzZUp9//nmu/YcOHSqbzaYVK1bkON1ms+mmm25yaps9e7ZsNpvGjx+v2NhYDR48WJUrV1apUqX03XffSZL27dun8ePHq23btqpSpYp8fX0VFham+++/X7GxsTnOT5IOHz7sdN/5letvdY/zzp07de+996pq1ary9fVV9erVdf/992vPnj3Z+q5YsUI2m01Dhw7V2bNn9dhjj6lq1ary8/NT48aNNXPmzFxfr9x8/PHHstlsGjx4cLZpV75e+/fv16BBg1ShQgUFBwfrrrvu0s6dOyVdvkf63//+t+rVqyd/f3/VqVNH7777bo7L27Fjh+677z7Vrl1b/v7+qlixopo1a6ZRo0bpxIkT2foPGTJEp0+f1rfffnvN6wYAcA+CMwCg2Dt//rwkqVKlSk7taWlpuueee7R06VJVqVJFvXv31m233aY//vhDTzzxhIYPH57rPNetW6eOHTvq6NGjuuuuu1S/fn0tXbpUnTp10v/+9z+Xaz548KBat26tzz//XMHBwerbt68CAwN1//33a/LkyS7P/2p79uxRq1at9Msvv6hz587q1q2b42zwxx9/rAkTJig5OVmtWrVSnz59FBwcrP/85z9q1aqVtm/f7phPnTp1HPeZBwYGKjo62vHVvXv3POv4+eefdeutt+rLL79U1apVNXDgQFWqVEn/+c9/dOutt2r16tU5jktISFDbtm31/fffq0OHDmrfvr12796tBx54QB9//HG+X4ekpCStXr1aderUsbyM3L59duzYoa5du+qmm27S4sWLFRkZqT///FNRUVF644031KhRI0VGRiouLk4jRozQRx995DSfzZs3q1WrVvriiy8UFBSkvn376rbbblN6erqmT5+e4x8L7H+AKE73XwMA8mAAACgGJJncfi117NjRSDKff/65U3t6err59ttvzaVLl5za4+Pjza233mokmZUrVzpNGzdunGNZzz//vMnKynJMe++994wkU7VqVXPx4kVH+8GDB40k06lTp3yvT/fu3Y0kM3z4cJOenu5o//77742Xl1eO84uOjjaSzPLly3OcpyQTHh7u1DZr1izH+owYMcJkZGRkG7d+/Xpz4MCBbO0zZ840kkznzp3ztawrderUyUgyBw8edLRduHDBVK5c2Ugy77zzjlP/KVOmGEkmLCzMpKSkONqXL1/uqH/w4MEmNTXVMe3bb781kkzNmjVzreNqixYtMpLMPffck+P0K1+vf/7zn47tn5WVZYYOHWokmYYNG5rGjRub+Ph4x7ilS5fm+Jrcf//9RpJ58803sy1r165d5vjx49naExMTTalSpa5pvQAA7sUZZwBAsZSVlaX9+/frscce06pVq9S3b1/99a9/derj7e2tfv36ZbvPtmLFinrttdckSfPnz89x/uHh4Y6PprJ77LHH1KZNG504cUJz58697toPHDigxYsXKzg4WFOmTHF6Enjv3r0VFRV13fPOTcWKFTVx4kR5eXllm3bbbbepVq1a2dqHDRum9u3ba8WKFUpMTHS5hv/+9786efKk2rZtqyeeeMJp2lNPPaWWLVvq6NGjOb62wcHBeuedd+Tn5+do69evnxo3bqwjR47k++ne9rPn9evXt+xXu3ZtTZgwwbH9bTabnnrqKUmXLzWfNm2aKlas6Oj/l7/8Rc2bN9fhw4edajl16pQkqWvXrtmW0aBBA1WtWjXHda1ataqOHDmic+fO5Wu9AADuxVO1AQDFypVB1u6hhx7SBx98kOM0Sfrtt9/0v//9T4cPH9bFixdljHFc3r13794cxwwcODDHj7YaMmSINm7cqNWrV1/3E7zXrFkjSerevbtCQkJyXMbXX399XfPOTdeuXS2f+n3hwgUtWLBAv/32m86ePev4XOwTJ07IGKP9+/erRYsWLtVgvwz73nvvzXH6fffdp82bN2v16tXZ+rRs2VLly5fPNqZevXrasWOHTpw4ke3+7pzY74MvW7asZb/IyMhsf3CpXbu2pMsPg8vpfvbatWtr69atTrW0bNlSixYt0hNPPKFXX31Vt99+e74+Mq1cuXI6duyYTp06lWetAAD3IzgDAIoV+/21qamp2rZtm3bv3q2PPvpI7dq1czxd2+7SpUsaOnSo5syZk+v87AH6auHh4Tm22wPR8ePHr734/88+Nq9lFCSrJ34vW7ZMgwcPdpwdzUlur9O1sK93butnbz927Fi2aWFhYTmOCQoKknT5fvb8sJ85t4/LTU73P5cpU0aSVKVKlRzP3NunX1nLM888ozVr1mjFihXq3LmzypQpo7Zt26pnz54aOnRojn84kS6fdZYu39sNACj+uFQbAFCszJ49W7Nnz9ZXX32lXbt26Y033pAkPfHEEzp8+LBT3ylTpmjOnDm65ZZbtGjRIp08eVKXLl2SMcbxUCZjTJGvQ2HI6wnh/v7+ObZfuHBBgwYN0unTp/XSSy9p586dSk5OVlZWlowxGjJkiKSieZ1yu2JAkkqVKpi3JPagmtcfAqyWdy21BAcHa9myZVq9erXGjh2rhg0batmyZRo1apTq16+f6xUP9oAfGhqa72UBANyH4AwAKNaeeeYZ3XHHHbp48aJefvllp2n2j/OZM2eOunfvrkqVKjkuvz1w4IDlfK8O4Ve3V6tW7bprtt/Xmtcyrubr6yvpcti9Wlxc3HXVsnr1ap05c0YDBw7Uyy+/rJtvvlmlS5d2hNi8XqdrYX/Ncls/+73BVk+7dpX9yetnz54ttGVczWaz6fbbb9fEiRO1ceNGHT9+XEOGDNHJkyf1/PPP5zjGfm/zlfdRAwCKL4IzAKDYe/311yVJ//nPf5xCmT185HSZ73//+1/Lec6bN0+ZmZnZ2r/66itJ0u23337d9drHLl68WElJSbku42r2wH31ZytL0pIlS66rFqvXaN++fdqyZUuO43x8fJSRkXFNy+rQoYMk5XrpvP0zrO39CkPTpk0lKcePgSoqlSpV0vjx4yVd/oznqyUlJen48eOqWbMm9zcDwA2C4AwAKPaaN2+ufv36KSMjw3HptnT5wVGSNGPGDKf+MTEx+uyzzyzneejQoWxnsD/88EOtX79elStX1sCBA6+73oiICN1xxx1KSkrS008/7RTQf/zxR33zzTc5juvUqZMk6f3339eZM2cc7b/99pteeuml66rF/hrNmzfP6R7nhIQEPfDAA46HhF2tWrVqOnny5DXdgzto0CBVrlxZa9as0Ycffug07a233tKmTZtUvXp1l17bvLRr105eXl769ddfC20ZV5oxY4YOHjyYrf3HH3+UJNWoUSPbtF9//VXGGMf2BgAUfwRnAMANwf7RUTNnztSff/4pSRo7dqy8vLz0z3/+U7feeqvuuecetWrVSnfffbfjo4Vy89BDD+n1119X48aNdc8996h169Z65JFH5OPjo9mzZ1s+oTo/3n//fVWuXFkff/yx6tevryFDhqhTp07q1auXHnnkkRzHdO7cWZ06ddK+ffvUsGFDDRgwQB07dlSbNm1yfVJ1Xm699VZ169ZNR44cUb169dS/f3/1799ftWrV0vHjx9W3b98cx/Xp00cZGRlq0aKF7rvvPj344IOaNGmS5bICAwP1xRdfKCAgQI888ohjm7Ro0UIjR45UmTJlNGfOnFzvxy4IQUFB6tChg/bt26ejR48W2nLsZsyYodq1a6tRo0aKiorS4MGD1axZMz311FPy9/fP8Q8eK1askCT17Nmz0OsDABQMgjMA4IbQtGlT9e/fX6mpqZoyZYokqWPHjlqzZo26dOmiAwcOaOHChfL19dXcuXOzfY7w1dq1a6eVK1eqSpUqWrhwoXbt2qW//OUvWrFihbp37+5yvbVr19bGjRt1zz33KCEhQd99952SkpI0a9YsjRkzJscxNptN8+fP16OPPiqbzaYff/xRZ8+e1fTp0/MMrVbmz5+v559/XhUrVtSiRYu0efNmDR48WBs2bMj14VSvvfaaRowYoYyMDH399df65JNP9MMPP+S5rL/85S/69ddfNWTIEB09elQxMTH6888/dd9992nTpk2Fepm23UMPPSQp90vGC9Irr7yi4cOHy2az6eeff9aCBQuUkpKiBx98UL/99pvat2+fbcycOXNUoUIF9e/fv9DrAwAUDJvxlMeNAgAA6PLHRYWHh6tSpUravn27u8txsn79erVr105jx47VxIkT3V0OACCfOOMMAAA8ip+fn1566SX9/vvvWrhwobvLcfL6668rNDRU//jHP9xdCgDgGnDGGQAAeJyMjAw1atRIwcHBRfagsLxs3bpVLVq00MSJEzV27Fh3lwMAuAYEZwAAAAAALHCpNgAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFrzdXQCkrKwsHT9+XEFBQbLZbO4uBwAAAAAKnTFG58+fV7Vq1VSqVPE+p0twLgaOHz+uGjVquLsMAAAAAChycXFxCgsLc3cZlgjOxUBQUJCkyztMcHCwm6sBAAAAgMKXlJSkGjVqOPJQcUZwLgbsl2cHBwcTnAEAAACUKDfC7arF+0JyAAAAAADcjOAMAAAAAICFYhucN2/erNdff10DBgxQWFiYbDZbvk7hz549W61bt1aZMmVUrlw59ejRQ+vWrbMcs3btWvXo0UPlypVTmTJl1Lp1a3322WeWY44ePaphw4apWrVq8vf3V7169TRu3DilpqZe03oCAAAAAIo3mzHGuLuInPTr10/z58/P1m5V7qhRozR9+nQFBATojjvuUGpqqn7++WcZYxQTE6N+/fplGzN37lz99a9/VVZWljp27KgKFSro559/VkJCgp5++mm9+eab2cbs27dPbdu21enTp9W4cWM1bNhQmzZt0oEDB9S+fXv9/PPP8vPzy/e6JiUlKSQkRImJidzjDAAAAKBEuJFyULENzhMnTlRycrJatWqlVq1a6aabblJaWlquwXnp0qXq1q2bypcvr/Xr16tu3bqSpPXr1ysyMlKlS5fWwYMHFRoa6hhz9uxZ1apVS0lJSZo7d64GDBggSTp58qRuv/127du3T8uXL1dkZKTTsm6//XatXbtWTz75pKZPny5JysjI0KBBg/Ttt99q3LhxGj9+fL7X9UbaYQAAAACgINxIOajYXqr9j3/8QxMmTFDv3r1VpUqVPPtPmTJFkvTCCy84QrMktW3bVo8++qgSEhL0ySefOI35+OOPlZSUpL59+zpCsyRVrlxZb7zxhiRp8uTJTmN++eUXrV27VpUqVXL0kSRvb2+9//778vHx0VtvvaWMjIxrX2kAAAAAQLHjER9HlZKSomXLlkmSoqKisk2PiorSW2+9pQULFujpp592tP/www+5junZs6f8/f21dOlSpaamyt/f32lM7969s12OXblyZXXo0EHLli3TmjVrsp2pBgAAgHtkZGRwYgMoRN7e3vL29oh4mSOPWLM9e/YoLS1NFStWVFhYWLbpLVq0kCRt377dqX3btm1O06/k6+urxo0ba9OmTYqNjVWTJk3yHGNvX7ZsmbZv305wBgAAcLOLFy/q9OnTSk5OdncpgMcLDAxUhQoVVLp0aXeXUuA8IjgfOXJEknIMzdLlDRgaGqpz587p/PnzCgoKUlJSkhITEy3HhYWFadOmTTp8+LAjOOe1LHv74cOHr3+FAAAA4LJLly4pLi5OPj4+qlq1qvz8/PL1KS0Aro0xRmlpaTp79qzi4uJUq1Yt+fr6urusAuURwfnChQuSZPmXjcDAQCUkJDiCs32M1bjAwEBJ0vnz5/O9rJzGXC0tLU1paWmO75OSknLtCwAAgOsTHx8vLy8vhYeHy8vLy93lAB4tICBAQUFBOnjwoOLj43M90XijKrYPB/Nkr732mkJCQhxfNWrUcHdJAAAAHsUYo4sXLyokJITQDBQRLy8vhYSE6OLFi5YfI3wj8ojgXKZMGUmX72HJjf2+lqCgIKcxVuOuHpOfZeU05mrPPvusEhMTHV9xcXG59gUAAMC1S09PV2ZmpgICAtxdClCiBAQEKDMzU+np6e4upUB5RHCuWbOmJOno0aM5Tk9OTlZCQoLKli3rCLTBwcEKCQmxHGdvDw8Pz/eychpzNT8/PwUHBzt9AQAAoOBkZWVJEmebgSJmP+bsx6Cn8IjgXL9+ffn5+enUqVM6duxYtulbtmyRJMcDvuyaNm3qNP1K6enp2rFjh/z9/VWvXr18jbFaFgAAAIoeDwMDipanHnMeEZwDAgLUpUsXSdI333yTbXpMTIyky5+9fKWePXs6Tb/SwoULlZqaqq5duzo+w/nKMQsWLHB6wJcknTx5UqtXr1bZsmXVvn17F9YIAAAAAFBceERwlqTRo0dLkl599VXt3bvX0b5+/Xp98MEHCg0N1QMPPOA05sEHH1RwcLDmz5+vefPmOdrj4+M1duxYSdLTTz/tNKZ169Zq37694uPj9Y9//MPRnpGRoccff1zp6el68skn5ePjU+DrCAAAAAAoejZTTB939sMPP+iVV15xfP/LL7/IGKM2bdo42l588UXHGWBJGjVqlKZPn67SpUurW7duunTpkpYsWSJjjGJiYtSvX79sy5k7d64GDRokY4wiIyNVvnx5LV26VAkJCRo9erQmT56cbczevXvVtm1bnTlzRrfccosaNmyoX3/9VQcOHFC7du20bNky+fn55Xtdk5KSFBISosTERO53BpAvU5fEursEPdWtXt6dAMBNUlNTdfDgQdWqVcvp6kEAhetajr0bKQcV289xPnXqlDZu3Jit/cq2U6dOOU2bNm2amjVrpnfeeUdLliyRr6+vunbtqhdffFHt2rXLcTkDBw7UqlWr9Oqrr2rDhg26dOmSGjZsqBEjRig6OjrHMXXr1tXWrVv10ksvafHixfr2229Vs2ZNvfjii3ruueeuKTQDAADAPYrDHyHzgz9Uoqj99NNP6t69ux5++GF98MEH7i6nWCi2wXno0KEaOnRokYxr3769Fi1adE1jatSooVmzZl3TGAAAAAAo7uwnK1u3bu3mSooPj7nHGQAAAADgOntwbtWqlZsrKT4IzgAAAEAJcejQIdlsNnXq1EkJCQl65plnVKtWLQUEBKhp06b64YcfHH2/+uor3X777QoKClKNGjX0z3/+U+np6TnOd+fOnXr44YdVq1Yt+fn5qVKlSrr//vt19OjRHPuvXLlSo0aNUsuWLVWpUiWVLl1aDRo00DPPPKOzZ89m679r1y498MADql27tvz8/BQcHKz69etr6NCh2rFjh6Pf559/LpvNpmeffTbH5Q4cOFA2m02bNm3K9XU5e/asRo8erZtuukne3t4aOXLkda1ncXit9+3bJ5vNpm7duik5OVkvv/yybr75ZpUuXVoRERF64403dOUjr9544w3ZbDb9+OOPki5/FK/NZpPNZtNf/vKXHOspKQjOAAAAQAnx22+/SZJCQ0PVvHlzzZ07V23atFGDBg20fft2DRgwQLt379bQoUP14IMPqmzZsurcubNOnjypiRMnasqUKdnm+cEHH6hZs2b65JNPVLVqVfXp00ehoaH6z3/+o9tuu00nT57MNua+++7TzJkz5evrq8jISHXt2lWnTp3Sm2++qcjISKfQuHjxYjVv3lyzZs1SxYoV1a9fP3Xo0EFZWVn69NNPnea/detWSVKLFi1yXP+tW7fKy8tLjRs3zvF1CQwMVJMmTfTll1+qdevW6tatm+Os67WuZ3F4rbdt2yZJKleunJo2baqPP/5Yt9xyi26//XYdOnRI//jHP5xuP61UqZLuvvtux/+jo6MdX/fff3+Or2lJUWzvcQYAAABQsOxB6vvvv9e4ceP04osvysvLS8YY9e3bVwsWLFCvXr1Uvnx57d27V1WrVpUkzZ8/X/369dO8efOcPpJ1/vz5euyxx1S3bl198803atKkiSQpMzNTTz75pN577z299tprmjZtmmNMRkaGpk+frh49ejg9dfnixYu68847tWbNGi1dulR33XWXMjMz9eCDD6p06dJav369mjdv7rQ+GzduVMOGDR3f24Pz1f0kKSEhQQcPHlTDhg2zPe3Z/rosWrRIDz30kN566y2nPteznsXhtbbX8N///lfPPfecXn75ZXl7X46As2fP1rBhwzRv3jwNHz5c0uXnRdWuXVvffPON7rzzTs2ePTvb61hSccYZAAAAKCHsZ0HvvvtujR8/Xl5eXpIkm82mu+66S5J09uxZzZ071xHkJDmmnThxwtF24cIFPfzwwypTpowWL17sCHKS5OXlpTfeeEOlSpXS0qVLnWrw9vbWgAEDsoXX0qVLO852xsXFSbr8MbDHjh1Thw4dcgzDbdq0UVBQkNP6BQcHKyIiIltfe6hu2rRprq9Lu3btNGPGDKfarnc9i8Nrba9h4MCB+te//uUIzZLUvXt3SVJ8fHyOY5o1a5btdSrJOOMMAAAAlBD2UDRu3Lhs05KSkiRdPusYFhbmNC0xMVGSVKFCBUfbhx9+qPj4eI0dO1a1atXKNr/AwECVL19eZ86cyXFZP/74o7Zt26YzZ87o0qVLTvWVK1dOkhQSEiKbzaaffvpJ77//vu6//34FBgbmuG6HDx/WuXPn1LFjR9lstmzTt2zZIinnQGhf7qRJk1SqlPO5xetdz+LwWtvPOL/00kvZxpw+fVqSVK1atRzrJjg744wzAAAAUAIkJibq0KFDioiIUKNGjbJNt4esPn36ZJv2+++/S5LTZdHfffedpP97oFROX6dOnVLZsmWd5vXWW2+pevXqGjJkiF5//XV99NFH+vTTT/Xpp586aqhX7/JnV1etWlUTJkxQenq6Hn/8cVWsWFG9e/fW7NmzlZqa6jRfq8u0r5x+9Rln++tSpUoVtW3bNtu461nP4vBaJyQk6MiRI6pVq5bTGWq77du3S1K2+73tteV0Zr4k44wzAAAAUALYA1FuHzH022+/yWazqWXLljlOk5xD6bZt2+Tn56fBgwdbLveWW25x/H/69OkaNWqU6tWrp+eee04dOnRQtWrV5O/vr8zMTFWpUkXnz593Co0vvPCChgwZoq+//lqLFi3Sjz/+qIULF+rll1/WunXrHJc55xWcf/nlF0nZA6H9dcntTPX1rGdxeK3t88nts5jtr9eVZ5YzMjL0xx9/KCwsTOXLl7dcVklDcAYAAABKAKtLcFNSUhQbG6uIiAine4bt7EHQHuYuXbqkpKQk1apVK98PkMrMzNS//vUvBQQEaNWqVapcubLT9GXLlun06dNq2bKl0724khQREaHnnntOzz33nA4cOKDBgwfr119/1SeffKIXXnhB0v+dqb36DKp92t69e1W5cmVVqVLFaVpOQdXuetbzynm667XOaT5Xyyk479q1S2lpaZxtzgGXagMAAAAlgFWQ2r59uzIzM3MNWVcHQV9fXwUEBCguLk4XLlzI1/L//PNPnTp1SrVq1coWmtPT0/Xiiy/mWt+VateurXvuuUeSlJWV5Wi3f47xlfcG29nDdU6B0Op1uZ71zGueRfFa51WDdDk4BwUFOT1IbdeuXZJy/uNDSUdwBgAAAEoAqzOreZ113bVrl2rWrOl4aJckdevWTRkZGXrkkUd08eJFpzHGGC1btkyrV692tFWoUEHe3t7at2+fYmNjHe3nz59XdHS0Nm7c6FTDTz/9pEWLFikzM9Np3nFxcXr//fdls9nUu3dvR3vFihUlSQsXLnS0paamatSoUVqwYIEk6ydq5xYwr3U985pnUbzWeS3nyJEjOnv2rJo0aeJ0ebr987OvXga4VBsAAADwePZ7V6tXr+4ImFeyuj/4jz/+UHp6erZpEydO1Jo1a/Tll19qyZIluvXWW1WuXDmdOXNG27Zt04kTJ5xCrJ+fn/72t79p1qxZatmypbp27SpJWrlyperWravIyEitWLHCsZyvv/5as2bNUrly5dSqVSuVL19ef/75p1avXq309HS99tprTjX1799fP/74o0aMGKEvvvhCZcuW1S+//KJy5crp3nvv1eeff57t0mn761KtWjVVqlQpx9fuWtezOLzWGRkZ2rlzZ541XP16tGnTRj4+Pnrvvfe0b98+VapUSf7+/poxY0aOr01JQnAGAAAAPNzu3buVlpaW60cMWd2Ta7/k9+ppDRo00NatW/Xvf/9b//vf//Tzzz/L19dXlSpVUuvWrdWzZ0917tzZacy7776r8uXL6+uvv9ZPP/2kOnXq6LnnntOTTz6pRo0aqVSpUo4nQA8ZMkR+fn5at26dfv31VyUlJalChQrq3bu3Ro4cqY4dOzrN+4EHHtDp06f14YcfauvWrQoPD9fjjz+usWPHOj4f+uozzvbXxery8Gtdz+LwWudVQ27BuU6dOvr666/16quvatWqVUpOTs7xAWYlkc0YY9xdREmXlJSkkJAQJSYmKjg42N3lALgBTF0Sm3enQvZUt3ruLgEAcpWamqqDBw+qVq1a8vf3d3c5QIlxLcfejZSDuMcZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAgMcyxri7BKBE8dRjjuAMAAAAj+Pl5SVJSk9Pd3MlQMliP+bsx6CnIDgDAADA4/j4+MjPz0+JiYkeewYMKG6MMUpMTJSfn598fHzcXU6B8nZ3AQAAAEBhqFChgo4dO6ajR48qJCREPj4+stls7i4L8DjGGKWnpysxMVEXLlxQ9erV3V1SgSM4AwAAwCMFBwdLkk6fPq1jx465uRrA8/n5+al69eqOY8+TEJwBAADgsYKDgxUcHKz09HRlZma6uxzAY3l5eXnc5dlXIjgDAADA4/n4+Hj0m3oAhYuHgwEAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFjg4WAAgOsydUmsu0vQU93qubsEAABQAnDGGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALDgccH5119/1aBBg1StWjX5+PgoNDRUHTp00KxZs2SMydY/MzNTU6dO1S233KKAgABVrFhRgwYN0q5duyyXs2DBAnXq1EnBwcEKDg5WZGSkfvjhh8JaLQAAAACAm3hUcJ47d67atm2rb775RlWrVtWAAQPUokULbdiwQcOHD9d9993n1D8rK0t33323Ro8eraNHj6pnz55q1KiRYmJidOutt+qXX37JcTnTpk1Tnz59tG7dOrVv315dunTRL7/8ol69eumdd94pilUFAAAAABQRm8npNOwNKCMjQ9WrV1d8fLy++OIL3XPPPY5pu3bt0u23366zZ89q2bJl6ty5syTp448/1kMPPaS6detq9erVqly5sqTLATwqKkp16tTRrl275O3t7ZjXnj171KhRI3l7e2v58uVq27atJCk2Nlbt2rVTYmKidu3apTp16uS79qSkJIWEhCgxMVHBwcEF8XIA8HBTl8S6u4Ri4alu9dxdAgAAuE43Ug7ymDPOu3fvVnx8vOrXr+8UmiXp5ptvdpxt/vXXXx3tU6ZMkSS98cYbjtAsSQMHDlSfPn20b98+zZ8/32le06dPV2Zmph599FFHaJakevXq6fnnn1dGRoamT59e4OsHAAAAAHAPjwnOfn5++epXvnx5SdLBgwe1a9cuBQQEqGfPntn6RUVFSbp8L/OV7Pcx26fnZwwAAAAA4MblMcG5du3aioiI0J49e/Tll186Tdu1a5c+//xzlS1bVv3795ckbdu2TZLUuHFj+fj4ZJtfixYtJEnbt293tCUkJOjIkSOSpObNm2cbU6NGDVWoUEGHDx9WUlJSwawYAAAAAMCtPCY4e3l56dNPP1VoaKjuvfdetWzZUoMHD1aXLl3UpEkThYWF6eeff1a5cuUkyRGAw8LCcpyfvf3w4cOONvuYsmXLKjAwMN/jAAAAAAA3Lu+8u9w42rdvr5UrV6p///7asmWLtmzZIkny9fVVt27dVLt2bUffCxcuSJJKly6d47zswfj8+fP5HpPbuKulpaUpLS3N8T1npwEAAACg+PKYM86SNGfOHLVu3Vo1atTQxo0bdeHCBcXGxmro0KGaPHmyunTp4hRY3eW1115TSEiI46tGjRruLgkAAAAAkAuPCc579+5VdHS0KlSooIULF6p169YKDAxU3bp19cEHH6hXr17asmWLZs6cKUkqU6aMJOnixYs5zi85OVmSFBQU5GjLa0xu46727LPPKjEx0fEVFxd3DWsKAAAAAChKHhOcv/rqK6Wnp6t79+6OgHulQYMGSZJWrVolSapZs6Yk6ejRoznOz94eHh7uaLOPOXfunCMg52fc1fz8/BQcHOz0BQAAAAAonjwmONsDa0hISI7T7e3nzp2TJDVt2lSStGPHDqWnp2frb78/ukmTJo620NBQR3jeunVrtjFxcXE6ffq0wsPDCcMAAAAA4CE8JjhXqVJFkrRp06Ycp//666+SpJtuukmSVKtWLd18881KSUlxfDbzlWJiYiRJvXv3dmq3f+azfXp+xgAAAAAAblweE5z79u0r6fKl2O+//77TtA0bNmjq1KmSpKioKEf76NGjJUljx45VfHy8o33evHn6/vvvVadOHcd87UaOHCkvLy/NmDFDGzZscLTv3btX//rXv+Tt7a2RI0cW7MoBAAAAANzGY4JzixYtNGbMGEnS448/rsaNG2vQoEG6/fbb1b59eyUnJ+vhhx9W165dHWOGDx+u/v37a+/evWrQoIHuvvtude7cWVFRUQoICNDnn38ub2/nT+yqX7++Jk2apLS0NHXo0EE9evRQv3791LRpU505c0ZTpkxRnTp1inTdAQAAAACFx2aMMe4uoiB9++23mjFjhjZv3qzExEQFBQWpWbNmeuihhzRkyJBs/TMzMzV9+nTNnDlT+/fvV2BgoDp37qyXX35ZDRs2zHU5CxYs0KRJkxz3Ojdv3lxjx45Vr169rrnmpKQkhYSEKDExkXujAeTL1CWx7i6hWHiqWz13lwAAAK7TjZSDPC4434hupB0GQPFAcL6M4AwAwI3rRspBHnOpNgAAAAAAhYHgDAAAAACABe+8uwAArsRl0gAAACULZ5wBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAAC97uLgAAAAAFY+qSWHeXoKe61XN3CQBQ4DjjDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACAhUINzqdPn1ZGRkZhLgIAAAAAgELlUnDetGmTJkyYoJ07dzq1f/vtt6pSpYoqV66s8uXLa/r06S4VCQAAAACAu7gUnN9++239+9//VuXKlR1tBw8e1ODBgxUfH68qVaooOTlZo0eP1ooVK1ytFQAAAACAIudScN6wYYOaN2+u8uXLO9pmzpyp9PR0vfnmmzp27Jg2btyoUqVKcdYZAAAAAHBDcik4nzx5UjVr1nRqW7JkiQIDAzVixAhJUsuWLdWhQwdt27bNlUUBAAAAAOAWLgXnzMxMp4d/XbhwQVu2bFH79u3l6+vraK9WrZr+/PNPVxYFAAAAAIBbuBSca9asqc2bNzu+/+GHH5SRkaGuXbs69UtKSlJISIgriwIAAAAAwC1cCs69e/fWkSNHNGDAAL399tsaM2aMSpUqpb59+zr127p1q8LDw10q9FqcOnVKY8aMUf369RUQEKBy5cqpRYsWeuaZZ3Lsv2DBAnXq1EnBwcEKDg5WZGSkfvjhB8tl/PHHH7r77rtVsWJFBQQE6JZbbtG0adOUlZVVGKsEAAAAAHATl4LzmDFjdNNNN+m7777TyJEjdezYMY0aNUp169Z19Nm4caOOHTumjh07ulxsfmzevFk333yzJk+eLB8fH/Xt21e33Xabzp49q6lTp2brP23aNPXp00fr1q1T+/bt1aVLF/3yyy/q1auX3nnnnRyXsX79erVq1UoxMTGqXbu2+vTpo9OnT+upp57S4MGDZYwp7NUEAAAAABQRb1cGV6hQQdu3b1dMTIxOnTqlli1bqkuXLk59/vzzT40cOVL33XefS4Xmx6lTp9S9e3elpKRo/vz56tOnj9P0X375xen7PXv2aMyYMfLz89Py5cvVtm1bSVJsbKzatWunp556St27d1edOnUcY9LT03XvvfcqJSVFU6ZM0VNPPSXp8v3dd9xxh7755hv16NFDQ4cOLdyVBQAAAAAUCZfOOEtSmTJlNHToUD3zzDPZQrMk9e3bV1OnTlWTJk1cXVSexo0bp9OnT2vSpEnZQrMktW7d2un76dOnKzMzU48++qgjNEtSvXr19PzzzysjIyPbx2h9++23OnjwoJo2beoIzdLl18F+hnry5MkFuVoAAAAAADdyOThfKS0tTSdOnNDZs2cLcrb5kpKSos8//1yBgYEaNmxYvsbY72OOiorKNs3etmDBgnyPadGihWrXrq0dO3bo0KFD11I+AAAAAKCYKpDg/OGHH6p58+YKDAxUWFiYxowZ45g2b948DRgwQPv27SuIReVq06ZNOn/+vJo3b66AgAAtWrRIo0eP1uOPP65p06bp+PHjTv0TEhJ05MgRSVLz5s2zza9GjRqqUKGCDh8+rKSkJEe7/fOoW7RokWMd9vbt27cXyHoBAAAAANzLpXucMzMzFRUVpe+//14+Pj66+eab9ccffzj1adq0qaKiotSyZUs9//zzLhVrZefOnZKkSpUqqV+/fpo/f77T9Oeee06ffPKJhgwZIkmO0Fy2bFkFBgbmOM+wsDCdPn1ahw8f1i233OI0LiwsLNcxknT48GEX1wgAAAAAUBy4dMb5nXfe0fz583XXXXfp8OHD+v3337P1iYiIUJ06dbRo0SJXFpWnc+fOSZK+//57LV68WO+++67i4+N16NAhjRkzRikpKYqOjtZvv/0m6fLDvCSpdOnSuc7THqjPnz/vaMtrXE5jrpaWlqakpCSnLwAAAABA8eRScJ49e7YqV66sr7/+WpUrV861X8OGDQv9DKz985MzMjI0YcIEPf7446pYsaLCw8M1adIk3X333UpPT9ekSZMKtY78eO211xQSEuL4qlGjhrtLAgAAAADkwqXgvGfPHrVp0ybXS53tAgMDderUKVcWlacyZco4/p/Tw8HsbStXrnTqf/HixVznmZycLEkKCgrKtpzcxuU05mrPPvusEhMTHV9xcXG59gUAAAAAuJdL9zj7+PgoNTU1z35HjhyxDJIFITw8XNLlS6grVqyYbfpNN90kSYqPj5ck1axZU9LlS7yTk5NzDP9Hjx51mrd93Llz53T06NEcP2IrpzFX8/Pzk5+fX35WCwAA4IYydUmsu0vQU93qubsEAB7GpTPOjRo10ubNmy3v542Pj9dvv/2mZs2aubKoPNmfjJ2SkqK0tLRs0+0fkWU/YxwaGuoIz1u3bs3WPy4uTqdPn1Z4eLiCg4Md7U2bNpUkbdmyJcc67O1F8bnVAAAAAIDC51Jw/tvf/qYzZ87o0Ucf1aVLl7JNz8zM1BNPPKGLFy8qOjralUXlqWbNmmratKmMMY7Lsa9kb7vyo6d69uwpSYqJicnW397Wu3dvp3arMVu3btWBAwfUuHFjxxluAAAAAMCNzaXg/PDDDysyMlJz5sxR/fr19eijj0q6/FnHI0eOVL169TR37lx169ZN9957b4EUbGXs2LGSpDFjxujEiROO9t9++02TJ0+WJEeNkjRy5Eh5eXlpxowZ2rBhg6N97969+te//iVvb2+NHDnSaRn9+/dXrVq1tG3bNk2dOtXRnpycrCeeeEKS9PTTTxf8ygEAAAAA3MJmjDGuzCA1NVVPP/20Pv74Y6WnpztN8/Ly0vDhwzV9+nT5+/u7VGh+DR06VJ9++qlCQ0PVrl07paSkaN26dUpLS9NDDz2kDz/80Kn/1KlTNXr0aHl7e6tbt27y9fXV//73P6WkpOitt97S3//+92zLWLdunbp27aqUlBS1adNG4eHhWr16tU6cOKGoqCj997//lc1my3fNSUlJCgkJUWJiotNl4QCKp+Jw/x4u4z5GwBk/ny7jZwNwY7iRcpDLwdnu1KlTWrFihQ4dOqSsrCyFhYWpc+fOqlatWkHMPt+MMfr444/1wQcfaNeuXbLZbGrSpIkeeeSRXC8XX7BggSZNmuS417l58+YaO3asevXqlety/vjjD40bN04rVqxQcnKyIiIi9MADD2jkyJEqVeraTuTfSDsMAN6YFie8OQac8fPpMn42ADeGGykHFVhwxvW7kXYYALwxLU54cww44+fTZfxsAG4MN1IOcuke56ysLCUlJWW7RPtK6enpSkpKUlZWliuLAgAAAADALVwKzlOnTlXZsmVzfIq13cqVK1W2bFm9/fbbriwKAAAAAAC3cCk4f/vtt6pRo4a6du2aa5+uXbsqLCxMc+fOdWVRAAAAAAC4hUvBee/evWrUqFGe/Ro3bqy9e/e6sigAAAAAANzCpeCcmJiokJCQPPuFhITo3LlzriwKAAAAAAC3cCk4V61aVdu3b8+z3/bt21WpUiVXFgUAAAAAgFu4FJy7dOmiXbt26euvv861z3//+1/t3LlTnTt3dmVRAAAAAAC4hUvB+ZlnnpGvr6/uv/9+jRgxQtu3b1dycrKSk5O1fft2jRgxQn/729/k6+urZ555pqBqBgAAAACgyHi7MrhBgwb67LPPFB0drffff1/vv/++03RjjPz9/TVr1iw1btzYpUIBAAAAAHAHl844S9Ldd9+t7du365FHHlGdOnXk5+cnPz8/1alTR4899pi2bdumv/71rwVRKwAAAAAARc6lM852derU0XvvvVcQswIAAAAAoFhx+YwzAAAAAACerEDOOEtSRkaGzpw5o7S0tFz71KxZs6AWBwAAAABAkXA5OC9dulSvvvqqNmzYoPT09Fz72Ww2ZWRkuLo4AAAAAACKlEvBeeHCherfv78yMzNVtmxZ1apVS0FBQQVVGwAAAAAAbudScH755ZeVlZWlqVOnasSIEfLy8iqougAAAAAAKBZcCs5//PGH2rZtq5EjRxZUPQAAAAAAFCsuPVW7TJkyPPALAAAAAODRXDrj3LVrV23atKmgagEAALhhTV0S6+4SAACFxKUzzhMnTlRSUpL+8Y9/8MRsAAAAAIBHcumM86xZs3TXXXfpzTff1Ny5cxUZGamwsDCVKpU9j9tsNr344ouuLA4AAAAAgCLnUnAeP368bDabjDE6cOCADhw4kGtfgjMAAAAA4Ebk8hlnAAAAAAA8mUvBOTo6uqDqAAAAAACgWHLp4WAAAAAAAHg6gjMAAAAAABZculRbkowx+uKLLzR//nzt3btX58+flzEmWz+bzab9+/e7ujgAAByKw+fmPtWtnrtLAAAAhcyl4Hzp0iX17NlTy5YtyzEsS3I8dRsAAAAAgBuRS5dqT548WT///LN69eqlvXv36m9/+5tsNpvS0tK0a9cujR8/XoGBgXrmmWeUlZVVUDUDAAAAAFBkXDrj/PXXX6tcuXL68ssvFRgYqFKlLudwHx8f1a9fXy+99JI6d+6szp07q379+ho+fHiBFA0AAAAAQFFx6Yzzvn371Lp1awUGBl6e2f8PzpmZmY4+HTp0UPv27fXee++5sigAAAAAANzCpeDs5eWlkJAQx/f2AH3q1CmnftWrV9eePXtcWRQAAAAAAG7hUnCuXr26jh496vi+Tp06kqQNGzY49du+fbvKlCnjyqIAAAAAAHALl4Lzbbfdph07digtLU2S1KNHD0nSqFGjtHjxYv3+++/6+9//rl27dqlNmzauVwsAAAAAQBFzKTgPHDhQ/v7++t///ifp8hnnUaNG6ciRI+rZs6eaNWumd999V6VLl9Ybb7xRIAUDAAAAAFCUXHqqds+ePXXixAmntsmTJ6tVq1b67rvvdO7cOdWrV09PPvmk6tat61KhAAAAAAC4g0vBOTeDBw/W4MGDC2PWAAAAAAAUKZcu1Z4wYYK+//77PPstWLBAEyZMcGVRAAAAAAC4hUvBefz48fruu+/y7Pf999/r5ZdfdmVRAAAAAAC4hUvBOb8yMzNVqlSRLAoAAAAAgAJVJGn2jz/+UNmyZYtiUQAAAAAAFKhrfjjY8OHDnb5fs2ZNtja7jIwM7dmzR5s2bVK/fv2uq0AAAAAAANzpmoPz7NmzHf+32Wzat2+f9u3bZzmmSZMmmjRp0jUXBwAAAACAu11zcF6+fLkkyRijLl26qHv37vrHP/6RY19fX19Vq1ZN4eHhrlUJAAAAAICbXHNw7tSpk+P/0dHR6tChg1MbAAAA4E5Tl8S6uwQ91a2eu0sAUICuOThfadasWQVVBwAAAAAAxZJLT9U+efKkVq1apZMnTzq179+/X4MHD1bjxo3Vo0cPbdiwwaUiAQAAAABwF5eC8+uvv67OnTsrMTHR0ZaUlKTbb79d33zzjXbu3KnFixfrL3/5i/bu3etysQAAAAAAFDWXgvOKFSvUsGFD1av3f/dwzJ49WydPntSQIUO0Z88eTZkyRSkpKZo8ebLLxQIAAAAAUNRcCs7Hjh1T7dq1ndp++OEHeXt7a9q0aapbt65GjRqlpk2bauXKlS4VCgAAAACAO7gUnM+fP6/SpUs7vs/MzNT69evVsmVLVahQwdHeoEEDHT161JVFAQAAAADgFi4F52rVqmn37t2O79esWaMLFy4oMjLSqV9GRoZ8fX1dWRQAAAAAAG7hUnBu27attm/frmnTpun333/XCy+8IJvNpt69ezv127Vrl6pXr+5SoQAAAAAAuINLwfnZZ5+Vn5+fnn76aTVr1kxr165VZGSk2rVr5+hz6NAh7dy5U23atHG5WAAAAAAAipq3K4MbNWqkNWvWaPr06Tp9+rRatmypZ555xqnPTz/9pKZNm6pfv36uLAoAACBXU5fEursEAIAHcyk4S1KLFi306aef5jr9kUce0SOPPOLqYgAAAAAAcAuXLtUGAAAAAMDTuXzG2e7IkSM6ceKE0tLScu3TsWPHglocAAAAAABFwuXgPHPmTL3yyis6cuRInn0zMzNdXRwAAAAAAEXKpeA8a9YsPfjgg5Kkxo0bq169egoKCiqQwgAAAAAAKA5cCs5TpkyRt7e3YmJi1KdPn4KqqcCcOXNGN998s06dOqWIiAjt27cv176zZ8/We++9p507d8rX11e33XabXnjhBaeP1rra2rVr9a9//UsbNmzQpUuX1LBhQ40YMUL3339/YawOAAAAAMANXHo42N69e9WxY8diGZol6emnn9bp06fz7Ddq1CgNGzZMO3bsUNeuXdW6dWstWbJEHTt21HfffZfjmLlz56pTp05avHixmjRpou7du2vv3r2Kjo7WmDFjCnhNAAAAAADu4lJwLleunCpUqFBQtRSon3/+WZ9++qkeeughy35Lly7V9OnTVb58eW3btk3fffedFi9erFWrVsnLy0vDhg1TQkKC05izZ89q+PDhyszMVExMjFasWKGYmBjt3r1bderU0eTJk7VixYrCWzkAAAAAQJFxKTj37dtXa9euVXp6ekHVUyBSUlL0yCOPqGHDhnme/Z0yZYok6YUXXlDdunUd7W3bttWjjz6qhIQEffLJJ05jPv74YyUlJalv374aMGCAo71y5cp64403JEmTJ08uqNUBAAAAALiRS8H53//+twIDAzVs2DCdO3euoGpy2csvv6wDBw5oxowZ8vHxybVfSkqKli1bJkmKiorKNt3etmDBAqf2H374IdcxPXv2lL+/v5YuXarU1NTrXgcAAAAAQPHg0sPBnn76aTVs2FBz5szRDz/8oJYtWyosLEylSmXP4zabLduZ28Kwfft2TZ48WcOGDVOHDh106NChXPvu2bNHaWlpqlixosLCwrJNb9GihWOeV9q2bZvT9Cv5+vqqcePG2rRpk2JjY9WkSRMX1gYAAAAA4G4uBefZs2c7/p+YmOg4e5uTogjOWVlZevDBBxUaGuq4ZNqK/bOncwrNkhQYGKjQ0FCdO3dO58+fV1BQkJKSkpSYmGg5LiwsTJs2bdLhw4cJzgAAAABwg3MpOC9fvryg6igQb7/9tn799VfNmjVL5cuXz7P/hQsXJEmlS5fOtU9gYKASEhIcwdk+xmpcYGCgJOn8+fM5Tk9LS1NaWprj+6SkpDxrBQAAAAC4h0vBuVOnTgVVh8uOHDmiF154QZ06ddLQoUPdXY6l1157TS+//LK7ywAAAAAA5INLDwcrTp544gldunRJM2bMyPeYMmXKSJIuXryYa5/k5GRJUlBQkNMYq3FXj7nas88+q8TERMdXXFxcvmsGAAAAABQtl844FycLFy5UaGioHn30Uad2+5Otjx07psjISEnSV199pSpVqqhmzZqSpKNHj+Y4z+TkZCUkJKhs2bKOEBwcHKyQkBAlJibq6NGjatiwYbZx9vmFh4fnOF8/Pz/5+fld+0oCAAAAAIrcNQXn2rVry2azaenSpapVq5Zq166d77E2m0379++/5gKvRUJCglauXJnjtNTUVMc0e5iuX7++/Pz8dOrUKR07dkzVq1d3GrNlyxZJyvaAr6ZNm2rVqlXasmVLtuCcnp6uHTt2yN/fX/Xq1SuQ9QIAAAAAuM81BWf7Rzulp6c7fV8cGGNybD906JBq1aqliIgI7du3z2laQECAunTpokWLFumbb77RqFGjnKbHxMRIknr37u3U3rNnT61atUoxMTG67777nKYtXLhQqamp6tWrl/z9/V1cKwAAAACAu13TPc5ZWVnKyspynEm1f5/fr+Jo9OjRkqRXX31Ve/fudbSvX79eH3zwgUJDQ/XAAw84jXnwwQcVHBys+fPna968eY72+Ph4jR07VtLlz7gGAAAAANz4PObhYNera9euGjlypM6cOaNmzZqpX79+6tGjhzp27KiMjAzNmjVLoaGhTmPKlSunmTNnqlSpUoqKilKXLl109913q379+tq3b59Gjx7tuJ8aAAAAAHBj85iHg7li2rRpatasmd555x0tWbJEvr6+6tq1q1588UW1a9cuxzEDBw7UqlWr9Oqrr2rDhg26dOmSGjZsqBEjRig6OrqI1wAAAAAAUFg8PjjfdNNNud7/fKWhQ4de8+c/t2/fXosWLbrOygAAAAAAN4ISf6k2AAAAAABWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFjwdncBAAAAgKeZuiTW3SXoqW713F0C4DE44wwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAVvdxcAANdi6pJYd5cAAACAEoYzzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABa83V0AAAA3sqlLYt1dgp7qVs/dJRSL1wEAgMLCGWcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAAL3u4uAAAAuGbqklh3lwAAgEfzmDPOFy9e1HfffacHHnhA9evXl7+/vwIDA9W0aVNNmDBBFy5cyHXs7Nmz1bp1a5UpU0blypVTjx49tG7dOsvlrV27Vj169FC5cuVUpkwZtW7dWp999llBrxYAAAAAwM08Jjh/+eWX6t+/v2bOnCkvLy/16dNHHTp00MGDBzVu3Di1atVK8fHx2caNGjVKw4YN044dO9S1a1e1bt1aS5YsUceOHfXdd9/luKy5c+eqU6dOWrx4sZo0aaLu3btr7969io6O1pgxYwp5TQEAAAAARcljgrOPj48efvhh7dy5Uzt37tR///tfLV68WHv27FHz5s21e/dujRo1ymnM0qVLNX36dJUvX17btm3Td999p8WLF2vVqlXy8vLSsGHDlJCQ4DTm7NmzGj58uDIzMxUTE6MVK1YoJiZGu3fvVp06dTR58mStWLGiyNYbAAAAAFC4PCY4R0dH64MPPtDNN9/s1F61alW9++67kqR58+bp0qVLjmlTpkyRJL3wwguqW7euo71t27Z69NFHlZCQoE8++cRpfh9//LGSkpLUt29fDRgwwNFeuXJlvfHGG5KkyZMnF+zKAQAAAADcxmOCs5WmTZtKktLS0nTmzBlJUkpKipYtWyZJioqKyjbG3rZgwQKn9h9++CHXMT179pS/v7+WLl2q1NTUglsBAAAAAIDblIjgfODAAUmXL+cuV66cJGnPnj1KS0tTxYoVFRYWlm1MixYtJEnbt293at+2bZvT9Cv5+vqqcePGSk1NVWwsTzgFAAAAAE9QIoLz9OnTJUndu3eXn5+fJOnIkSOSlGNolqTAwECFhobq3LlzOn/+vCQpKSlJiYmJluPs7YcPHy64FQAAAAAAuI3Hf47zjz/+qE8++UQ+Pj565ZVXHO32j6cqXbp0rmMDAwOVkJCg8+fPKygoyOkjrXIbFxgYKEmOsJ2TtLQ0paWlOb5PSkrK38oAAAAAAIqcR59x3r17t+677z4ZYzRp0iTHvc7u9tprrykkJMTxVaNGDXeXBAAAAADIhccG52PHjql79+46d+6cRo8erZEjRzpNL1OmjCTp4sWLuc4jOTlZkhQUFOQ0xmrc1WNy8uyzzyoxMdHxFRcXl481AgAAAAC4g0cG57Nnz+qOO+7Q4cOHNWzYML355pvZ+tSsWVOSdPTo0RznkZycrISEBJUtW9YRgoODgxUSEmI5zt4eHh6ea31+fn4KDg52+gIAAAAAFE8eF5wvXLigu+66Szt37tSAAQP00UcfyWazZetXv359+fn56dSpUzp27Fi26Vu2bJEkNWnSxKndfrm3ffqV0tPTtWPHDvn7+6tevXoFsToAAAAAADfzqIeDpaWlqW/fvvrll1905513as6cOfLy8sqxb0BAgLp06aJFixbpm2++0ahRo5ymx8TESJJ69+7t1N6zZ0+tWrVKMTExuu+++5ymLVy4UKmpqerVq5f8/f0LbsUAAACAazR1ifs/HvWpbpxMgmfwmDPOmZmZGjJkiJYtW6YOHTpo3rx58vX1tRwzevRoSdKrr76qvXv3OtrXr1+vDz74QKGhoXrggQecxjz44IMKDg7W/PnzNW/ePEd7fHy8xo4dK0l6+umnC2q1AAAAAABu5jFnnN955x19++23kqQKFSro8ccfz7Hfm2++qQoVKkiSunbtqpEjR2r69Olq1qyZunXrpkuXLmnJkiUyxmjWrFkKDQ11Gl+uXDnNnDlTgwYNUlRUlCIjI1W+fHktXbpUCQkJGj16tCIjIwtzVQEAAAAARchjgvO5c+cc/7cH6JyMHz/eEZwladq0aWrWrJneeecdLVmyRL6+vuratatefPFFtWvXLsd5DBw4UKtWrdKrr76qDRs26NKlS2rYsKFGjBih6OjoglspAAAAAIDb2Ywxxt1FlHRJSUkKCQlRYmIiT9gG8lAc7tcCAAD5wz3OsHIj5SCPuccZAAAAAIDCQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALHi7uwAAN46pS2LdXQIAALiBFIf3Dk91q+fuEuABOOMMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABa83V0AgPyZuiTW3SUAAADccIrDe6inutVzdwlwEWecXZCSkqKXXnpJ9erVk7+/v6pVq6bhw4fr2LFj7i4NAAAAAFBAOON8nVJTU9WlSxdt2LBBVatWVd++fXXo0CHNmjVLCxcu1IYNG1S7dm13lwkAAADAzTjrfePjjPN1evXVV7Vhwwa1bdtWsbGx+vrrr7Vx40ZNnjxZp06d0vDhw91dIgAAAACgABCcr8OlS5f0zjvvSJLeffddlSlTxjFt9OjRatKkiVauXKnNmze7q0QAAAAAQAEhOF+HtWvXKjExUREREWrevHm26VFRUZKkBQsWFHVpAAAAAIACRnC+Dtu2bZMktWjRIsfp9vbt27cXWU0AAAAAgMLBw8Guw5EjRyRJYWFhOU63tx8+fLjIakLhKg4PdAAAAADgHgTn63DhwgVJUunSpXOcHhgYKEk6f/58jtPT0tKUlpbm+D4xMVGSlJSUVJBlogClJl9wdwkAAADAdSuOWcNekzHGzZXkjeDsBq+99ppefvnlbO01atRwQzUAAAAAPN1z7i7Awvnz5xUSEuLuMiwRnK+D/SnaFy9ezHF6cnKyJCkoKCjH6c8++6xGjx7t+D4rK0tnz55V+fLlZbPZCrja65eUlKQaNWooLi5OwcHB7i4Hbsb+gCuxP+BK7A+wY1/AldgfcKWc9gdjjM6fP69q1aq5ubq8EZyvQ82aNSVJR48ezXG6vT08PDzH6X5+fvLz83NqCw0NLbgCC1hwcDA/7ODA/oArsT/gSuwPsGNfwJXYH3Clq/eH4n6m2Y6nal+Hpk2bSpK2bNmS43R7e5MmTYqsJgAAAABA4SA4X4f27dsrJCRE+/fv12+//ZZtekxMjCSpd+/eRVwZAAAAAKCgEZyvg6+vr0aMGCFJeuKJJxz3NEvSlClTtH37dnXq1EktW7Z0V4kFws/PT+PGjct2WTlKJvYHXIn9AVdif4Ad+wKuxP6AK93o+4PN3AjP/i6GUlNTFRkZqY0bN6pq1arq0KGDDh8+rI0bN6pixYrasGGDateu7e4yAQAAAAAuIji7ICUlRa+99pq+/PJLxcXFqVy5curevbteeeUVhYWFubs8AAAAAEABIDgDAAAAAGCBe5wBAAAAALBAcC5hLl68qO+++04PPPCA6tevL39/fwUGBqpp06aaMGGCLly4kOvY2bNnq3Xr1ipTpozKlSunHj16aN26dUVYPYrCmTNnVKlSJdlsNtWpU8eyL/uE5zp16pTGjBmj+vXrKyAgQOXKlVOLFi30zDPP5Nh/wYIF6tSpk+OzGSMjI/XDDz8UcdUoDL/++qsGDRqkatWqycfHR6GhoerQoYNmzZqlnC5ay8zM1NSpU3XLLbcoICBAFStW1KBBg7Rr1y43VI9rtXnzZr3++usaMGCAwsLCZLPZZLPZ8hx3Pb8P1q5dqx49eqhcuXIqU6aMWrdurc8++6ygVgUF4Fr2h6ysLK1evVpjx45Vy5YtFRQUJD8/P0VEROjRRx/VwYMHLZfF/lC8Xe/Phit17drVMe7o0aO59iu2+4JBifLRRx8ZSUaSufnmm83dd99t7rzzThMUFGQkmQYNGpiTJ09mGzdy5EgjyQQEBJi+ffuaO++803h7exsvLy/z7bffFv2KoNBER0cbm81mJJmIiIhc+7FPeK5NmzaZ8uXLG0mmUaNG5q9//au56667THh4uPHy8srWf+rUqUaS8fb2Nt27dzd9+/Y1AQEBRpJ5++233bAGKCgxMTHGy8vLSDItWrQwgwYNMp07dzbe3t5Gkrnnnnuc+mdmZpr+/fsbSSY0NNQMHDjQdOrUydhsNlO6dGmzceNGN60J8qtv376O9wlXflm5nt8H9n3LZrOZTp06mYEDB5rQ0FAjyTz99NOFsGa4HteyP+zdu9cxvUqVKqZPnz6mf//+pnr16kaSCQoKMqtXr85xLPtD8Xc9PxuuNGvWLCPJ8R4zLi4ux37FeV8gOJcws2fPNg8//LDZuXOnU/vx48dN8+bNjSQzZMgQp2lLliwxkkz58uVNbGyso33dunXG19fXhIaGmnPnzhVF+ShkS5cuNZLMww8/bBmc2Sc8V3x8vKlQoYIpXbq0mT9/frbpVwef3bt3Gy8vL+Pn52fWrVvnaN+zZ48pX7688fb2Nnv37i30ulHw0tPTTaVKlYwk88UXXzhN27lzpylXrpyRZJYtW+Zot/9xtm7duubPP/90tMfExBhJpk6dOiY9Pb3I1gHX7vXXXzcvvvii+f77782JEyeMn5+f5Zvj6/l9cObMGRMcHGwkmblz5zra//zzT1OnTh0jySxfvrygVw3X4Vr2h3379plu3bqZn3/+2WRlZTnaU1NTzdChQ40kU7NmTXPp0iWncewPN4Zr/dlwpfj4eFOuXDlzxx13mPDw8FyDc3HfFwjOcFi3bp2RZPz8/ExaWpqj/a677jKSzNSpU7ONefLJJ40k8+abbxZhpSgMFy9eNBEREaZhw4YmNjbWMjizT3iuxx57zEgy77777jX1HzlyZLZpU6ZMMZLMiBEjCrhKFIXff//dSDL169fPcbr9WJ84caKj7eabbzaScjzL2KdPHyPJxMTEFFbJKAR5vTm+nt8HEydONJJM3759s42ZN2+ekWR69erlaukoBNcSlq508eJFExISYiSZFStWOE1jf7gxXcu+cM899xh/f3+zb98+y+Bc3PcF7nGGQ9OmTSVJaWlpOnPmjKTLH7m1bNkySVJUVFS2Mfa2BQsWFFGVKCwvv/yyDhw4oBkzZsjHxyfXfuwTnislJUWff/65AgMDNWzYsHyNsd/HzL7gefz8/PLVr3z58pKkgwcPateuXQoICFDPnj2z9WN/8DzX+/vA6udGz5495e/vr6VLlyo1NbWgS4abBAQEqF69epKk48ePO01jf/Bsixcv1pdffqnnn39eERERln2L+75AcIbDgQMHJEk+Pj4qV66cJGnPnj1KS0tTxYoVc/xs6hYtWkiStm/fXnSFosBt375dkydP1rBhw9ShQwfLvuwTnmvTpk06f/68mjdvroCAAC1atEijR4/W448/rmnTpmV7s5OQkKAjR45Ikpo3b55tfjVq1FCFChV0+PBhJSUlFck6oODUrl1bERER2rNnj7788kunabt27dLnn3+usmXLqn///pKkbdu2SZIaN26c4x/f+Nngea7394F9X7FPv5Kvr68aN26s1NRUxcbGFkLVcIesrCwdPnxYklSlShWnaewPnis5OVmPPfaYGjRooLFjx+bZv7jvCwRnOEyfPl2S1L17d8eZBvub4px+IUpSYGCgQkNDde7cOZ0/f75oCkWBysrK0oMPPqjQ0FC98cYbefZnn/BcO3fulCRVqlRJ/fr1U48ePTR16lS9//77euqpp1SnTh3NmTPH0d++L5QtW1aBgYE5ztO+n9jfMOHG4eXlpU8//VShoaG699571bJlSw0ePFhdunRRkyZNFBYWpp9//tnxh9a8fjawL3ie6/l9kJSUpMTERMtx7CueZ86cOYqPj1fFihXVrl07Rzv7g2d76aWXdOjQIc2YMUO+vr6WfW+EfYHgDEnSjz/+qE8++UQ+Pj565ZVXHO32j6cqXbp0rmPtb5gJSTemt99+W7/++qsmTZrkuOTSCvuE5zp37pwk6fvvv9fixYv17rvvKj4+XocOHdKYMWOUkpKi6Oho/fbbb5LYF0qC9u3ba+XKlapdu7a2bNmir7/+WsuXL1epUqXUrVs31a5d29E3r/2BfcHzXM/PgCs/9pJ9pWSIi4vTqFGjJEkTJkxwug2E/cFzbdmyRdOnT1d0dLQ6deqUZ/8bYV8gOEO7d+/WfffdJ2OMJk2a5LjXGZ7vyJEjeuGFF9SpUycNHTrU3eXAzbKysiRJGRkZmjBhgh5//HFVrFhR4eHhmjRpku6++26lp6dr0qRJbq4URWXOnDlq3bq1atSooY0bN+rChQuKjY3V0KFDNXnyZHXp0kVpaWnuLhNAMZWcnKwBAwbo9OnT6tevnx599FF3l4QikJmZ6bia8c0333R3OQWG4FzCHTt2TN27d9e5c+c0evRojRw50ml6mTJlJEkXL17MdR7JycmSpKCgoMIrFIXiiSee0KVLlzRjxox8j2Gf8Fz2bSspx4eD2dtWrlzp1J99wTPt3btX0dHRqlChghYuXKjWrVsrMDBQdevW1QcffKBevXppy5YtmjlzpqS89wf2Bc9zPT8Drvw5w77i2dLT03X33Xdr06ZNuv3227M9K0Fif/BU06ZN09atW/XGG2+oQoUK+RpzI+wLBOcS7OzZs7rjjjt0+PBhDRs2LMe/CNWsWVOSdPTo0RznkZycrISEBJUtW5YfaDeghQsXqnTp0nr00UcVGRnp+Bo8eLCky39Ysbf9+eefktgnPFl4eLiky5dIVaxYMdv0m266SZIUHx8v6f/2hXPnzjl+mV3Nvp/Y540bx1dffaX09HR1797d6Q2N3aBBgyRJq1atkpT3zwb2Bc9zPb8PgoODFRISYjmOfeXGl5WVpejoaC1atEjNmjXTggULFBAQkK0f+4NnWrBggWw2mz799FOn95dXvp+8++67FRkZqcWLF0u6MfYFb7csFW534cIF3XXXXdq5c6cGDBigjz76SDabLVu/+vXry8/PT6dOndKxY8dUvXp1p+lbtmyRJDVp0qRI6kbBS0hIcJxBvFpqaqpjmv3R/+wTnsv+ZOyUlBSlpaVl+ziis2fPSvq/vwqHhoaqZs2aOnLkiLZu3arbb7/dqX9cXJxOnz6t8PBwBQcHF8EaoCDZ36DY38hczd5uvzfefpvPjh07lJ6enu3J2vxs8DzX+/ugadOmWrVqlbZs2aKGDRs6TUtPT9eOHTvk7+/v+Pgi3Hj+/ve/a86cOapXr55++uknhYaG5tqX/cEzGWMcf1jNyYYNGyTJ6VbB4r4vcMa5BEpLS1Pfvn31yy+/6M4779ScOXPk5eWVY9+AgAB16dJFkvTNN99kmx4TEyNJ6t27d+EVjEJjjMnx6+DBg5KkiIgIR5v9bCP7hOeqWbOmmjZtKmNMjn9Msbdd+dFT9s/rtW/3K7Ev3NjsHxmzadOmHKf/+uuvkv7vSoRatWrp5ptvVkpKiuOzOK/E/uB5rvf3gdXPjYULFyo1NVVdu3aVv79/QZeMIvDCCy/ovffeU82aNbVkyRJVqlTJsj/7g+dZsWJFru8x7WeL4+LiZIxxCs7Ffl8wKFEyMjJM//79jSTToUMHk5ycnOeYJUuWGEmmfPnyJjY21tG+bt064+fnZ0JDQ825c+cKsWoUtYMHDxpJJiIiIsfp7BOe64svvjCSzC233GKOHz/uaN+6daspV66ckWT++9//Otp3795tvLy8jJ+fn1m/fr2jPTY21pQvX954e3ubvXv3Fuk6oGBs3rzZSDKSzHvvvec0bf369SYwMNBIMkuWLHG0f/TRR0aSqVu3rjl58qSjfe7cuUaSqVOnjklPTy+ydYDr/Pz8jNXbxev5fXDmzBkTHBxsJJm5c+c62k+ePGnq1KljJJnly5cX9KqgAOS1P0yZMsVIMlWqVHHaH6ywP9yY8toXchMeHm4kmbi4uGzTivu+QHAuYaZNm+Z4I9S/f38THR2d49epU6ecxo0cOdJIMqVLlzZ9+/Y1d911l/H29jZeXl7m22+/dc/KoNDkFZyNYZ/wZNHR0UaSCQ0NNT169DCdO3d2/IJ86KGHsvW3v1Hy9vY2d911l+nbt68JCAgwksxbb73lhjVAQRkzZozjd0ajRo3M3Xffbdq3b29KlSplJJmHH37YqX9mZqbjj7Nly5Y1UVFRJjIy0thsNhMQEGA2bNjgpjVBfi1cuNC0adPG8WWz2Ywkp7aFCxc6jbme3wcxMTGmVKlSxmazmc6dO5uoqCgTGhpqJJnRo0cXwZoiP65lf9i6datjetu2bXN9j7l69epsy2F/KP6u52dDTqyCszHFe18gOJcw48aNc7wJsvo6ePBgtrGzZs0yLVu2NKVLlzahoaGme/fuZu3atUW/Eih0+QnOxrBPeKqsrCzz4YcfOrZtYGCgadu2rZk9e3auY77//nvToUMHU6ZMGVOmTBnToUMHs2DBgiKsGoVl3rx55o477nBcQVC2bFnTuXNn8+WXX+bYPyMjw0yePNk0atTI+Pv7m/Lly5uoqCjzxx9/FHHluB6zZs3K8z3CrFmzchx3rb8P1qxZY7p3725CQ0NN6dKlza233mr5cwZF71r2h+XLl+frPWZO+48x7A/F3fX+bLhaXsHZmOK7L9iMMSbf13UDAAAAAFDC8HAwAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAIBLhg4dKpvNphUrVri7FAAACgXBGQAAAAAACwRnAAAAAAAsEJwBAHCzuLg4Pfnkk6pXr54CAgJUrlw53XrrrXr55ZeVlJTk6Hfx4kW98soraty4sQICAhQSEqKOHTvqq6++ynG+NptNN910U47TZs+eLZvNpvHjxzu1R0ZGymaz6dChQ/ruu+902223KTAwUOXKldOQIUN09OjRbMv49NNPJUmdO3eWzWZzfB06dEiSZIzRF198odtvv12VK1eWv7+/atSooa5du+rdd9+9vhcNAIAi5O3uAgAAKMlWr16tPn36KCEhQTfddJN69+6tlJQU7d69W+PHj1ffvn3VrFkznT9/Xp07d9bmzZtVsWJF9erVS8nJyVq2bJlWr16t9evXa/r06QVW13vvvacpU6aoQ4cO6tGjhzZu3KivvvpKmzdv1rZt2xQQECBJio6O1po1a7R//37deeedqlKlimMeZcqUkSSNHTtWb775pvz8/NSxY0dVqFBBf/75p7Zv3659+/bpiSeeKLC6AQAoDARnAADc5OzZsxo4cKASEhI0adIkjR49WqVK/d/FYOvXr1e1atUkSc8995w2b96szp07a/78+QoKCpIk7d69W506ddJbb72lbt26qVevXgVS27vvvqvVq1erbdu2ki6f7e7WrZvWrVunOXPmaPjw4ZIun7keOnSo9u/fr3/+85+KjIx0mk9qaqrefvttBQUFadu2bapVq5ZjWkZGhtavX18g9QIAUJi4VBsAADf5+OOPderUKXXv3l1jxoxxCs2S1LZtW1WqVEnJycn65JNPVKpUKb333nuO0CxJDRo00AsvvCBJBXrG+amnnnKEZkkqXbq0Ro8eLUlatWpVvueTlJSktLQ0RUREOIVmSfL29laHDh0KpmAAAAoRwRkAADdZunSpJOmRRx6x7Ld582alpKSoRYsWatCgQbbpf/vb3yRJa9euVVZWVoHUdscdd2Rrq1evniTpxIkT+Z5PpUqVFBYWpt9++03//Oc/deDAgQKpDwCAokRwBgDATeLi4iRJERERlv2OHz8uSbk+6Cs0NFQhISFKSUnRuXPnCqS2sLCwbG32M91paWnXNK9PP/1UFStW1MSJExUREaGbbrpJ0dHRWrRoUYHUCgBAYSM4AwDgAWw22zX1z+vM9NWXjbuiS5cu2rdvn7744gv97W9/U1ZWlj777DP16NFDUVFRBbYcAAAKC8EZAAA3qVGjhiRp//79lv3sDwg7fPhwjtMTExOVkJCggIAAlS1b1tHu4+OjCxcu5DjGfra7qAQHB+uee+7RZ599piNHjmj9+vUKCwvT3Llz9eOPPxZpLQAAXCuCMwAAbtK1a1dJ0ocffmjZr2XLlgoICNDmzZu1d+/ebNM///xzSVL79u2dzhRXrVpVZ86c0ZkzZ7KNsd9fXRB8fX0lXX5Kdn7ddtttjnuzd+zYUWC1AABQGAjOAAC4yYMPPqgKFSpo0aJFmjZtmowxTtM3bNig+Ph4BQYGavjw4crKytITTzyh5ORkR5/Y2Fi9+uqrkqQnn3zSaXynTp0kyTHd7o033tCaNWsKbD3sZ8T37NmTbdqRI0c0e/ZsXbx40ak9NTVVy5cvl/R/Z94BACiubObq39IAAKDIrFixQn369NH58+dVq1YttWrVSikpKdq1a5f27dunrVu3qlmzZjp//rw6d+6szZs3q1KlSurUqZOSk5O1bNkypaam6sknn8z2cVR//PGHY37NmjVTRESEfv/9d8XFxWnYsGF67733NG7cOI0fP94xJjIyUitXrtTBgwezPYzs0KFDqlWrljp16qQVK1Y42jdv3qxWrVrJz89Pd955pypUqCBJmjhxouLi4tS8eXOVLl1at956q8LCwpScnKx169bp1KlTuvXWW7VmzRr5+fkV1ksMAIDLOOMMAIAbRUZGatu2bXr00UdljNF3332ntWvXKiQkRBMmTHA8cTsoKEgrV67Uyy+/rAoVKuj777/X6tWrdeutt+rLL7/M8TOcGzVqpGXLlikyMlKxsbFasmSJIiIitH79erVq1arA1qFly5b6/PPP1bBhQ/3vf//TJ598ok8++UTnz59XRESEJk+erMjISB05ckTz5s3TmjVrFB4erqlTp2rlypWEZgBAsccZZwAAAAAALHDGGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwIK3uwuAlJWVpePHjysoKEg2m83d5QAAAABAoTPG6Pz586pWrZpKlSre53QJzsXA8ePHVaNGDXeXAQAAAABFLi4uTmFhYe4uwxLBuRgICgqSdHmHCQ4OdnM1AAAAAFD4kpKSVKNGDUceKs4IzsWA/fLs4OBggjMAAACAEuVGuF21eF9IDgAAAACAmxGcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAA4DJjjC6kZbi7DAAoFARnAAAAuOzBTzep8bifdODUBXeXAgAFjuAMACgxElPS9f2240q5lOnuUgCP8/PueEnSnF+OuLkSACh4BGcAgEfbf+qCluw8KUl65D+b9OScrXpp/g43VwV4LmPcXQEAFDyCMwDAo/1l8ko99Nkmrd9/RhsOnJUkzd1y1M1VAZ6L3AzAExGcAQAlwu/HEhz/5409UHg44wzAExGcAQAlwpVv5nljDxQew5+mAHgggjMAoETgrTwAALheBGcAQIlw9Vnmlxf84Z5CgBtURmaW/rPhsPbFn7fsxxUdADwRwRkAUCJcffnorLWHlJSa7qZqgBvPV7/G6cXvdqjrlFXZpl1Iy3BDRQBQdAjOAIASy2S5uwLgxvH70cRcp01avNvxf8MpZwAeiOAMACgRcnovn8UbfCDf/Hxyf9v4x/GkIqwEAIoewRkAUGJlZBGcgfzy8879bWMpm83xf44qAJ6I4AwAKBG+3XosW1smwRnINz9vL6fvl+w8qf7vrdXB08kqdcU7Si7kAOCJvN1dAAAARWFf/IVsbRlZ3OQM5Fcpm/P3D322SZI05pttTmej+RxnAJ6I4AwAKLEyMnmDD+RXbkfLmQtpOnTm4v/147AC4IG4VBsAUGJxjzOQf1cG4o0Hzjj+f2VolrjHGYBnIjgDAEos7nEGrs9Hqw/kOo0zzgA8EcEZAFBipWdyjzOQX1feu8wfnQCUNARnAECJxZt/IP+uPFzSLZ8PwHEFwPMQnAEAJRZP1Qby78pLsC9l5H7scKk2AE9EcAYAlFiccAby78pLtdMt/uhEcAbgiQjOAIASK4vkDOTfFYfL1iMJbisjN8YYGVI7gEJCcAYAlFhXvsU+mZSq1PRMt9UCFHf5jaTGDfc4G2N094z1+uuHGwjPAAoFwRkAUGJl/f832PviL6jNv39W92mr3FwRcONz9fPRjTHacOCMEi5eyveY+PNp2nT4nH45eFaJKekuLR8AckJwBgCUWD/t+PPyv39c/vfQmYvuLAco1mz57Gf14LD8WLj9hAZ/uEF3TV+d7zFX1sYJZwCFgeAMACixPl1/WJK4tBMoQK5+Pvri//8HrROJqfkfdEVyzuJ4BlAICM4AgBKPZ4QBBcfP28ul8aVK5ffc9hVjbP835txFLtUGUPAIzgAAAMhTfs/kVijj59JyDp6+kK0t5ZL1g/uujNrPffu7S8sHXGGM4RMbPBTBGQBQ4nFlJ4pKZpbRo//Z7LZw58ptCfnNAq5cKp1w8ZJ2HEtyanvzpz26ZfxP+vXQ2XzN45eD+esHFIboWb/qzmmrXL5lAcUPwRkAUOJxTySKysaDZ7T4jz/15cYjRb7sPxNT1epfP+vNn/Zc1/jMfCZnV46nYwkpTt8bY/TO8n3KyDL6+tc4i2Ve9yKBAmOM0arYU9obf0F/HE/KewBuKARnAECJx3tuFJUTCf/3wKuivpzzneV7dfpCmt5Zvu+6xuf3bHV+A3ZO/Lyd35rGnvy/y7brVw7S2n2ntS/+fPbaOIpRDFz5UWyP/mez5vzy/9j77jC5ifP/j3b3in0u5967ccUFA8ZgegsttNBLKKEFEhJqQgKh5ke+EFooAQKht5AQDIRibFww2Lg33Hv3uZ3P1+929ftjd6SZ0cxopNWWs/V5Hj++lUYzr6SZ0dvf7CvIQmQOoeCcBmbNmoWLLroIXbt2RUFBAUpLS3HMMcfgtddeCzO0hggRIkSIECEcaEzY7pvZ9nRIV07Phqt2QZRlTSctL7P+XrK1Ape/8gNOftJZbz1ku0LkA+hSbNsqanHPR2G8/f6EWK4JaKr4z3/+g4svvhjxeByjRo3CMcccgx07duDbb7/FtGnTMGHCBLzzzju5JjNEiBAhDmhoKzFDrjtElkBbpHZX16Njy+KsjZ3uNNcViNOxOPNYvs22Ln+9ZLu0XbiEQwSFRMLE2l1V6Nu+BIbhLcN7ujXMQ+Q3QouzDzQ2NuLmm29GPB7HO++8gzlz5uCDDz7AN998g4ULF6Jt27Z49913MWnSpFyTGiJEiBAHNHT59zAWLUS2QAuVj3y2NIeUeIe+xdn/GLzQTSsaVIJ76KodIij8+fOlOOmJKXhm4krP19aHCcH2a4SCsw8sW7YMZWVlGDhwIC677DLm3ODBg3HFFVcASLpyhwgRIkSI7GL66l24418LsKeqXtviPHFZmXujECECQGPcnpNrd1ZlefT0hEvd9ZRO7PZ94xYzv7/6cRs1vmLMUG4OERBenbYWAPD0BB+Cc2hx3q8Rumr7QFGRXn3Cdu3aZZiSECFChAjB49J/zAAAxCIGHjnv4BxTEyIEC9qimm0radZctdMY6LtVu5jftCBCPy/TNBk32jC3TIh8QGhx3r8RWpx9oG/fvujXrx+WL1+Od999lzm3dOlSvP3222jTpg3OO++8HFEYIkSIECE2lVeHcY8hcoLFm/di694a4bnGJmwa1SU9yBhnGvR65scI13qIfEB5dUOuSQiRQYSCsw9Eo1G88cYbKC0txeWXX45DDz0Ul1xyCU488UQMHz4c3bt3x8SJE9G2bdtckxoiRIgQBywihuEru295dT1+9e5cfLdqZwaoCrG/Y+3OKpz17DQc+eg3jnMN8QQmLLUTXGVb2Evb4qwpEGfjvhrioeAcIv+wcXd1rkkIkUGErto+MXbsWEyZMgXnnXce5s6di7lz5wIACgsLccopp6Bv377Sa+vq6lBXV2f9rqgIk9KECBEiRNCIRvSzobZuVoC9NUlLwciHvgYAfLZwK9b95cyM0BZi/8USRaK5Fyatxpz1e6zfWRec03QN11VEZcoFne61IZFAM0SVY1bXN2Lb3lr07dAiI/SECMGjKXuUhHBHaHH2iffeew+jR49Gjx498MMPP6CyshIrVqzA1VdfjSeeeAInnngiIxzTePTRR9G6dWvrX48ePbJMfYgQIULs/4gaRmiFCpF1FMXkrNXLU1czv7Ndxzld6MoEmbotOo65gUvCJKLtjGe+xYlPTMG8DXucJ0OE0EBVXaOn9k1tTYfwhlBw9oGVK1fiqquuQvv27fHZZ59h9OjRKCkpwUEHHYSXXnoJZ511FubOnYt//vOfwuvvuece7N271/q3cePGLN9BiBAhQuihvjGB575ZiRXb97k3zjNEIvqu2h5LdYYIIUVRgc1aNXCJgqrq49kmJ1DorieddqZpYuveGlz04nT89v15Wv3S7tl1nOAsSg62blfSbfaTBVu0+g8RgsdV/5zp7YJQbt6vEQrOPvD++++joaEBp512Glq0cLr/XHTRRQCAqVOnCq8vKipCq1atmH8hQoQIkY+49B8z8NfxK3DqU+L9LJ8RNYyQh8kSahuatkCog8Wb9+LFKatdy80UxWz3YbfnsmzbPnwwa0Mg9OkgXWOY7vU67e4btxhHPvoNZq7bjY/nexdsK2rZJEy0xfnkwZ2Yc7UNYabjEP4we703b4Wwnvj+jVBw9oFNmzYBAFq3bi08T47v2RO6BoUIEaJpY45HpiGfEI0Y2iVqoqHJ2TfGzd+MQfd9iXd/yJ4AmAuc9ew0/OWLZXh7xnplu1jUnks1GgqF3/1nUdq06SJdll7f4uze5u0Z6c0XZ/Zie9AJS7fjzenrrN8HgmInRH4g9NTevxEKzj7QuXNnAMDs2bOF52fNmgUA6N27d7ZIChEiRIgQHCIRfYuzl0RiIVj85v35AIA//Dd7AmAusbJMHbZAM84JztBZEM3OPPt+1U5c9c+ZgWf4dROczx7RNfWXd+lBN2M3AR97ypP2p3E/Wn+HgnOIbCHMDbZ/IxScfeCcc84BkHTF/vvf/86cmzFjBp566ikAwAUXXJB12kKECBEiRBJRAzA1PTTdeJ35G8txycvTsWjT3rTpCtG0Qbtii2HPJl7QPGdkN/EVAZupLnvlB0xZsQO/4WKH0y5H5XJ9Y0pT4GecLxZv89SeH0NFm47lP0SIIBC6au/fCAVnHxg1ahTuvPNOAMDNN9+Mgw8+GBdddBGOPvpojB07FlVVVbjhhhtw8skn55jS/EA8YeKuDxfg/Zn7txtfiBAh8gtJi7Oma6mLRHDu899hxprdeOLr5UGQFqIJo7hALTjTU4mfVTKBkk90FRQ2cBbndJl6NwGfWNj9ZBa+5d253mhx/JaPWdPEk7KFyH+YpompK3Zg297aXJMSIoMI6zj7xOOPP46jjjoKL774IubMmYPly5ejZcuWOO6443D99dfj0ksvzTWJeYPPF23Fh3M24cM5m3DJ6J65JidEiBAHCLyUo9Jl9NfsqEqDohD7A4oL1DYHWgnDK2Q27hG7Tu+qqke30mbpE8ehLuCkWHEXBdMxA9rjyx+3ZcXmxgvxvFs8jR/W7s4wNSEOdExcWobr3hSHcIbYfxAKzmngvPPOw3nnnZdrMvIe5TV8Ao8QIUI0RcQTpjIWePHmvfhu1U784ug+iEXzw6FJl4HXjUsrKQo/mwc6ClzmNmNx5ubV7qp64TVf/7gNV4/tky5pDvCW7Pkbyq2/TdOE4TEpnts6IW7s2Yjz9GJxDhEi05i2ameuSQiRBeQHZxNiv0aYcidEiP0DjRKTzs7KOuytacBZz07Do18sw+vfr8suYQrILMmPnHswAKC0eUGynYLTp2vxtihyi2/NP1TXN2Li0u1hgqQsgbaE8sKcbJ51yYC1GQDquTrSa3baHhN+hFs3zwyiVws6ZlsEfoh8y2YcT5gorxYrSkLsf4iElRkOCISCc4gQIUKE0EJj3MmZVtU14rBHJmDEg+OtYzPW7MomWUrImOnWzZICM2F1VAIBXX6oMNb0Ppu/fX8+fvHGbNxPZRkO4R9u/DEtkPLCaaNEWs2GoMlDpghTwY1Mw9BrFwzYQfJNcL74pekY+dDXWL2jMtekhMgC8sTJKkSGEb7mEBlHqIQLEaLpYkR3u169iOnnkw8BovqquYPMfZNYB0jMZlzBdS/dWmH9bTRBH5rxS7YDAD6YvTHHlBwYoJUwvEAsixHORQkbH3KzhsU5uT6y4TbtsDjnmav27PV7AAAfz9ucY0pCZAORsKThAYFQcA4RIkSIEFLQMc0ipl+kGFMJodmEYUAa5Exui9ySSnChheVQERjCTXlCC5f8vJILzplfMyu3s/Wn/axTXTqzsQXwQ+Rr/dxwyzgwELpqHxgIBecQGUdTtNCECBHCiYa400QlYhbyRG6GacqZaWIdsOvOyonOFD+0q7IOr3+3FnskCaNCBIv3Z27A6c98m3a5GLf5QE8lfl7JXLWzIfSd+bdpzG+3DNkiuF1Cko2lowgY0KmFVjtnjHOebDwhDkiEBucDA6HgHCJEiBAhpKAZ5T2CRDciXiGfGFg3V23irqoSIjIlON/w1hw88OkS/Pq9eZkZAMDWvTUZ67up4fcfLcLSrRX48+dLPV/rZU7Tc46/Ki7xj87UmqHnLp8ozK12uQhudEYCiHEma/Pgbq3UtPAxzv6HzCh2VdXjnOe/w3szN+SalBAZRGhxPjAQ1tUIESJEiBBS0Izyjn11GNSZPS8qZ7Ng095Mk6UFw5Az8ITBJ8KErgzhtXyPCnNSMZCZLGOyfNs+90YHGKrqGj1f40XGpGVj3vKabVdtFTPvx1XbzUpNPMzSuR1ybdTjWssnhR2Nd35ICswLNpbj0tE9c0xNiExBtdb8lH4LkZ8ILc4hQoQIEUIKmk+uqXeWM8p3XkDGStO1eLeUu1llDcFfTQNuNYcPRMjcpVXwIpSxycHYc1LB2UeiLh2o5qsfi7PbJZbF2Yf9l2SsJ8/PLdkS/2yfmbjKla4QITKFqGKS5alOJ4QPhF/UEBlHvjPWIUKEkINmgBsE5ahkyztfagbLhANSjgoAtqYZ85rPCAVnJ2Tu0irQ08jtk8aWo9KNcc68qzYPPxZnNwVCOuWojhvQIXlt6rebxZkfYuqKHQq6QkYkRGahVFKFkvN+g7xy1a6vr8f333+PKVOmYP78+dixYwfKy8tRWlqKDh06YOTIkTjuuONw1FFHobCwMNfkhggRIsR+D1rGECUHk7ED1fVxFBdEM0NUADAB9GrXHOt3VaOuUS7kvzVjPePa29T479DS5oSoHrkbvDC+tHA5efkO9GzbHC2Lk4qaukZZjLNnkrSgSs6Zb8nByCX6Fmf9MfJ1GSQSJh76bAkO6VmKc0Z2yzU5IdKAajbma8b3EN6RF4LzsmXL8OKLL+Ltt9/Gnj17pJvhuHHjYBgGSktL8fOf/xw33HADBg8enGVqQ3gF/cEK4zxChGhaoBlgPrkQIGde80XDLiPDNE3EUox5vUSYAYD7Pl7M/G5qu5fIS0CF175bi91V9bjj1IEZoij38CMw0vPI7RNGd//4V8sxeXkZPrzpKMxat1tOUxYszm1LCrGbyuCeiTrOZDg/dzNh6XbUNyZ8xzirkOnETQs3lWPBxnJcfkQvT/V8xy/Zhte/X4fXv0feCc71jQlEI4bSBTmEjUbB95HAz54TIj+RU8F506ZNuPfee/H2228jkUigZ8+eOPPMMzF69GgMGjQIbdu2RatWrbB3717s2bMHS5cuxcyZMzFlyhQ888wzePbZZ3HllVfi4YcfRvfu3XN5KyE0MXFpGU4e0inXZIQI0eRQXd+Ic577Dkf2a4eHzjk4a+PSfLLI4izjB/JFcJbRYcJ2Y1YJzjyamuKv0aN09OCnSwAAZwzrgsFd1FmNmyr8xDh7mc9821nrkkngeCWM3/69gJ6uo3qWYsLSMuu3aG40xBP41btzMaZvO1wzto+ATrfxDK12MszfWG49CzeBzdMjy/CyPfu57wAAPduVWC7nOtixry5TJElhmiY2l9egW2kzx342c+1udGxZhC6lxRj954loV1KIb+48Pus0NkU0KCb9vroGNCvMXw+sEPrIqeA8YMAAAMD111+PK664AmPHjlW2P+mkk6y/p02bhrfeegtvvfUWPvzwQ1RWVmaU1hD+Qe/Ld/17Aeb96dTcERMiRBPF1BU7sbKsEivLKvHg2UOzJsDRDH2DQMCUMfyZSnbkFTJWpn1JkSU4y9xnRWhaYrNY2aGDm96egyl3nRAwNfkBP0IqfY3M/bmyrhGGon+V1SlTBimWVpZuEZ2fLtiCr37cjq9+3C4UnFXu0c9fNsoODfCpCGhWENV31fZg187kuqXX2O4qb4JwLgyRb81Yjz+N+xG/Pfkg/PbkAdbxVWX7cNFL0wEA/7v1aOytacDemobsE9hEobI4V9Q0oGPL4ixSEyJTyGnWkBtvvBFr1qzB3//+d1ehmcfRRx+Nl156CatXr8b111+fIQpDBI2wzl2IEP7QprmdzCqbzAwjOAvcfmUCcr5YnGlG/+TBnfDkRSPwzCUj0bNdc8Si7q7aTR1eXbUJ1u+qDpiS/IGfEl1uAk5dYxwH3/8VRj403pfgnI06zvwYIj6/0qVUl+ze/nDGIJwxrLOdHMwTlTaKCyLWnhIL0OKcSd6DjpkvjCatiroZy3OxT/5p3I8AgKcnrGSO/7ilwvo7HcVnbUM8b0uDBYWa+jjmbtjDvGeVJ0t5daiA2F+QU4vzU089lXYfXbp0CaSfEJkDrfEO5eYQIfyhIGbrObMp6NH8jyjGWWpxzhPGiVDRqjiGV646jDlXEPFhcW5ie5iXRFgyZvfVaWvRt0MJThjYMSiycgov75tg8vIy5flNe5IlzRriJuoaxP2rGGs/paF0wOQY4c6JBHkvGcNp3HBsv+T1aSQHIyDz0E3Y9TJEJtctHZ9OrOC64QC5sDi3KIoJFST0O2NLqunnptmxrw5j//INjhvYAf/4+WHuFzQRJBIm4wFx5as/YPb6PXj4nKG48sjeANR7bWi5338Q1qkIkWU0Ma4zRIg8AS3UiATYjI1L/S1iDGTMa964aivcPm2Ls5fSWU1rD/Piqi16l7PX7cbDny3BNa/NCpCqpoffvD9feZ4WlmWCeU5ctVMCz97qBocwKxRuXQSkVWXqsLg0PbWRMKlyVC4cqpchMmlxpt8ruW/dZFC5sMzKPSLEbUiMvg4+nrcZ9fEEvl6y3Td9uli7swqTlqkVWkGhmiuvOHt98pm8N3OjdUyVTyIUnPcf5JXgfO211+KGG27AqlXyIvYA8Oqrr+Laa6/NElUh0gb1vWpq1poQIfIFNB+WTYtzQmBNkZ3XOZ5p8Iwo+SnaemJ+Ypyb2B7mSXCm/h7UuSWA/bvGtV+I5kAtpXxZWSZ2BVcx1hlLDoZkwqcRD43H5OVsnWOvmX73UBm5ZYikmRwsnjDtGGdXi3N+xDhf/+Zs629Cu25SPuKpkE3I9oRlWylXberRXvTSdGzQDN0oLsieWHHCXyfjmtdnYeZaebZ6NyQSJlZu38d4fKwSrN/Tn5kqnG/0EVVYTJVLCESIpoO8Epxff/11vPrqqxgzZgwmTpwobTdt2jS88cYbWaQsRFBoYjxniBB5A5rJ9Ru36ges+576vM7xTIMe9rtVuyyhWORqWJCyQvtx3U0X2bJAeMkgTb+zolhesQd5BdHUpi3OVXViDwZ1jHPaZAlhGMBfxy8X0+Nx0D3V7oKzFePs0ndNvfgZJUzT2mdcs2q7UkMT5qWxN9CC2+vfrwOgp5TYUl5jtc8mZN+PV6attf5+7bu1zLnVO/US8BYV2Jmj1++q8kGdd8zfqG8R5/HX8ctxylNT8cTX9hr56kentXzj7hrX724uPEpCZB9592Xs27cvqqqqcPrpp+O5557LNTkhAgD9vQo3jxAh/IFN0pU9Qa+m3h5LxAznuhzVpj3VmLfBZpzoUTeX1+Dmd+YCkFmcvScHC8q18qmvV3hq/9w3K/GH/y7yPH66rtohnBDNbZpploVSqBjrzNVxluUAF8dVq+TLQg1lChnN7Xb+PXeTmCbTtOa4a/3gPJyv8zaUA9BTWM1YsyvD1PjHZwu3Mr91a2oXU4LzlBU7FC2DQzqfwxcmrwYAPD9ptXXs8a/Eiia3b5pqr93fk6UdSMg7wfmYY47B5MmT0a5dO/zmN7/BjTfeiMbG0MWhKYN1ZcmTwMcQIZoY6O9utiyk1fWN2Flpl1cRMQ4yhuCODxdmjC4aR//fJJz3wveWdYOnccPupIthbYPTwkVctb3EjAel/Nvm0QX6r+NX4N0fNuB/i7a6N6bgxTuBdctPoqm5pmcDoidKPzuZIkaVPCiTdZxl71CYHEzxvl0FWcAqR+VWKkpWuidh2mtMV1jTQobkFtn+p2NxrhHsSfkKnRhx0zQZd/6OLYsySZKFbClp3b5/ovX90xFdk+0yR1aILCPvBGcAOOKIIzBr1iyMHDkSr7zyCk4++WTs2pW/mrkQapgaDEWIECHUyIXFee76co4GZxsZf7hgYzniCROfLtjiWUjUBb2fbNydjBWU8VBVAtfQQhLj7IGB9cKkqawMujGQPH717jxP7f3OldBAIodoDuisT5VVOWOu2pDXnfZq5daKibZcte1DDfGEQ3EVk2T+SrpqOxP6NaMsmQS8cF5Klexztg0Gm8tr8PykVShPua3LHomOxVnmrp6PiGhIC3/5Yhnu/+RH63eR4J1lApnKSM/Dbf6L9nQyg0Nvy/0HeSk4A0D37t3x3Xff4YILLsDUqVMxevRoLF68ONdkhfABesOo85S9NkSIEAT0Oqquz44XDm9hchMYeLw1fR1+/d48nPTE5KBJA8DGXBKm2c3SRSPmI8ZZV9b4ZMEWHPrIBPwgccf0FHucBtcls+wJx3FJBBciCdEc0BGcc1GOKmIYTCk7Gl6Tg+nM/YigHNUx/zcJwx8cz3z/ZdbkRMK0xqHb/PzIXq70jOheCgA4Y1hnQdtgnu9FL07H418tx93/XqjsN67h6cErE7LlzuvHkK9jcX5p6hrmt84zCAJBhDkURO37O3lwJ2EbN12naH1HNGP+QzQd5K3gDADFxcX44IMP8OCDD2LdunUYO3YsPv7441yTFcIj6A9oi6Kclg4PEaLJgl5H2yvqFC394/2ZG/D4V8us37Eoyyy5CQw8vkll8RVZe4OASEDxwp/4yaqta3G+9b152F1Vj5veniM876W+cjqMoRdXbXqYpsrn6ZaoSYeRFcb6U1OooVHct0qJkblyVEBzieVPXMfZXvO8MK8z961yVOR/08S2ilrUNyawbqedmTkmcftOumo7Lc4i6yUZo7YhjtOenmrF1B7aq62w3yCwuTzp2TI+VW5JbnF231Pq4/zzTY+2TELHTZ+HX68arwhC6UTf34Sl4lJabvNftKeTpJRNdT8N4UReC84E9913H/79738jkUjgggsuwPjx43NNUggPoPe0y45wao1DhAjhDppZ361RFsYPfv/RIjw/aTUWbCwHILA4CxgUFUOQaRc6mlEhdHhhUMoqki7k/523GQDQulkBLh3dU3mNVwYoHcaawKtlkIYXV23G4txEGT1SombWOnWJGi/PdDbXl+jZ0MqNOkX8rgxe4zSTllmdaww0KxQLzm5j8gobQn/LYrkCnAi7ovVIj8cr5eg2lsWZ4lCFrtqpdhOWbseybXYJIVHXmfCgqKmPK2oiu4/H74/prHMv8BM57qcOthevmnQQhMVZh1S3cUR7ra1IaqIbaggHmoTgDADnnXcepk2bhu7du2PrVm/JUULkFvTHPSxxEiKEP9BylijRVZAoT5VK2rGPtWyLPv0q5ptOLJYJ0IwZocOLADKRs0xGDLkljB9HFwWSWE5PFuc0GFBPbuiSv5siiPJHBi/M9gUvTmd+i14H/Z1r8JHLw4sFvCGewOnPfIvr3pjt2jZi2LH8PETyPS0f8fNOp76yJSik2tI90GtH1keCquNMu2qL6gMTYYSnMyq430wogkQhMyRkREdo5PeSSkWt3321wZWvE5Xmc4Mvi3O2XLUDMGzrrD83RbDonRtp1jUPkX/IK7/ZSZMmoXNnZ2wKwYgRIzB79my8+OKLiMfDWNmmAnqzCfeOECH8gWayMi04k7FIKScRDfYxeT+0FSgToK22Jve/H0QjBppLrHME3gVnMcPpxRqTjuWm2oObvEkxoPt7TF46XqQi6xHNvMu8CVoWxbBPIhx5ecXzN5Zj+fZ9WL7dfX0ZhtzaJVLIfL96l/S8aQnO6vEAex3KvBhkspvMVVukgJJ5mYiUX5mYzRHDcOwHJK5XR9nFK29OfGIy5v/pVEe792ZuwD0fLcLD5wzFlUf29k9wCv4szt6vueejRTj3kG4+RvMGt72qtiGOFdv3YVi31lKlgc5256ZsE4Vi2HXN3fsP0TSQV+a/4447DgMHDlS2ad++Pe69917cf//9WaIqRLpgvh/h7hEihC/Q6yhbZUycjLOzTbZKgYhAWzQIHekIfIZhuOZh8Nq9zLLmpZt0LM41HhLJ0QLWsm37sLeatXI1dWG6W2kz6+905i15HbPX7cafxi3GvtoGrXJUpx0sNwx4sYB7mQ8GDKlFTvQMPl2wRUoTGVZpceaSg0ldtSVpmpNZtUkbg2ovF4Z5xYDIOpqJuRunaKWPAXrKLj55Vnm12Kp8z0eLAAD3jftReD5dVNQ24JKXpyvb+HHVztV3isf1b87G2c99hze+Xydto7MfiMahL1MlB8vldzJEsMgrwTnE/olwwwgRIn3QjF9NfYaTrkiWrJc6ztkAzagQMrzImL89+SDmd8QAWijiN+lxdEGSCfHwwobSFsy2JYWu7XdV1llxuV4szvyz++PHi5jfTWkrF1mWGDfkdG4mde0FL07Hm9PX44nxK7is2uK+VbKHl++km8sofd4w/Nca5l3OCY38s6WFWttVO/U/tZnQw5UUSRKWmaa1/9AWZ2H5KtNEZV0jbvtgAXNYlLE7E3NXFGdO9qS4hktDELG5fsA/npemrMaMNeqcAPm89t2e47crdwIAnhi/QtpG57uhGmZzeQ1+3FLBHBvatZW0FFyIpoucumpfe+21vq81DAOvvvpqgNSEyBSYTK25IyNEiCYN+sNem+GybjImXuiqncPS7DRzatqcujZG9ihlfkcMQ5iEiIYfRWBVXSNKOEu2FwMO/YxVVkuCYx+bhKr6ON64djRj9WnlqhRg7+2HtbtxWK82Nh2miUgTZgTpZ55O4jr+0tU7KjGiR2vrd71LcrALDu2OlWWVTBy27rSqrGt0tWbSgoQBuWDhNpf5+HgyD3mD7le3HWv9HeGyCMsszrKhTVMc4ywKeZi3sVxohRUlHssE7yGyONc3JvDk1ytwVL92rtdnq/4wj6QwZ49dWevulZLPya10n+O+ukZs2lON7m2a+xpHpWg645lvHcdeueow/G3iSk80hsh/5FRwfv3116Xn7BTuMs1tKDg3FewPmVpDhMg16HXkpTavH8jWqejbn0uPEtqyJ3PbVIF3P4wYBgpdEhj6ud99tU7B2YvrY6NIQaAAKf/1zdLtDL08DTz4nk0TeODTJdbvfOD94glTK1FRQzyB056eipE9SvGXnw0HwD7zeMJEeXU9ohEDLYsLlH0d1a8dE/vLzzHDYN2hZeuTvIqDOrbAT0d0xVX/nGmd02GsN5fXYOxfvnFtR79zwzDkmd1dkjfxuRREycGuHdsH/Tq0oMZL/m8lB6MV54wSXWYFt8eJMq7aznX50dzNwj6y5aqdMMX9/m3iSmk2e9M0Lf5WpNBYtq0Cgzq3Yo4VxiJS9/9swe/jW7+rCr3alQRLDAcvlvvvVu3ExYfLKyeo5olqnL01Tjf7Lq2bgfhg5MHWGSIg5FRwfu2114THTdPEtddei6OPPhq/+MUvskxViKCR0PhYhggRQg1WcM7sOpL1bpomquoa8fBnS3DGsC44dkCHnApTtAWACB5e6HEIzhF5FmxrHB/3K2LkvSTbYe/Tw3VUaR/APe6SVwrwTGSuw26++nEbbn1vHh6/cATOHtFV2Xby8jIs27YPy7btswRnGtX1cRzz2CQAwNpHz1BmGu7Ysoj5LVQgJdzXJ3mehgFHEjqdefXxPLGg6KRFThtDj0s/tQ1sR+T103OXnxMRK8aZjEErzt3nMR3jTK9PWfkqEUTx0Lrr1jRN3PLuXHQrbYY/njlE2Tbpqi0+t21vrfB4PGFa9yKSrU97+lvHfCzKsOCsE4/td+X/sHZ35gVnD4/GzXX64/nyNebHahwmB9v/kFPB+aqrrpKeu/baa9G/f39lmxBNA6HFOTdYv6sK7VsUuVqZQjQN0GunIQPSKsPUylw7E8Bzk1bh/Vkb8f6sjVj3lzPzJ8aZ/O+BHp6/jhiGq+Ds525FgrOX2Df6Pr0mkfK0/5rKnznfv298aw4A4Nb35rkKziLQ9K/dWcUcVzkA8HIw/xwMcIotF0HVgIHDerXBmcO74H8Lk+U1/ScnMh1CP+OqbTj7TsY9a2QjbhRbnOnxZHQTgZkmmVWii8HHZxN4KWXpJ5EVwaLNe/H5om0A4Co48+uLhszizDwPyTxJmGwt6nTuRwguZOGdHza4XuJ3n89GCdIgv0HvzJA/Cz8x6WFysP0PYXKwEBlHWI4q+1i+bR+Oe3wyTnpiSq5JCREQmORDGbA+yFwqeRo27q7mjgVOijboGGe7jrP+9bzAYcCd0fPDpAmv8RTj7LSs6yDJ2LvQQY9j8r/zy+LsBbSy4q3p6wCw1k9asHG7K971WuQ59dp36+z2EnMqbXE2DAPPXzYKt57YnzmnpMMlqy8BLWBHDEOaHd9tSKmrdsR5jIC3sNH3paOco5WCdBO3EAqWBlnJIfdn7KXuuSjGmUDmdUDft0wQk5UBCwr009Etded3n8+G4Oyp6oDLvqt6/6Jl7eZFaYSu2vsdQsE5RMbBMm65o+NAwvgfkxrzbRVid7EQTQ/0OpIx5un1Ty9OuSWEX8O5FKaYxyBwDXWDyOKciRhn0RXesmr7szg3cll/3Wjnnx1fHiffsqqrEKWkO5JAip4vjODscg98lmyHxdkAU1NZllWbvEZasDM412Y1Hc51L7qMt9rK+nZbK7xA8lnKOk7fP0+S01Wbapu68NuVOyzvAeeYYoWGN4uz+LjOVHVTTLVpbsfDJxKm9BnK9mhReImqDZBZoUtf6PRHhReFh18ElZ38prfmKJ+Hn70/YimS8nOfDOEdoQ9niIyjKVkp9hfI3MRCNF3olLtJr3/x3zRMmCivqZfSlW0kGKEw9YcHciIch20YQJ/26ng8PzoL0TPy4n1JM3NerCu8xdntUj/n06kxnUm4hcSKEsvJwAtBPBOs+yrJVRGBG67OOvr75NXOPk3TQYGz/rK4b7ch+Xf76rS1AIBNe+wSax24+G97XievNalHR8a78tWZkIH5dFEEFkbV2e5ppFM73W0606EcIkUiQX06FucM76n049FVwvolKXA3cwG8eOHw1BRGI1YW/C9/3ObIPUDDrY6zcDwuy3yIpo/Q4hwi42BctUKHlaxA9tEO0XRBr6NMWJzZJD4yGoDvVu1yHMsVWBt58pe35GD8bwNdS5vhn1cfJr3Gl6JAcInfGGcvwzdyMZhuDKabVUR0Pk/lZsbiTCBbQ27vlHe79XvPVowwdYyPgZy+ehf+8N9FqKxzLxEEuFucTVMVh6yGSiny8pWH4tyRXXHjsX2Z4+rkYC4DgnWLp5t7slxKlpbO2nX3PrDpU8U4yzKr01u37PnGHUH1SpI8g957dBVffknIhmLVC7vjqEHOadhEieXscbzfi8Gt7xBNH6HFOUTGwezL4d6RFYQW5/0PjKt2BhQjTIyz1FVbJDjlblGzbsipY542GWc5KgAY3ce9BqsXiCjKisU5zluc3QRjdX/5Vo5MBVGON5rShkZ9gY7fT52u2pov03S2J14PRKC69B8zAAAlhVHX5FSA+PnHmXVhOuZMUSyCusaEu8JAMddOHdoZpw511hQndyYqR6UzV2RKIrqOc7OCKFOfnIfU4qwxVfmcAPy7pffe8up6tG4uLmUm26P5d+PWJhOg90jdGGe/JGXCO4pHkDWSeS+kdMcJY5z3P4QW5xAZB5PVNYd0HEjIdJ3fENkH66qd2RhnGX8gFpwCJ0UbosRX6VicCY+sKhXlR1AM1FXb4/gi5YK8rfq8OMbZEzlZg6juL01rgwevDX698c+hQlDDVQQisOi4atNZv5V9Cp5/nBM++TbHD+wAAHjw0yXYXF4DGfyV32EFhYSGoEiDtTjb7elkb251vGVndZRqdBvR7ddT9D346RJrfRUXRJg47Jnrdgv7Z1y1Jc/XERrgSrU36Fi9efiN0c1UKMdfv1pu/e1lT3bbdtUxzs5jbiOHFuf9DzkVnKPRqPSfYRh44403pOdjsdBY3lTgJasrn8UzhD9oW0BCNBnQ6ygTWnxTY53mm8VZVGrJWzkqscVZ5Ubthw8UkeTNVdvmdL3UczVhehJc/JznBflcJsGhxxZZjhhLW9w5d2Rws8rNXr9Hiz7rNdIWZ4ux5toKhjykZ6nWOLur7DwEpsDiTOZ5fWMCv3h9lrQfXWskDXJr5dUNuPDF71FLzVctV22JxZkWlt1qoKdjcWaSdwkuoJUoy7fvo2pbG64CPcAqI2T6z0zmDZizfjcj/Gc6q7aXOeRFIfzcpFXW3+k8L/6NqZRFfsaxpkQoN+83yKngbJqm73+JDMT4hcgM2HIU8nZzN+zBoPu+xKNfLM0CVSFCNC0w8ZkZtjjLY5xFFsdcCs4UHSQZkSeLMyc4p76IPN/dt0MJju7fPjWm3gBtSwopOoOzOHtRLpoma11yI93tzkTXO0tW6dGWCdDPiU8ONm7+Zk75JLZsikAUVSN7lALwryxSWZz5dSScM6I+BaTQybcSprxkFAAs27YPMvgRFGgaZ63bg08XbPHUH92GTsTlyeIsOb2rql58ggKrQHTSJivZZmjQBWi6ameoHNXMtbvxs79PZ8fSVML6zU+j+61avHkvDr7/Kzw9YYXnMWTP8e0Z63Hu898xx9z23Qalxdm/B0aQCuaZa3djzP+biK9S1VNCZBc5FZwTiURa/0I0Dei6av/l82UAgJemrMkwRfs/spHJMkR2QWvCVR93XcQTJiYs2Y5dlXXJ/gVCqJMGwbEcCkqMG3KCHNO/nl8mZN3w6+ebO47Hb04+yFP/hRTT73aNG8PtW3CGN1dZP+dNbk7kMss2PTTvdfOb9+czwjLtteFGMmH+yTv1ywNb5ago8VLmyil6jiJPItE72Uu5jvNeB/z4KvixOPNrp7LWTnKmE2bw6BfLrL9LiuwMx1FDX3CWff8275G7pROwrtosvcJyYJTFWZVYyu7T/luaHCxDa+i7VTsdx7QTTWbY4nzfuMWoa0zg6QkrPY8he173frwY8zeWK691xrDLn8eOfXW46a05mLy8TJs2vq65G9bvqsIf/7sI63fJQzWu+udMbKuolZZ0C5FZhDHOITIO3W9ANur9HSigvwVh/cD9A2xysPQVh29OX4fr3pyNs56dljygsLTYNDhP5DY5GPW39b8+PTyDTZgoEd/ttR6nWzZheuyoi6KLFZw9uGqb3pIzucc4C2jTsJRmC6zXhJMOui61lzrOhPkn3yjfFmdLyLKP8Vmo+baufbqcT5hO935+uv17zibxtWm4ahPQbsFe+2teaIfk0a73bqFIvGDdrCApgOsIpCoPDV5wPrRXG9vibIgzuTv7tzvVFZyDWlEiwV53LvulQVcJ4CUExTFGGspSHipy//z5Unz54zZc/Zo8vIEfx2tysCtfnYl3fthgeY2IsuvXNYYhjblEXkkqkydP1m774IMPZo6QEIFC5gK6t7qB+RCFgnNwoL+P783cmDtCQgQGeh0FkVX7i8VJN6+te2sd/Ted5GBO65AnV21uyyHrhhZqrxzTCwDtcqdLm/23SJin9zs3Zo622tR6Ypr4GGd16xeouEERRIKP01U7PwTnj+dvUbSUlz0SgXynyDvze4cry5Ju0fT7JoLlJwtYeoUWZ0GfOiXE6hyCM9vTnR8uAOB8v0FYnGmByGt/Q7u2sv6mlUtuwhifUb1FcVIA15mbrIccb3Fmfx/dv711TxW1jVoWZ5p2mbU3UxbnmCDVfKazausKxKr6yW7wYhzgvS28+Obt2FenPbbtvZT8rbsvbthdbf3/2JfLcPD9X2Hi0u0eqAyRaeSVpHLOOedg7ty5ru3uu+8+PPTQQ1mgKEQQMAUM5PaKWox4aDzO/Nu31jk6I2VYTik90MzLH/67KIeUhAgK9DrykhFYFyJrXe92zTkaBK66+RLjbJJj/i3O9nH77yP6tmXaaltoXATW0mZ2GRtesOHh11UbcAp5qvf10bzN6r5EFmfu5vIlxtkNdK37DbuqcfZz0xzCK0GDw1Xb302u35VkindW2rG2/50rfua6cfFutyy0OEva8kJUPIB9hl5jXpUqh/Rsg2cvPQTjbhnLWJx3u8Qq84qBDi2KAOgJiWz5NvYc7+mTME28+8MG67dWcjCNXBLOhHuu3WpBJNjrKmH9xjjf/8mP+Avlfi/tP0v7RjpRbF6uJY/aq6s2jRcmrwYAPPTZEuZ46EOYW+SV4AwAZ5xxBlaulMc4/PGPf8Sf//xnDBgwIItUhUgHog/FN8uSMSIrtlda52jB+b2Z9scohHeE1vv9C6bJWg4DyarNdSESQju0TDKcJw7qCEAco5hLjwaRsO/lyciYGZrxJsxmxCMDxCYZcl5UVGCv0UJR0WEKtEC4vaIOsySlbkQ0BJm8yy3LMJDbGGdVbV8etBB01rPTsHDTXtz63jxJ2+Q9FcT8xzgv3Vph/b11rx1ru6daLARql/5yoUVkcZbJd/y785VFmOucrr8cT5jaSgfSzU9HdMWIHqVaQikBbZ2+7Iie1rU6ruKswottXy+Y63S8a1CCcxAeRSKI6NN9x+ks6xenrHZtk7NdQ/HKzhrehfmt41Fgd8tWaEhHwRxG2+UX8oq7HjduHCoqKnDqqadi82anFvb3v/89Hn30UQwaNAiTJk3KAYVO7NixA3feeScGDhyIZs2aoW3bthg1ahTuuuuuXJOWN9B1OaPdiNbtrMbybftyyoQ1ZdAfZzemPER+Y3N5DcY8OhHPfsOW31iypUJxlXfQFgUiIJP1RxgGEUNHCwQy9GlfEgSJDjAZ+1P/T1vpTIAjg05iHBK3SBggXauZm4s03c3Azi2VffH74E2aSWESpulgutJxpRZdy8+JXHog1NR7EJx9lMkhQqCfWzz9Gdu7inYXLS4Qu6jy5D382RJh2Ss3S2DCdAp8shhhXjHmqxyV4pwoK7UMvDeIWx4Apm3EwAuXj8JxAzrgzlMHWsK8VlZvhWDLKyzjponWlOeI1+Rg0iSMvMXZh1iZSJiO+NgYn2oeXly1M7uucxHisbe6AftqnTHEBI9dMJz5LfJQklFNmloKVz8EWmOEfHA+Ia846uOPPx7vvfceNm3ahFNPPRW7du2yzt1111147LHHMGTIEEyaNAmdO3fOIaVJzJkzB4MHD8YTTzyBgoICnHPOORgzZgx2796Np556Ktfk5Q1EdZxF30D60D+/W4ufPD0Vfxq3OLPE7aeg+aSTBnfMHSEh0sZfv1qO7RV1DiboDCrMIQjQfAthMAmfSMrC5DKEQsT0ihJf3f/Jj2n1yYPonby63LFdC2hn2qo75ZlbXRnCFPSdnuDsPMYrH3Kp7Kyq0xeceWFSBfL8i9JMDkZAv78Hzh4ibsP9fnXaWmE794RuprarNl+aiH6XuoITPzfpuXvXvxfilW/1qmY4BGcv1j4DOGNYF7xx7Wi0LSm0SpPpZPVW3bPDVTth4oxhtlVSVDtc1T/5s1OrInacANbQr96bi4Pv/wobUzGzABATJC/TXa+ZXtXpLCm/1z77jTqDtwEDw7q1tn57sThb8zeAclS8fje0QOcWeSU4A8k453/84x9YtmwZTj/9dFRWVuL222/HE088gYMPPhjffPMNOnXqlGsysWPHDpx22mmoqanBuHHjsHjxYrz//vv4/PPPsW7dOnz//fe5JjFvILIKiSDS5r3zQ+iy7Qcqd7MQTQteY1r9QpRoi7g2EqbVr+CcrrXirRnrMeRPXzpclEXu5V7AM40itohYnIOOcWaFfjc6eVdbPQZO5KqdHoMqsDjnUYxzTYPcesTDS2Z6YlW3Ypy9keUA/fb6tm8BAGhZFGPaiKzLIuhkSnesW5mrtsLiXK1pzefnJr/GHtWIdwWcArgnV22uLREYdVy1RYItgcPinLCTWp02tLPQBZ73+BIJ5necMpBpw9PpZ81+viiZ/PFdKuxNJPj9+X9LmDA5KTK8rnNhkVVZm4HkHHz7uiOs316VN4D3EJ8Q+Y+8E5wB4Oqrr8bjjz+O2bNnY+DAgXj66adx8MEHY+LEiejYMT+sZ/fffz927tyJxx9/HGeffbbj/OjRo3NAVX6C/rAv2rwXgLiOZFh6ODjQH+fQ3b1pI5B4Zg0ILc7EVTtlsvFrCUn3Du77OFnj87fvz2eOe6lRLILO2iggMc4RMo5e3zQ51785WyB06lvzeLlHl4GbsmKHgzlMZz8QWpy5+ZktRZ1ICKJjeTu2LHKcp6G7rkzTtKzT6Zajsvqk/ibvMlNryzRNpu8zh3WRKl547wH6GT/+1XItevieyTffK3ga6SnfpXWx8lrerZusXZ1nTM9nfl06E+HZrufRiCF8rrxQyiZLTUFhpU8XdB3tYkHm6gWb9romJwSy4C6cpX2DDlNw4zkNA2jdrADHHNQegDgruQwtUoowO8THI6EUQuNHfiEvBWcAuP322/GHP/wBW7duxYgRIzBp0iR06NAh12QBAGpqavD222+jpKQE11xzTa7JyXvQG8a8DeXJGnTUhkU+RqHgHBxoy0EoODdt6MThBoGEYM6QY4WWq7a/uRTUh5/fI9K1ZHdqpWbAAaAoFYNKmGLdMel7Xr+rGvs4V3s2eZi6Ly8WZ54+unYxT5dXiK6dsWaXa5tMQJggjbHiq+nQddWm908SspCuDEG/PaKU8rtPu7tq232fOKgjnrhohNRVm99qaAHuy1T5Ojfw8dNzNC3nzn7k/d58fD9PNFjJwTTmJl2xgH8lvEU+njAZ/kWk0OITdcYZZV/qf14gD7COMyHpX7M2Yv6Gct/9ZHpZZ4tN8cJmEqHXLi0lvlr0Tbj48B6pa6xWHkZmQUpGhsgPxNybZA4nnniia5uCgmTihQsvvJA5bhgGJk6cmBG63DB79mzs27cPRx99NJo1a4YvvvgCX3/9NWprazFgwABcdNFF6Nq1a05oy0fwH6vahgSzeTXEE4hGovC2pYVQgf7wZslgGSJDyJbiQxQvTBhny+Ls21U7PdoIeL4l4UFQEqFHW7bcFs1w33hsX2zYXY1RPUuT5zyOw7dq4Kw6ouctA2+BEoQqCvsVIUjLx5z1u/HI/5Yyx7I1X4Ux7x7o0K0vSz/7oCzO9Dwj1lG/CjI3RU7CtIW7xy4YjuKCqFRJrYpX1/VyCEoBrlIORVULAE5aSV86c9OLxTlumnjy6xUAgI17arQszkwfqf5JnWmLBtpdPOGMUXcD/f0vLohizvo9uPs/Cz314egz467a3gaIRgzrWXpZjvQrcpurZBopFS+mc/znLjsEJwzsyIyRJf13iCwgp4Lz5MmTtdotWLDAcUyWFTIbWLIkWVOtY8eOOPfcczFu3Djm/B/+8Ae8+uqruPTSS3NBXt7BLa6oPp5QfsxDeAf94Q2iFmeI3MFLXFU6YF21k/+TtUtiBEUuhD3aNsPG3TWO47K+0wHPmDL5EwJm7O45YzDzm3xzdIdxK2VDM4pughjPsKua67ju+gV/6Yw1Tquvn1I68zbswf8WbsVtpwxASZEeW/L2D+txVP/2zDG3TOY0dHMH0PHBRHA2EVyWYZsp99en6Io2zQuwJ+VpQK9Zu7SaeE/hPxW0dVTXQq8bf09w56kDUBSLYvn2ffj3nE3WcVU3bh6zvKt2zENWbfp9860difaozNULNpZjRPfWjv6KuKzpotwHzTkXavq5f7pQXFtcBfpdxaIG1u+q8twHj4xn1fbIphRGI6hJJNewf9LUc9XgLM0yBQY/L84c1sW6Nqr4doZomsip4Lx27dpcDu8be/YkXY8++eQTRKNRPP/887jwwgtRXV2N5557Dn/9619x1VVXYfDgwRg5cqTj+rq6OtTV1Vm/KyqCLSuTb3DbLwijFcrNwYFOqibORmxiT3UD2pYUZpOsED5QkIVyYuPmb8aI7qXWb8IIEAaOlOERMQ5tmhe6Cs5Bgd8jWIuzs/2DZw9FXWMc/+/zZThuQHqhPhHLcqDrqs3+rmuQW5z5Hj9ftBVfLt6G/3f+MLQoinkSnN2E8CAtziKlzk+fnYZFD/7EU7/nvZBMpmkCuO8scZZpHiT5EQ3Giu9yo7qCMylxZRh2yALt/pwu6EzHjhjahOmaqVn0uvt1aGElFxNZjf1YnHWtnl6/4/06tMDpw7rgAS4bvkoAdzOcyBKL6QnOcoWWW51rEV3K5GAgbt4GmhVErTrkdHbzH9bq1WynQdNtwAjEWpxuF6ZpKt+bat+qbYijKBZhrqcTnfmNv9a1OJP/RWvAhKjqgd1xgZUfJD0Dhs5eECI7yKng3KtXr1wO7xuJ1AJobGzEn//8Z9x8883Wuccffxzr16/Hhx9+iMcffxzvvPOO4/pHH30UDz74YNbozTVEWlp6o3HTZO6uqg8FvDQgYhZ++8F8jJu/Bf+68UiM7tM2B1SF0EU26nD/5v35+CNlYXUmB0vSILI66cQ9BxX3yjPTbFy2k7arjuqNRMLEYb3bYkiXVq791zXKhSk7xlmPVn5fq1X0zfd58ztzAQCH92mLK8f0cjBmqj1TpzyRX/Bbiai2Lh/L7QXLt+3z1L4hnmAUS8x8cLlP3SzRa3cmrXXd2zSzmHXT1K9H7IYoVVuXf88b91SjVzt1DXSR0CC7d1tIl1icHeWXfAjOHnn7iESYV8kIblmgpa7aGnOfDkfhm/PKGL4/kSKpqEAR45wair+KbqOV8Zq/XpDELF2k20U8YQrrSLthZ2UdDntkAo4d0AFvXmsn3Y366Avwpji0rcYpxbHE64JXitIge4YfTxwaCdNEJDQv5QXyNjlYPqNFixbW36LkYOTYlClThNffc8892Lt3r/Vv48aNmSE0T+D4+JgmVpdVUr+Tm6NsW3n8K73yFSHEEAnO4+Yn3b9enro62+QccPh+1U6c/sy32LrXn1U2W67a8zeWW39bycFS/5PM0iLmmRdYCwQMTWBefo7kYPbfMle4SMTAqJ5tUFzgzCjLY/FmufcPYb731TViX22DtJ2INkBgcab+ljG2NfVJIVTHsmX362ZxTkdwdhcUAKDKp/Ds1YrLJz6jr3brq0bT4kxccds2L7Td9c30niP9+mjLGU/z69+vc+1LdJsya3vUctUW9+UsLWb/Pu3gzq60AN7D6IjyxZEJ26Wf7m2aSc/JakDreIvQrtoOi7PACMCMo2FxvuwfP1gKInK1qoSXzr7Fg7/NYNys3ftQfavcFKwyEj9dkORVpq7YwY5FPTMvt0c/C92Z6mbprYvL95KYlVgzTYtz6OmdN8ip4Lxz58686kcXxFLevHlzYabv3r17AwDKysqE1xcVFaFVq1bMv/0Zoo9PFaXtn7+xHIc9MgHvSmo276qszyh9+yNoLbXqe8XHX4UIHpe98gOWbq3AkY9+4+v6TMnNvIBFu5LN3bAndYyzOHOC8+LNex3Mtsi1PKhSJmqLc2Y5C3ro575ZpWxrmqayJm6yjexv+we5hr/2iL5yLxE3JjIdHppnwGWM8phH/SXu1LEIMu0Vlng3wbZW0+Jcm1J4FMWidk1WmGnNN5o0ek7zzPU3y8Q8BNuXF4uz2lWbvyd6T+jVrjnfXAivFmdLmOfmklI5ZAK/OqG/9DyfOyzixVWb8YZjzzmTg7HnRSTzFmcAuPb1Wan+iau2fBw/FmdHlu4gXLU1+hApDgjobOWmaWLuhj2WUgqQr1cZ7X7dlulhdOeq6r5M01RanIniJN0Y57AkVf4gp4Jznz59cM899/gWfMvKynD33XejT58+AVOmxiGHHAIgWZaKjlUm2L07GZNCW6YPZPAL3jRZK9Wb09cpr89GjOf+hm6ltjZelRysOBYKzvmObCVCpJm1b1cm92SydomLHe+qtqOyzuGCJtKsp/PNp62XuyrZ/ZbuNl1XODfQr6Fsn3Pfp9GYMIWeNjRkycFo/orcE88ItyoukI6dTVdtGe/K147W7t8jc8krZNhM5eprt2iWeCHu+0UFEWsSJBJ6Qn6rYnE0HC3sFkQNtG+RDEVaRXliAaw1WgYRGSLjlmFQbtESW5sqhlf31XjdrSKWFZwXnOXXHNKzFBcf3gOPnj9MeF6WHExHeKGz37utYYfFWeSqLfjGbq+oZfqPGAZzv+lbnIN31dZ5/yqLM70/f7JgC85/4Xtc8vJ06xjdPZv0Ua0E4q91gx8lrpvXlypxHvl2pmtxDuXm/EFOJZILL7wQjz/+OLp3745zzz0XH3zwAbZtU9cK3Lp1K9577z389Kc/RY8ePfDkk0/ioosuyhLFSfTs2RMjRoyAaZpCd2xyjAjYBzr4/SJZ+9D+7bap+4mLOdBBP1OVQPGfuZuk50LkB7KVbV7EVBIGriBlwuEZ64hhOI6JXPLSUbavpIQJVexeputd04y9255VJ3BpdwiFEoszHedL4i09xTi7umorT6uvdZTFCnZyugmjLbiM207BhuorIA8E2uJM7taEqSXkT7j9OOFx+lLDMNCxZbKeeCXn4t6muXtuD9EjE9FGCxqy1+awOCtKM8ng9bnHJO7jIhpn33syxt92LHq1K4FhGLh0dE9hnw5XbaLw0LiHV6attf52JgcD99u5H/JQ5aiwkoNxx9fvqrb+1lGe8GBrRQcTj68jcKpo3V1lKxs/XbAVABsaI6uQIHtntDDrReFGfyZkCiQvMAxDHeOcev/b0qzFLNsb/ZaIDOEfOU0O9s9//hO33XYb7r33Xnz22Wf49NNPAQBdu3bFwIED0aZNG7Rs2RL79u3D7t27sXz5cmzdmlxwkUgEP/3pT/Hwww9j6NChWaf97rvvxuWXX44777wTX331Fbp06QIAmD9/Pp544gkAwE033ZR1uvIR/Ac3WVeSiiNyWfcqN5kQYtDfERETH6LpwM1NLCiLtIjhtZODiccwoCuw+ufcbvtgvj2eoxyV/XelTyunLmim2I0JEsaCOyzONmjm8Iy/fWv9TdxGea8RlYDpanFOg4vWSQ6WDtxoc0t6lImSOcTiXFwQYRLE6QiIMYnQxFuwZFnre2q4R+smB6Pnr2zPENUplp2ToXmhN7aS0MULnSIhtH2LIrRvUeTeJ58czHLV9kSae1Zt7ryuq7bdP7mQPb5+t10+ys/+znteBLEudLpQKdK+WVaG/h1bAgDaCZK9mpK/ZdOutHkBNu2pcR1XNY7o0cYiBhoTJqOkW7ipXN6faWJzuTx/CckPsrKsElv31qBLa3lsvgr0XOzboQRrdiTnyN6aBrTTWBMhgkNOBWcAGDZsGMaNG4dNmzbh1VdfxWeffYb58+dj8+bNjraxWAyHHXYYzjzzTFx77bXo3r17DihO4rLLLsP48ePxxhtvYMiQITjqqKNQU1OD77//HnV1dbj++utx4YUX5oy+fILTvYm1oLhpgXWTuISwQX/gdcuuhMhPqJLk+M1UKoLIM4FMI5kAYBh6LtLp8G0kq7EItKD1t29W4fZTB/ofyAU0b+ZWIkaUnZt3X9apQf33yatx8/H9HBbnippGvD9zA04d2tlRccBtP81GjLNfuFmcRWE/7PlAyQFgW5yLC6IWo50w9Vy1ZYqFm0/ox/wm64tXcuooPXkyTNN0uHwDepbLIFy1Cz3G5MpinL3Wgxb1af32YHGmwbd2c9UW0ayKUSbrKWKwtk/aPdvPEotz/BV/2ycP7ojvVu3yxFvpPDnVHKMVHs2LnO7nvJWZ+HfIXlnzAlt8ESWklMF1f0z9/9cLR1jHqurUyb+2VcgVqfS3c9rKnbjwsB56hPJ0UVtBx5ZFluC8pzoUnLONnAvOBN27d8f999+P+++/H1VVVViyZAnKysqwd+9etG7dGh07dsTQoUPRvLlegops4LXXXsPYsWPx0ksvYfLkyTAMA6NGjcKNN96Iq666Ktfk5Q34D27S4uzONNLt9xckEiYSpikVRIICzeCGgnPThop/jJtmYJu4yuJcIGGIDBhacYNBrWCn90pAHevAAwMrsjjf+NYcTLj9OPTvmMx9IbM483j3hw0OBv2TBVvwyYIt+HDOJvznl0cx59weidcEXDScMc4BC84ucqI76ZmzOBfFIpQQY7p6SgGAIdjmWxTFLNdsAiJwOARnhQuoTQmLKVz2YQJamKyQZIV3Jgfz7qrtFeRTyG8x6UwtXmFBShd5dSN3lJ/yUY5KpEggz1VicGbqOMvWWDxhYndVPTq0dApN63bZysZEwpnIzp8V2/3ZqSy/9J4oeiayxH7ypGFUGw+eBG4JBMmzom+lul7uzVQYjSg9ZYJSbMsSYXqZ0w3xBD5ftBURw8AZw7pkrWLH/oa8EZxplJSU4PDDD881Ga4wDAPXX389rr/++lyTktcQZdXmNaIqHNGnXUboygXOef47bK+oxbTfnehZM+8F9Ac9tNg3baiYnCAzSYtcri3BWWFx1qEhKOWXM0w4e5KzFyFRZil84JMf8fZ1RwDQT2SVMG1GuzAWYRjQOev3ONpnNjmYWnBIF3uq6pXhBw5392xbnEEsl3rJfkQWZ9GtFVgWZ3av1nlXfJvdVeIqFLSy9qO5To8+IOn2yfTt4TvtF2RdLd1aITzuB/yl5D14zWzM52twc90WW5zlyb2s5GARg5nzNJ0y2ebW9+fhfwu34uNbxmJkj1L72ngCl/3jB4pmYOpKVpliwPveqfP6VRZnWoFfRM1Fst4ZV20TzHkhPdTfXuam7r4rex88CmIR5TeQKZulR6KFQZ1bYlkq5wV9j/S89HLvtQ1x/Ob9+QCAU4d2QjQSJof1gzBdcYiMQ+RKxmjMXBb+/qQUW7R5L8r21TEJgDIBeh+vbUhkzFoQIvNQOScEVRIHcJZWqW9MWNlCVVpzIkCcPLgjAOBno5whNEEJ+M4Y10C61QLPFKvWlEyoopkcTgQEALxKJSYiKCmKulr+BV3JT0vonrzcvfQRz6S1LApW976toha//88i7fHdXLeDAG1xJlPANE2hOz4PkUVHdIysL97CrOXNoekFoLIukVO/fm+edPxMrbVYKvHgyu2se3laFmfeVdtDHWcavNLZLcZe9IhVCnKrHBV3nMlpIHkQ/1uYzPfznzlsgs/1u6uZ3wnTxOTlrOAcMQzP71NH0FYpO+okFueP52+26LTG0hBuZe3dwCQQVIQZ0e9SNRWLohGl8Mo8E4/PvHmhLdgy1RaY0l7u/ZimiR+37EUNVYIvaG+hAwmh4Bwi43DGTXn7IGfVHTODoBnWoC01PNxczGiEQnX28Jv352HNDmf8oQpuMc5BgU9A9daM9dbfMotznHIDfPCcg/HZr4/G7053xhkHJjhnIRmUDDxT/OCnS6RtdZkZAvJ4Hv7M2Wezgqi1XxZoeKm4WSBkr+Lq12a59s2X4SIMcJAC9AezNwqPm4JYTf5WMmEVZWOcbYuzTvyxaOmK1jMRHvk+9QQ9to1su1BZA0kPvKDu1yXUC/iay9bxdGKcJYnGvH5361wE5+9W7bL+vu+sIZ5jnEl3vIeFjsWZoLQ5W5quisvMLloTkQg8C3Fe9zQe9Nymvye3fbDA0b+OUEwf31ohT87luI66cdV8oF+Jai4WxtwEZ/HYOqCnG/1sGz1anD9ZsAVn/m0aLn/F9kQIk+76Ryg4h8goTNNEOef+dd/HixmNmRtzkK8xzrUNcYz/cZujhIgMsg9DJsD3r7Jc7C+KiaaAcfO34I4PF3i6RvV58+p6yPTLdcwn+ZpFJcCSWasaEwmLhqJYBAd3ay38IKdDJw03i48ffHTzUe6N4GRuX/9+HfP7xy17cd/Hi7Gzsk7K7MmMD6r7aN+iyNojVaVtRP2Kxk7nmW1yWLOS//dNxW0z5wLeWETd8Yy6aMTRvdumNS4RnpJZte1xRHHsPERrQbSUSHIj3oqtk7HekelcslZVFmfZlGBzkdh/B8lzy+hKZwz+WmLR15mTdDblWg+u80f0aSv0QlCVFLPKUXH0xhnBWf0g+PO8e7lI4ZFupm4ZVI+XXi+id864ZzN9ijudv7Hc+nvjbn3BmaZRNR+YdG2Kx1UQNZS5GdKpekFTRwv5XmtCvzk9qQSnSzuGcrN/hIJziIzitg/mO2KXZq7bjQUb91q/3ZjqfBWcH/x0CW54aw5u5dzbZGC1qJkWnNnfqmecKUtCCDFICQ1dqD686Qgn/BTk58iXP26z/pZlLaWZIWLREtGbDVdtL5lVaYzq2UarnRujcebfpuGtGevxu38vlFoWZHVDVe/RhGlbnHUEZ8neErPcVV27kEJWT1ok+wTtVSP6DvBHhPeeJoNISoLFIryrtobgLHgwQouzJKu2zrvSddV2y6otenayrNoFMjOxD1jKBY689GKcxRbnScvLXPciep7Vcq7zKnklGjGE2ZfbCEovEVgxzoaBS0fb2Za9WJz5OaYTzjJr7W4t62fLYluJoLOaVbwaPZ5wLUuSdonmZVq1i1P9bdpTjY/miWP9AX3BssDVVdsxtDZEXkmAt6o0gDMXhmGkJ9Af6AgF5xC+MHfDHvzfl8tcMzZ/PH+L8DitMdsnyfBJEE8AuyrrlG1ygfdmbgCQrE+oAya2JsOllR2u2nH5hyhfFRP7K4oVdT1FUH3fgrLkAuo6yDKBrU5gRRCRG0+YgSuLeNfdTOt/dBn5JVsrpAzSwk3l+H7VzuQPiYWFRyJhM8Mqt0+rvaQzQr/ueheNxXsPkZ5k7zxIiJlt9W/Au9zM753kdzRiMHWcdSzOIuZUNI9I7Drfp47FmReAZIKWW61b0etiPMOoh+tXSaWky6EACGwIK0/Eiu2VeHP6OmVbVnDmXLVNokQRv1eR91kbzpWaBhnKAHDXTwbhtKGdk+NQL0OkbKP3Ul5w5oVK0b5btk/uFUPj6P7tccLADtJ+eKiWvNteTR+iSy+J2gZRGeDo/5ukbEevXdVwJtSKT3rdeaWazRxuIpEw8fSEFVi/q5pq47FThPHN6SIUnEP4wvkvfI+/T16Nv09e7ev6empzd2Ow/jp+OQ59ZAL+zSXBaGrQKbGQibEAngFStw2RX1C9niCFE1UtSpngTCuNSJym7JscBK3dSpsBAJZsqcBhj0zA21QcdjxhSsvsBAFdRj5hmtI1VVHbiMte+QFlFbWMwOPGmJFn16zQPQuqzJJEmH3d5U7XkiV494cN7FiE8c+glwGBSIZ0umo7x/TKJPLKKPIuaQY4YZqe3SUJVMnBnIKae3/8+/zDfxcL29HC3hnDOgv68WBxDrAihMwSXl4d3FqmXeZJUi0Z+MSazDmF50c0AlQJyhY1L5TH/1vJwYxkrOwpQzoBYOegaPqqXLkbuPkrW4c6q7MhnrDWdroxzqzhQK0Ec+OVRMPoKmZ129HTUp0jRi3IpyOkLt5se2uaJvDF4m14esJKps2Wcm8ebEmafJMUAqHgHCJNrCrzluiIgN5n+gvi42iQTev+cWKGoCki6Pg/HvxGrkryErpqZxfp6inG33as9Xe23p1McB6X8igxDNtCKXNHDsI6ftaILgCAu/+zALuq6h0Wnqe/Xim6LBDoMkCm6c6Ubt1bq53zIGHartolCibcJkB82Mos7OImSaA3t1KMv+BMkN4QgJ6rtki49sq3yjJ3RwwwFme/tyeiR+aqzSfsE0G3HFWUcq++/Ihegn6c1zCCM/W3Tqy9LiISV20+EV1aY1CSgts6pu9TllVbZHGPGGJXbZ3kYISmmFVv2n7vIqUUvbb4V8HPmQ8lBged9T1haZn1WnRcu1Ut6D1GtN8wikSXPnWFaRFEtz2gUwsM6MTyofR3TG3hNpk+O3J1tYMSUhOmic3l1Y7jN78z13NfocU5PeSV4PzGG2+gtlZu9Qixf0LH5Q0InhHLNpgC9jmMcXYwhhl2Gw+RHngmY0CnllbsmY4rZxBwY5RbFMVs5lTyTQ5i/RJmRrZ8tius5mmPrclrmHC3aiQ4N3Olxdm0meHmRe4WZ9ljJsKZiOkUvRsdxtq2OAvoyIWrtuA6nrbubZopx3FanJP/RwyDSbDm11NHnFWbJAfTj6kl0CUj5iI8mjDRp30JAFiWz7jku6ETa68LVYhHUKDv3W0dq1y1yTlRiamIYaBnW+fcUnmJ8MIoeRaNitAqIBmfS49Lg08OJsJdP3FWPpDBjut3b6uOcabbia4V9yPaR0TXV7uEDYroIBjatbVD4UsLvCWKd5gwbRqPG9ABk+48nutHz+XbDQnTDEzgDQXn9JBXgvM111yDrl274te//jUWLFiQa3JCZAm6Lm9N3aWY/TBkeCyFVZl/jpkW4kOIsaqsEq98u8Y1TwCNTq2S2ux9qXjk334wPyslmQpi6g9tq2I7jk/qqq3jdyoALejIstDS54d0aQUA+MXRfXyNJ4PMks5jx746JgZNBBPuyXKstpTFubmAgdNxVwZoi7PzHJ9RHQCevfQQAMk4x34dSsS0pf4XPZvALc7UZ4KU4OHvVfQcadq6lTbDx7eMVY7Dz1Oyl0YMSnEDmwnu2ba5p3JcQldtUo7KYeEMTjEWZQRn53n60Q3s1BKAvGykW6z9hzcdqU2XruI8HXiyOFP3KStHVRRzrsNoxMDTFx+CM4d3wakpxQMgDnngx7Iszql5QD930Sp6YZIdIueMcXZfd6pM307Yc94NKmVZQsGDXPfGbOl5UTJN0Tr/7fvzNSgUKyKSybL4g/af/7z6cGV/hIfq26EEJdxeEJSQmjCDS+gVumqnh7wSnK+77jo0Njbi+eefx6hRozBmzBi8+uqrqKqqyjVpITKIem3BOcOEZBi5jHGmlRP8cwxdtXODk5+cgkf+txTvcHGjPOi3QyxCBIs3V+D3/1mUAepYuGXkpUu4yFoGYh3nGE0eiYTt7ji2f7v0x6MgegQN8QQmLSvD7f+azxy//V9qxa9pQt/iDHs9NytwCmj88pX1Zcc4Oxs0CN7NyUM6Yfkjp+Ht647AYxcMF/Zp7TMii3PAexzdnzReWzBkq2b2M/vZod1dhb4VZfuE4xqGYc8B036OnVsVI+ohWZZo6trlqPjkYO7PUPc502tYVg6IfCdIAkM2xMem7Y9nDlaOdXjvtvh/5w3ToouvQ5wJ0DHOqoTgvOBXy3sApJ61iGbDAHq2a47nLxuFwSnlHcBmpubBe2yQd8S8d5fXy79JnX3Wi+DkxeLsto8R8M95wtLt2EeH3VCnRaEHpuAWJyzd7k4gxOtle0WtNBM7ABzSsw3atxArG+j9WVR+ji1B6H9PTFqcfV/OILQ4p4e8EpxffvllbN26FS+//DIOP/xwzJw5EzfccAO6du2Km266CbNnz841iSE4pLMREDQ06vXR1AU8JlNkBu/FNE2lcJyJWrgh9ME/brc8AXR7kTXhg9kb/dHhcp6O43P70KqYQ/Kx97t+TQEPKdO80y7QuhZiXYiewUdzN+Ga12fho7nisiY92zYXHv/1u3MxfonN6KktzrZVTvSc+ecq68mrxRmwLWv9OrSg2tobmUJuDtziTHvGyDKEi55jr3a2ssmAU2jkf/Nx8+Q2og5XbbtT0f1ffVRv4X2ImOuYpI6zzneCVcDI20cZd2WxqzaZB8RK2pjKhv/SlNX46sfkfL3t5AE4aXAnx/XO8dTnWxXH8PEtY1HqyfrpD8y9K/YFfv44XLVT76OtoMQUPQa9JtV5CYg3Q/LaqCDGWcRjtaOEOP6sjsVZlWGdVs5ecngPqna5vhLnmUtG4tNfHY1h3VpjZI9S5lzyb+e1jRLlvui7kQ7PIrr0u1W7HLOC/y2rN067aovaMIKzC9nfr94pPZcwzcC+aKHcnB7ySnAGgJKSElx33XWYMWMGFi5ciFtuuQWxWAwvv/wyjjjiCBxyyCF48cUXUVFR4d5ZiCYBXYtzPoFncHTAumYGSQ0Lum+RBptnrpq6QqKpw80CRs+bqnrv884vCIN58uCOUqaBoAUl0LUsZq0xIvdDLxDV9JSRY4J6XgEzByJmY3eVOvOvKBYSALbsZWOxk66/4ueTMO2awSJXbWfOAnE/ZC8QrXc3KxXtmkpbRW1FhoDuDMU4026VOjHOtKBqGE4FCP9MHa7aZM5FbIGTnmeyuShbM+IYZ3FyMJ01Q7dQ7eUxShEmoi1BWZzJnpQwTcxcuxuPfrHMSpTVtkTPQuzmVjq4SytLqAKAM4Z10erXD1ilgbwd//gcWc5Tr6d1M+czoOcZm7xLJaizNFnfaybG2XkdnVDVoSTXECplitB3rzsCH9w4xvp9zshu1ndAZzmTJof0aINh3Vvj018fbXn+uCVDlMU4i+4nHcFZdh/8I+Hnr0jhBST37UnLdwiv8YqrX5slPZdIuJeU00VQ/RyoyDvBmcbBBx+Mv/3tb9iyZQvefvttHHvssViwYAFuueUWdO3aFb/4xS8wZ86cXJMZIk1kI8YpaPz5f0s9X5Mt+ZR2yyaWgzCrdv7CLUaZPn3W8OCYS7dPJxGmIobhanFuwcV1tW9hZxaNKgQ2HbCCMyyahG0TphULGzRrIGKK3FzYDQAPnj3Ute+EaSrLxpA9spkgXlLbVTdlAhTNNzfvTtr7gN6vrXI6WYhxJmRHDUNqNRQ9C/oVGXDOZV5xxTPq9JwjV9IWZwOG2IIruX0R00qeL/8tJMqHvTVyBQ39PlWPnL5vkRBgmiYlOEet8Tdz5W6iKl9nup3LnsELlDILfRBg47v1Lc41DQnsqarHcY9PwhPjl1tzQxS3TM8BtzVJyurxikDRXsn3tHpHJeoVycN0FFbRCHDTcf0cx1sUx5h7K4xFbIFSY5+hlVsEdiZ6fcMBfVq0j6Szteh6SfLLVBaOYZq219jWverSUG4j8zW4aQRpcXZbmyHUyGvBmaChoQH79u3Dvn3J2CPTNNHQ0IDXXnsNo0ePxgUXXIDy8vLcEnmAIggv36ZocX5z+nrP14gsZ5kA/aEhlvF3frDp5RnD0FU7t3CLcSbWh77tS3Dz8U5mxy/c3rrlohoxXC3OfJZdurkwbs8DRK7aMnISpkm1yTxz4PZcIoahpd03TbmlyKQszqIMvQ5XbcljJu9B9BrcmEkmM6zovICTCDKxVbI/261VanEWEEcLNIbhfGd8xniZYtGgFEjJGHXKEu3hPkTTQVaOiqyZbXvl2eJlljrHGC5W14Rpj1dUELGO8ck73ZRFBKK10aGlU6Fm9Rtgpm4e9PxVkS9y1X5l2hqs31WNZ79ZZQmlIi8h+n74Z/Yal1yqMZGAaZrYY9WqTl5LPA8YwZl7pSc9MQWz1u62fn+yYAt3D7K7sxExDPzutIE4d2RXx3H6/RZRgrPO7m0lO2Nc4510uVYdkMTW616vguxSPkSDXyMxicKI3jtdayq70C0bw7o0oG9aUEnGDlTkteA8Y8YM/OIXv0CXLl1w8803Y+HChTj//PMxfvx4VFRU4J133sGwYcPw3//+F7feemuuyQ3hEweKxZMRnDM4Dq21JKUp3ptpx8Hye/eB8vzzFSqBsiGesGKYzxnZLScfvEjEcGWWeSGV/imK2/MCluEi/Uu0/6CsoFl4VG6Cs2HoC1Wyx0PHOIsEZ/46mfAkiwsmY6hAP0uRB4DIAhy0PpS2ZsnuRZYx1/obTsGJd6eX5YCIMC7ibCy9aq7xpX9U5ahk5Y/UlSdoS579Nz8MKzwKCDadrtrxhMlYNwH3OS8bHwAuG91TSkMmvUf1Lc7s79qGOOMFsCVlURSXo7L/JlUPCE4Y1BFH929v/Y4nTHy2cKv1m5AUFSgZRUqtLxbb1y7ctJe7B/fveVL5ZDC5C8hx+lkVxiKuJQBpiEJp7PAGudebsx/7b1HMdloWZ02h2+GqLYtxppZmtUsoldvIarf+IJODBdPPgQr9GgpZwp49e/DWW2/hH//4B5YsWQLTNNGjRw/87ne/w3XXXYfOnTtbbS+99FJceOGFOOSQQ/D555/nkOoQ6aCpC268m6oMbBKXDBED9zqOYXKwpoM1O+yKArl6TzpWU95YRDOnRCjQqS8qgmk6mUgZNfTUDjo5mHg89T3RVko3iDJbA8mSYwQ6rtpCi7BhW4XF8YXu92EYyX0rIXofglv0m0VdllDNctWm5iJPtfjeKaEpYjD3AjhLC8ks+FHa4gz1vdNwCCdCV+3ky+FdtYkApRKc6VdH0x4xDMaLgbZmi95NwjStNWq5apumw300pplBnBcCurdpxhzjz2fSQ4SNc9d31a5vTDBCLEkCOG7+FsQiBnOOfq8q13og+Z6+/HGbfa1Vjkrgqi2Y1Kq9VEcwJM+en4uRCGv17NCiyNpsdfrly2vRf3spx+lWqi8TMc48+FkiUx7T7+qgji2EbXShUlCHdZzzB3llcb7iiivQrVs33HbbbVi6dClOP/10fPLJJ1i7di3uvfdeRmgmiMViOPzww7Fnz54cUBwiCGQyw3Q2QNeZVSF7rtpJRke2CVfXs9rwJugpnxNsr6jFOc9Nw798ZrH2A1mm1mwianiPV2TiKdOMcaavcotxZiyBWeANREmCaBge6NCpcy2KrXTG5Dr7oePURVuPznYkuv5fszZJ23uRm7u2Lrb+ls0T2lWbxN2WVbAuzKJviWgbpOePyuK8Y18dZq5LusUalPtAwqRi6Q0DKr+Cwhi/NpxtiDBKLM68AKXySpEJJPya3E49K5EgTitu6ORglZz1VNfizK/RcbeMVVp+Myo4u9SwJuDnT/I9O5/97qp6R3gKvUeeO7IbAGBYt9bCcRoTrEKCXGtbnJ0J+HSh56otO560OH9405F497oj0KakkFEWuY/tVGyKwircBN+EmdzHVpVVCpUEQWfVvuiw7o4+decn2R8A4FcnHuQ4Tytw3chWlbWjK0aki9DinB7yyuL87rvvonPnzrj22mtxww03oGfPnu4XATjvvPPQq1evDFMXIlPQyQKZz+A/oDKYEgYnaNz14UIAKW2yYKDKOj5TaNN+/tnC/32xDAs27cWCfy/ERYf18N2PSLAxTdM1+ZTMIplp6Fmc5a7aQWbVtmiSLLlk0qbsuWq73ZOX+Fed58PH4wK6Fmc7gZWOq/ZLVx4q6AOIU9c3xBOYvmYXgKSAycOLxZkWXlXZxQH2vd7w1hw8cu7BuODQ7iguiArvnY9xBpKCSjzVWiU43/uxXSM9YrDKA5M6LvZ8TrZQCVgEZJ0Tq3BhLILG+rhFi9riTFnn6GzO3DgrqbJ3fCw1wFq7i6w6zsC0VWyJHN0YZ1rQuPHYvmjXoogN4eCmcibXq19X7Ya4KV2XsagBUIZlut8zhnXGp786Gv06lgiuTM4x2gWZ7GcxK6zF/zdZ51pCq8yd//Deba1jpIlWVm1LaelUVDCeQ66Cs4m/T1mNx75crhzHD6asKMOtJ/Vnjp0xrAu+W7WLOeaIcdbwtOhCKQBF8BLb7ThnAs0Kg7F1hlm100NeWZw//PBDbNiwAY888oi20AwAP/3pT3H//fdnkLIQMgQh82ZDcFu2rQKPfrHU1YXKD3S1n2wdQ/17rmuM45kJK7FgY7lrW9M0LUZHlq28mkuCUdPQKGwXgkVVfeaekyxBHmNx9unqnC4iGsnBeCadEZzTjXEWlFKTZ9WmSiR5cNV+5NyDAQAvXD5K2e6jm49ifrvVTBVlcZZBZx8UMW/8YxUyZ4bNwIosOLRr5BF92uInQ53eXbbg7aSXj81NttOfr3RLmSKVTlZH496PF+PZb1YybWjQz5/MCVrxUsA9U1pQokMl2KzadOyzeKaR23AmzhMJzmxyMHINecZrd1Y5rrHGof5ma11LL0HHlk4G/8vFtuswcdU2TdPhVaGdVZsiYGDnlsljAk8U2W9dkHJHKtCCgop8fs7GE6ZwHp8zsqvzvVI/DcPAsO6t0VxSw7kxYTJ7Pu+qzQjrHpks3RhnwMkjiF4BHdevgimZewalbCJwM5aYJqRCM2Dfo5c5Q2pvz91QjhXbK5lzsUjEkT2eX6c6Y4nWtm4Wb0CtnAjSMzN01U4PeSU4/+xnP0MslldG8BBZQDZiN097+lu8NGUNHvp0SeB96+5nTIyzh/7/OW0dnpqwAuc8/51rW5ElgcfUlawF4U/jfvRAzYELv4ydDmobxO+N/r4FXd5HFxHD3crkiJUTxLj5VVrRd23XPZUIzqbpKznYFWN6YdnDp7nWkh3OuV66WVUjai9eBuoEUEmILM6y8kk8HeQ93PT2HHzIhRvouWon/ycMHL1viywYE5aW4YpXfnBkqxWBHr+6Tpxghy6PxmP66l2pftSu2uRS1lWbi3GWJNiKRNjSOnYpLvFcIyExvGAuassrRApjrOD8x/8udl6UgswFVsUc9+/YAs9cMhLvXT/Gej6PUCUWrfFN03dWbbrZvA3lDpqCctV+/ZrR+OaO4zC4SytpG+0YZ26PbeQsw/06JC3Ilxze0/EcvNCf4Pol3xailKCVpF53fZ21TMbj24ruQfeu6EfHKKvIvsEYDtR9yYR0ctxSonl45nQpxxXb9zHndL7tOvPeTafk9mpUytOEGVxYXWhwTg95JTjPnTsXt99+O2bNkhcBnzlzJm6//XbMnz8/e4SFyCiCsDjrxgz/uGWv7zFWle3Dr9+bh1Vl7KbLjz1pWRl++fYc7KmqZ477jXFetq1Cu22NS1ZHAHhxymrm949b9Ps/kBGUllb05usEFjuAZWx0BKtMIBpxt5ryDAzdnljLrn19tq/xWYUTsfJJ2oK2OHuDKH6YB89guSY8Mwztta5ncRa4avPJrATXJWOc7d8PfMIqy7xYqUhTPhEVj79PXo1pq3bi0Ie/lvZJyuXR4++ra3QkpEokTJzxzLepsZz9EMWTWGlAW5yToOdrCZepPE6NTduS+TJYIrdUGlUpBQCffEzEpPPWS6Ig0QljYl21XZtbOGdkNxzZr50wezAdY80r7LRjnKl2ltJDmRxMn3YaBdEI+nZQJ2Xy66odTySYd0CeRUHU0HLBl6ExwSokVBZnr3YFnbUsI1X0bkUWY7dx3ZKDubtqixMhksssbw8PEgxNUw33vRV58sjc2FUQzQF6D3Hb4lXr3TTF3g9+EFqc00NeCc7PPfccXnjhBfTu3Vvapk+fPnjhhRfw/PPPZ4+wEFJ4cUORIV25eePuaox5dCJemLwqbVpUuOTlH/Dpgi245OUZzHF+M7vm9Vn4YvE2PPrFUq6d/BoVvGxx/AchRHDIhcWZnidubsGZQkE0gsJYBO1Srm4iqGKc04Wo/JG0piYVfJqJ0l18n27uc5I0A0LoeBTw1kvAuZcIY8KpGGcAqOIUbDok8iWgaCFNtTRkXjDTV+/CwHu/xLMTVzqY8gouIdXm8hrrOdYJ1sqSrUnln+h7RL8yUkd4H2UFX7qVVRzSy4yxOFMu2SZMyvtBHBZAnhNvCVWVoyIgFl/TdJ9jJpJK3YZ4gn33mtOff3exCJtIzmFx1s2qTd3nlWN6O8Zyhndkbn+toLxdRGEFBPzaaUyYjIKI7MGxaMSxFr3GjTYwiqfk/6JEil4TiersN5bFmTsuegX0nFePK557ouvdFDwmTBw/sIN0DLeQHQJS8eTzW49h7u0PHy1i2om+7Xzfe6rrHW14iPohYQqAxrtUuWpr7AW6COXm9JBXgvO3336LUaNGoUMH54Ih6NChA0aNGoUpU6ZkkbIQ+YznJ63C9oo6ZUxMENhZWZf6n7cki9tv3ctmfPVrGfCiHQwF58zBi0XBK2Quv/TcCjo5mC5DRiwrzyvif3mmMcgnJap/XiSoo5psy9bdzTTcLIIG9JVkOsm0RIkIZeWTGDoM9fOg54Jsmluu2qm2bDyt94dNEm898fUKxzleWNNVWonuvTFh4rnLDsGVY3rhnFS2Yxq8Kzkdi89bzug4b9r7gb79h88Zih5tm+F3pw2yaP/tyXa2XZGAxQujtFAWN02hiz7B5a/8gJOfnIpb3pnry3uLF/oLohFGgOO9Kkokcbs86GfXsjh5DWP5FTyH3u3Epch0oJohO6vs5HXtWxRJ2/HPL84JzmRexiKG0PvDC+icJeS5kHmQXlZtfe8RfsEIXbU1Lc70efrVirLxu+2biYR4zZOrZPkOnDQlGzYvjDLz3OFFIbxv9jcfFy2CSPkTjRg4f1Ry33Fdn4rbSZhmYIl0Q4tzesgrwXnz5s1KazNBr169sGXLlswTFCLrGNGjVKvdD2t24cnxyxFPmFImWoRMhFPrJwfzfg0AT1KIX41kmFnbHUFZRJJunu5WQv540BZn3VdOhDXV3acT7+cGUZ4cueBs2zWywRu4rZuIYehbnDXeL58BGnC+R5m7suqdsMYicTsi6IiSgxmGgXevO0Lavxv49cA/V1XtZroP0bP+btVOnDW8Kx4+92Aho827Ujcy92UfpwVkeiz+eV15ZG98e/eJ6N3ezqhMKzyE5ag4Dwr6PccTJs4Z2dV5EYfxS7Zz75FF3w7iDM+i7MHkWNw0HSFHJOO2G+hbInOH3kNFwspntx6j1bdXNKfcflXCOb92GuNsCSDbVTuiXU1DB+T5kHlAWxc9u2prbDgyi7NYcIZFkwqs4Gz3I0ou5l6OyhTGFPOu2q6CM0WDqqmOxZlvI6s3L+w/1ZeOolWGuGR/84PtXBm/EN6QV4JzUVERysvLXdtVVFQgGnWPRwvRtPD4BcPx0hXOMigiXPzyDPztm1WYumKHay1VGkG4lgPAmL52uQbZXsgfZ8oxeBjLS3ZgfmMdTZWVUKHfHz7Hn8bJE9CEcJZPSQf8e5J9EP3OGR3o9hcXxCfyyFRN1qq6RibzK8lRIBIgAVL/k/zKvOQsy1xvUWDoW/Z1lFciZt1ZjsrZD50cTAQdCunEWPy4pmniqP7tNXoRwxlbKreEyZ5nPGH62t95l9uERHA2KOWDmSQkSZtGyTFaCBC6avPJwaKs4KzLMLOJzdg+37hmtPAanpyoYVDJo0ys313NnFdZbPl++L/pY6L9hLjWBo2LD7ertKgepSirNj2ndqeUCLGoIQybUEG1JZI5wVRRIG7JnkbRU4iSfA6O2sWCbVXXVZtO/sgKzsm/P56/xZMyQDQ/CA3EIO/mBWblIYChfP4qF3UCeg2/e/0ReODsIcqxaZD36qbUUNFomuKa4gCwfpc8674IfChMCG/IK8F56NChmDZtGnbv3i1ts3v3bkydOhVDhuhP2hBNA62bFWgle6AZp7J9tSguzL4ShS4zoWMtTP62//YSt+RFBuHHfDhVZkdnzDenr9cf6ABEkDHOIgZN3E58zf0/TX//052DlrVI0SZTMc6Tl+9gfi/clBSc35+1UdQcCzaWY0OK0c+Gxdkti71hGNpeIFUa2adFVhiH4CyzOCvmr557Z/J/wtCn66VCC3Y8U87TQ79L2bBx0xTeu5unSItiVlijLc60ABCNsMnBEgxTrh4jxlicBYKzwuKcjLPVC9NgEzSxSoEeEgsZTw+tIOBdlQF9wVlUAoqJcZZ863X794JmhVFccGh3AGqhjbcINibEc6owGtHOLq4DIgDSfcYtIdPbOtNZy0VUDD0NlcXZrdu/jrdD5XhPDYJPF27RovHLxdvEpZ04i7NbXDnZVwwXxaHIOMGvafrdtG5W4MkDjS/l5weJhPy5bSkPLcjZRF4JzldccQUqKytxwQUXYNOmTY7zmzdvxkUXXYTq6mpcfvnlOaAwBI8gXZ8jhiFN+iMb0zAMh3Y+G9BxO1Il7fFCppfPMz9mx5Y2E5IJN/UDCcG6H+sKzuLjQTBtuuECBak1qbp9p+AczLNqVuj/E1XgJeWqT5Cs0DIYYJNNqfDa9+tc24gs7bIYZ3qOGIa6HJjOVLCYv5QMd+eHCzxd7xyTtliz5/hcDWycu3gwUQiEDnirFWtxpi3FthBAZ7jVmeq0ACtauvyxAu6bpluKTka7Cnwr2juBH/a4AfL8MzxEicCYrNoBa7bcutOxnDpDBsQKixZFscDcZgGbdnof9Vt+UGcNWMnnuOPiclR672nDLtszQZTJHgAWpRSfbrf23KRVwu8TOUT+5+eQcj/2anHmjvEx7V74AXKpG3+qetYJ06nEsq7LgpI4hI28Kpp83XXX4b333sPkyZMxYMAAnHbaaejXrx8AYPXq1fjqq69QU1ODsWPH4qabbsoxtSG84ubj++GFyaul5w1D72PKa4XpeMf6xgSacRboVWXuSR28gqZAZgxwumpT12TI4sx3y1pqTESy4L66vyIoi7MJpxVDNh2YuUW1CaKms26uMZvZld+/w2qlScO2vbWYuGw7zj+ku2PdAs74U0BfOMpkFnQCN1ftiAeLc5lG3JnQVZsjgQgGzQqiVvbohMkmI+Kh5TqZepxk7/o+VTuZHtMv+Kv/3+fL8Oa1o4UNpBZniUuzahY0F8w5JsaZ7oea43RIgA4DTSuEhfGU3DFecNZVCLMJ23Tfq9NbRLZ2/CrsSH9MsrUsF5PVsZzyj7mmIS5sX1IUw3xqPf3y+H5p0WYlB6MtznF9t2YaOlOF8EwOV22FAOnXS471PNBzWZa1sVy1JTHO363aiRMHdbLbE68QQ22VFa1h/hit/EokvCWfjFqKKP+u2mwYEosw2Vd2kVeCcywWwxdffIFbb70Vb7zxBj7++GPmfDQaxTXXXINnnnkGsVhekR5CA5eP6YXS5gX4f58vE56PGAaiGnFD9Oazp6qeiXGubYg7GPCTn5xi/R2U1ZXuR/ZB4Q97sTg3xBP4zfvzMLp3W0YLOf7HbYgnTJw+rIvwOqeLI8vshfCPjFqcNbwWaOEkkNrnmu0Ig6CO0eN+axp7z33+O2yrqMXK7ZV44OyhjvO7qpwlQHRvXbdsTjpwd9XWV3KIFAc8RPckCwkpogTnBhc66T5OHNRR2EaUHZfAz77KuGpz109dwbro06elMc4SV20lDZJ+bBrt4/QcT5ajsi3ObltDjLE4uzPpSe8rwyqHpG9xtv/W9vow+J+GVCjwpoyiPR6M1PVUX3nI7JN3WhiNoD6eQG1DAvVchvfCWMTh+XHLCf3TGteKAaeer98qCnqu2sm9xuGqrajj7JocjFqlohjn5N/6NIrmvNNV240mG58ukCcU1otxtgdLmKYnfsDQFZwV50xTnlU7D5fSfo28kz6bN2+OV155BQ8//DAmT56MjRuTsWw9evTA8ccfjy5dxAJDiNzAC59S2qxAWVZDx+Ic52KOHv1iGeM+5sbIBiU7MhZnSae8FcY0ZT+cGDd/Cz5ftA2fL9qGSw7vYR2/4a05AIB5952CNoK6ujwtPLMXwj+CFJwbGvVctVkFjf23p6zsEmjHOKfuW3X3PEOt+6y2paysk5aX4QE4Bedb35vnOKarNMiGFl7H4qwbnzqocyvMWCPP7wGILX48M0XeK+OJE9dnwq8Z29Qj/nYAAQAASURBVFt4nK/jzIyp3bsYbnORnfuSNpIYQJ2kTDTilG+9wbUlZ9ga1urEQwBrrRK15b970UhyTTUmksyy7pznY5x14BTa5dbg3QJFlhw2LSKLc3GBWFGUqWVLFNCquUaec6tmBdhVVQfTdIY4iBKYpRs6Q+7ZSCVmo70MvH63dUoWkczo/LNIJ8Z51ro9VD/U9VQbXcurrI21/nS9PSyLs7qdytJOQCu/EqbpaZ7a5d30r+GRMOXfvqArboRQI+8EZ4IuXbrg0ksvzTUZIVzw9ZLt2m1LimIoFLheEiQT2Kj72FNd73Cvm0JZJ9wY2aCgE+OcjsW5vNpmUEQbtExBoLI4hzHO6cFPVm0Rk2aawAezN7i2A/g5Y/8diKu2ZEzCuBFEKKZOBv5cJkVWXaVBkMl7ZCjbp3av1rE4D+nSCku2VmhZnEWKR37ukF/0XsrXReZBnmnX1sXS+rQqa5Gf2OJ9tbZAIvIsYPqH+34bN72rBiMRA0O6tsaCVOwloE4OJlqPBtzjQN1ctfmlFY3Yma3jcQ8WZ9parrkK+bENQ37l7PV7JGfUsGKcGcE52BwErjHOGgIgOVcQNdCquAB7axqwp5qdmyLBOV0lncHNM9rLwOvSIu1H9SzF3A3lwjakFjevIBEKkKRfD6uLzQ0gsji79yESEsn8JvuN23O3XLpd34+7Fwj9PUmYXmOcdV215X3GTRPTqfAYGm77O49rx/bx1D4Ei7xKDhZi/4eqhINhOLOL8lhVVqncdIMqEO8GHQuIOjmYmk7WQiTa1GV08Rpk+pw/BjdEEn5i8mSPe+V2Nu6+IW5ayVU27KrGA5/8iE17qqXzJAgNs4w2h/VYI6s2/2i8Jgdbv6saFbXy5FU03LxKCLIR4+xmITYMQ2kt/O3JB6FVsyQT26jB/IiEWv4yUeytbg1W1XuTJYzyg1VlldheUafdXlTL29nG9Ly/RQzgVyeybras1ZZl+olImaDcwg0NizOdKVrUVOSxYQnOpn5WbV7hpfM0RPVqg0ruZ40RsfsmkFmcu7Qu9jVG8wK1HcgSnBVtEpRARkLA9laz+1JJBizOor6sGGeNaxhFfmoOyBRggDimGpBllyZjaBAiABvuYDA0qiDaDm1XbWff9HkC+30CF6ayqoug8/qYWvIeXbV179vNVXvaqp3W71upfcurweigTi08tQ/BIi8tzpMnT8bUqVOxdetW1NWJP66GYeDVV1/NMmUhNu2pxqY9Nb6vl9VfBVIWZ5e9yC1RShAurDqgta/SGOfU/3trGnDzO3PQkyoHUulSR4925RXtz/KYWPY3/SFMmPr1QEM4QWutTdPUYi5lj5tnav40bjFWllXisQuG49lvVmLj7hp8v3onHj1/ODWm3T4IzwqpxVkQbwl4c3n1w0ee9tRUfH/PScyxo/u3Z5gFQN9VOxsxzq2bFSizVRtQW5yjhl36p4FShtx28gA8NWGFFg3OrNq2JVQXpAedOPYFG8vRqjg91uG8F77z1F5HIE5ISgepFT4GmnMCHPO+OKbfNGwroIx5F2FUr1Lr7+XbnckqRcKr7d6Z0FaUrdi+zyZdk7Hn3a9bFMUC8Rih34VtcbaPFUkE52cvPQT3frzYc8KtR382DNe/ORu3HC+LNyau2vI+vly8DQCwubwGw7u3xobdTldtUeZmHaXqjcf2w7crd7q2I++9MaUs0WFpTNNpzdUR5nlDhciwYbm4u5MhRIRbQ0ka3XsT1Sbma8i7W5xTfxjA6cM648M5zmo9gHit8O+UFpyTFmfl0GxfqbbpGHbofX5E99a4/dSBmL5mF2at2+PZ4pzlvHz7HfJKcN67dy/OOeccfPvtt64fylBwzg2O/r9JaV3vFuPs9rFP1lWUzw33WLlgJEedeFOyzz0/aRW+W7UL38F2s3ni6xW46PAe6NRKrF1vpCwM7/6wwXFeJyYWcGbVzla5rv0R9Ee6MWEqvScIhK7aAAq5a1emMr8/9uUy7KxMMrIrtleyJXuo9l5iVuW0iY87rV/kf5U1kv/t/cu8Za/T7ZnUnr3h2L54eeoaAEB1vXu9YyA7Fme39RQx1G2iUbZmLgD0atccvzn5ICRME89MXOlKg8xV24vVUKe0EqHzoc+WOM6Jsn2rsM9FcciDn6sjepQ6soTHTVO4F7vHN7LnmZJOXLuEYTPuRHkaMdyVFHR2+LU7nYIz//iiqeRgQNLyprtv/2/RNpt2n9O/VbOY9NozhnX21acoxrlIokTv1a4Eb/3iCM9j9OvQAt/ccbz0vG1xlj9LuuoHsTjziq81O5wCnQ6OPqg9Zv7xJPzi9dlYtHmvtJ393onF2f3d0y1kGacJOlAlKulv2MmDOwqt1FY3Pnknkdu2znReKaiGQp4JbUlmx7L/pvfFZB4ChbejxjHaCBFPmIgqjEA8VIkVXQlJgZ6HROlE9l2v/EDQHiUHGvJKcP7d736HqVOnon///rjpppswYMAAtGzZMtdkhXCBzPomEhpU7kOqmKyiWAR1jQnEEwnlpuue+dGmLZ3NQ8dVmzSSJVSZt6Ecpx0sZkTcNkKZ554zxpkiR3A+hD5oDXQ8YUJiMGEgetqmKV8H/OuhrY7Zsjjzq0JHCHbEOAf2XXYygU+M17PE6tSETxdumn7DMJTWwljEdvNdWZa0FhLrXGdNl1VncjAyttbl7DWKPVjVn8x6mA5mr9uNTxZswV0/Geg4d+OxfXHzO3OZYwnTu1XMMACDmyayGOeIQccqss/Mm5JCRAenrIrYCpXGRMKi6ZlLRuLlqWvw45YKYd+0u3/EMHwpilsVFwjnwb1nDsalo3tq90PfErk/+nnqKB6DhFf5rxVVrSModGxZ7Gqdjqb2LS8xzsn3bFB/yy3O/7rxSMdYQFJhIYJuVm0ZaDr4knZeQfY6UTgKfZz/O0J59oigk+mehtfkYHT9dxVUXcYFCj0iODd4DN0Ky1elh7wSnMeNG4dOnTphxowZaNu2ba7JCaGJTXtqLMsQDdFGq3KfVH1PBnZuiYWb9qIxLrYqEOho5j9ftBV/GrcYz102CmP6tnNtL4JOshpyVBbXomIc+KzLPORWbvY4vUGaCSBuhIKzX9AuzA3xhDRGj4boNe2rbcC6nWKrBd/8u1W7hGd5xYofRZBsJoiSBfHHTxjYAZOW20n5nLVg3WmpqXe6PPKYtCw5Bs18fbOszPU6IDsWZ7ekTRFDzSTSDB2Jl16Tmhu65Xr4PY9OWqUPsQWHp1WG4pT15fnLRuGWd+dK26lQGIswCqELXpxu/X3d0X05WpzXJxLib4NbIhz+vpg4YUqojkQMmCnyEqZp7ete9TOi7wH/rmMR2+KcoCzOrYoLlAw7raTxO/1LipwW5xZFMVx3TF/xBRKIpn2UEaKyLDhbFmc9tHYRnA3DnxGWfy+927G8E29x1gHdkgiXov3v1hP7o097W0CmeRA3126/VTmYdx7RtLxKkKDWH6D+zvAZ5lV3J+qGP0YrPA7v3RaLt8i9BmSdkVe6cvs+dGpdjFbF+soZpr58ihQS+uhVkR66aqeHvEoOtnfvXowdOzYUmpsYZFYXERNToOAyVJpY4uIdT5jKBAuumkwTuPmdudhZWY9LXp6hbqvqRqLZFNEiY65V9+uWDEYWK8MfZgRnqJUOIdSgjcRVde5CHyBmNuoaE5goEf5UzCT96ng3Rz/WAFVWbRo92jQDwFoj3WKa6d+Pnj8MgDO/gU6dUlKuinaPFDGUxxzU3nEsH1y1AQM3HSeP1fzZqO5SJkY2FQijTbL78q9RJ9EXj4TGNaq5SSzOZw7vgrOG+ysZKWPcV26vVCoECeISk/OR/eTKUdN0zl3WssPOedqtXtzKHaJ1J0rIR74PtMXZbU7TaypiGPjNSQMAABcoEiPxOLRXG2UpHl2IvGpEVuhswXpLmt9AmSt5unBTMtoxzsS6quGqLfBGEs2VG7i9qH9HO0mUbG6lnxzM+d3wGzJG+B67HJW8LT1Csja5N28a/hidBV6Vq0fYv0WTicWb9+KUp6Zi7F++EYyp4AkpPpvM5ULL4uxVcA4l53SQV4LzQQcdhKoqf/EjIXKHmgaxECEUnFVZtRVjkA933CXBldfNnc+YqQt6mMo6cbweoUX2kVDdr9ttyJQHDldt5pzcxTuEO+iP2phHJ6JWMu9peJ2PqlBRuqtbTuiPvpTlwA8jIpsLPAN1ypBOANRMr8NKTc28AZ2S4TaduXh+L89mR6WdJLJRQLgod0I2ylG5IWIkXa5/cbTT6vnfm49Cm5JCKRMjO/7Vbcfihz+chH4dku/fkRwM3i3OOu7dqnPFFCPpV2FRTXkgHDegg/U3K6TKx0hIYpzdyOHF3rjAskP6sVy1E6blFl0YdTFnOegU0MBdHzUMKwnnkq0VliLVbU7Po8sPGcCvT+yPz289Bn9JKa9EoMfu2roYlxzew/FM/Kyl4d1a4+TBHXH1Ub2tY6zF2XOXacGrxTlT+4cjLpc7T3ikuJfkYNRd/Wt2MgFWbYNzn+THotfZ+aPEyhWd5GBrJR5UAB9rnPzfrwKf8D06ycGY0mwR9XwTCaz8GihWlFJ1A618IB5TojwPSm8SgScMmSteBedQbk4PeSU4//rXv8bkyZOxatWqXJMSwgNEGzQg3vCVMc6K1UziFeMSdzwCrwJEZSrR0ObyGu1yOAAcX5HVO5yJLAgpIkZf0IUnaGfVNuhzZtbKde2P4DX/dAbboKCyXdHjt29RhHG/Gmv9DtKTgGZG2pUUCl21+WXsjNGk+xPTKFL+bNxdLaHJ/lsUMyyyiGWK8Z1w+3EY1Fkv94aKQSHxabJ9TyaAFsWi6NTKjpV0vHvLGuPF4uwubOtYnAF9F3MRrkspGAZSz3f+pnL849s11m/DENNCl4ii4WbZ5E+zgjNrLSP3ljBN1KYse0UF0bSzUPPvmv79x/8uZizOuks9YhipOtWtlN9d+llee3QfxKIRp8XZR76ASMTAK1cdjgfOHiocK+uu2qn/Vc+vK5VXIJqhHAmO+cgrTYjF2VM5KuexHwWuxKIwnFV/Ph3z7juFsT6LrlE9t12VeqXltJNkSRDnrPDq5GDsuGpvGucxvnkRV3fcy+yllQ+qb7Wqz0aBJwz5huiWaCQILc7pIa8E5+uuuw633norjjvuOLz22mvYtEmcOj5EfkFmeRPFMKoszio+l/6YqDYerwJELGJg295ajP3LNzjkoa+1rjEMpwuuKMaEbO7rd4mFAXV2cDUNMgWBMzmY/VBN07+LVAinUkLHXdsrg6D6nqmsbn4EZ9k1bO1a6m/qsy6qOyv7TVvp3Mafx2VKFo0nCn0QCZkqYSEd9O/YAj+TWGd4qFwTyTmvrtoEtBBHQ7dMEr0/67h3q+QI2uLsp945QTRKGGv7nuobE0xlAUMyRm1Dwpcykp+7tItsNeVNZBj2M42bJupS372iWCRtt2PV+gHs+ROL6gvOuiXDRJTzx4Iq7cbsLYH0qA/yjlSxumeP7AYAGNWz1FXx5pd+twoEtJEA8C9kipQdIsVsLBpBm5JCRT+sBVwE1Zqn6Se36lfRS9YmMbDy645N2spaupUWZ8Fz4d/L0K6tPFJL9U8pH/yyYI0Cq3JBLHTVzgXyKjlYNJrUWpumieuuu07Z1jAMNDZ6K2kRIjMQ1TUEgJemrnEcU2mu+U2wZVEMz18+Ch1aFuGvXy0HkHLbU2w8ulm1acxev9vqWwcGvH3Mlm0TWyZVbtNuiTj4azftqUa7kiKhMJ5MUJRcV6HY7B/8o9Uti+QFqs+ZKn7dl6u2RowzzWzQy5NPjObU/FNW6xbJ8idl++pQXd+I5oXJz47I+0HGnLl96DNlHZJBl+9QMYrknFdXbf48zzPJVvnfLx+FX1KZqJ8Yvxz3njWEuUapvFQlB6PmQzqGfsLAqqazYRjCMZZsqfDMkJsQxTgnH+it78/H7PV7rOO0tTeRsK08RbFI2kKgs46z/fegzi0tt85oJKJ9j//3s+HujSTgv8VBeW+w+0lumHedxzeqZ5uM5UhwxDhz5/kYZx3Y89K+piAWDP1WySOFVVOZmwOmo51fzzcy92XlqJhxaYEdBrqWNpO2FcY4c79/feJBqG9MSCuhqECZL5QGE9WcEyYH8x3j7Kl5CA55JTj36NEjrC+Wx/j1e/OEx2UC4Koyp8BIbwwDOrXAiu22izO/mBOmiWNTMTj0x0QlJDw9YYWyBiS/afnRfBqG4WBN4ym6XqaUBW59p+NeS394Vmzfh1OfmorOrYrxyLkHC+mFaaIhYWLayh2O8yH0wL8vWWw/DTdVRd8OJUziK9X+x/dEMyt+Ytdly4h1sxZbiEhiKlE7vm3X1sXo3KoY2ypq8eOWChzeu62UZllZDbfvQj7EMxMQRRVg0y2OvU2ekzFLroJz6j3xfZOfjvrEHAmz1u12XKMKFVAx8kUuMc66Wd91YiANyIV4f1mOOetuqo9PF2xxtqMtzpbgnH4pLocVMmJgVM9SzN1Qjp+O6Iq3pq8HkJznut+Ngzr5L+XptDgHo5jK9xhnywWYymoeNJzJwdjzVj4XlzrOdBZ60oZODieqre6HvbYyNytKHukKsLbg650OQOSqLR+YiXE2gB5tm+Ppi0fitx/Md7QVdcP3XVwQxR/PHEJdo/8wWYuz/OY7tSrGzkpx+VJRCImtmNUmhbkuhD/kleC8bt26XJMQQoKqukYHI0Eg3wicq5MWNk4a3IkTnOWMXoxKmKHaeL5duVN6ToR1O6uxS7JR0aBrMUcMpwBumsBHczfh/75cZh1z28zen7URpw4Vay+9uGp/vWQ7gGQGYllynDiAl6asxpspBiyEd/BPVhbbz1zj8h75LPNKV22us3RdtWWX8LVrbdrsHyWc4MzTzV/XvmUhtlXUopJKiCKyOshqHrt957Nf2kY+XiwSscqFkVYigwB5RrKu3GSViEQot0u1sO33cTkcaPdKneRgstrBAHDFmF4OumiYpt330q3yfnRjIEWuoXHTtuYUF0RQ25DA7acMUPbTp32JIMZZvK6TcrP9zImnVVGBMybYKxwxzoaBAZ1aYu6GcpimycQ4ZyPcxhnjHMz6EmVYzhasOFMT+GDWBvTv2AKH9mIruJBnaxh22EDgdHDdnsbxAM6s2uJ+WjcrwI59dUwbev8UJUz0A52SR7rVIMjfXuqLnz2iK6as2IG9NQ3W+7GzanNKL2pt0MuEtDvt4M7AB84xhArDAF+/Qe1rfpPb0kplQpoVruNxTwgNlOkhr2KcQ+QvVNYG/pRpmliwsdxRaxYAurex3WVEGlG2X6eLT2NCnVUb8LaJXPqPGbj/kx+VbeIJE6MetuOfDTgtzgnTxKoyNkGY28dBVY/W7Q7kdZydx8gm+e85+1/OgL3VDXhpymps21ub8bH496mVVdvlPM8we/me0Zf6cX2TzU9pjDM1npvFWRa3R7uUidapLJGeGzIUziyF6jXR8aC2IChy1TaYNrLzMlix47wSz7qebd+rXQnzmxaGrEzcPhiqR88fht5UhnehxZn6+/RnvpX2ZVtmFBZnQ16Oilx1w7H9sO4vZ+LWkw4S9vHxLWNx5vAueOaSkY57lldBsF3ETdMWJGKRiDSxEo07T00K8feeOdhxjh8yGjEYKxWdVTsbaSocrtoBCZH03Mg2707G+2HtLvzuP4vws79Pd7QhzzZqZM/ifA1XZ5yPKZatBXoPJi1owTkwizNx1Va4A+sqQexyUvqT+KTBHdG8MMpcJ1MOMmuDEZzVdIpetXs2fu8wIS6napom3pqxHktSSkXR2DS/QW5D9g1wQxjjnB7yyuLMo66uDrt370ZRUVFY2znHUMVQ8Iv27Rnrcd84sTDavkURvvztMSgpjDkEOX4x0xu1bV1xF0gr6xutwvJBaOfX7eJKLRhOzWBCED9M4v6KYhHPWQ/dILsvIYPuck1Txh0fzseEpWX4z9xNGH/bcRkdi5/neuWo1M+cZ0i9fNAMI8lcm6Z3jTOgSg5G/U3xXjRlrq7a3O8Czv1QNr7MVdsN+RTjLBIORGvPjnEW9+M2Fyw3cH5rkbhdj+nbFhcc2t3adxlXf+sa7xjWrTXzW2xxNrV6txM4KdrAECpKTNNOHOk20sgepXj+slHCc1LB2WDfLXFdjUUMPHTOwWhRFMMlo3tKx/zViQfhosN7oGPLYsc5UbI9+llk2+LMw09WbRGYvSXrFuckVKWT6DJHbnsKCYHyTAdvzee+AbzFWeYiTQv2hG7aVVukwPJWcTyJAsviLP/eKT2lqL/J3JWxk8cP7IDJy53hZHySRZnFmf6msK7aREkpHldYjirA+Um6+t/CrcI94usl23Hfx4ut39GIgQT33l//fp31d7MUb2lYz8UbPXkU2dQkkZcW55dffhmHHHIISkpK0L17d9x5553WuY8++gjnn39+XpWs2rVrFzp27AjDMNC/f/9ck5MRqATnyrpGRohwcwce1LkVerRt7rRScbNRlI3RNN1LKq3cXmkJLON/3MacWyfJcK1CEVfs3oCTsUuYTk0i0ZKqqF0n+Yi7fY9lQs8WgeU13RIQ+YwJS5NWe9rlP1Pgn59MGdIYT+CiF6fj3o8XebY4yzKwA+J3bmdWdhlI2F/y/0e5Gq9sHKLY+sy7avMfYl6wsSzOFKEiAUCmaHDjYXjr0LvXy/McBAEVOfS5iOL92Fm19a0govNOi7PJnLfoMgxceKidDZx+z3Zsp3pMEZxu+mqLs7Kv1P9KC4ohZmrpxJHpCGVypSTrIt6QWv+RiIH2LYrw2AUjMKpnG2XfIqEZSLrd0hUnohGDeRZWVm3N5GDnjuzq2oZA5k1GP8LgkoPl3uKsgiU4ZzHGmf9dmIqZJ6FAomzKAL9+kWpLe+k5r/HzzIusBFT+GAjRfJXt8w+f48zRAjgtq7L9ava6PcJxybOQ55NwHgvy7ZPVXFUfZwRggpWct6Lb/kUSbJLvbGhxzi7ySnCOx+M477zz8Mtf/hJLly7F4MGDHQtsxIgR+Pjjj/HBB4JAhRzhjjvuwM6d3mJrmxoaGuUL8+5/L8Thj0ywPu6lzQu0+nQwdoqtij7j5s35s79/b7lf76n2UJsZ4g2djxUykw2ZYwnTyRxamWYVe9rP/v69jBIlnbJn8PBnSxzH7AQS+6HknEXw/KWMwZ63sRwz1+3G2zM2wHSZq3yMswqi1+c3S+nG3dXYW5NcG11as8w8bWmRJwdjEyI56jhLXD1pJlCcaVq8B7h95nmGyE2ASRe61giVIGjHOEuYORfGXaYQs/YGQb8tqBJFIsbbj0VKlRGa71+3L9X3xoA4Odj8jeXapbhUkArOMJlxiTI5qDCBm47rZ/3Nu2pbFueooeVd0pNzy/cD+r3yNWz9gvXGyLLFWUOBTGdrzlRW7cnL2RAt/jGUpBTuNamqDTLFBkNfqglt4Ajq+erEOKt4i4O72h4phL+StW8rKYtF7pXcHu0ZQOOf362laLKP28m0ZN8X53F3jx/laa4v+28djxG3udcytY/LShJW1Dbgr18tx8rt4oouodycHvJKcH7uuecwbtw4nH766Vi/fj0WLVrkaNOvXz/0798fX3zxRQ4odGLixIl44403cP311+ealIxCFd8CAPvqGq0EWrw1SganpULV1v7o6QiAxOpdGPM2xYVWMP63wC07ISiTZSXCUAjBu6rcE5OJQAtKbpugLIlQCG/glSoyhqYZVZqnvEb9fr0wZ0LBmWicPZqcj//rZOtvPhbu1CGdrL/puUX/7WZx5oU+MgZtERFtKfRVJjPH1c8pU0yuDCpyzhrhtPaJ5opbHWeecWvdrIA7n/xfGuMs6JN2sWcEZyvGWUyLCo59XBjjzFqK3Pr6YPZGZTvR+/54/hZAYm13w9VH9XYtA1QUjTLvxBacg3Jj5i3OqW8eqDrOEUNLSeZ3NbB1220UB5A5HJAnHswGdIYje1IkgzHO/PTi13mzlOBcnaqzLuO9ROuX5l9EyiU/d6QjOKum5JCurdC7XXOmnexzJdvH7bWZpIEoB9VVKMR7gdC6LFjCQQqXXvtSlf4DgFtO6J/qV8zbDX9gPJ6btAqnPDVVeH1ocU4PeSU4v/766+jUqRM++OADdOrUSdpuyJAhWL9+fRYpE6OmpgY33ngjhgwZwriT749QbZoEX6XcokUxKiLwm55qEyRnTJie6ht6FpwFXwB+UxLVko6bpkNAtkpF+JBXSbZMGbwISvazY/HiFYc62nrJdnmggX8ykyTJ3eiPv5vHQ7pJd8gH1mvcI8NgUfQO6tzSYtwA3uJs/83XcebXLv/hp4WSLxdvxferdwppZowoHm5JFCOaSah6v+vUgdbf5BY6tSxy9pHqhKf1J0M7OcY4dUgnfHPHcdz1hGli+zVNufDYstgWvuk93c6qnb7FefrqXY42pH83d08dWcUw5IyoDkMtwgNnD8Ub14wGIF5L3UqboXXzAsY1lMSeujG5uqDncNSwLc6gXLWjmsnBgpj/dBf8evcLWkeXdeZdYziydqIRw9Xjo9gjb6GLkpQbblVKcNZx1a6sS1qnaf6le1tn3WI/65soPesUiR1pHunXJzrDFY/q3z7VztmehmxOkDVmcterPp+yPU00hqgbt0flxTtH1vaqf84UhkGq5l5hNIIOqe8Jb4nXRSg4p4e8EpyXL1+OI444AiUlajejkpIS7NiR+3q0Dz74INasWYMXX3wRBQV67slNFToF1hdsLJeeEwlpezhrq3ItU25rsnIhIhR41BqL4y653xDXTuXbVdR6cxOnQWJ3ZfAiKNEufzSO7NfO0TaUm+XgmYVFm/fizenrrLI0BPS7cVM4uWWWpyHyXAjCm4C2rNCxlQDLUNDr0xmnx9PF/ibxm2X7anHT23Nx2T9+wK4qp3Lo25U78b+FWwFwyV1c7iGd7OS+oBhAVMbm1pOd2Z2thDXcFPjVCcm2ZZTybESPUrRrwQrfMoszX0OaRhsqjKZHm+bOaxxXuIN/16qyVW5Z03UY+2RyMHG7dCzn5D3EEybKKthcEdeM7Q0AGXXVZpPy2euQzgsQi+i5agcx/+mdgM/z4btPiUU7G7DLUcmfH12Oyk24eP3a0ejauhgvXenkbbyAH6d5KgymOiUMy0r00XPx+UnJnD/MrZnAAz8dwlwThMV5055qHPbnCXjy6xVWG7pc59VH9Xb0QRs+ALniX7Suh3Vr7Qg308llkJAoEEVCqWE49xQ3wZge+og+bfHC5eJkg3xbGlNW7MAXi7c5jqvY1kjE2c57OSpPzUNwyCvBuaCgALW17mVlNmzYgJYtW2aBIjkWLlyIJ554Atdccw2OOeaYnNKSDehkEO7TQazwOH5gh2T9PA7vzdzA/FZtgrTbmuxDIkLMI1ej56otzqrNg8SQZkIWlbnrHd67jeOYTHspOpzv7ty5zAwuGvlP437EC5NWM8foZ8gL1Ty8aH6FCaZ8lqOgEeUEZ5om2frh3Ridif7Y3yQ5WDllgd9a7tzrJy4rwy3vzkXZvlr89LnvNO/AnZ6goeqdHpsw6a2KCzCmb1uuXaovjta2LZJxfpWU4k3E7MtLXZkOGg1qrBMGdgAAdKNKA5I+/D02/l3LO3GzOOuOL3u/tgLA+42QORo3TTz6xTLhePRaIYJzUHONXjNRw54Xf59s7y/RiIEupXZOgmYSS3AgXsZUH0VBWZxpwTnrMc7ubehyVG7P8PDebfH9PSfhJ0OdvI0X8OPwFmeZqzY9X7ZXECWbKW0D+FvfhVZysCQdz05chd1V9fjbxJVWm6tfm2X9zSv46HHdXLVFz7xvhxaOfA52OSr5DcktzuJx/3fr0RjevbWyHY0hXVphdJ+2OHtEV3xw45E4Y1gX9QUSrC6rtJTFBCo2hwl30Pj+76x0KqizHdq0vyGvBOehQ4dizpw52LdPHNAOAGVlZZg/fz5GjhyZPcI4JBIJXHfddSgtLcVjjz2WMzqyiWoNwZlkgeRBuwfS4F2u1THOyf+TFmd9IaF5obcP/oSl2x3HhHX3+Damk4El7lNe3Z912ss0jKJLZY9VxPDlc8WqzeU1OPSRr/Ho50ulbSpqG3DWs9/iuW9WStv4hezjNGUF6/1Cz+s6yZog8PL9OmWwM3zFr6sWDdrqTSclAlghiD7uZuGVJQejmcDqVPIbET5fuBVLt8otlzzcLOBBQ8WA6g4ti3Emv+k4chGDKMvYbTOMzrYA0LU0KTAv2rzXvkbQThc8/SLB2c76K5+oo3u31RpfZQ20LVGu3TgQpSzOROlJj5n8nxKcSR3noGocczHOVXXO9RGLRPDcpaNw/MAO+NeNR2LRA6fi69uOdbQLQiilewjK4pzvMc52mIMRmELEDfy7as4nB9OwOJNnSX+iTEHffuYFb3EWedS4gTZ8APJvqcEpLLql9ire4qxKAkiU1TKLs4y+QZ1b4ZWfH8bQokIkYuBfNx6Jv116iHv/ir6embjSqt9MoBKE6Z6sUC1F+8MemeA4lqn4/QMFeSU4X3nlldi1axduuukm1Nc7k+rE43HccsstqK6uxlVXXZUDCpN49tlnMWvWLDz++ONo187p7uqGuro6VFRUMP/yHTX17oKzzLom22d5AVjHSuA1xtmrJW7qCr3s6Lxwm0g4HWnt5GD62Li7GocKNjoeMi20OHuvzOIsEpzzV3J+acpqlFc34KWpa6RtXv9uHRZvrsBfx6+QtvEL2aPhhUhaqeGWVM8LH0PcRWkE4arNx1bySYr4sQD3+tN83Cf5UDdQrusqZdzUlew6dHtOTkE+0xZnef+MxVnVh+FsT/d9zshuaho4ZpJ4BZnWebGQQp7V10u2Y+PuZPkzy+KsHFEMZ1ZtgeCcokq1d597SDet8Q3JGEB6lnOS5KsxbjrWk2hNkBjnwCzOnCVJ1G00YqB3+xK8fs1ojO7TFjEq3jFo0OMXBKQcYOrC58jirFqTcWr+ZIs8fio352OcJeENogzljKe2aQbiDk8szkRw5quM6ICO16f+E4L55qSG4i3OxPotomVfbSPTVoevJPR1bFWMpy4egRcuHxWoVdZrT6rnwyqf2Oeii9DinB7ySnC+4YYbcPzxx+O9997DwIEDcdNNNwEAFixYgN/85jcYMGAA/vOf/+CUU07B5ZdfnhMaN2zYgHvvvRfHHXccrr76al99PProo2jdurX1r0ePHsESmQFoCc4S65rMVZhnomytvrMtve/qxFsTeLWgFgvKbuhsSgnTmTCMj8fRwVNfr2DihWT4zfvztSzhgJwBEB1/6uvgBc6goBMPzFuKgoTME4Cf3l5inL24lIrcpsnY6biwxzjhmL4fmcXZq6s2+VDTa3fZVrlnkUOp5sLFRiV0ZgqyMUb3bqs9Pnn3Mmu5W2JD2uI8aVkZBt33Jf4xdY3txihoyx9ftSNZP1QnZlAG/hLRPLUszop5Go3ojc9bpWjIytTogMzphGkKMh9TdBrsXA6KCY1wCiwVja59+VwEBvM3rSwIvhxV9i3OyQGJYAUIFODEVTuSO4tzCYlxTlmcZUuG9ShJ/s9/ooK4B8viTIRVH94HloCX+r25vMa1Lf03n8+BfFdFnhDknCeLM9XmvEO6+3a71ulfB0qLM/3eLY+zZPua+jgeSJViVSEW0Ho+UJFXTy8ajeLzzz/HL3/5S2zZsgUvv/wyAGDevHl49tlnsWHDBlx//fX4+OOPs66tJLjllltQX1+PF1980Xcf99xzD/bu3Wv927hRXXojH1Cj5aotbqO7eZNNQNSadFHXEMcv3pit1R/g3RLXQlBK69OFWxzHnEKyOGGYV3hhwioFrq6ij6y0dqHgsMqam2vouAvqxOL7hZyBYR8k7TblJjin+/2KUsx+VV0j7vjXAkxcuh0VtQ1YokjSJOqD0MMw8LRACidDY/+G8jd5RrTg9MkC57oiaFmsV9KOgBf+Mw2Zy/GYfu309zuJolC0XkVd2syyidv/NR8A8OfPl7q6aldRStDSVIkrKwGVj0enZ3FOQpXEJhqJpB3jHE9DARCh5igvUDHW+9SaDVpwpo26kYghKT+nN1YQSyATFufc1nF2HuMVdGR+qly1S5tnNhGsZXGuS3mQaGSgtgVTefib38dNBOUGicXZSyhawjQxb8MeZRv2m5P6nwtLId6NIiGeULN8W1IxW8UZffzUbE4XXntXKcLpdUOWJeE9n564Aq9/v861/9DinB68cSdZQHFxMZ5//nk88MADmDx5MtatW4dEIoHu3bvjhBNOQNeuzhqZ2cRnn32G0tJSyxpOQJKabd68GccffzwA4P3330fnzs7EEUVFRSgqyox7VaagI4DKFrtMe96ldTG27rUTBJFWEcNwjEc2u9U7qjSoteE1vnh491LHsce/Wu7sl/tAJUxBbWcfknOBB22u6LmK7le2RTa1kgRFGrVEZXH2QUBWj1tlca5z8Y4gzJFf0FbHl6asxn/mbsJ/5m5Cp1ZF2F5Rh/dvGIMxfdXhJM4YZ/uGaJdserq4uUbz58lc1U3s166kUKudaLxszGrZPCuIsNZQejnyDJuVVdtFCSG6lr4uYZrM8xfFK9ND0LHlhbEI3pqxHvd9vDg1TvoQVTIg+5LaRVNPmFKVCiKCjx/GkMz1RMLpqi1SQliCc1Cu2hTN6QqqfvniCw7rbv1NdxGUhaqA6Se7YUGiuRU3TYYJtqyUEblXw4c3HpkWHWP7t8N3q5Il20b3aes4b8c4s6EXPCKCOemMcbZ/+51RZC7WSSzObuFIoOgwTWD6Gme5OhpRgUKAtzjXKSzOZK950oP3XKa/GV6VRLqsI58c7MfNesryoPIyHKjIO8GZoEOHDrjwwgtzTYYQ5eXlmDJlivBcbW2tdU4nQ3hTgU66e5ngLGNy/vKz4bjqnzPtdgwjKWZcymvc3ZhpeKhcBThGVbTjLc6C2s6mCXy/Si9mmsBL/JDl2k5t+0IrhWTTbkqCs2maeGqC+4fQixu/V8gzgXIWZyY5mFowViXI0gFdQmcbVUKHZFn9YtFWV8GZj2Om65PSLpr0XbpbnMWCtCheb1DnlhjYuSXGzbct0G9MX8+0cZupsljsTEGWjCUWjWiP32hlZWaP6zJZpN3/+3wZSqgkiKI4X1o5Qs9P04QlNCdp8WGp5ZUkAqbMsjgrOMJoJKIl8MUihlRY9eKeyYO2OPPTlIlxtgRn/0K6anzAm6AqTgjpj6ZWVCJPVU4Dv6DnRga3aiFEr4l/z/T8Ea3De88cjIM6pVfR5Zqj+liCs2geE8G5qp6N1eUhCk+h2545rAvKqdAlvxZ+OjmYaZqOkCk3ryqATQ4m4nFuPr4fjh/YEQC3f6T+5CsIWK7aBVF89dtj8ZOnp1qX1DYk8Mq3axijjCt9mbY4e+xetU/SxhHyXMieXilIKChCU+L98hF55ardFGCapvDf2rVrAQD9+vWzjvXu3Tu3xAYInRBKGTMp4yu6ti4WtxNZXFLHvMawerX66lqondZl59GEaeKufy/0NL6XbIciUsXJwcTX55u3zoZd1Tjt6an4aO4mxzlVTFS2QObGsQM6MMf5jxD9Dty08XS8nR8QxithmkIGXscawLs5027SzHxUWJx54eno/u3Z8yk66wUW56hCELKGdpmrsljsbCNpNbV/y3aTM4Z1tpI6aVmcBcfodrQ7oh3jbDegrUT0lPjpc9Ncx3EDf8ntpwxwtOHLyIgQNfTEvaTFWXyOMJC6Ls00rBhngcWZse65zH2/oNdULGI45k4biYtwC0FYQyBrgOojqCy8tMU526UFRXsMz7OQtcHneiAIQsCiBUPRnk2y6Ve7WJzZ5H9EMLVbnzKkkzS3gRcURW2lXEPcdNQtr9MRnCnBnha8WxbFcMcpA3D3aYMs67vY4ky+c+yYhdEIBnZmFRl/+O8iPPK/pVq8YtuSQvTv2CLjvJDXeaPKkk2Dfy66ivgwq3Z6yCuL80MPPaTd1jAM3HfffRmkJgQNHQFUlvhFppF3MByWdlHUOnmQrgOrA6/fZp377NWuOWPVMU1xcjAT3mM1vWQMF7X8URDXKo9xzt7mubm8BqXNCpgSOzz++PEiLNu2D7f/awHOH9WdOaer/8jkLREajujTFlOpElT8mLRg4qaNr9ZIuqdChGL2Re+TJOwzTfF5gE3mFDHY8nGMQAo5w8ev2ZMGd8Tr1xxuMTVkCFFsMO8eLsLtpwzEVz86S8VZ42dZcJbNx4jhfi8A8MLlh1p/65SMEfUosxoQSyh9upCxONvvgL8PP8+Ot0Cdd0h33PbBAraRjuAc0RNCVYmbSP9+3KdtrwjnXs7EFXI0BsWEMkn5BFa5vh1aCK8riEZw8/H98AJV79nL3v7L4/sxtaKtPqi/ZfXcvYK2XGe7goNobvHCu0nNn0x9H3dX2l5zopwclsWZK2d51vAuqKxrxOTlyW8P66qNVNvk/51aFcHgFFHpxjgDSUUsv/ZEZdN4WPSBtVi/e/0YDKNqJwPcXk7+T/1hu2onn5vIVXvm2t1KWmjyv//9iSiIRrJQhcF7e53VQR4l8QjVDYUKY5zTQ14Jzg888AAMw5Ba/ayU+ykmMBScswetzNISoY8koOHhtLQkf4vsDpbF2aPgTObS4b3bYGVZpavgzbtubd3rtHTWNsQtATAWMdCQKl8iyqotEpyP7t8e0wQu3LUNca3EDgS61vFce+Vs2FWNYx+fhNbNCrDg/lOl7SoU1lf+HmSCID2nGuMJbYbv6yXbcf+4xXjq4pE4QuLaLGP0nK7a9iRyE5xlcdO6IGNX1DYKhYX6eAKTl5fhzg8X4PELRuCEQR0dbfj6sc0ot19ZjLNzKKfwR1zvAJsZEn3YZdYdGr3aNVeez3ZyMNl74xkSnTWqSqw2okcpFmwsF2Z5lVldSbgCvT5o5lelnPPz7ESVCHiYMPHIZ0vwyrS10jYJEygucM9joPJQILfm5z7smuimw+JD9+YWluAXrKu2ODmYDO1asDlTvFAkW3v0/AkqORi9TrNucVbE3hPQ5agyJVvQllBRBQ2SHKyuMYE4pcQ5aXBHHD+gIw55+OskjYpkjVbG/gD0HfS7b2hMsPkUTFPLa8rm3dlvSoMgdMeLxblIY+85dUgn5jf9yguikawIkV63iLOGd1Umz7T7Jc/F21oKY5zTQ14Jzq+99prweCKRwMaNG/H111/ju+++wy233ILDDjtM2DZEZiBamGcN74LPFm61fssYslOGOBOkAU6rAJ8IggY5tI/SbhbFIq5uQlayWMPdHTTZnr2Hj+ZudrSpbUiA5C6KRSJoiMeFtT95tySCUb3aCAXnVWWVrvQx/Wu2y7Xg/O2qpIbczXWqVmF9dbpDi+uD04cO//METL37BMaCKsP1byYztV/88gys+8uZwjaykj38h5c2qrp5EKRrdNm8J6nYuf7N2fj5kb0c5+sbE7j6tVkAgGtenyW8N5q5ihgGw9zKytA4166aTtJe5DoeMdyFj4hh4OvbjsUpT02Vns8H+GHCHEwv9fs/Nx2Jqro4WgvcdGVWEqKcoM8WSmKcg4BO0j7ThFJoBlKKRoVHCkGMszg/e+kh+PV78wAE46odN9V1nHWyiPsB46odNTwp1dyy2qsgc5Cn9+ug7pGes7ruqEFBdAuOrNqpnzKvhiCewnDKwrpmpzPZaXNKcVld32jNAwPi2t4AK5gmfyf/Z121/VGfzNuQfDb18QTz3Y0nTFTUuhszyCV8bekGAf8mit0mnyGi6CCeVDo5YVRzN1uGVy/PvmVRTLlH02cM/pjmMKHFOT3kleB81VVXKc//6U9/wmOPPYaHHnoIN9xwQ5ao0kPv3r09Z3BuShDdGZ9dUWZx7tRKnEGc/wiQn8IPlmCdd2vTDGtcsmzTyT50mClR0i8edY1x63kUxiKoaYijIZ4QCknfr3ZmkDyoo9jlzitEZWdEyLVQofPRqKxrxPLt8rq+PBKmiU/m2UoNcos0Y7anugETl5bh3EO66ROrHDP5vxuTSjOEuq5TfkGXiRO95/FL5O7NousKYmxyK1qIZjJES5ReMqiSg6liVu3xgH4SV1WA1aCn6/6eDvjnoPP2HTGz1M9YNILWzcUPR7adkX2IJoVm1lVMmeo9/uqE/nhu0ioHDTrWSJ3nEE+YaF3iruSKcPOlXYukFrMwGrFdtX1Y2uiaqI46zlR/jozxGUgO5rVPvrUX11OdpgUZqPuqk3A0SIjmNi+80+WoMvXZPKy3M5M2jaJY0goaT5ioro8z33n6W0rTNygVEmML2QKkcT+xaAT1jQk0xBPMftWYMLF0q8Z3O3WJabLfkcFdWzma0lONrzyQME2sKqvE/xYlDTZFGh4qKr4vW+FqXoYxDFj3596WVZjoIqzjnB6a3NO7++670b17d/zhD3/INSkHFEQWZz62S2ZdEyUvAURxksnfok1GJHwN6qzObrl2ZxUlOBtasWg6Li+1DQlLACBuijLBmUdhNIKupcWu7bTgMhyJ/8m54KwxvFv2cZ7ZjydMZxwlRBmK3cdWtVu5fR8e/PRH7NhXZ1lLHQof3hpO0SoSFGkEqWvz+57p6wo51zX6b9oCxo+kYzEG5K7abgyMAfW7zPYcV8U4e4Vf119Zu29XJj08DACf/fpoXH9MH9z30yHWeXWNUPl4orCTolhU+O66lTZjfrutAyC599LWNhliEYNLpBWxrqf3e69g3YhZerNhcWbLUXljzZyKLP1rB3V2Ci88MuHamQ+u2rKs2klX7dx8Nw3DQPOUQEgLzgBgMEIlcFivNgCA1qlwOFvIZgVOID1ruZWEMsH2mTBNYZw2DzqrNvmOjO7dlsnizo8F2PPYoMZ//Ktl1nlRjDMP/j3WaNAbNLw8ey/CvB077g1BldA7UJFXFmddDBs2DBMmTMg1GQcURIwi/yGSCZ3NJFpBh4tiag9sVhh1xLuK1rmbm84d/5qPnx/Z2xprZ2Wdsj3gTCYm25BIHVcSk1fXmNDSoI/o0ZrZGGkG0+teRj5AojjaM4d3we9PG5Ts11u3gUNn/CfGq0tNidzgRWP4/R4YEL/rM/82DfXxBFbvqLISgvEu5zw/RitQdJiKoOCXf6efmVMooU5SD8jpXqweg/S5bJvTOpF0i3SjUS1c54sG3bElaXA0/LPUTTblVmauqi6Og7u1xsHd2OQ7vdqVYPb6PcJrVSOLXGtl8c0T7zgOu6vqcdRfvgGglz0+ntATVjq2LOYS1SX/N6lx0i2r1dCosMpztxwUE0oL6xHD8MQNO6aMB5rOGNYZD58zFCN6lErbBJUcjEa2/fNEgrPD4mx5LMgT0GUDzYui2FfXiKo621U7YrDmg88XbcOJqZwVpA3/TGW13L3Civ83TabPZAy2+5sklyQTqSb/biXLfUO9p3UpV3ZyKMGVw+K9HoW055oBgrdn77b903up/Vy9rSYjPz6XTRZN8vGtXr0ajY3plXEJ4Q0ioZD/sIisSS2LYlKGl2dASKuXrzwM3Uqb4YXLRznO0ZBtvATbK+qwcNPe5PWGnW1WBV5AkykDalLuoMWp+L6GuKllVUkmQqKseGmYHJNuTyae/NopdF5+RE/0aJtMqJRr5aLO+G5u2vz0498LmWO8Z4Ku9lbGJBEr88JN5dYxPmunI/6aIvZfs52ltTIFv5YvmnzeVZvuk34H/IdX11Vbds7terc7ywBf7wv8feiUA6MfTavimLaQIntk3ywrAwCM5UqCEdx75mAFLfInHRfsn7JkXsUFUXSllII61RASHFMuw19+Noz5dpBEjfGEae33OnsxD1phwb83UR1n63dAFmdSex0AKjyWXeQng1cL15VH9sbw7qXSNkGWr7nw0O4Y0KkFjuPK+mUawvKNfIyzazmqDBAmQEmhXZKKdtWm52Gr4pgjmzZfw52m12+MM2DvUfGEyTwXUViD6vodlXV4/KvlzDEe9PoiZfbsOs6s54tOfoWgysWlAy/5awzDQPtU+IlrW9jPJflbD7n2QmzqyBN2Qw979uzBHXfcgfnz52P06NG5JueAgmhz5BmGJVsrYJom4z7TVxHPK0sONqJHKb77/YlMJlmRAPTbk531QmlsLq/BP79by/TtBl6QlZVrIfXyaFdtDR5Zy7qmCxN65auyWXZKOH4ANm+Hq7Yk661MGeMGt0dEn+bnhCOrtgdliK7iRFbDlYbue+afJRPjrLA4054jhdEI2pXYH3edGGUZeEuK1+sBeRKzTEH23vi5oOXGSF3TpkSPYRKNxUMmf7cpKcSATuJ9WdXlcQOdgo6OqySgJwgmEu6C8+Q7j0f3Ns1RFIvi/342DA+dMxTtSpw5NHbsc/cu4kGP3aAQnHlGPCjBmVYG9uvQwpNFlqcgaMY4SMH58QtH4KvfHquVQT1IEKUKDWdyMDrGObcWZwCoqm+khCKWnmcvGwVQLtD0/4T0oF4bUebxyq24oOa5CIT2TxdssTKJyx4vfbxr62RYG21xpueNH1ftXGCXIHu6DBED+P3pcuUmDcvi7NF/I/dPpGkjr1y1+/btKz1XWVmJXbt2wTRNNGvWDI8++mgWKQsh2hz5DWlvTQO+WLyNaatyX5aVo9LB3acNRFtPTCasmssquLlqF0aTmbxJO5KcIik4u0vOvHUtHXc10zTlcWKMW20agwQBanxVPWEVHAoN7lHbGna/VlcDft8GP2S6tbhF+PiWsa5tdLKbAk5rHE1+LBrhylPZjEnr5gV45pKRiEUiKC6IMvftKsQpzifXpovF2eV8kIx9OuDjgHViVelnpyuIJq9Tn1cpE+TvS96pyCLpJvyQPVcnrjAWjbgqSHq3L7H+vvjwngCAXYIQHD/KOloA5r2n6GedKYszyRQMAD1dyq/x8Bo64RVeY67dkAuhVHQLvJIzbgnOmf1ukizVMpAwtPrGhJ3wy2D3hzF92+LtGesBJNdYbUMc94/7Mdk2Nf/p55zOI5flqKDLZakgGtst1AQATkmVkrLLLrEhenqu2rn/NnijwWDm6vXH9ME/vl2rvMKr42I+KBOaMvJKcF63bp30XEFBAXr06IHjjjsOv/vd7zBkyBBp2xDBQ7QuRWvvw9kbmQ+Cl/pyqrXMnxMxyqqPEbFquVHjEEQ5+mMRAzSbRj5k9fEEurdxZ3aiETZbZzrJoT5ZsAWXpJhHHoxbbY71i/To8YTpK9GMjMFRjQV4SA7mdl7R0c7KOsQTpsVAe8kWq/v+e7UrcW3z7g8blOf7p7w/lBbnaISxHvPv6pyRdIZyygrnKsTJGwQRT5htdzzZe+vQkrV+0jXsZbdIk+7lOfi1OCdpkTGt2sMDcBf0yZ6rUibdetJBmL56J84a3sW1SoJwjICYQJq5dSiXFO8oKCaUL63oJYyHJyHo5bA/lK8RufU6XLVTPyOGkdE9JWIYSt6IPG/TNBk33Fg0gnn3nQIglZgv1T5hmnhz+jos2py0qluKZKrPdO6G7CUJ02Ssm3FTM8ZZcExn3RDlXzoxzvngqu2V56GfzWVH9JIKzn6zaodyc3rIK1ftRCIh/VdXV4d169bhjTfeCIXmHEC0OYo2vpKiGGtxVixoZ5yqvC0v/InGVo1lGHrMuSMJFXee34SJxYXWDN90XD9p/1E+xpnJVOxtN3vw0yVSAZLpN8ebpMEwpP40BTKXOmuM1LNzWF40n6kXV20eM9bsxn3jFlu/vWSLNYykNfmunwzUvsYvSJwV/w7oey+Msq7aujUw3YQX1dqTxRN6QbZ5I9EbPm5AB4xIWWXvPXMwerVrjjs13itT/ssDg5XOM5fam12G78gpBtzKwRAaVd44t58yAB/edBSKC6JSl/8WRTF88iux14WIZD/7DH3vzjVCKYky5Kotc5/XgaOaQMDK0kxk1c42hHWcHaFZdHIwZ/ugnoJrTgdr3Tjdr9uUFFohHbarLhsjLxonHQVT1KKHtTA3xvVinCWlUoRgc2ywx0yTVbzreBrlQ/4Lr8krmf1GyRcn4dVVO0R6yIMpFaIpQOyq7WzXoijGbKwqbSRJ6mL3p2D0NCzOKhBXbTfw5PK/+XGLGVft5DG3uqay+/Sz+cmENJru9buqPfebKegwtH07OK2rjvrakrTa/KPVtzi7MTLq62lrrycvCwAje5TilhP6a1+TDuZvLHckeaI/0gWcq3aBUnA2hH+L4GpxTlP44GNSs40j+7bDG9eOtu7jumP6YspdJzAJsmSgH93izRXaY6Zj5ZfxcXzGeB78nu3mqk32J1E2dSFdknn0yLkHS5NXia5p9DEfDEqB43TVtsco4UpmBSU4nzOyG/501hB89uujATiVM6pRHPtXwHJu0K7auYBIySwqcwgk12Qm3cnd5Cir/BNl0RXRY81LrpQbaRmleJF07saqcW6yXApdAk55vWBwqat2hP6bVYibYOeizqc2H9yS06nLHjEMvHGtOKcT9fpTv/XGyYdn0pTR9HfDEFkB+b5cdkRPXHxYD7zy88OEi+/9WRuZ3zyjRaO4IIr/d94w67eaMWAR9fghT7pq+7E4s7/5DbA45SrU0GhaVhWVdnHisjLmI5JuHV+ZWzB9NBd1C2nQT0yHoRW51DkszpJu+Des+3lw/47QXgJquCkHVjxyOjVueh8wrzz7tr01Svpi0QgjxKoEWpp0NzJU/US48AU/oONDswFZ0kAVrhzTCwBwRJ+2zPEg6m+LoFIuyq6dsWa38DgBX1pQNyb7pSlrtNrJSFbequCcX0UK2d+dycHsv+k4a/qadBGNGLj26D5W+TBP3waOhHxODpYriKaEs45z8v9Ml6Pq0lqtUIswrtFJiKihLc7NaME5daKA4kXSUU6S95/gLc4JlkOSrl8B9VKvF+oMGZfcZyJh+jCaNL25y9Msy0BP18f2gib4SPIKeRXj/Oabb6Z1/c9//vOAKAnBgwiUzQuiuPe8pKv8nA17lNd0K22Gxy4YrmwzvHtr62/VBseXB/Ga8CES0dssnGWP2N8OwZmyOFsfq5ieGxbAbniij0tB1FCW0ZK6aqcrkWcIOm7MItpl9TYJyJPjBVHdaeLuOseO1a6kUJop0+0edeKydBGLRLRKHhFU1DYq6SuIGszaUimbPFmcXV210/uSZ1s5xCvUdMg/fVgXTLzjOPTgciH45WfdrlMqPfwNyVi1AHeLs3fYlPVpX4K1qTquKgWT6DZPGtzJ1+jJeWiqwxm49Zut5EOqYZwhKsEiX+qkpwNRuIDju5IgycFk5aiCebIvX3ko7v14sbQyCHmfCTrGWTA2XY7oxy0V1PEkaLfmdBQ8lsU5wcY4J7is2rJ9fPZ6p0JORo4oEZ/9PFieTOSdxiMf4vO9hG8BJptTIUV/NGII8pNYl3hC7p9I00ZeCc5XX32174y7hmGEgnMGQfZGxhrl8qq++O0xaFWsLqNDa+9Vr37r3lrmt1eto2HoWbUcFmdecOY6IeWoPpq3GecdkkycVODCZNBd7NhXh427q62ayzxikQga4nKhQMfinGvQz1SnVJPI9YsXpvkPCHmmzg+33jzxMptMAP+66Uic9MQU4XkvycHS5kc9bpdVdY3KGreDu7Ri1nhQMoHKQSRqpB+jfGivNul1kCX06+CMY/VrCXJbSyqBzq8A0KzQn8VZF/SjoENelHF+gnsZ0aPU1/hRSkCQjeEoo5gHMiX/BIKmaX+IceYT9wHAC5NW4eWfH2b9Jt8ew8ispfKgTi3xwY1HSs+TsZkYZ1FDYok1Tfxv4VbrMLkPdg35vx9ZjHPcZH/Lxpi3odxxTEqPIJ8AnRyM3NspQzqhpQt/SV+bS3gJ3zJNLn9I6v+IAfCcoEE9Fy9oilb4fEJeCc5/+tOfsGbNGrz99tto3rw5Tj31VPTu3RsAsH79eowfPx5VVVW44oor0K+fPAFTiOBBhAF6uaVjZSJoURTD9HtOREE04omZ86pF1HXVdrPURjkGgnYrXrMjmXzJjRHmzz782RLm400jFjUARdihVEbLI8mZZkI1KnYJtbO8UVX2mvx+JN2mHl0X1jRN9G0v13R7qeOcbhIfr1cnY/Gd9H3yq7FYsb0SY/u3R2Vdo3Vc9Ty9MOfK5GABuEV2alWMvh1KfGVl9gNniL1/+v0KsbtdaoMq3eMlp9zyMxRyGhCiOAwKTKI0aoKpni9/RscKJYPsm0Uf5b89mbLGetnCnfXrg2WM3eZFU8Avj++P5yetZo6NX7Idy7ftw8DOLQHYAkhUU9GeKZApVtMQp2KcRe1sizMNkquAnpvpuNtbCiUuxrkxbjKKYk/PTMPiTGgmdaTr4wlrvPYt9MqR8vvg1Uf1xuvfr/NAaPrwItjWNyZAPxz1M029f+ZXEof2aoM568VeoaHcnB7ySnC+/PLLMXr0aFxxxRV4+umn0bYtGwu2Z88e/Pa3v8Wnn36KGTNmYMAAsZtLiOBhZ3akF7SL4Ky5UbvF+wCCGGcfycF0LnEzFvJMEs04kgQ4Xj9QpAyJKDkYnQhjdO+2uPMnA3HRS9OtYzpZtUX4781HeaIxHdDxgjpCpaiJblZt38nBPH5JDMPAs5cegl+/N89xzmtWbYInLhyBOz5c4JEOT81R35gQxjgP715qJV+i528LRY6C7qXNsXF3jda4yuRghpG2C6RhJDM+Z01wDrAvv66+VXVq93TVPiR73sWC/AJMnwrFYRBgS6PpWZx5pQsv3HsaXzIQPX/ZTMX54QrKC8pBM8aqmuBNBS2KYujcqhjbKljvtZ88PRXLHj4NxQVRa2+MRTMb4+yGSct3AADu+3gxhqVi3kXkkEP8frSnOiU4B+SqzXhiMFVTWE5DNoboqOz5MsaZVH/kO1RV12jxRLrvh99frzumD96asR5H9WundX0Q0DEYENQ1JrRLFNrJwZxfpEx4HIVIIq92w3vuuQdt2rTBa6+95hCaAaBNmzZ49dVXUVpainvuuScHFB64IIIKu6DV1wT54eG78mVx5joZKXDnc0sOxg9Lx/gRAdgtAylPB2FwRQIjbVX6f+cfjNFcYiGpq7aCs3/w7KE4pGcbJY1Bgq5PquPGrOWqLYl5dsQ4a9Loh6doWSwWKj0JztTfPzu0O5MsT+96b4T/dfwKV/ro+RtTzOU/n3ew9rjudZy1uxKiIBJJO9FeOkhnq+taWuzrOrfb1S0lRsMt/p6fD0FnW6afY0yzXiv/7NPJIUCeWe92zYXHk3RRwn0GBUoveSr4GP+gGeP9ITkYIF8TB9//FQCgNvUci2JRsaCag8dAeBBhjLNCcALY9ektzpYFeW68oNzIxTh7UQLKWjLlqAxecI5bPIQuD8jzod3bNMe8P52CV686XJvWdOHFC60+nmCVc4q2vOKEycWyfyzZvEReCc6TJ0/GmDFjEI3KtdixWAxjxozBlClTskhZCCvG2UtCoAA/tryA4D3Gmd2Aigsi+PgWZ11Q/tvCl+7hLc4iJs1zsXuF4My0EzxvnXJUzn6cx+gSK5vL9ayIuqCTV8lcln46oivVxnleVm/T/i0eW5eBVGV/l6FVM3F8lSdXbYkSRf96T80BOEvt8IhGDJw1vAuaF0Zx0uCO0nadW+sLfGqtefrWnUjEyG50QoBSevsWzthLPRLUNLg9cxHcYpb58mRBVymiLb60xVmlwHEIzulYnFOdtS1h3UCjku9eJmN/+beret3bOStq0FTtD+WoAPl+SSzNRMlbXBDJmzhQKzmY4Bw5JuMD6O8Jb2n3AibmmhqKz7Lt5TsqtThThwkPSfrdV9to8Qe670fkRdKquCDQJJ1u8BqDzArAyR/CzOQSV30gjGPOJPJqN6ypqcHWrVtd223btg21tf43gRDeIbI4uwnGmVRSe02oE+FilmSMAM+M8m6t/Lii+Da3eDD+LF2zUQWh4Cx11VaML+iHZkyXbdWvJauDhkYqOZiG1lvUxqHQcJQRSTbg35/uLGlHCS90jK8IZARZ4jsvycH4V+F1XvtZYjrv4NlLD8Gce09RhlF4+TCr2kYjAe0VWZSc+aHSsfD5ZeAyYXF2A79vBs2cRSR7tGpP5RnKdBhiq44zt0boZ0k/gmy6aatj1g3l73SxPyQHA9TvyzRNq6xdUSwqzqqdKcIUUGbVTh2rlZTjC2pLpF216W9sI/e7hcQLS/TgZM4aTCK+1Lhk/TcmEhbPQ8/xl648VE57HgiQXngCwBkOkvzD2Y63ODN95JV0t38hrx7t8OHD8e2332LChAnSNhMnTsTUqVMxfLi6zFGIYCFyhXXbj4J0F+O78l7Lj6WHWCXeuHY0WhXH0CVlPeOF11bch4AfV0SHV+08+Tj4YYRlmZ0P6Vkq7UfEVFXX28KizJLqF/VUVnCZcoA+LrKk8R8evp+4JTiz1+lOweaUy32Zi2a+c6vkXCkpEnvGqOokt2nOPluePD/Z4r1ClVWb7pfPoKyCW0y9Oqt2+jHOOjRkErwl1tO1fq15LrerEhL8Jo8qiGVacKYtzpSrtgeLczrWUbuOs1xhGpXQmGmonjT/qoOWFfYXV23VfH30i2Woa0y5ahd4S1aaSYgSPxEQEmsbxfkO0vG+oEFn1Z6yYod1PFmOym530iC5hxIPnQSfZD0y5agsV2273U+GdkYfScLOfJi6lx3Ry/e1OjHOoocZWpwzh7wSnO+55x4kEgmcddZZuPbaa/HVV19h2bJlWLZsGb766iv84he/wJlnngnTNPH73/8+1+QeUEhYWk/7WC4Xpr8YZ/s3YXiOG9ABC+4/FWcM6wLAacnkORCH1l9AhluWVZmV0dXirHnP7UoKle6fom66U7Vlg2YG6+kYZ8kt0sKy0FXbRXAmP724N8r6K6MyaItw92mDAMg12Srt8j2nD2Z+88xZpi3OhpFerBvfl02Hm/eJwloWQFZtIFDvaU9jdWldjPvOGuK7r0y5DKqYZtkW5fYIu7dhPRCC/gYwMc5MXLHiXvjkYGlZnIngzH4IYozFOZhMxV7hxYsoaKpUz78pQTVdX566xvr2JC3O+SF4qLJqk31X9s2Rlbn0CjqrNklcRn6Tb2fbkkLcdop+wl7Zd4j+ltiCc/K3aZr4fvWu1DHey0I8jt9yf0FidJ+2+P73J+KCQ7trtdeNG7di3Mlv6tnly/zdH5FXWbXPPvtsvPDCC7j99tvx+uuv44033mDOm6aJoqIiPPvsszj77LNzROWBCXGMc/bHJ/AqOPNxlAUxgztHxuEENG5z5xklOjmY1bfAra0wFkF9YwLXHd3Hcc5ODubmqq08bUFUr5Ltx9lRs4IoWhXHUFHbiLiXFJAuME0T//h2rfVb9rGkj4vcz3lBWUaio52mNEU3o5OZidC2eTL+UWaRUMU48x/xdC3OXjnkPu1KlBZxLyiKRXHW8C6orGtEj7bqzPhuWbWD2EuyaW+mrdvT7zkprb7o/aJbqXuFARENIqjca/0yVdeO7YPJy3Zg5rrdqX58dYOiWES4ztg9WtdVm0XrNDxmyDxt5ARnJmERJUNm0uLcnouzVr1u/nWGycHE0HXbLYpFxHM7B8LIzspkglBxjGvyf68xtF4xf2M5AODJ8cuZ442Uxfnqo3oL+SEZZN9JWqlHsvzbLulxK1Z70x42F4tszudD1nsA6FraTHu/pD/RfAlUGmRO2IYD+8I8ue39EnklOAPATTfdhDPOOAOvvvoqpk2bhi1btgAAunTpgmOOOQbXXHONVds5RHbw16+W472ZGwDwWbWztzL5TVa0GUYMuUWTTw7GW2NsVyCxC7A1LnXP7VsUolCwqYm087efMgAnDuqI/h1aODZ8WS1GHroffTemSXa6fcsiVNQ2uiaPUmHdzipU1TdiaNfWAIDpa3Yx50WC8/erd+KrH7dbv4Wu2poCMX9Y907o/lwVB6nnJ/sgqyy6/BXpxiZ6XYGNCTOt98vjuctGabVTaf2jaVqco5qKp3xFEZUM85WrxPXc/UAl1OmUghGhuCCKxy8cjuMen5zsxyd3JlPesHWc7b+9uGrz4RBeIHPVZspRSTJsB42fH9Ubb0xfb/32EooQumqLobvPJAVnZ1s+23o2QDJ9i17Bht3VAID3Z23MKA0ka/u6XdXM8aRhwZn/hofolDShJ9WaCOKk7wlLy6xzY/u3dx3Dja5sQ1dnbQoszio9jmhv2B9KyOUr8k5wBoCePXviwQcfzDUZIVJ4btIq62+aaaioVSdRChI8Uyxyh25WEEVVvTjWJxnjbP/mmUrDEpzZ62iL88PnDMXni7ZZv7u1EX9EZdaRAZ1apsZijxNS3DZVXSaVb9ayOIZ91LsSMQQmqDimNASQ4/86GQAw596T0a5FEfZUNTDnRQLvla/O5No4++Vd6HVp1BWmWMFZ3ZY8PtnrUAnO/LTVid0f0b21ghZvXEE8YWrFOAcNt5qS6TD6JA9BVi3OAQ5GC1/NPFhsRDRcf0wfy8NDZaVt7iF+nYeXygoyyNYIPf3pDN/KeG2OhhZF/gVnMgzvqs0kB5MI90GjX4cWmHPvyTj0EXnOFwJ+LgSh1D5jWGfre5cvVrt0ofMNLYxFhHvSNWN742hOWMsGrHUsIH3m2qTnx74s8mI0GhOm5f3l9VskU35X1No8Q3FBRNp32xIuX4hk+HxyWdb1DKC3R8IzCF31Cd9K3gE1SX5Yu8t5QYhAEKokQngCzTT8Z86mrI2r46p9ypBO0uv5Os7O7LDJ/x0W59QOduOxfXHlkb05Bko8lshdie6WT76lazHT/QDwzfi4Wpkiks6c6Qc0/VvKk+5UvCZUJ35ZnFWbPbZqe6UrDbLxRKDbud0/eQ8yJkwleJMP24mpJCrXcq77onl9108GKWhRkiqgzQwsxtkL+Puiy59FI/5cS4lb85MXjQSQ5RjnAPuiBcRSD9ZS0f0eRTH2Kotz80KxzlznvphEWQFzEPQ86N6mOX4ytBNGdG/tKVYzHZpsi7NCcI7IvyNBg872r5rffEhREKLCwE6t7P7ySPhIBzr7ZXFqPfLf26uO7J2T5yCKX80XJKg6zl4FVNl3aOGmvdbfhJcSd63nrZVPSh/dbxTNx6hy5pA7E3WbK2XKgYC8tDgvWbIE//jHPzBz5kzs3LkT55xzDh577DEAwPfff4/Zs2fjiiuuQNu2bXNM6YEHehNyiwUNErzgJNoM+3dsgSl3HY8Xp6zGezNZ16WIwX52eGuMzF3aqhmYGk/GQNEQxdjRAmTrZgV44KdD8MCnS5ix3eQZXVdtt6QZog+waZqW5ctvDCzt3khiyPnnKfpYGgZXG1LDVfvu/ywU0sBf6Sc5mJtgSZ6e7H3sq20QHgeScxRIls9Yt7PK+k0gC0GQwWvSnm0VtTkRnPk5WUx5h/iNcf7F0X1w2RE9LeaqaTpqJ9/h+zeMQX1jAqXNC90vSEHknkdXAVAxjLKM8DqvIZPhOnTfBoCXrvTuup5OMiByP3w4A73W6eeazTJNqvnNnwvCS3Ns/3Z4akIyJGl/gc58LbLcg/UEs0yDKEXyUXdBxzh7pU/n20wszqJnr7vM80lw1rE4//Pqw1BTb/PWShd44qqtSCAXInjkncX5ySefxMiRI/HMM89g+vTpWLVqFXbu3Mm0ue222/Dhhx/miMIDG/QGVicpgZAJ8Ly+yEUuFo2gV7sSlAisKQbnqs1nXpVltia/CeNEb8KVdXGIWE2h4MzRf+yADo57cbU4a65WR6ZmPuOqZHMlMTFxnzGw9ZSVhlhidGKTeXJEj0FX2PObHIwRnKm/iwQZei2Ls+BBVtY1YvyS7Y7jw7q1xstXHoqDuyXdrguiERzUqaXjXQ0XuGWrrBx+yiDtqqr3fE264JkX2ivDb1btiMF5d2TR5HzlmF4AgHNGdg2kvzF92zF7gl+0pGqLq2LZZS7hOha1IFy1dfqeunKHoqVeH15hWZwTehZntwoKQeD8Ud0AAL86sb+0Db8/BmGdPKx3W3z6q6Mx4fbj0u4rX6CjVCmyLM78tZmgyB1WHefcDK9EPGFaCjzVoy2MOfcbnW864aVEfTv5GjEB+eQtwe/Jo/u0xe9Ptz3K7j1zME4c1IlRiqroz0cvhAMBeSU4/+9//8Odd96JHj164KOPPkJZWZlDmDjqqKPQoUMHjBs3LkdUHljgXcBopuHasX345pmjQ8PibAmggusjfFZtiat2PGHid/9eiEc/X2r9ps/T4y7dWiGkta2G1Yim8Y3p6zFn/W5Xi5kuQ+hW01MY42zaz89vjHMD5YFQIOEyxBZnw7WNjtycSJgCjwFNwTlB/21fw7tsAlSMs+AWX566Rtj/kC6tcOrQzq50lDYvxJx7T8Yj5x5sHVNpzP2UieEzBmcD/C0UFVDxqx4szp/9+mjrb54Zy6bFuWtpMyx/5DQ8ffHILI7KQjS1S4pspaFo7hIEYZVN/u27G9e+WzfzZ+lMhyaZxZl+Xux3JPOM6xMXjsDMP56EEwbKa+Q6lK4BkTWse2tPXhD5Dp25QQRnNwV0JvHYz4Zbf8cta2L+CUkN8YSw4gqPpy4e4av/tiXJUAXRvTv5GnEfup562QBvbLrk8B4MfcSgI+J3RGEh5NJVZZX4bOGWrIYrHcjIK8H5ySefRElJCb7++muce+65aN9enIhh5MiRWL58ufBciGDBC1E0A6Fbky4IyGKc6cRJqhhd09TLqr2yrBIfzN6Il6augWma1v2T+3ZLBtOyKKbFlPIKiZ/9fTrWcxkreei6HPGtdBiAuGnaycF8uvLurbFdlG0XIraNSJDlb0voqq1BU0MiEYirNnFVn7N+j1JgFz3HPRJrrheXznYtiphYV7WrtnemIKhyVF7APyq6bngkYmgzhcRiDzi9RrLNNBTFojllZkW3G4sY6NQqyWwO6tJK0CIJGdU6t8O4agcsOdPj333aQF99pCPgkPvh1whbxzm7FmfDMNCxZbGyDb+k8ykhUj5B57nwmZy9XBsULjq8h/V3whKcsza8NhqpGGfVXnhwV6cnlY5SuzRlcRb1rO9J5zpM1lDbYCszx90yFucd0o2rXZ8kVuR9+ODZQwEAlx3R0zpGLm1MmPjVu/OwfPu+4IkO4UBexTjPmTMHY8aMQd++fZXt2rdvj2+//TZLVB3Y4IWoKKNtz96OxMfzEUbmzWuPwIiHxiePpegZ2Lml4/r/ztvEWOf4WsfkI0myVAJJRpwIbOS+/TKK/EYosur+4b+LlH3oW5x5QZk/77wmkTDtGqY+BauXpq529quov2yaJt75YYOj9Ivoe6rzkY0LLM66JVyYGOvU/T/yvyXCtuT5ijTZMuWG1+y7tGJH7artfQ3mIsaZv4dO1PqL+XTV5t3ovZTr2R8gWhKGAUy9+wTUNybQokj+eZc9bj3BOX0O/qh+7fD96l2OjPHFBVFcfFgPNCQSOIiL/88GZHoo+pbpNb5wU3lmCdIEvz/moYyVF9B5LkWS5GDZDpUtjEZQH09Y38w8CtW10BhP2DHOinZ+9wzCbwmv5z3rJBTkk6WetjiP6FEKQJwzQbS3nz+qO4Z0bYW+7al9MX9u7YBCXgnO9fX1aNnSKfTwKCsrQyyWV6Tvt3C6SNt/ZzMxCs/sE6G9NWWZI8LJhYd2xz0fsULonuoGK9EEANx9GpupWLS5mrDvX9fiLGPd+Y3QTy1d3Q+nW1IT0b02JuzkYPU+k76tFGS6diQHow5MXFqGez9e7LhGpFTQcR9PCuByQV0FUYyzm1XOy/fYq0t1UQGddVrlqu19DeZEcOZ+08+jMBbxxRTm2uKcezhvOGoYKIpFUSSIKaRB7wmdWhVhe0UdAL2YuSCszOeP6o67TxuEAZ2cwvH/XTBccIU+0pkHorV27IAO6EBlt6YVZrLyh9mGIzlYHgkL+QSdx0LWjm4MbaZAhrO/Tfn3ThvidFZteTuRftfLMhVdr21xzqO10KtdCWat28McE4V+yAwFgzqzXkRu+/VBHVtgZZm4AkkI/8gjJwagT58+WLBggbJNfX09Fi5ciAEDBmSJqgMbPJPNLPIsZsugXVwAscBAmB6ZkELHHrctYeO2RIxGwjSt0kKk712VrCuu7p7MM5utir3XGtV21eY1sRoW53jCtMr7rN3pb6Oly4GRfZ/f/mmXa9mG7tdVu5HSzqv6chvTzeJO5oqIkZIJpelYnFWXduQ8J3SQC1dtfn3RsaFJwTl9izPR4B/I8BPOMeWuEzyNwWe+9ouRPUqlZbHSQTrzm5+Hz1wyEm9eO5pZ6/mUpZfg8N5tmN95JCvkFXQUQyT/gsGxEdl+7eQdkvmcj+/0oc+W4H8LtwIAogrlcLqKHNF7c+ZyEY+RT+v1D2cMxiWH98B/fnmkdYwmj7hq625hbo/1UyonSIjgkFeC89lnn41169bhySeflLZ57LHHsGPHDpx//vlZpOzAhcNVm06SksXZU9vAavZFsWVuwsnLPz8Mh/Vqg3/fdKTjnOhS03Rm1Z62aqezIYVDe7VxHOvboQRXpLLwEvRs19wRZ827jwNslmVdjbdTcFZboIHke26XKjtSVefPikJnEydus866yu5fBNP0V495T3W9w11Xl4UWuWrLIHsLpmlKmXavlmHamqpiOv583jD07VDiqW8/3g7pgr8Feh8pjEY8MYU/HdEVgzq3xFH92BwY95w+CLeedBC+vu3YdEhtMjhxkLNuvbY1WJIZ26urtt+ZpKMI84u4rpuJADyTLdpzg47rDgJ84rD8o7DpQO6qnWWLM/cWRaM/nqZ3RpBQ8V8i4dWLZ4jYU9v9+ciuzRXalhTiLz8bjkN72aV06f3EzeLMw21OFhdE96tycvmCvBKc7777bnTr1g133XUXLr74Yrz//vsAgO3bt+O///0vfv7zn+P+++9Hnz598Ktf/SrH1B4YUAnObu6AQYKvGS3KZupmxT24W2v8+5dH4bDezvrfcouzN23vExcls0f2aJu03h7Wqw2+ueN4YYmqa8b2Zn6P6F4KADikZ6l17OObx+Ks4V3whzNY13IVXGOcBau+MWHa5ah8MrX0ZZbFmXfVphqpnilPgg5NJz85Ff+avYk55lbiy+pfUMdZXt5C3Idp2u+dR6923oRbNsZZ3q5raTN8eKNTEaRCOoKFX/BMToHDVVufu3n20kPwxW+OcbhqtywuwO2nDMBBndzDffYHXHx4D4fHge5zpFt5tSAHYcHRZQx1cd3Rfay/00kaz9+b6Fa9eo9kA/xelU9xnXkFjcdC9qZcJgcD3BXgANBKwFdkAjcem8w7pJr6qn2BNxIkob8H6NRxlicHy++1QN+bZRAKwOKc7/fdlJFXgnObNm0wYcIEDB06FB9++CEuv/xyAMCXX36JCy64AG+//TYGDx6ML7/8UisWOkT6cGTV5lLnv3zloXjuskMyTkcdb3GmNuJ7zxyM8w7phhMHyct1uEG0AZmmff+iTejGY/s6vsPtU7Fw7143Br88vh+ev3yUYkz2aiLQdG5lZ1CNRAw8d9ko3HBsP53bSF7jFuMs4B4Sppl2OSqaGTYFx+jfjfGEkoeprGuU9u2NJt12TsFZBhlTaoKto0vjZ6O8ZaCX1Y0VwStDlw9ZtWOMAi7i2XslFAyS8+LS0T2ZY6K64yLQc4aeXzrPlW7i9y2M/f/s3XeUFFXaBvCnuifnBAwwwwwMOWdBQMICIkiUpIKAYQ0YYVVcJRj2c02sqxh2VwVFBZEkKCqigARBguQoYcgwpMl57vfHUDXV3dW5p6tn+vmdw6Gn4u3qrq5669773obaI2a46oXbmyuv3QnKzc8lrf6R6mWcbe3hLTw9XCf/NlnUaHr5btn8I9T6SL31Md9843w172OrZuuBklYrDXunqbpyRPP77OCDDV/q46zFpKn2jfc8sHVt1IwMxtC2dWyua+udvafcl/v2+6+KfC7DVuPGjbFr1y6sXLkSq1evxsmTJ1FWVoakpCT069cPd9xxB4xG79V0+jvzyinzm3h5bNpHv/yjUsvRt3ktfPZbuvK3+kf6/h62s7A7QrMpEYRSYynPrx0dgvOZBQCAAS0TcS1Pe/ih5LgwPDvAdi2x+UNYOaDp26wWQoOMmkM4OMLiSbWd+UB5sKgM5+ViU16tG1bzIK1MAKv2nscTC/9Acxvvb8Y3+/Dvse1U67lWJoeHo1J9z119cCCEQKlGddfMwc2dfvpr0iXCzoXf2fsCV9+fOywCZ7MaZ/MWJeQY81wNjo50YFLLrG6q7dC67t+IJceF2V/IRe4Ezo401VYv8/GETi7vqzIxOZjr5CDPMjeIt2uc7e/PW2WSv/K2zi1nr3H2zlLTPB+2H2ABtppq+/a5YFIZdeM9RwQH4Lfn/mL3mNp6awkRzuc/Icf4VOA8ZcoUxMbGYvr06Rg6dCiGDh2qd5H8nvlNtnlNoLdMu60pNhy9jBOXcwE4PxRWt4bxNudrN9WuqH2U5z8zoAme+mq3Mq1HoxpOlUPNWjPmwAADZo9u6/J27TXb03yvHhiOSt1vUahqltVKywQeW1D+kGX36etWt/XNrnMmgbOrzS8dvYlWN+nWWqV/81pYfeCi7W1A+9hpNdO3xzRwtr2ss3GCqw9G3GH+nVM/+AoyGhESaL1M9p66+zNX7wmtrufA9tQ1OPa+Sc/d1hSvfn/IZJq6RU1lcCdrvPm5pnXjqv5NiQ3zTlNZZ/l4rKAbh7oi3Dh45r9Zjrbm8BTzzzBX497LW3lm5GNi63rq6THN1SNLaF0DHR/H2bdPBpOm2qr7WsfKbX0ZJRe7b7/9KsmnmmrPmTMHe/bs0bsYpGKexOXQeX0GWA8LCsCIdnWVv539MZzcu6HN+drJwSyzapsn0nFnLOvsAtMLoRx0uduHznz1mlGmTx7V23+qb3l2+leGt6xoqu1iH1iTPs43/rescfZcbbbM1nfB0T7O6mLK+1Lvc2jbuuarWJg0dxv+1MgU7koG9QAnapytHZuwIO2WOT7RVNssq7bcv9/cwr92wdtj2lZewfyU1f77Dq1b8dre73BEiPefzbvz9XZk7N5i1VO8UCvnmN54r+w6+bfJ/LN351rvCvPPsFN9y9ws3qpNlfdj66GU0zXOVq5bvZuUV0bca5IDxnLbjvQBLy+XU8XyOq1xnB1lM0+MDtd5f+FTX6mkpCSU6ZC4hqwzv8nWSsrlLe40MbXXz0XrR7dMI6u2+sbK3WZSuUWmgbN8UXL3Cal5udrXizWdr9r+E30bYffM/hjatq6yX/NxBh1VplFra/79cTnxmI31+je3zC6slMPR7dsZAsva975OdEXt2cY/L2PhttMWy4QEOn9z7UxTbWs12taGotBjHGeL4agMpk21E6ND0LdZLYtxfRMign2+qZ2ePH1kHOvjrLrRc6H/fWV/nI4+LNNikRxM4/2px7nXTnqkP54z2pzJGq93c3fzz1ArcPdWCeXTwNa1w95vwUM9TfO0WNvS+3d3wKIHu+LhXhWVHdo1zqZ//3FK+75F78/RHnXxnB3i1dY7Y9hceXzqV3/YsGFYv349srP1qdUkS+Y/lPfcnKq5nDdqhdxqgudKgiV1U20rNc7uML+/q6waZwBoVLMiIDG/OZQDr+MZ5c3gz17Pd2m/psGndlNtVz9CWw9NtD67mBtNKB196mpS43zjD/U+4630F1r1RA/N6equAc4+RQacC5wDjAaM6mCZfEz9sKhhzQikxJf3K9Wlxtnsb5Ma5xs3hB9N6IgfnzQdSspXAxOf4eKPkLXVnN2a0c6Nntb2Kvs21q3kYBZZtW0Hzr4aoPposXTnyDjO8m+v3sfQ2aHhKpMj3biMdq5zjla6hAYZ0bl+nN1roPm5V2ylC5KvB86mTbWdrXG21druxjIulYps8am7khdffBH16tXDwIED8ccflZtsihxjfhNirU9XbzcyWjvKmcDZPPi0F4tq/WCVCaG8f3l99XY8/YMsN5F2v0+O5fqmNeXaa6VfzXNrr1ofj/nFzNXmQ7ZW0/oY4sKC7K6nZtLHWd6nKuZvXjsKU/o1xmt3tDJZz9pnFaIaqs2VVhomTbUd+JXWahKr/syjQwOVMunRhMv8Am+eHMz6crzsO+rebvUdXjYyWLsJtbM/aa7VOFfuZ+rWcFQONNWuConsfD1Y8GUVgbPONc4OLOPI53xri/IWWT0auZ7JXj4WZ65Zf6hu77fA/HmW++M4O8bX+ziry+fsvYLNGmcdkoD6C59KDjZ06FAEBwdj06ZN6NixI2rXro169eohJMQymYgkSfj55591KKV/sTWOs5o3fpucaaq99m+90OP1taoptguolfhDQNVUW+Ni6n6Ns+n7KSmVa5zde56l9VmYllW74O6O76vdVNssOZirfZyd7FslB1yO1j6py6XVx9lgAB7/SyOL9azdXKmbZ7vSN05d8+XIDVxMaJDFNPXXyCBVbNMn+jibDUdljbf7FVY16sPqzG9w2+RYzemO1Mip2b0p1eGe1ZNZtbW6+JhnMvdFvh0q6MeRa7avDF/k7NBw1rw5qg36Nb+Ifja6NNnjSPBpbxmhajwsSeWJVh3lSFZtZ9b1JSbDUTnbVNvGW2NysMrjU4HzunXrlNdCCJw7dw7nzp3TXFbvp4H+wiJwtpqAofI/D2dqysyHO7FXPK0moWVCWGTVrswa54IbY1V7uo+zOWuzS9zMtmySVVvepof6ONsKuLU26ewFyDQj+I19qqZZ+95bO9IhJhlB3atxdsR9PerjX2uOmEwz6VYASUmS4u4DElfYyqptK3BmU23H3d0lxeFlWyVF461RbVDLLMO1s19Vu8OlOLc5j3CnpsUyU6/lOxjYqjZ2nrqGzqmWyZp8BW+PXOcrNZSOFMORzzkyJBAjNbryOMOR65G9Bw7q0/Lwy7eZtDSyR7PG2cGPyUc+Tqvcaapt696ica1IAO61wCFtPhU4nzhxQu8ikBnHa5y9EDh7MFuqOa0f8bIyoTTX1cqqLR+KWxrXwK9HMjCodW3XCwgg/0bg7G7zVK2YUTIpt/b23a2JNMmqfeN1kVmzRldvaq3VIo3tlIxNxy5bTHe2xlmYlN2yj7O17721r1VIoMHuMrao9+fIMYvQaHpr3h9frk3UI9mm+SEoUl3Nw600GwaAwAAfv+vxIfUTwp1a/g43b6YB+0GGHsnB3EoiaTD/27KwRoOEmYNbuLwPb/D1Wja9OHJYMvOLK78gDrFfWG99zo4kuAyw85BT/ZDOmaAZ0H6A5eh795UHIdZojePsrpmDm6NGZHlelmJGzh6na+D82WefoWHDhrj55psBACkpjj8xJ+8wvwmxVtPvjR9wd5KDmdesmNP6IV+47bTy/pUaZ9Vi8rF4Z2xbrD5wEQNbORc4m7+b/CLP1DhrNbd0pElnlJtDx5g01b7x7vIKS02W8XRWbaNBQqjGRV1+Qu7o7kzLbrlP68P32G+q7QrTwNmxdf4xvCWeX7ZP+du8dYT8tztNWV1l/vtwPa/i5tRW4OzpsUGrG0//7Drbksve72pekeXYs5V1qejWMB6b/ryCsZ3qubwNi6zavn3PTU5ypCvCwm2n8c87WnuhNLapzxNr3QO03k1SbCj+b3grjTmuc2TYNXv9c+/sXA9HLmajZ+MaTu/ffvczG+tWoZPY3sMHc9aOQYMaFclgzSsvyH263pVMnDgRH330kfJ3gwYN8Oyzz+pYIjLnaPNorzTVdvGGPyzIiMRoO4Gzxg/W4h1nLIaI0qq5jQkLwuiOyZq1fs4oKC7/gXM3q7a9C4q1m4ep/R3vc6RF67tiMeSWq1m1rfz2n7qapxl4yRcgR78yWrXljtRcOVTj7EKDVXWg6eh7uPumFJN+bOobBoMBSmH1yBlifpxuaVQDgUYJbZNjbK6n5/B3/qhmpHb2eHPv390ezw5oavfzW33gosU0V84HR3x2703YOb0fWtaNdnkb5g8OqtJNtxprnKs+9Sf4zxHagbDW93Pjs31wiwvBqS1aD6fjzYJ5ew85gwIM+MfwVujfItHp/Wu2XHHwd8TXzwX1fYazrQ2t5QBR30MWscbZ43QNnA0GA0pKKm6sT548iYyMDB1LROYcrSE0//2ubSdQrcyymHMk43ewxoVBQCjNZCuyaqv7jXqW/APnykOIrg0qhj/SulBM6ddYeW3tOiI37XE1cNcKPvOKTGucXc+qrb3ehqOWzbSBioDL0Yct6uWU5GBuXG+C3cyqrb4gRlvJZK9F/XbN+zjLf+lR42z+nYsND8SuGf2x5OGb7azn2zc9evNUEDp3Uif0aJSAV63coJsb2Ko2Hu6VZne58CDLh1qV9ZEaDZLbibsss2pXze9fFS12patKx0VdVj2TsgLagbP5UIyV+ZBT63NztDGSryR7s0bdFcvZcZytNe1Wf1/caalJ2nRtql2zZk3s3btXzyKQHdbGxjNnfoO74tHuHi9LZd7wp9Ww7B8oBHApuxCAehznivnu3lRZezuuNE8NVtVwal1M1U+g7Y3h6upx1sqqLTc/DwowoKikzPWs2jbW05olH0Nr/YOX7DiDnw9dxOzRbRESaNRsqu3IeNbWjmWAUcKDPRvgcnYRGqrG0HaU0SBh+eRuKCopU8bZdowqE7ikniqUv/W4kFrU5EmSzSba5F29m9RE7yaeH1Lwib6N8MP+Cx7fbmWpLk21q2rAr4dRHZLw9Y4zehfDgvqhmPWWD975nLUSOMaFB6FuTKhynXS2mbEzokIsr4EO1zj7eG8fdYWw0zXOVvqKs6VW5dL1zqVv3774/PPPkZaWpvRv/uGHH9CnTx+76+o5HFVeXh5Wr16NlStXYuPGjUhPT4fRaETDhg1xxx13YMqUKYiIcP5m2Rf936qDTq9TPyFcqb30pLtvSsGC30+7NR6hNVrNrIUAzmcWmEwzT7hUGVypcVZf2LSHbqh4be2CIwc3roZVpRp9nOWANORG4FzsYn8bZ4N5+cJhbbWpX+8GALRNPom/3pJmUlvuzL6sHcsAg4Tnbmvm8Ha02GsGq6VGZEVLD/ObLfnzVb/XNVN6ulQ2Z5l/pXlj7xm+fhi1Wh5N7tVQh5I4prp8L6vJ2/CK+3rUx4CWibjv0+0AYNI/uENKLHakX8OI9nW9Xi5HHtJ768GOVuBukCSTexV3u5jZUjcmVGP/jq3r6+e0SVNtJ4+h9RpnH39aUMXpGjjPnj0b169fx/fff48TJ05AkiRcuHABFy7Yf0KtZxO+L7/8Eg888AAAoFmzZhgyZAiysrKwefNmzJw5EwsWLMD69etRs6bnn+B724HzWXoXQdGybjR2Tu+HGKdq4Byj9X1S18zVjw+/sVzFfHf7v1kLz+z9eH55/02466OtJtOCVE2DtWI5k2zgVn5T5UVcrdg3zUx94/8bf4cGGZFVUIJCFwNnZ7vpyBd0e5WrcpIqk1pYJ96/tZ8hvS5cz9zaBJdzCjGqQ5LFDYP8tVpzsLzfae8mNVyqDXeF+QMGWz/fA1okVqlaSj359i2hZR+8abc1xehOyTqVxj7z+1BXf6/05uuZhH1JgEFCUmzF8JVtkiv6yP9nfAf8dOAihrSp4/VymeZT0V5Gz6BQgtkYxJVYy6mVbNPRGMDXzwV1qzhn4xprgXNlPsQgnQPnhIQErFixAsXFxTh//jxSU1MxcuRIvPHGG3oWy67AwED89a9/xZNPPolmzSpqlc6fP49Bgwbhjz/+wJNPPokvv/xSx1JWT+72YXOGenimuIjy/VZmH2eZvR96rWBH3TTH3hAw1hJKmCakEk7/iGv1X5YvCnIfKXmsamfZ6hutNUduNmav9tggSRbbdq7GWZteF67Y8CD8756OACwzGlsGr94ro/nhsLXrprUjGThXE+ajFTTy0oMaV5k/DHX190pvldHiq7oyGgwmD0zUXaUSIoJxZ2fXs7S7Q/3Qxlo/XXeGXnOXJJleQypzBASteyLHx3H27SDSna5T1ob1YkKwyuUTncwCAwNRr1491KtXD6mpqT4/LNWECRMwYcIEi+m1a9fGe++9h5tvvhlLly5FUVERgoK8F+iRZ5WWWV64TMdxrpwfZHtPbmtqDAGjfvKoPXSDhIk3p+JaXhEaWBnvVb1amQCcfYCsdRGXrwnyE2NXa3BsBrMa8wINjjU7lyTLbXviXsQXnnJbZOY2D169WRgnmmq7mkDOH/n6TaH5AyRrD+18hXmAUuLqMAA6i2T+AE2FxZbXH6Mkwai62FVmzakzLucUKq+tPeS8nF2oOb2yBQUYIEmSaWKrSjxuWg+iHd2bD1yKbSpx43pn7Zh7qyWZv/Kpq9jJkyfx+uuv610Mt7Rp0wYAUFhYiCtXruhcGn1YS8jkbfLQKre6MPwBYPqDJgdCpv2OXC8bYP04ORJ0dU6NM/k70Gi7jzMAzBrSAv8e286hsbhd+Qy1mmorfZyVwNm1GhxrT9atZQGWs1rbex+/HLpkkQCvTDj+/q0dS19oKmWtqbYeLMtivTB61qJUNYNa10ZKfBju7OybzZ8lSTJ5qOfu+OaVzfy3t1tDz+fT8AZmo9dWrDFUgtFo+h31xSzM1u4JmiRGerkk5eQWZOqYrzKTg2l1i3P0IZwvPMS2xZ37ZWvHXCuZGnmOTwXO1cHx48cBlNeix8XF2Vm6evKVWpDVT92ChX/tgsGta7u0vrrmS35P6hsSd29OrPdxtn9aqrNoA6YXEZeLpVrPlYegppmpy1/Lk0IdqHFOiLDevNBaLeRN9bXPscAAuY+z6XpFJWUY8f4m5e/957Lw/b7zJssICIffv7VD7QsXa/MiXMkpMvnbm6ep+a5sHZ6miVGVWpbqJDw4AOv+1guvjmitd1GsUtfgaQ1r40vU166YsECrTSGpagoJsPz+BRgkk89Zj+H67LH2e5kSH45Vj/fQnlmJ5PNY3czY2w+LHW0Z4Cv3o9awVXXVw6uCh/373/8GAAwYMADBwVW/n5FLGax95HcqJiwIXRrEuxzgFmvUOKs3pWdW7WCzGwA5UHSH+fBFztIajkp+mioPm2CrWdKTfRvZ2Lb29ECjQbOk8oME83ugXw5dxM5T102mfbPrnMnfQjje78jWcFR6M2+qffRSjtkS3uzjbNa/2sa+b29dG68Ma4lvH/P8kHbVka/XLqpvqEMCffuWwyT5o48fV3Jen6aWCVuNBslkVApfDJxtPcBpXsf7Dxq1zmOvB84O9qn2hYfYttzaohYAy1aE5LvYEcaDVq1ahY8//hiBgYF4+eWXrS5XWFiIwsKKvilZWb6TudqcOtuko3z7Z8pxpSY1zuX/FxRVNDUOC6qc2hNHfujDg033rW5qtvHPyy7tVzILtJyl9eRUPoRyn+NSG30Gbd2nWruZMRq0QzD5ptc8/tUal9z8cAshHL55sha0+MJwEPbu+71a42zev9rG4ZEkCeO6+HaeC3JcQBVqqr3sj7PKax+/3yYXTOqWisiQADSqFYk7PtgMoPxaoW6xZf5Q2hf4WssH+TxWX+a8HaA62qfa1x+AxUcE4+BLAzTHyibfxE/KQw4dOoRx48ZBCIE33nhD6eus5dVXX0V0dLTyLznZN/unAa71v/D1HypHyYGz0SApAVJmfrEy391mh/d1r6853ZGj98yApiZ/q5++aiVAcYRJjbMLgbMwaaot/1/+Sq6BtdV/1db3xtpqAUZJs5+P/F4+XH8MmXnFFvPVzGvBBdyvdfCFfnL2aiK9WULzslSX3wiyT/274uuB89lr+cprX6/JJ+cFGA0Y27keUuIrKgSEEDAaJLwwqBke69MQyXHOVxZUNl9LqidfzwMN6i5i3m6q7dgx8YFn2HaFBhndHt6UvKcKfKV839mzZzFgwABcu3YNU6ZMwRNPPGFz+eeeew6ZmZnKv9OnT3uppM5T17q2SYq2sWSF6na/oQ6C1MN8uHuhaF8vFn9M74d29WJMpoc7kBG1bkwoProx7BBgGujFhrmWyV1dd+tK4GjaVLv8tZyLRb7I2RrexdbRtFaeQINBs4mYOjD715ojNrZsmTm3TAiT733fZrbHY9f6Gvh68zBvMz9GPDz+I6ugYli02DDfTlrz4tAWymt+R6uvCNU1Niyo/PX9PRpgav8mehXJJmvj9Zp7rE/DSi5JOfn6qud1ztGm4b7wEJuqFzbVdtPVq1fRv39/pKenY9KkSXjzzTftrhMcHFxl+j/L8cPAVol4c5T1WnQ1W/0XqyL1E8sOKbF4eVhLNPZQuv/Y8CCTH/ZeTWq4VK56qqfkrvYjVF9fXKlvVTfVlteXA165qfaGo9abkdt6gmwtcA4wStpP41XvxXRYD8tFzfszC2HaxPv1kba/9xIsj5cvZNVW0+qz7s37CWf6OFP1UqRKCFiZmXc9Ia1Gxe86v6PVV0igEUsevhlAeW2fr3O0qba3aqYlHwicHX2vbN1EnubbVzEfl5OTg9tuuw0HDhzAiBEj8L///a/aNe9asvMMAKBdcqzyZNae6nIIIkPK3686sJUkCeO7pOCmBvEe24/6eDnzdFT9XfNEMir1rrUC1Tm/HMXs1Yetri80koPJHOnza6vPkrVcXQEGg+Z7tzbetlb8XWI2REmZMM3iHR1qu5ZM65w3+kByMHu8GRiY76m6/EZQ9aIOBHzs2Rd5WIeUWHRIidW7GA6xFyTKTc8HtHRt6E1HPNq7ojZbPje8GTinxps2oXd032wCTZ6ma43zqVOn3Fq/Xr16HiqJ8woLCzF06FD8/vvvuPXWW7FgwQIYjb7/5NIZJy/nKq+dudGtLg8PujSIx08HLlb6D6+rQ1ypA0KTDJMuHn9bAWZBcSneXF3e5PnuLimoFRVisb5WsC1PC3Ig67etLJnW+toHGCXN9dQfmb0LrGUGbdPkYPY+fq3ZvlbjrEXPGmfWApAvUp+25zIL9CsIkYq9RFg/PnkLruYWoU5MaKWVoW1yjPJa/v325nVu9VM90fiF751ej92myNN0DZxTU1NdDrIkSUJJSYn9BStBaWkp7rzzTvzyyy/o0aMHli5diqAg1/qV+jJ1Iixnfnyqy89UyY22x5X9w2sa5FX+etao36V5oJqt6qeYXVCCWhojYJh2Fb7Rx/nGNEeGjrBVa241OZhBQtvkaKw5eNFkuro21WDyYMJyG+bJwcrKKpKYSZIDSbaqQB9nreOnZ1ZtHzs8RAD4QId8U7id1n4hgcZKDZoB0+uzHjXOQQEGBAcYUFjiXPJTXmvI03QNnG+55RaLm9LCwkJs2bIFABAbG4uUlPJhSU6dOoWrV69CkiTcdNNNuvYRnjNnDpYtWwYASEhIwCOPPKK53JtvvomEBBfGQfYRJmMWu7heVSYHVJWdXEId5Dn3gEK9nvuRs60a55zCisD5kS92YNXjPSz6K2qN4yxPc+R92W6qrR05S5KEB25poNSGA+V9vNW7s7driz7OEEpSM0c+e0mjl7OjY0x6i94jk1oGztXkR4KqFX4vyRf5QnPjAI0M2t6+zlm2DrPP38/pxKgQXMhi6xlP0jVwXrduncnf2dnZ6NOnD1q2bIk33ngDt956q8n81atX45lnnkFxcTF+/PFHL5bU1LVr15TXcgCtZdasWVU7cFYFZs60DKguv1NyUpvKvmiZtrJ2pql2xWt1kylXS2urj3OOqsb5yMUcrNxzDsPbJZksozUclfzCkTEXbV2EbV0vgwOM+L/hrfD3ZXsBABHBgSbH0d6Fs9hsAOry5GDlO3Tksy/SGMDa12qctXi3j7NZcjDfPzzkh6ryTfZ/xnfASysP4PP7b9K7KORBD/dK07sIALRrnL3NvHWYI6rCtbgyfX5/Z7y1+gi+33dB76JUGz5VLTJ9+nQcO3YMv/zyi0XQDAD9+/fHmjVrcOzYMTz//PM6lLDcrFmzIISw+y81NVW3MnqCq/cQVfnmQ81bNc7q4+VqcjBPXBzU2zO/PJkHh5eziyzWVz8NNq9xdiSbbmmZwFN9GwMAWtQxbQtub3gs9WELMkomf9sLfs2fYpeJimmuHtYqcbHWsal2dcmDQNWLjzUUccqtLRKxaVof1E8I17so5AHrn+6FD8e1xzO3+sYQWTGqoeTkexZ1SzRfVV3uR61pbzacqbmGNSPxwbgOaJoY6Z0C+QGfukwsXboUffr0sVlLm5CQgD59+tis6SV9VZefqWIv9XFWc2ZfziTAcpR8jTEPVM3/1nryez2v2GKavFigA+XLLy5F6+Rok3LI7A0rrV7cYJDMsmrbXtf8vQgIZX+uPjRh3Gyqut+8UPXA7yn5ipT4cAxoWdtnHjLWjKxICCo/WG6fEgMACHZwuCw9VIVrsTvmTuyMiTen6l0Mv+JT4zhnZGQ4lPCrpKQEly9bHw+WPMNkXF97kYu1Fauwiqbalbsfa0Mn2WOtxtmdw2+QpPLEWKqPWwiBE6oM6wBQajaEU1ZBMbaeuGqyDlAxfrAjNc55RaUIDw64sb7pPHs1zubHUOuhQklpGf44dd1iXfMa56U7z6pqnF1MXuhrj480k4N5s6k2ke+rEi1FiHSgDo7l6/EzA5qiVmQIbm9TR69i2eUrDx4qS3RYIIa0rYN5m0/qXRS/4VOPiVJTU/Hzzz/j9OnTVpc5ffo0fv755yrfDLoqcPXmv6r+TN3ZuR5CA40Y36U8IZ33mmprv3ZmPU8NCyFvRR1LvvPzn3hm8R6T5cxrafedyTT5W54rx9eOjDPds3ENzf2X/+14lbPRIGn2cX71+0P4eOMJi1W1as+/2XWufF0Hjmvz2hopxslENb93oWqCcTORNtNkseV/RIUE4rG/NGL3AJ2xpYx3+VTgfN999yE3Nxc9e/bEZ599hoKCikxwhYWFmD9/Pnr27Im8vDzcd999OpaUbKmqNx+vjmiFvbP6o3ZMeZMkuam2N8dxdiqrtjo49FAZ5R9goaqi/NeaIxbLlZkHm1aaVss1z4F2qu37N6+FGpHBysXZvIWDvZwgJk21JdOLvPyetIJmwHamztrRluNVm4uPsByKjtcxU9X9qT9VD/yeEmkzqUjhaeJT+HF4l08FzlOmTMGYMWNw8uRJTJo0CeHh4UhMTERiYiLCwsIwceJEnDx5EiNHjsSUKVP0Lm61J5nV4jm+XtU9jQOMBiXQKinVocbZqeNc8doTWbXVK9sLVB3NbikvZe/7ExVannjE2pNTe10FDGYPH5zq46yRFVtWI9L+sHdV4fsuNNpq/2w29jWRv6vs33qiqsrV4Un1sOOFvnoXwatY4+xdPhU4GwwGLFiwAAsWLED37t0RGBiIS5cu4dKlSwgMDET37t3x5Zdf4quvvoKhKqe/rIKcGo6qEsvhDfLNU5GXkoNJTgR5aubBoifIm5ED1T1nrmsuZ15La96sXw7U5CbW9oajksz+t0xOZnN1s4c8BpPS2Ds2tmqcHbkgaW2+KlzH8opK9S4CkU/hDSiRNl8/Nfo0rQkAGN8lBfERwdg0rQ/+mN5P51J5h69/NtWNTyUHk40ZMwZjxoxBSUkJrly5AgCIj49HQIBPFrfaUgcvziWtqozSeI9c66s01a7kN6SuTXWmxsO0j3PFg6T6CREul0UOgOUiXcwq1FzOInA2L7YyHNWN8tlJDiaZRc7WkoMZDZJmoBsdWjFUhtFglizMTuBsq/bckecRWt8Pn0sORkR28Xk8kTb1Nc0X7/Hm3NUOW49fxc0N4wEAdWNCdS6R9/ji51Gd+XQkGhAQgFq1auldDL+lDl4cSIqsqOpBgxwsFZd4p8Z5zcFLFft2ajgqdY0z8PVDXbF4+xlMu62py2UxmAWuxVaaMZsHm1biZuWhgL3kZfJ3RgnczebLuzNIgFY9ae8mNZXXRslsHGc7h7QyapyJqOphjTORNq3kYHpJjLLMPRIWFIDeTWtqLF396f15+BufDJxLSkrw3Xff4ffff8fly5dx00034d577wUAnDt3DpcvX0bz5s1ZA13J1DXOTvXjrOLnsBwoF5cJk7+9sm8Xb9yMBgM6pcahU2qcW/uXP2f5s7cWOFtbT1aRHKz8/0AHn7yYNxWv2F7533/r3wSvfn/Icj3VZ2SwklXbGls1zo705D6ekWsxzdfuv50ZTY7IXzFwJtKmPjP0Pk0iQ3jvr6ZuKdOuXoxu5fAXPtcwaePGjWjYsCFGjBiBV199FR999BE2btyozP/tt9/Qrl07rFixQsdS+gd1POHMDUVVr4GTg65iL/VxVnO1xtljw1HJgeuNv+WxrM2Z90E23/3nW9Kx+/R1ZTl7w1HJ+5WPvbWm2o0TI9HtRlMsa4xm4zi7czNsqzZaduKK7wfORHrpkBILALi1he+3HmNTbSJt6ofRel/f+IDLlLrG+Y2RbTSX+Uuz8tr4BI1RQMg5PvXY5sCBAxgwYACKi4vx2GOPoXv37hg9erTJMoMHD0ZYWBiWLFmCESNG6FRS/2Dax9nx9ap6sxGjWfDmzUyrrvYl91Rwb56cq7hUO3A0D2zNi/3D/gv4Yf8Fpe9xgJ070orA+cb2zebL40EbJMnuMTKYZdV259jYHT8a2rW5Vf0cIPKU/47vgFX7LmBo2zp6F8U+1bncuJbruSKIqhuTGmedr2+Mm02pj0dMWKDmMo//pRHqJ0SgR6MEL5Wq+vKpwPnll19GQUEBVq1ahf79+2suExQUhPbt2+OPP/7wcun8j/DX5GBm5fdmLYQzfclzC0uU13HhnnmKKNd42+vjbD680dXcYs3lHM2qLV+W5e+ZeU2vvB2DVDFMmDXBAQan+jjb4kiNs5aqfg4QeUp8RDDGd0nRuxgOSYgIRqu60dh7NhNLHr5Z7+IQ+QxfuqaxxtmUZOW1WnCAESM7JHmjONWeTzVMWrt2LTp37mw1aJbVrVsX586d81Kp/Jc6ZnCqi3MV/00zby7t1abaThy88OCK514hgUaP7F/eu/zQxFqNq/nkJxZqP8iSA8+gANs/NfIDAznANh9bWd6fQZIwvmv5TXjXBtpNtoPN9uXOOMuOjlft6wSAB29pYDKtqp+nRJ5mMEhY+Vh3nPznIESGaNfcEPkjn2qq7VORi29x536HHONTNc7Xr19HcnKy3eVyc3NRXKxdw0WeU6YKGpxJOlXVnwaal9+b78eZfTWrHYV/DG+JlLhwj++/Iiu29nLm8aS1MYHlwDM4wDSwrxsTirPX85W/5abc8v/FVmqcJQkY2Ko21kzpiXpxYZr7DAowmHx33Wqq7WqNs8t7rDzTbmuK//x6XPm7qp+nRETkf3jtMuWpFnbkGJ8KnGvWrIk///zT7nIHDx50KMAm98gxQ0RwAOr40Zh45s2lvZpV28l93X2TZ5tAyj/A9vr2mme9tsZajbN5rbD8vq3VOFc01S6f37Cm9f6HwQFGk4bk7vRRL3UxHbUvXtfNn0TzAktERM7SO3Blrap1evc/9wc+1eChT58+2LVrF9auXWt1mWXLluHPP/9Ev379vFgy/yQHR84OJF/Vf9TMLwreTA7mzSBdi7Ws1uYcjSflwNki67fZn/J8+f2bN5GW/3Lkgh1kNLjczcCcvf7U1vnWOaD1oKOqn6dEROR9el867KZM8TuS5kuqHD4VOE+bNg1BQUEYNmwYPvjgA1y4cEGZd+3aNXzyySe47777EB4ejilTpuhYUv8gBx/O/khW9fP29NU8k7+dGSLKXXpfkMyzaltjnhzMHvPhqCyawys1zuU/SeYBa0UfZ+v7kBOkDWiZ6HJiO3N5RSX2F9Kg9+doTuvTYo0zERE5S+9Lh9413r6Mh6by+VTg3LRpUyxYsABlZWV49NFHUbduXUiShE8//RQJCQl44IEHUFhYiC+++AL169fXu7jVnhwcOVszVdVvyA9fzDH526s1zjr/6hnMapythcfOtmA2H47K/Dsi1zjLAXZJmXZTbVvfxZ+n9MTih7qid9OaJuVzpxY/p9C1wLkq4M0HERFVNbx2mZJY4exVPhU4A8CwYcOwb98+PPbYY2jatClCQkIQFBSEBg0a4MEHH8SePXswZMgQvYvpF8ocqOXTUtWbgMaajYPny32cPU0ZR9lOYOxszizz4ajaJcea/C2/byU5WKkwqTVWD0dlTWx4EDreSGKnrjF35+uYU+BijbPru/SaqlBGIiLyLXrf49kbpcOf6f3Z+AOf+vZlZWUBAFJSUvD2229j//79yM3NRX5+Po4ePYr3338faWlpAIBdu3bpWFL/YJ6QyVFV/bR9oIfpsD2V3VT7H8NbKq/1viBUZNX2THIwWaBZxrUmiZEmf8s17eoAWz2GslwB7eh30VOjSJln93aUr128tD4uvR/SEBFR1aPXleOVYS1RNyYULw1toVMJfJMj4ziT5/hU4Hz77bejoKDA7nI7duxA3759vVAi/yYcqOXT4mMxg9NizGucK/n99G+eqLw2DzD1Yi9edDacrBUVYnN+fEQwACBA9f7VCcKEajgqR6gDfxcTYwMwDd6dUcVPASIiIk163eON65KCTdP6oEEN66Nq+Luqfv9dFfjGXfoNGzduxKhRo1Baqj0mLABs374d/fr1U2qnqfLItXzO1p75Wm2bs8yD18qucVZnnNa7FlDuiiwHqtZqlp2tcTZIQK2oYJO/ZcPa1sHIDkkATI+FOnCu6Dbg2PFRF8/Rso7umGQxzeXAuQqcAu48UCAiIn9VBS5wfkR9z83hqCqfTwXOjz/+OL777jtMnDhRc/7vv/+Ofv36IScnB19++aV3C+eHHOlXqqWqn7bmwWtlJ+xSB+Z6J72Qf3TtxYtlAliy4wy2nbxqd5sBBgmSJJkEauof+rfHtlOaqKvfvlYfZ0cPT7HZONCA/WbwrZNiHNu4mbmTOrm0nt6MHNODiIicVBUeDPsrfjaVL0DvAqi9/fbbuHr1Kr744gvExsbinXfeUeZt2bIFt956K/Lz87Fw4UKMGDFCx5L6B2dr+WRV/cQ1H3O4smuBTWucK3VXdlUUxXbkvPXEFazYfQ4AcPKfg2wuKwe96i1a+46ov2vq4N3Z7+LV3CLltbB4oc3Vz7l3k5qYfntzvPztAWWarz311XrrQXp/2YiIiMhjqvr9d1XgU4EzAMydOxfXrl3De++9h7i4OMyaNQubNm3CbbfdhsLCQixatAjDhg3Tu5h+QbicHKxqn7nmTc0ru6m20ZdqnCXHapwvZhU6vE15WyY1ztb2r3qtrnF29ruoTj4mb8be2NSebFlQFS5ewYEMnImIyDlV4PLmt6r6/XdV4HN3TkajEV9//TW6deuGl19+GY8++igGDhzIoFkHcsDjbBAQUs1uyCu7qba6T3VekfX+/d7g6HBUrjFpq625hDowFiY1zs51GxjdMdlir/YCZ3cekDjb59tbejRKAABM6JpiMY81zkRE5Kyq8GDYn5hUODidupWc5ZN3TiEhIfj222/RunVrfPDBBygqKsKSJUswdOhQvYvmV5wdjuqloS3QsGYEnr2taWUWy+squ6m2evuNauqbLVIuidK82oO/wQ7VOKtmlJn0cZbnO/ZZBBoNGNKmzo39CpNtWGPeRN8dvnJj8fGETlj91C0Y3q6uxbygAKMOJSIioqqMtZq+JSyo4lrOz6by6dpU+7PPPrM5/6677sL+/fsxbNgwXL161WL5e+65pzKL5/eUwNnBxyv3dE3FPV1TK69AOvFGpusNz/TGnxk56JASW+n7skUZx9nB5s3OcKSPsyRJkKTy/Ze5UeNsvg/zGuGEiCDUiQnFnjOZyrTKbpKvh6AAAxrXitScN31QMy+XplzXBvG67JeIiNznKw+GqVzNqBD8fWBTBAcY7SZBJffpGjhPnDjRbg2SEAKLFi3CokWLTKZJksTAuZLJsYbe/W710LdZLaw5eBGAdwLn5LgwJMeFVfp+7Kloqu1YLa0z1EF4sI3aToMkoVQIsz7OFfMcZdpf2nIfM25vjpEf/qZM02qS//rI1g7ty3z7vjokW1iQEXlFpVjwQBd0TdMngJ1WzVqkEBH5Ex+9vPm1v96SpncR/IaugfOMGTN89gaT1EMA+d9nFB5cEdj504MDpcb5xt+O1DgXFDvfL/v21rWx8PdTuKlBnMW8iubiFdOc7TYAVHxvy2uvLd+HeQ2zeZffZrWjTPpKO8NXvzG/PfcXnM/MR9PEKK/ve/sLfXHuer7Lw34RERER6UnXwHnWrFl67p7sqBgCSN9y6OHbPeeV10cuZutYEn1U9HG2Hzh/tOG4zflD25b3NVZ/jUICjVj88M2ay5cHxwJFJWX481IO0mqEOz2Os5qAsKg5Lywps6hhNpr1SQh1IslddFigyd+++qwlOjQQ0aGB9hesBAkRwUiICNZl30RE5BnsR0v+jI3hySpXavmqi1JVpFXqyfbKPs6yj7P9dc5nFih9s0MDLZtgvzikhVNlkL9uzy/fi76z1+Oz39IrHuI48RTHVqbJ3MISiyb47iSZNk++xRsLIiKqlnh5Iz/mU4HzxYsXsWLFCpw4ccLqMidOnMCKFStw6dIlL5bMP8m1jf7+G+lPyRbkoLVM6eNsP3KWpIr+wff3qG8x39k+4nLwvuHoZQDAmz8eVo3j7MSGVENrmb+NkjJhUSvsTpeEQKPB4f7QREREVZW/3xOSf/OpiGD27NkYPnw4CgoKrC6Tn5+P4cOH49///rcXS+afnB0CqLoK9qPA2bKPs2Prld6ITFvUibaYF3CjCbSj9fbmwXFuUYmq24AzNc4V7+XoxRyTee/e2c4ioHd3OCr1uMh+fsoQEVE15e/3hOTffCoi+P7779GiRQs0a2Z9mJTmzZujRYsW+O6777xYMv8k/LiPs5o3smr7CvOs2o70cQbKa3AB7eDT2eNnflEuE661fpBUNc6PLdipTN89oz8Gt6lj2cfZzZuBAGPF+v7zjSEiIn/C6xv5M58KnNPT09G4cWO7yzVq1AinTp3yQon8mz/3cVbzp/cvWfRxdixwLrsROGsFyXIw7eiQ0FqH253WDwICGdmFyt8hQeU/e+b9pd0dxzlQ3Unaf74yRERERH7BpwLn0lLHhrWRJAmFhYX2FyS3KP1Kfepb4n1+FTjf+L9MmP5vex1JSaCmFXwalMDZscjZ1vF2JrZVLxoaVDGAgLx98xpmdz9nk6bajJyJiKga8qNbIiILPhUSNWjQAL/99htKSkqsLlNSUoLffvsN9erV82LJ/BP7OJfrlBqrdxG8xqA0b3Y8ORgAHDifBcD95s7qMmjPc2Yc5/L/hQDCgizH5TbflqOBvTXqptpERETVEa905M98KnAePHgwLly4gGnTplm9iX3uuedw4cIFDBkyxMul8z/+3FQ7IriihnJUx2QdS+Jd8kMS+aGJI7HkySu5ymvzoLdtcozTZbBd4+x8cjDANHCWp5q3pHA3e7q6bH54yhARkR/w98oU8m8+FThPnToVderUwb/+9S+0bdsW77zzDr777jt89913eOedd9CmTRvMnj0biYmJePrpp/UubrVX5sfJwRb+tQt6NamBVY/38KvkYBY1zg601c4trGghUmK2vPrYvTi0fDznh3ul2dyerWuy5MQvljrRWbjqQYg83fxzjQwJxBtuDCmlLrf/fGOIiMif8PpG/izA/iLeExcXh9WrV2P48OHYu3cvnnrqKZP5Qgg0btwYS5YsQUJCgk6l9B/Cj2ucW9aNxrxJnfUuhtfJwaQcADvSx1m9SElZmen2VN+d4e2ScEujGogLD7K5PVtPs8MCjVbn2RKv2qdkpY+zJJW3Lnh68R6X9qGu4eYTeSIiqo6CXbwOE1UHPhU4A0CzZs2wf/9+LF26FGvWrMHp06cBAMnJyejbty9GjBgBo5EnrTfITbUZA/gPOTN0cWl5AOxIH+dSVXRdXGq6vPl3Jz4i2O72rFXwx4YFIsDoeJWzuo9zgxoRAC6a7sc8q7ZFIO3cF9/AGmciIqrmwoN4D07+y+cCZwAwGo0YNWoURo0apXdR/FpFU22GAf4iyCxwdiRhVkFxRTb8klLrTbUdZe375kjQbepGNm9UvI+/3tJAmRtoFoS72yKftcxERFTdhQX7ZOhA5BU+1ceZfEtFcjCdC0JeIweTRTcCYEfyTBcUVzTPNm+q7UrCLWuBs7PfQ3WNs1briUCjdo3zPV1TAABT+9sfU15rf+aviYiIqotWdaP1LgKRbnz2sVF2djaOHTuG7Oxsq7Vet9xyi5dLZSo/Px+vvvoqFi5ciFOnTiEuLg4DBgzAyy+/jLp16+paNk8QrHH2O4E3At3iEsebaqtrnNskxZjMC3YzU7Was99DeWkBodl6wrzGWfbikBZ4+tYmiAwJdGl/5a95zhARUfWxfHI37D+Xib7NaupdFCLd+FzgvG/fPjz55JNYt26d3WaipaWlNudXpoKCAvTp0wdbtmxB7dq1MXToUJw8eRJz587Ft99+iy1btqBBgwb2N+TDDl/IBsAmqP5EroWt6ONsfx25j3NwgAGpCeEm84ICnO8LZT5MlDLdje+hVuuJAPM+zjf+liTJ6aBZXq/iD+fLSERE5KvaJse4NMQkUXXiU4Hz0aNH0b17d2RlZaFbt244f/48Tpw4gbFjx+L48ePYuXMnSkpKMGTIEMTExOha1ldeeQVbtmxB165dsXr1akRERAAAZs+ejalTp+Lee+/FunXrdC2juzLziwEAWQXFOpeEvMWVPs6lN5ZpmhhpdXvOsNpU28lNqZtqa7WekCQJgUZJSWjmfh9n7ddEREREVPX5VB/nV155BdnZ2Zg7dy42bNiAHj16AAC++OIL/Pbbb9i/fz+6d++OAwcOYPbs2bqVs6ioCHPmzAEAvPfee0rQDABTpkxB69atsX79euzYsUOvInqEXPvYjk8Y/YZ5H2ezLsualLGeNaLF4EBP9nF2tql2RXKwij7OptsIUEXj7javZqxMREREVH35VOD8yy+/oFmzZpgwYYLm/IYNG+Kbb75BRkYGpk+f7uXSVdi0aRMyMzORlpaGdu3aWcwfOXIkAGDlypXeLppHyWP5Roc632yVqiZlHGcnhqOq6D9sOc+VGmdr8bHTgbPSyVlUBM5my6gThHkyqzaDaCIiIqLqxacC50uXLqF58+bK34GB5QFbQUGBMi0mJga9evXCt99+6/XyyXbv3g0AaN++veZ8efqePXu8VqbKIPdddWVIIaqa5OD09LV8PDh/O7advGp3nZzCEgDawaIr/ZI99W1Tb8fa0GrqBGHu9uU3GceZbbWJiIiIqhWf6uMcFxeHwsJCk78BID09HU2aNDFZ9tKlS14tm9qpU6cAAElJSZrz5enp6eleK1NlkGvpGDj7DzneW7n7nNPragXJrnx1rAXb5kNdOap8HGft8pgGzi5tXmFgjTMRERFRteVTNc7169c3CTbbtm0LIQS++uorZdrly5exbt061KtXT48iAgBycnIAAGFhYZrzw8PLMwtnZ2drzi8sLERWVpbJP18k1zhzOCr/4c4zEq2vicGFDVoNnEsdGVVaXZ4bfZxFRZIz8/KoHwp58nvOU4aIiIioevGpwLl///7Yt2+fEjwPHjwYCQkJeOmllzB27FhMnToVnTp1QmZmJkaPHq1zaV336quvIjo6WvmXnJysd5E0yRV85sP2UPXlTvCo1TzZlc1ZW6fUkbGxNJSP4ywnBzOdF+DRPs7urU9EREREvsunmmqPHz8ehYWFuHjxIlJSUhAeHo6FCxdi9OjRWLRokbJcv3798Pzzz+tWTjmLdl5enub83NxcAEBkpOXwPADw3HPPYcqUKcrfWVlZPhk8y01jXak1pCrKnRpnzWkerHF2MnBWD0dlrY+zusbZ/azantsWEREREfkWnwqc09LS8Oqrr5pM69OnD9LT07FhwwZcu3YNjRs3RocOHXQqYTm5mfiZM2c058vTU1JSNOcHBwcjODi4cgrnQXLLWCOr0vyGrRrnTqmx2Hbymt11B7epo/SR9mSNs6t9nIGK/vrmz4DUrSkkN9vfcBxnIiIiourLpwLnFStWIDAwELfddpvJ9PDwcAwYMECnUllq06YNAGDnzp2a8+XprVu39lqZKkMZs2r7HVsfdVJsGOZN6owWM3/UnC8Hi++MbasEzh5NDuZkH2f5gU+pEKrkYKbbVv/tbh9nBstERERE1ZdP9XEePnw43nnnHb2LYVe3bt0QHR2NY8eOYdeuXRbzFy9eDKC8j3ZVpiQHY+DsN2w1MRZCIDzY+rM2OfCU3AxGrX3dnG2qHXAjY3ZJqbqPs2S2jOcyYZtk1eYpQ0RERFSt+FTgXKNGDcTGxupdDLuCgoLw6KOPAgAmT56s9GkGgNmzZ2PPnj3o2bOn7k3K3SUHG0wO5j9sfdT2wlatYNGVb461MZBLSp1rqh10IyguKS1T+jibb9loqPgJdLvG2a21iYiIiMiX+VRT7V69euH333+HEMLqzbOveOGFF7BmzRps3rwZjRo1Qo8ePZCeno6tW7eiRo0a+OSTT/QuottKOByV/7HxWdur8NXOqq1fjbM8RnORqsbZZh9nt7NqMzkYERERUXXlUzXOL7/8Mi5fvoynnnoKBQUFehfHppCQEKxduxbTp09HWFgYli9fjvT0dEycOBE7d+5EgwYN9C6i29jH2f+4NY6z5vac36C1YNvZ4agCA8p/3opLy5TqcotxnNnHmYiIiIgc4FM1zgsWLMDAgQPx7rvvYuHChejbty/q1auHkJAQi2UlScL06dN1KGWF0NBQvPTSS3jppZd0LUdlKRVy4KxzQchrbAWPQpgGrvUTwnHickU3Ba2g25Vg0mqNs5PJweTa5OLSMqt9nI0erHEOVDX7FnYbthMRERFRVaJr4NygQQOMGjUKr732GgBg1qxZkCQJQghcunQJX375pdV1fSFwru5K2VTb79j6pBOjyh9ghQYakV9cip6Na5gEzjmFJRbruFKDbbWPs5PDUQUFWCYHs2iqbfRcjXNyXCiGtq2DsKAABAcY3doWEREREfkWXQPnkydPIiMjQ/l77ty5OpaGzIUFGREeZFT6ilL1Zy2DeoBBwhN9GwEAfp7aE78du4KbG8Zj3uaTNrfnyT7Ozm6roo9zRcBtHhyra5zd7ZEgSRL+PbadexshIiIiIp/kU021J0yYoHcRSGX1Uz31LgJ5mbXY9N072yEyJBAAUCcmFHd0SMLV3CKXt2eLtZpfZwNbdVNt+bXt5GBsWUFERERE2liVSEQKa9mgAzRaHTgSyLqSXdpa/OpsYBukSg6mDEdlo8aZiIiIiMgaBs5EpLAWR6r7AivLOhB0uhKXWqtxdnZTclPtYpM+zgyciYiIiMh5ujfV3rVrl8tZqWfMmOHh0hD5N2tBqzpjtLVlhUYi6YY1I5wug7WaZWdbUlcEzmUoE+XJuszjZKPG+yIiIiIiMqd74Lx7927s3r3bqXWEEJAkiYEzkYdZC061amaNZguXqSLnpY/cjP1nM9GnaU2ny6De1fguKTifmY81By9hUrf6Tm1HriUvLi1ThtIyf38BrHEmIiIiIgfoHjinpaWhW7dueheDiGC9tjdQo6m2+aLqCuf29WLRvl6sS2VQ12T3aJSAHo1qYEf6NdzUIM6p7QTJNc4l1ptqRwTr/hNIRERERFWA7neN3bt3xyeffKJ3MYgItvo4WzZpNq+FLtNoqu0K9VYlSUJokBHdGyU4vR2lqXZZmdKM3PzBQMu6Ua4Wk4iIiIj8CDv4EZHCWsNlrSbN5uN7C61Ozq6UQRXcutOQOlDVVLuixtl0mcFt6qB+Qjh6Nanhxp6IiIiIqLrTvcaZiHyHtUzZ5kGyrFHNCBy9lAPAtI+zW2VQFcGd3F2BJk215W2bvr+woAD8PIXjlRMRERGRbQyciUhhrY+ztWGb1IGoh+Jmk226Mg60TJ1VW1ipcQYcG1aLiIiIiPwbm2oTkcJaCKmVHAwwDTo91cfZpJbZjZjWtKn2jc05O6YVERERERF0rnEuKyvTc/dEZOZYRo7mdK3kYACgnuyxPs6qaNnauNKOkGucswpKUFJa5vb2iIiIiMh/scaZiBSHL2RrTg/0YlNtdWzrXnKwip+33WcyAVjPGk5EREREZAsDZyJShAQaNac71McZnkoOpurj7IGm2mqscCYiIiIiVzBwJiKFtebW1ppqqwPRklLPZ9V2KzlYgGWZ2ceZiIiIiFzBwJmIFF0axGtOt5YcTD01v7jUI2VQ1zi707Q6UGMsK/ZxJiIiIiJXMHAmIsWkbvU1pwc4MKBygJXg2mmSlddO0gr22ceZiIiIiFzBwJmIFEEBBvRpWtNieoCViFPd9LlGRLBHyuCpcZy1+mWzxpmIiIiIXMHAmYhMmNfUGiTT8ZqteX1ka4/sX70rd2qItfozM24mIiIiIlcwcCYiE4FmicCsJQYDTFtSN6wZ6ZH9m2bV9mykyxpnIiIiInIFA2ciMhFkFihbG8MZqJwaXMlDw1FpYeBMRERERK5g4ExEJsyTfNmucfZ8ICp5qKm2FiYHIyIiIiJXMHAmIhNGswza1hKDVRbT3Xl236xwJiIiIiJXMHAmIhPmwaXNYaYqIRA1VGJT7bCgAM9ukIiIiIj8AgNnIjJhXsHsyBjOnt2/pPnaE2pGembILCIiIiLyLwyciciEebBqPjyVWmW0fFbv3tPbjwoN9PAWiYiIiMgfMHAmIhPmgbPN5GCVkVUblddU29v9tYmIiIioemDgTEQmLPo42xqOqhLqnA0mWbXd2/7jf2lk8renx4UmIiIiIv/AwJmITJgHw7aSg1VGHGrwYK1wt7R4j22LiIiIiPwXA2ciMqF3cjCTPs5uxtCeDMKJiIiIyH8xcCYiE+bBqs3kYJU9HJWbTcE9nZWbiIiIiPwTA2ciMmHeD9hoo9ZWiErYv+q1u5XdtoJ+IiIiIiJHMXAmIhPmoWagjazaxaVlHt+/J2ucQwKN7haHiIiIiIiBMxHZZqvGuaikMgLnitfutrQOVQXOI9rVdW9jREREROS3GDgTkU22koMVVkLgrG4q7m5ur9CgisD52duaurcxIiIiIvJbDJyJyCZb/YQro8bZtJbZvcg5OKDiJ66krBI6ZBMRERGRX2DgTEQmzANMW021UxPCPb5/kz7ObtY4hwUFKK/jw4Pc2xgRERER+a0A+4sQkT8xT/hlKznYqyNa4bUfDuGerqke2786Tnd3OCmjQcLuGf1RKgQThRERERGRyxg4E5EJ88DZVo1zragQzB7d1qP7l0yyarsvOizQA1shIiIiIn/GptpEZKK41LSptrfHQvZkU20iIiIiIk9g4ExEJkrMapxtZdWuDJ5sqk1ERERE5AkMnInIRLETycEqA4NlIiIiIvI1DJyJyERxiXmNs3cDWXXczBiaiIiIiHwBA2cnHTp0CK+99hp69+6NhIQEBAYGIjExESNGjMCGDRv0Lh6R2yySg+nYx5m1z0RERETkC5hV20l9+/bF2bNnERERgS5duiAuLg4HDhzAsmXLsHz5csyePRtPPvmk3sUkcpn5OM7ernFWNw1n3ExEREREvoA1zk5q2rQpPvvsM2RkZOCnn37CV199hb179+LDDz+EEAJ/+9vfcODAAb2LSeQyixpnL0ev6jhd8siAVERERERE7mHg7KQ1a9Zg/PjxCAkJMZn+4IMPon///igtLcXXX3+tU+mI3Gc+HJWwslxlMbDGmYiIiIh8DANnD2rTpg0A4Ny5czqXhMh15sNRmTfdrmwcx5mIiIiIfA0DZw86fvw4ACAxMVHnkhC5rsisxtk8kK5sbKpNRERERL6GgbOHHDt2DN9++y0AYMiQITqXhsh1j/VpaPK3t2ucJdY4ExEREZGPYeDsASUlJZg4cSIKCwsxZswYdOjQwebyhYWFyMrKMvlH5CsGtqqN357ro/zt7Vpf9d44HBURERER+QK/G45q+PDhOHjwoFPrfPbZZ+jcubPV+Y8//jg2btyIBg0a4P3337e7vVdffRUvvviiU2Ug8qba0aHK677Na3p13yY1zl7dMxERERGRNr8LnE+cOIHDhw87tU5eXp7Vef/4xz/wwQcfoFatWvjxxx8RFxdnd3vPPfccpkyZovydlZWF5ORkp8pEVNnWTOmJ9Cu5uDktwav7VQfLrHAmIiIiIl/gd4Hzrl27PLatDz/8EC+88AKio6Pxww8/oGHDhvZXAhAcHIzg4GCPlYOoMjSsGYGGNSO8vl91sCwxciYiIiIiH8A+zi5auHAhJk+ejLCwMHz33Xdo27at3kUiqhZMA2f9ykFEREREJGPg7IJVq1bhnnvuQUBAAJYtW4Zu3brpXSSiakOdjIxxMxERERH5Ar9rqu2uTZs2YeTIkRBCYNGiRejfv7/eRSKqVtS1zMyqTURERES+gIGzk26//Xbk5+ejfv36WL58OZYvX26xTPfu3XH//fd7v3BE1QzjZiIiIiLyBQycnXT9+nUA5dm5T5w4YXU5Bs5ErjEdjoqRMxERERHpj4Gzk4QQeheBqFrjcFRERERE5GuYHIyIfAqzahMRERGRr2HgTEQ+JcBQ8bPEptpERERE5AsYOBORT4kJC1Res8aZiIiIiHwBA2ci8ik1I4OV1xyOioiIiIh8AZODEZFPaVAjAvd2q4+wICOMBgbORERERKQ/Bs5E5HNmDG6udxGIiIiIiBRsqk1ERERERERkAwNnIiIiIiIiIhsYOBMRERERERHZwMCZiIiIiIiIyAYGzkREREREREQ2MHAmIiIiIiIisoGBMxEREREREZENDJyJiIiIiIiIbGDgTERERERERGQDA2ciIiIiIiIiGxg4ExEREREREdnAwJmIiIiIiIjIBgbORERERERERDYwcCYiIiIiIiKygYEzERERERERkQ0BeheAACEEACArK0vnkhAREREREXmHHP/I8ZAvY+DsA7KzswEAycnJOpeEiIiIiIjIu7KzsxEdHa13MWySRFUI76u5srIynDt3DpGRkZAkSe/iKLKyspCcnIzTp08jKipK7+L4HR5//fEz0BePv754/PXF468/fgb64vHXl7eOvxAC2dnZqFOnDgwG3+5FzBpnH2AwGJCUlKR3MayKioriD5aOePz1x89AXzz++uLx1xePv/74GeiLx19f3jj+vl7TLPPtsJ6IiIiIiIhIZwyciYiIiIiIiGxg4ExWBQcHY+bMmQgODta7KH6Jx19//Az0xeOvLx5/ffH464+fgb54/PXF42+JycGIiIiIiIiIbGCNMxEREREREZENDJyJiIiIiIiIbGDgTBby8/MxY8YMNG7cGCEhIahTpw7uvfdenD17Vu+iVSm9evWCJElW//3www+a682bNw+dO3dGREQE4uLiMHDgQGzevNnmvjZt2oSBAwciLi4OERER6Ny5Mz777LPKeFs+ZceOHfjnP/+JESNGICkpSTm29njrGJ85cwaTJk1CnTp1EBISgsaNG2PmzJkoKChw6n36Mmc/g1mzZtk8L6ZNm2Z1XX4GpvLy8rB8+XLcd999aNKkCUJCQhAeHo42bdrgpZdeQk5OjtV1eQ64z5Xjz++/582ePRsjRoxAo0aNEB0djeDgYKSkpOCee+7B3r17ra7Hc8AznD3+PAcq15UrV1CzZk1IkoSGDRvaXJbngAsEkUp+fr7o0qWLACBq164tRo8eLTp37iwAiBo1aohjx47pXcQqo2fPngKAuOOOO8SECRMs/u3Zs8dinSeeeEIAEKGhoWLo0KHi1ltvFQEBAcJoNIply5Zp7mfx4sXCaDQKSZJEz549xR133CFiYmIEADF16tRKfpf6Gjp0qABg8c8Wbx3jo0ePioSEBAFAtGzZUowePVo0aNBAABDdunUTBQUF7r59n+DsZzBz5kzlGGidF4sWLdJcj5+Bpf/973/K8W7WrJkYNWqUuPXWW0VkZKQAIJo2bSouXrxosR7PAc9w5fjz++958fHxIiQkRHTu3FkMHz5cDB8+XDRu3FgAEIGBgWLlypUW6/Ac8Bxnjz/Pgco1YcIEIUmSACDS0tKsLsdzwDUMnMnE888/LwCIrl27iuzsbGX6W2+9JQCInj176le4KkYOnE+cOOHQ8j/99JMAIOLj48WRI0eU6Zs3bxZBQUEiJiZGXLt2zWSdK1euiKioKAFALFmyRJl+4cIF0bBhQwFArF271gPvxjf985//FNOnTxcrVqwQ58+fF8HBwTaDNm8e427dugkA4vHHH1emFRcXi+HDhwsAYubMmS6/b1/i7Gcg3zTNnTvX4X3wM9A2b9488de//lUcOHDAZPq5c+dEu3btBABx5513mszjOeA5rhx/fv89b+PGjSI/P99i+nvvvScAiFq1aoni4mJlOs8Bz3L2+PMcqDxr1qwRAMRf//pXm4EzzwHXMXAmRWFhoYiOjhYAxM6dOy3mt27dWgAQ27dv16F0VY+zgfNtt90mAIh//etfFvMef/xxAUC8+eabJtNfe+01AUAMHTrUYp2lS5cKAOL22293ofRVk72gzVvHeOvWrQKAqFmzpsXT1AsXLojAwEARGxtrcjNRXVRG4MzPwHmbN28WAERwcLAoLCxUpvMc8A5rx5/ff+9KS0sTAMTu3buVaTwHvEfr+PMcqBx5eXkiLS1NNG/eXBw5csRm4MxzwHXs40yKTZs2ITMzE2lpaWjXrp3F/JEjRwIAVq5c6e2iVXv5+fn45ZdfAFQcZzVrx/67776zus6gQYMQEhKCNWvWVM1+JB7mzWMsrzN48GCL8Q9r1aqFHj164Nq1a9i4caMb78h/8DNwXps2bQAAhYWFuHLlCgCeA96kdfxdxePvusDAQABAUFAQAJ4D3mZ+/F3F42/fiy++iOPHj+PDDz9UjrsWngPuYeBMit27dwMA2rdvrzlfnr5nzx6vlak6+Pjjj/HII4/g0UcfxTvvvINTp05ZLHP48GEUFhaiRo0aSEpKsphv7djb+syCgoLQsmVLFBQU4MiRI554K1WaN48xzyX7fvnlFzz55JN46KGH8Morr2DHjh1Wl+Vn4Lzjx48DKL9xjYuLA8BzwJu0jr8av/+Vb/78+Th8+DAaNWqERo0aAeA54E1ax1+N54Dn7NmzB2+99RYmTZqEHj162FyW54B7AvQuAPkOOaDTOpHU09PT071WpurglVdeMfn7b3/7G6ZPn47p06cr0+wd+/DwcMTExODatWvIzs5GZGQksrKykJmZaXO9pKQkbN++Henp6WjdurUn3k6V5c1jzHPJvvnz55v8PX36dNxxxx2YN28eIiIilOn8DFzz73//GwAwYMAA5Uk/zwHv0Tr+avz+e94bb7yB/fv3Izc3FwcPHsT+/ftRp04dLFiwAEajEQDPgcrkyPFX4zngGWVlZbj//vsRExOD119/3e7yPAfcwxpnUshDZ4SFhWnODw8PBwBkZ2d7rUxV2S233IL58+fj2LFjyMvLw+HDh/GPf/wDAQEBmDFjhnJjBdg/9oDl8VcPdcLPzD5vHmOeS9Y1bNgQb775Jvbv34+cnBycPn0aX3zxBerWrYslS5Zg/PjxJsvzM3DeqlWr8PHHHyMwMBAvv/yyMp3ngHdYO/4Av/+V6ccff8Snn36KxYsXY//+/UhJScGCBQvQoUMHZRmeA5XHkeMP8BzwtHfffRfbtm3DG2+8gfj4eLvL8xxwDwNnokry0ksvYdy4cWjQoAFCQ0PRuHFj/P3vf8fy5csBlI9lmJ+fr28hibxs3LhxmDp1Kpo3b47w8HAkJSXhrrvuwrZt2xAfH4/ly5djy5Ytehezyjp06BDGjRsHIQTeeOMNpa8teYe948/vf+VZs2YNhBC4du0afv31VzRq1Ag9e/bEP/7xD72L5hccPf48Bzzn1KlTeOGFF9CzZ09MnDhR7+L4BQbOpJCbxuTl5WnOz83NBQBERkZ6rUzVUf/+/dGxY0dcv34dW7duBWD/2AOWx1/dlImfmX3ePMY8l5xXu3ZtTJo0CQDwww8/KNP5GTju7NmzGDBgAK5du4YpU6bgiSeeMJnPc6By2Tv+tvD77zkxMTHo0aMHVq1ahQ4dOmD69OnYtm0bAJ4D3mDr+NvCc8B5kydPRlFRET788EOH1+E54B4GzqSoV68eAODMmTOa8+XpKSkpXitTdSUnyjh//jwA+8c+NzcX169fR2xsrPIDExUVhejoaJvr8TOr4M1jzHPJNebnBcDPwFFXr15F//79kZ6ejkmTJuHNN9+0WIbnQOVx5Pjbw++/ZwUGBmLMmDEQQigZgnkOeI/W8beH54Bzvv32W4SFheGhhx5Cr169lH9jx44FUP4wT5524cIFADwH3MXAmRRyk7KdO3dqzpen+3uSKU+4du0agIr+HU2aNEFwcDAyMjJw9uxZi+WtHXtbn1lxcTH27duHkJAQNG7c2KPlr4q8eYx5LrnG/LyQ8TOwLScnB7fddhsOHDiAESNG4H//+x8kSbJYjudA5XD0+NvD77/nJSQkAAAyMjIA8BzwNvPjbw/PAeddv34d69evN/knt2YsKChQpsnDRPEccA8DZ1J069YN0dHROHbsGHbt2mUxf/HixQDKx2Mj12VkZGDDhg0AKlLxh4aGok+fPgCAr7/+2mIda8d+0KBBJvPVvv32WxQUFKBv374ICQnx3Buoorx5jOV1Vq5cicLCQpN1Ll68iA0bNiA2NhbdunVz4x1VL0IILFu2DIDl0BX8DKwrLCzE0KFD8fvvv+PWW2+1msEW4DlQGZw5/rbw+1851q9fDwBIS0sDwHPA28yPvy08B5wnhND8d+LECQDlx12elpqaCoDngNsEkcrzzz8vAIibb75Z5OTkKNPfeustAUD07NlTv8JVIZs2bRLLli0TJSUlJtNPnDghunXrJgCIIUOGmMz76aefBAARHx8vjhw5okzfvHmzCA4OFjExMeLatWsm61y5ckVERUUJAGLJkiXK9IsXL4qGDRsKAGLt2rUef3++Kjg4WNj6WfPmMZY/5yeeeEKZVlxcLEaMGCEAiJkzZ7r6Nn2arc/g0qVLYs6cOSIrK8tkenZ2tnjwwQcFAJGYmChyc3NN5vMz0FZSUiKGDx8uAIgePXpYHDctPAc8x9njz++/523cuFF8//33orS01GR6UVGReOedd4TBYBChoaHi1KlTyjyeA57j7PHnOeAdJ06cEABEWlqa5nyeA65j4Ewm8vPzxU033SQAiNq1a4vRo0crf9eoUUMcO3ZM7yJWCXPnzlUuAAMHDhR33XWX6NatmwgJCREARIsWLcTFixct1nviiScEABEWFiaGDh0qbrvtNhEQECCMRqNYtmyZ5r4WL14sDAaDkCRJ9O7dW4wcOVLExMQIAGLKlCmV/E719e2334qbbrpJ+SdJkgBgMu3bb781Wcdbx/jIkSMiPj5eABCtWrUSY8aMEQ0aNFAeTBUUFHj6cOjCmc9AvphHRESI3r17i7vuukv069dPOU4xMTFi48aNmvvhZ2Dp7bffFgAEADF8+HAxYcIEzX8ZGRkm6/Ec8Axnjz+//54nX2sTEhLErbfeKu666y7Rv39/Ubt2bQFAhISEiK+++spiPZ4DnuHs8ec54B32AmcheA64ioEzWcjLyxPTp08XaWlpIigoSCQmJoqJEyeK06dP6120KuPAgQPi4YcfFu3btxc1atQQAQEBIjo6WnTp0kW89dZbIi8vz+q6c+fOFR06dBBhYWEiJiZGDBgwQGzatMnm/jZu3CgGDBggYmJiRFhYmOjYsaOYN2+ep9+Wz5Ev2rb+zZ07V3M9bxzjU6dOiYkTJ4rExEQRFBQkGjZsKKZPny7y8/Pdeds+xZnPICsrSzz77LOiZ8+eom7duiI4OFiEhYWJFi1aiKlTp4ozZ87Y3Bc/A1MzZ860e+wBiBMnTlisy3PAfc4ef37/Pe/48ePi73//u+jWrZuoXbu2CAwMFOHh4aJFixbiscceE0ePHrW6Ls8B9zl7/HkOeIcjgbMQPAdcIQkhBIiIiIiIiIhIE5ODEREREREREdnAwJmIiIiIiIjIBgbORERERERERDYwcCYiIiIiIiKygYEzERERERERkQ0MnImIiIiIiIhsYOBMREREREREZAMDZyIiIiIiIiIbGDgTERERERER2cDAmYiIfJ4kSSb/DAYDoqOj0aVLF7z99tsoLi7Wu4ia5s2bB0mSMGvWLL2L4jRJkpCamqp3MSrNxIkTIUkS1q1bp3dRiIioCmDgTEREVcaECRMwYcIE3H333WjdujW2bduGp556CrfddhtKSkr0Lp7PSU1NhSRJHtveunXrIEkSJk6c6LFtVhZPv3ciIvJvAXoXgIiIyFHz5s0z+Xvr1q3o1asXfv75ZyxcuBDjxo3Tp2DV0MGDBxEYGKh3MSrNq6++imnTpqFevXp6F4WIiKoA1jgTEVGVddNNNym1nz/++KO+halmmjZtirS0NL2LUWlq166Npk2bIiwsTO+iEBFRFcDAmYiIqrQWLVoAAC5dumQxTwiBBQsWoE+fPoiNjUVISAiaNWuGWbNmIS8vz2L5P//8E7NmzULXrl2RmJiIoKAgJCUl4Z577sGRI0eslmHTpk3o27cvIiMjERMTg1tvvRVbt261We4rV67g6aefRqNGjRASEoK4uDgMGDAAq1evtlj25MmTkCQJvXr10tzWrFmzIEmSUiMvN6lOT08HYNpH3NF+y+bLTpw4Eb179wYAfPrppybbNO/Dffr0aTz66KNIS0tT3tvtt9+OzZs3W+xH3fz7woULuP/++5GUlISAgAC8/fbbAIDz58/j9ddfR8+ePVG3bl0EBQUhMTERI0aMwLZt2zS3Z++92+rjfPr0aTz44INISUlBcHAwatasqbkvwPSzyc/Px7Rp05T1GjZsiNdeew1CCAeOOBER+TI21SYioiotOzsbAFCzZk2T6WVlZRg3bhwWLFiAiIgIdOzYEbGxsdi+fTtefPFFfP/991i3bh1CQ0OVdT766CO8/vrraNmyJTp16oTg4GAcOHAA8+fPxzfffIMNGzagdevWJvv59ttvMXz4cJSUlKBz585o0KABdu/ejVtuucVqX+CzZ8/illtuwfHjx1GvXj0MGzYMGRkZWLNmDX788UfMnj0bTz31lMvHJDExERMmTMDixYuRm5uLCRMmKPMSEhJc2mb37t1x4cIF/Pjjj0hLS0P37t2VeW3btlVe//bbbxg0aBCuXbuGJk2aYNCgQcjIyMCPP/6IH374AV988QXGjBljsf2MjAx06tQJJSUl6N69OwoKCpTa4G+++QbPPvssmjRpgtatWyMqKgpHjx7FsmXL8O233+Lbb79F//79PfLe9+7diz59+uDy5cto0qQJRowYgVOnTmHZsmVYuXIlvvzyS4waNcpivaKiIvTv3x8HDhxAr169kJubi/Xr12PatGnIzs7GK6+84vCxJiIiHySIiIh8HABh7ZJ1yy23CADi888/N5n++uuvCwCiV69e4vz588r0wsJCcd999wkA4tlnnzVZ57fffhPHjx+32Mcnn3wiAIjevXubTM/KyhI1atQQAMQnn3yiTC8rKxPPPvusUu6ZM2earHf77bcLAOKuu+4ShYWFyvQNGzaIsLAwYTQaxR9//KFMP3HihAAgevbsqXkMZs6cKQCIuXPnmkxPSUmxetzsASBSUlJMpq1du1YAEBMmTNBcJzMzU9SuXVsYjUaLz2Pbtm0iNjZWREREiEuXLllsE4AYPny4yM/Pt9junj17xL59+yym//DDDyIoKEikpaWJsrIyk3n23vuECRMEALF27VplWllZmWjVqpUAIJ555hmTbS5evFgYDAYREREhzp07p0yXPxv588nMzDR5z0ajUYSFhYns7GyrZSEiIt/HptpERFTllJWV4dixY3j44Yfx66+/YujQoSa1mCUlJXj99dcRHh6OhQsXIjExUZkXFBSEd999F4mJifjvf/+LsrIyZV6XLl1Qv359i/1NmjQJ3bp1w7p165CZmalMX7x4MTIyMnDLLbdg0qRJynRJkvDyyy8jKSnJYlvHjx/Ht99+i4iICLz77rsICgpS5nXv3h0PPfQQSktL8d5777l+gHTyySef4Pz583jyySdx9913m8zr2LEjpk+fjpycHHz++ecW6wYHB+Pdd99FSEiIxbxWrVopTfLVbr31VowaNQrHjh3Dvn373C7/unXrsHfvXtSrVw+vvPKKSVbuO+64A8OGDUNOTg4++eQTi3UNBgP+85//ICoqSpnWsWNH3HbbbcjLy8P27dvdLh8REemHTbWJiKjK0Bpe6IEHHsB//vMfk3k7d+7E5cuX0a9fP9SqVctindDQUHTo0AHfffcdjh49iiZNmijzcnJysHLlSuzatQtXr15Vxog+f/48hBA4duwY2rdvDwDYsGEDAGDs2LEW+wgMDMTIkSOVfrqyjRs3AgAGDBiAuLg4i/XGjx+P2bNnK9uuSuT+2SNGjNCc36NHDwDA77//bjGvffv2qFu3rtVtFxYW4ocffsDvv/+OjIwMFBUVAShvWg0AR48eRatWrdwqv3zMR48erZlRfPz48Vi6dKnmZ5OSkmLyPZI1btwYQPn3h4iIqi4GzkREVGXI/VULCgqwe/duHDp0CP/73/9w8803m/QnPnnyJADgp59+sjuWr9yXFQB++eUXjB07FhkZGVaXl/tUA8C5c+cAlAdNWrQSccnrWEvSJU8/e/aszXL7Ivm4d+vWzeZyly9ftphma1iovXv3YsiQIcr2tag/F1e589lotS4AgMjISADlgT8REVVdDJyJiKjKMB/H+Y033sAzzzyDyZMno3fv3koAKze/btiwod0gLj4+HkB5TfPo0aNx9epVzJgxA2PHjkVKSgpCQ0MhSRLuuusuLFiwoNIzJNsL9LWom5vrSS7HyJEjER4ebnW5pk2bWkzTaqINlGdGHz16NE6ePImHHnoIDz30EBo0aICIiAhIkoS///3vePXVV72SudrWZ2MwsPcbEVF1xsCZiIiqrKeffhpr1qzB6tWr8eKLLyp9T+Xav6ZNm1oE29Zs2LABV65cwciRI/Hiiy9azD9+/LjFtNq1awOAMvSROa3pderUsbmOXKuqbrYs94POycnRXOf06dOa070tKSkJhw8fxrRp09ChQwePbPPQoUM4dOgQOnbsiA8++MBivtbn4ipXPhsiIvIPfDxKRERV2j//+U8AwPz585WAp1OnToiOjsb69etx9epVh7Zz7do1ANpNbv/880/s3LnTYrrcZ3fRokUW80pKSrBkyRKL6fIwTj/88AOuX79uMV9OnCVvGygfRikgIAAnTpxASUmJyfLFxcVYv3695nuSA27zdVxlb3v9+vUDACxbtswj+wNsfy7Xrl3DTz/9pLmeK+9dPuZff/01SktLLeZrfTZEROQfGDgTEVGV1q5dOwwbNkzJpA2UZ2h+5plnkJ2djREjRmjWSp49exbz589X/paTOC1dutSkj/P169dx3333KUnC1EaNGoX4+HisW7cOn376qTJdCIGZM2fi1KlTFus0aNAAgwYNQnZ2Np544gmT7f7222/44IMPYDQaMXnyZGV6UFAQunbtiqtXr5pk2y4pKcHUqVNx4sQJzWMj16AePnxYc76z7G3vwQcfRM2aNfH6669bZCyXy/vjjz86lQG7YcOGMBgM+OWXX3D06FFlekFBAR566CGrD0Zcee+9evVCq1atcPLkScyYMcOk+feyZcuwdOlSRERE4N5773V4m0REVD0wcCYioipv1qxZkCQJn3zyCS5cuAAAmDZtGsaPH4/169ejWbNm6NKlC+68807ccccdaNmyJZKTk/HWW28p2+jYsSP69euHU6dOoXHjxhg+fDiGDx+O+vXr49y5cxg6dKjFfiMjI/Hxxx/DaDRi4sSJ6NKlC+666y60bNkSb7zxBh544AHN8v7nP/9B/fr18dlnn6FRo0a488470bdvX/To0QO5ubl4/fXX0bZtW5N1ZsyYAYPBgCeffBI333wzRowYgbS0NCxYsEBJmmZuyJAhAIC//OUvuPPOO3H//fdj2rRprhxiAOXJsVq3bo3t27ejc+fOmDRpEu6//36sWLECABATE4NvvvkG0dHRePDBB5GamoqBAwfi7rvvxl/+8hfUqFEDAwYMwJ9//unwPmvWrIn77rsPWVlZaNOmDW6//XaMGjUKqamp+OWXX0ySwrn73iVJwhdffIH4+Hj83//9H1q0aIG77roL3bt3x4gRI2AwGPDxxx8rTfSJiMh/MHAmIqIqr02bNhg+fDgKCgowe/ZsAOXJmj777DN888036NevH06cOIElS5Zg48aNCAkJwdNPP20xHu8333yD559/HjVq1MD333+PHTt2YOzYsdiyZQtiYmI09z106FCsXbsWvXv3xr59+/Ddd9+hdu3aWL9+PW6++WbNderWrYtt27Zh6tSpCAgIwNKlS7Fjxw785S9/wY8//ogpU6ZYrNO3b1+sWLECnTp1ws6dO7F+/Xp06dIF27Zts5oF+vHHH8cLL7yAiIgILFmyBB9//DEWLlzo+IHVsGTJEgwbNgzHjx/HZ599ho8//tikGXuXLl2wd+9ePPPMM4iKisL69euxfPlypKeno2fPnpg3bx769u3r1D4/+OADvPXWW6hfvz5+/vlnbNiwAX379sX27dutZjR39b23atUKO3fuxAMPPICcnBwsXrwYhw8fxrBhw7Bp0yaMHj3aqbITEVH1IAlvpKEkIiIiIiIiqqJY40xERERERERkAwNnIiIiIiIiIhsYOBMRERERERHZwMCZiIiIiIiIyAYGzkREREREREQ2MHAmIiIiIiIisoGBMxEREREREZENDJyJiIiIiIiIbGDgTERERERERGQDA2ciIiIiIiIiGxg4ExEREREREdnAwJmIiIiIiIjIBgbORERERERERDYwcCYiIiIiIiKygYEzERERERERkQ0MnImIiIiIiIhsYOBMREREREREZAMDZyIiIiIiIiIbGDgTERERERER2cDAmYiIiIiIiMgGBs5ERERERERENjBwJiIiIiIiIrKBgTMRERERERGRDQyciYiIiIiIiGxg4ExERERERERkAwNnIiIiIiIiIhsYOBMRkV2nTp3CpEmTUK9ePQQFBUGSJMTExOhdLCIiIiKvYOBMROSkWbNmQZIk5d/ChQvtrjNo0CCTdU6ePFn5BfWQzMxMdOvWDfPmzcPp06cRFhaGWrVqoVatWnoXjfxITk4OvvrqK9x///1o06YNIiMjERQUhMTERAwYMADz5s1DSUmJ1fWPHj2Kf/7znxg5ciRatGiBmjVrIjAwEDExMejYsSOee+45nDlzxm45fvrpJ4wePRopKSkICQlBaGgoGjRogLvvvhvr16+3u352djZmzZqFVq1aISIiAtHR0ejUqRPeeustFBUVOXVMtKxYsQKDBw9GYmIigoKCULt2bQwdOhTff/+9zfUmTpxo8htl7Z+tY2yLvP6sWbPsLiuXJTU11aV9ERFVCkFERE6ZOXOmAKD869+/v83lz549K4xGo8k6J06c8E5hPeCDDz4QAERsbKw4ePCg3sUhP9WwYUOTcygoKEhERkaaTOvUqZO4dOmS5vpvvPGGxfoxMTEm08LDw8WyZcs01y8rKxMPPvigyfKhoaEiNDTUZNpTTz1l9T2cPHlSpKamKsuGhYWJ4OBg5e927dqJq1evunR8SkpKxN13361sS5IkERsba/Lb89hjj1ldf8KECQKACAkJEbVq1bL6r6SkxKXyyWWYOXOm3WXlsqSkpLi0LyKiysAaZyIiFyUkJCA8PBxr1qyxWVP12WefobS0tMrWnuzduxcA0KdPHzRt2lTn0pC/Ki4uRvPmzfHGG29g//79KCwsRFZWFs6fP49nn30WRqMR27Ztwx133AEhhMX6zZo1wyuvvIK1a9fiypUrKCwsxLVr15Cfn4+lS5ciLS0Nubm5uOuuu3DixAmL9efNm4f//Oc/AICRI0fiyJEjyMvLQ15eHg4dOoShQ4cCAP71r39h2bJlFuuXlJRg8ODBOHnyJGrXro2ffvoJubm5yMvLw8KFCxEZGYk//vgD48aNc+n4vPDCC/jiiy8AAE888QQyMjJw9epVZGZm4s0330RAQADeffddvPPOOza3M2bMGFy4cMHqP6PR6FL5iIiqOgbOREQuCg8Px8iRI1FWVoZ58+ZZXW7u3LkAypsfVkV5eXkAgIiICJ1LQv5s3rx52LdvH/72t7+hefPmyvTExET885//xCuvvAIA2LBhAzZt2mSx/qBBg/D888+jV69eiIuLU6aHhIRg+PDhWLVqFQAgPz8fX375pcX6n332GQCgYcOGWLBgARo1aqTMa9KkCb7++ms0aNAAALBo0SKL9T/99FPlIdSSJUvQt29fAIDBYMCYMWOUoHzVqlX4+eefnTgywOXLl/Gvf/0LADBs2DC8/fbbiI+PB1D+OzV16lRMnToVADBz5kxkZWU5tX0iImLgTETklkmTJgGA1cB548aNOHLkCBo0aIBbbrnF5rb27duHWbNmoU+fPkhLS0NoaCiioqLQrl07vPDCC7h8+bLmeg8//LCSrMta3+kPPvgAkiQhICAAv/76q0PvrVevXpAkSXlvn376qUlfR3n6unXrlGkA8Mcff+Duu+9GUlISAgMD0atXL5PtFhUV4f3330fv3r2RkJCg9FN1pB9mfn4+XnnlFTRv3hyhoaGoWbMmBg4cqAQaqampJmWTmZfRGnmZdevWac4vKyvDF198gYEDB6JWrVoICgpCjRo10L9/fyxYsECzptO8XEVFRXjjjTfQpk0bhIeHIzo6Gn369MEPP/xgs2wAsHXrVkyaNAkNGzZEWFgYoqKi0Lx5c9x777348ccfleWmTZsGSZLQokULm9vLyspCRESE5jHzNfL30Zr77rtPeb1t2zant9+4cWPExsYCgGYLkvPnzwMA2rRpg4CAAIv5gYGBaNu2LYDy/tjmPv30UwBA79690bVrV4v5Y8eORf369QFUBOmO+vnnn1FYWAgAePrppzWXeeaZZwAA169fx/Lly53avq84efKkQ/2wHe1LTUTkDMtffiIictgtt9yCtLQ0HDt2DL/++qtFcKyubbYXtN1+++1IT08HUF4LFhYWhmvXrmHXrl3YtWsX5s2bh59//hlNmjQxWW/27NnYsGED9u/fj7vuugu//vqryY39vn37MGXKFADA888/bzeAl8XFxaFWrVrIzMxEQUEBQkJCEB0drcwPDQ21WGfJkiW48847UVxcjKioKIsAIz09HYMGDcL+/fsBlAeqUVFRuHjxIlasWIEVK1bgoYcewgcffGCx7atXr6Jv3774448/AAABAQEoLi7G999/jx9++AHvvfeeQ+/LVVevXsXw4cNNHjxER0fj8uXL+Omnn/DTTz9h4cKF+PrrrxEUFKS5jZycHNxyyy3YunUrAgMDERwcjKysLKxduxbr1q3DRx99hHvvvddivdLSUkyZMsWkmW14eDgCAgJw6NAhHDx4EEuXLsX169cBAA8++CBef/11HDhwABs3bkT37t01y/Pll18iNzcX0dHRGDNmjBtHR38hISHK69LSUqfX379/P65duwYASEtLs5jfoEEDHD58GLt370ZJSYnFd7u4uBi7du0CAHTs2NFkXl5enlILftttt2nuX5IkDBgwAB988AFWr17tVNnl3w0AJrXxanFxcahZsyYuXbqE1atX45577nFqH77AaDTaTEoohMClS5e8WCIi8is697EmIqpy5ORgcuKal19+WQAQEyZMMFkuJydHRERECIPBIE6dOiXWrl1rMznYPffcI+bNmyfS09OVaYWFhWLNmjWic+fOAoBo3769Zpn27t2rJCn6+9//rkzPy8sTLVq0EABEt27dXErsIyfqMX9/MvX7ioiIEAMHDjRJInbkyBHleDRt2lQAEL169RLr1q0TBQUFQgghrl+/LmbPni0iIiIEAPH2229b7Gf48OECgAgODhYffvihyM/PF0KUJ1waPny4CAwMFGFhYQKAmDt3rtUy2iIvs3btWpPpJSUlomfPngKAaNu2rVi5cqXIzc1V3tenn34qatasKQCIJ5980mK7KSkpSoK1unXriuXLl4uioiIhhBCHDh0SXbp0UY7f9evXLdZ/5plnlLLde++94vDhw8q869evi+XLl4sxY8aYrDNgwAABQNxzzz1W32/79u0FAPHoo4/aPC5VwYoVK5Rj9MMPPzi0TlFRkTh9+rSYN2+eqFevngAg4uLiREZGhs3tjxw5Uhw9elSZd+jQITFs2DABQKSlpYnMzEyTdbdv366su2rVKqvlee+995Tlrly54uA7F+K1115T1rt8+bLV5eLj4wUA0bp1a4t58nmelJQkGjVqJIKDg0VkZKRo2bKleOKJJ5Tz2FVy+SozOdhzzz2nJH7buHGjawUlIrKCgTMRkZPMA+dTp04Jg8EgwsPDRXZ2trLcJ598IgCIfv36CSGE3cDZluzsbFGrVi0BQGzYsEFzGTn7tcFgEL/88osQQihZgGNiYkwCcmc4Ezh37tzZanD+0ksvCQCiZ8+eStBobunSpQKASEhIEMXFxcr0rVu3Kvv4+OOPLdYrKSkR3bt3V5bxdOD82WefCQCiadOmmoGtEOXBkSRJIigoSFy8eNFknhw4BwcHa2Ymv3TpkggJCREAxOeff24y7/Dhw8JgMAgA4plnnrFZfrXly5crmZ+vXbumWV75/e7Zs8fh7QohxIkTJ0wySTv7z5HgyRlFRUWidevWSuBq7fsla9KkiWa5mjdvLnbv3m11vX/9618iKChIM6t2TEyMePjhhzUDXnXQbWv78mcGQOzdu9fh9//VV18p661bt05zmfPnzyvLJCQkWMyXz3MAwmg0WmTkDgoKEu+//77DZTInbyc8PNxm1u5atWop54IzgbP8e6t1/hMReQL7OBMRuSk5ORl9+/ZFbm6uSVIguZm2VtNbZ0VERKBnz54AyvtNa3nooYcwYsQIlJWVYdy4cfjvf/+rJBz63//+h3r16rldDnuefvppq1l3P/74YwDAlClTEBgYqLnMsGHDEBUVhcuXL2PHjh3KdHms7OTkZKVfuZrRaMT06dPdLb5Vctkffvhhk+bqah06dECLFi1QVFSEtWvXai4zcuRIzczkNWrUUPq97tmzx2Tep59+irKyMsTHx+PFF190uMy33347kpKSkJ+fj/nz51vM/9///gcA6Nq1K1q1auXwdoGKJrOu/vN0orknnngCe/bsgSRJmDNnjtXvl6xGjRqoVasWoqKilGmtW7fGu+++i9atW1td78knn8TSpUtRs2ZNAOV97vPz8wGU993PyclBZmamxXrZ2dnK67CwMKvbV89Tr2NPnz59EBwcDAD4xz/+obmMerpWcrD27dtjzpw5OHnyJAoLC3H16lVkZWVhyZIlSEtLQ1FRER555BEsWbLE4XJpyc3NxcWLF23+KygocGqba9euxYMPPggAeO6556psIkYi8nF6R+5ERFWNeY2zEEIsWLBAaQ4thBBHjx5VmubKTYodqXFeuXKlGD16tKhfv77S7Nj83+TJk62W7erVq0qTU/nf/fff79b7dabG+dy5c5rLnDlzxqS2y1Ztk1y7+tVXXynr9+jRQwAQ48ePt1rO/Px8ERAQ4PEa55KSEmWs3djYWJtlDwwMFADEa6+9ZrJducb5nXfesbpveQxe88+rV69eAoAYO3aszbJrefHFFwUA0apVK5PpOTk5yhjIVb127s0331Q+txkzZji9/pUrV8THH38s6tatqzSFV7d2kOXm5orRo0cLAKJjx45i9erVIiMjQ2RkZIjVq1eLjh07Kt9v81rlL774Qimjuom3udWrVyvLbd682an38fTTTyvr3n333eLgwYOiqKhIpKeni2effVZIkqR8P0NCQpza9uXLl0X9+vWV372ysjKn1hei8ppqHzx4UBmP+4477nCpbEREjmCNMxGRBwwfPhyxsbHYtGkTjh49qtQ233nnnSZJi6wpKyvDXXfdhcGDB2PRokU4ceIEioqKEBsbq9TSydvJzc21up3Y2FiTJFkNGjTAv//9bzffnePkmjhz586dU15fvnzZZm1TWVkZgIphsAAoCX/q1q1rdd8hISHKEDyedPXqVSVj8bVr12yWvbi42KLsapGRkVb3Iyebkrchu3DhAgAgJSXF6bLff//9CAgIwN69e7FlyxZl+sKFC5GdnY2YmJgqnRTs3Xffxd/+9jcA5bXBztTIy+Li4nDvvfdi06ZNiIiIwCeffIIPP/zQYrmnn34aixYtQpMmTbBhwwb069cPCQkJSEhIQL9+/fDrr7+icePGuHz5MiZPnmyyrvpzt/bdMJ9n67ui5f/+7/8wduxYAMAXX3yBZs2aISgoCCkpKXjttddw0003KZnH5ezhjoqPj8ff//53AOWJyOQEfXrLyMjAoEGDcP36dXTq1Anz58+3m4SRiMhVDJyJiDwgODgYd955JwDgo48+UoaT0WpWrOXjjz/GggULYDQaMWPGDBw9elRpLnnhwgVcuHABI0eOBACrQx7J5Ca4AHD27Fn8+eefrrwll1hrpq3Ocnzw4EGI8hwbNv/5SnNLddm///57h8ruyaFw3AkE6tSpgyFDhgAA/vvf/yrT5e/IuHHjNLOjVwVz5szB448/DgB49NFHlXGMXZWSkoIRI0YAMD1WQHmzaXna5MmTNR+GhYaG4tFHHwVQ3p1Cnd25Tp06yuuzZ89aLYN6nnodRwQEBGDBggX47rvvMGbMGDRt2hQpKSno0aMH3nnnHfz6669KYN64cWOntg3AZAit48ePO72+pxUWFmLYsGE4fvw4kpOTsWLFiir7XSaiqoGBMxGRh8hB8ttvv40zZ86gZcuWFsPSWCP34b3//vvx4osvomHDhjAYTH+i5ZpHW+bMmYMVK1bAaDSiefPmKCwsxNixY23WcnlDYmKi8lo9dI6j5JpsW0FHYWEhrly5ojlPPXSQtf6TWn1TgfLaNnl9V8ruLvnYubrvhx56CACwaNEiZGVlYe/evdi6dSsAKP1CnXX69GkkJia6/O/NN990ab+yd999F4899hiA8kD23XffdWt7MrlFg/nDpiNHjqCkpASA9lBVskaNGimvT5w4obxu1qyZcj7v27fP6vryvMTERMTFxTlZ+nIDBw7EwoULcfDgQZw8eRK//vorHnvsMQQGBmL79u0AgJtvvtmlbfuSSZMmYfPmzYiIiMDKlStNfmOIiCoDA2ciIg/p2LEjWrVqhaKiIgDOJQU7ffo0AKBdu3aa83NycpRgx5q9e/fi6aefBgDMmDEDq1atQkxMDA4ePIinnnrK4bJUhtTUVCUoWblypdPryw8g1q9fb7XG/ddff1WCG3PqpqnysTZn7fgGBgaic+fOAFwru7vkIOenn35yOmkSAPTt2xcNGzZEbm4uvvjiC5OkYC1btnSpTKWlpXYTPNn6l5OT49J+gfKgWV3TPGfOHJe3ZU6uSTVvJq1+iGXrAcbFixeV1+pthIWFoVu3bgCAH374QXNdIQR+/PFHAED//v2dLLl9f/zxBw4cOAAALo3hrG7qX79+fY+VyxUzZszAggULYDAY8OWXX6JNmza6loeI/AMDZyIiD3rttdcwdepUTJ06FePGjXN4PTlT8+7duzXnv/zyyzaz7Obn52Ps2LEoKChA9+7d8fzzzyMlJUVpXvrf//7X7Wy47nrggQcAlDdLt9dH8urVqyZ/y/1wT506hU8//dRi+bKyMrzyyitWt9e4cWOlGafWcSgrK8Orr75qdf2//vWvAIBVq1Zh1apVTpXdXRMnToTRaMSVK1cwc+ZMp9eXJEmpWX7//ffx+eefA6h4T65ITU11qMm6p5uyq4Pmxx57zKmaZmsPVWQHDhzAN998AwDo1auXybymTZsq35+PPvpIc1ulpaXK+RYbG4smTZqYzJ8wYQKA8gzQWg9pvv76ayVwdyWwtSUvLw8PP/wwAO3M7va6f1y9ehX/93//B6A8s721B3zeMH/+fLz88ssAgDfffBODBw/WrSxE5GcqNfUYEVE1pJVV2xG2smq/8MILAoAICAgQ//nPf0RhYaEQonzs1SeffFIAEPHx8VazW9sar/m+++5TMkKfOnXKqTIL4VxWbVuys7NFq1atBAARHR0t3n33XXH58mVl/rVr18SqVavE+PHjRfPmzS3WHzJkiJIR+L///a8oKCgQQgiRnp4uRo4cKQIDA5VM5FqZoseNGycAiMjISPHVV18px/jQoUNiyJAhIi4uTjOrthDlmbX79u2rjGf78ssvi7Nnzyrzc3JyxC+//CIeeeQRER0dbbFvOau2rQzWto7ztGnTlLLdd9994siRI8q8zMxMsXDhQjFs2DCr2758+bKSGVz+nuTl5Vld3hfNmTNHKf8TTzzh9PoNGjQQb731ljh48KAoLS1Vpl+8eFG8//77IiEhQeDGWNtaYyg/9thjyv4HDBgg9uzZI0pLS0VpaanYvXu36N+/vzL/xRdftFi/uLhY+f7XrVtXrFmzRgghRGlpqVi0aJGIiooSAMRtt92mWX75d0fr90MIIbZs2SL+8Y9/iP379yvf7cLCQvH999+Ldu3aCQAiOTnZYoxxIcrHKR8+fLhYvHixyfy8vDyxbNky0bhxY2XfCxcutH2grZDXdyer9saNG5VxtB988EGXykFE5CoGzkRETqqMwPnatWuiadOmynyDwSBiYmKEJEnKTaK1wGrJkiXKel9//bXFfnNzc5Vt9+jRQ5SUlDhVbk8FzkIIcfbsWdGlSxdleUmSRExMjBI0yP8aNmxose7ly5dFmzZtlGUCAwOVYWgkSRLvvfeezQD19OnTok6dOibry/uNjIwU69atsxo4C1EeoN5+++0m5YyKijL5nOSHH+bcDZxLSkrE5MmTTfYdEREhYmNjlX1rBexq8oMDAOLRRx+1uawvUh9jW0OC1apVSzz++OMW66uPXUBAgIiPj1eG5JL/1ahRQ/z444+a+8/LyxMDBgwwWT44ONjkgQQAceedd1o9x06cOCFSU1OVZcPCwkRISIjyd7t27cTVq1c117UXOC9btszkvIqLixNGo1GZ1rJlS6vD4M2dO9fkPYSHh4v4+HiT9YODg8V7772n/eE4wBOBs/oY2BvW7o033nC5rEREWthUm4jIB8TExGDz5s148sknkZqaCqPRiICAAPTq1QsLFizQHB4HKO+ve//99wMA7rvvPiXztlpYWBgWLFiA4OBgbNiwwWaT5spWp04dbNy4EQsWLMCQIUNQu3Zt5OXloaioCKmpqRg8eDDefvtt/PrrrxbrxsfHY/PmzXjxxRfRtGlTGAwGBAQEYMCAAfjpp5/wyCOP2Nx3UlIStm7divvvv1/pbx0REYF77rkHO3fuRM+ePW2uHxUVhZUrV2LVqlUYM2YM6tWrh8LCQuTl5aFu3bro378/Xn31VRw+fNj1A2SF0WjEnDlzsHHjRtx9992oV68eiouLIYRA8+bNcd9999ltij9q1CjltatJwfQkVM2J7fWh1kr0tnLlSkydOhVdu3ZF7dq1kZubi6KiItSpUwf9+/fH22+/jSNHjljtXxwaGopVq1bh66+/xtChQ5GUlKSUKTk5GXfccQe+/fZbfPnll1azy6empmLPnj2YMWMGWrZsCUmSEBgYiA4dOuDNN9/Eli1bnB4qStahQwc8/fTTuOmmm1CzZk1kZ2cjPj4effv2xX//+1/88ccfSE1N1Vy3d+/e+Mc//oHbb78daWlpCAwMRGZmJqKiotCpUyc8++yzOHjwoN1zzJvsDWvnTj96IiItkhB2OrYQERFVEampqUhPT8fcuXN9ZjgrX/HYY49hzpw56Nq1KzZv3qx3cYiIiKoU1jgTERFVc1lZWcrY4nKSKCIiInIcA2ciIqJqrLCwEE888QSysrKQnJysZCgnIiIixwXoXQAiIiLyvLfffhtvv/02Ll26hPz8fADA7NmzERQUpHPJiIiIqh7WOBMREVVD169fR3p6OoQQaNu2Lb766ivN5HFERERkH5ODEREREREREdnAGmciIiIiIiIiG9jH2QeUlZXh3LlziIyMhCRJeheHiIiIiIio0gkhkJ2djTp16sBg8O06XQbOPuDcuXNITk7WuxhERERERERed/r0aSQlJeldDJsYOPuAyMhIAOVfmKioKJ1LQ0REREREVPnkoRLleMiXMXD2AXLz7KioKAbORERERETkV6pCd1XfbkhOREREREREpDMGzkREREREREQ2MHAmIiIiIiIisoGBMxEREREREZENDJyJiIiIiIiIbGDgTERERERERGQDA2ciIiIiIiIiGxg4ExEREREREdnAwJmIiIiIiIjIBgbORERERERERDYwcCYiIiIiIiKygYEzERERERERkQ0MnImIiIiIiIhsYOBMREREREREZAMDZyIiIiIiIiIbGDgTeUBubi4kSYIkScjNzdW7OERERERE5EEMnImIiIiIiIhsYOBMREREREREZAMDZyIiIiIiIiIbGDgTERERERER2cDAmYiIiIiIiMiGAL0LQFQdGI1GjBw5UnlNRERERETVBwNnIg8ICQnB119/rXcxiIiIiIioErCpNhEREREREZENDJyJiIiIiIiIbGDgTOQBubm5kCQJkiQhNzdX7+IQEREREZEHMXAmIiIiIiIisoGBMxEREREREZENDJyJiIiIiIiIbGDgTERERERERGQDA2ciIiIiIiIiGxg4ExEREREREdkQoHcBiKoDo9GIgQMHKq+JiIiIiKj6YOBM5AEhISH47rvv9C4GERERERFVAjbVJiIiIiIiIrKBgTMRERERERGRDQyciTwgNzcX4eHhCA8PR25urt7FISIiIiIiD2IfZyIPycvL07sIRERERERUCVjjTERERERERGQDA2ciIiIiIiIiGxg4ExEREREREdnAwJmIiIiIiIjIBgbORERERERERDYwqzaRBxgMBvTs2VN5TURERERE1QcDZyIPCA0Nxbp16/QuBhERERERVQJWjRERERERERHZwMCZyEO2Hr+C3aev610MIiIiIiLyMDbVJvKAExeuYvQHGwFRio3P9EZyzVi9i0RERERERB7CGmciD9hxKhOSMQBSQDCW7b6gd3GIiIiIiMiDGDgTecAfpzOV14t2nENpmdCxNERERERE5EkMnIk8QB04n7legF+PZOhYGiIiIiIi8iQGzkRuyikswaGLOQCA3EMbAADzt6TrWSQiIiIiIvIgBs5Ebtp16jrKBFCSeQnXf50PAFh7+BJOX83TuWREREREROQJDJyJ3LQ9/SoAoPDMAZRcO4ebG8RCCOCLrad0LhkREREREXkCA2ciN+1IvwYAqGXMRceOHXFnpyQAwKLtp1FQXKpn0YiIiIiIyAP8KnDOz8/HjBkz0LhxY4SEhKBOnTq49957cfbsWbe2e/ToUYSGhkKSJPTt29dDpaWqoKS0DDtvBM6L3v8ntm3bhttaJ6F2dAiu5hbh+33ndS4hERERERG5y28C54KCAvTp0wcvv/wycnJyMHToUCQnJ2Pu3Llo164djh8/7vK2//rXv6KwsNCDpaWq4tCFbOQWlSIyOABNEiMBAAFGA+7qXA8AMP83JgkjIiIiIqrq/CZwfuWVV7BlyxZ07doVR44cwVdffYWtW7firbfeQkZGBu69916Xtvvxxx9j3bp1eOCBBzxcYqoK5Gba7VJiYTRIyvQxnZMRYJCw89R17DubaW11IiIiIiKqAvwicC4qKsKcOXMAAO+99x4iIiKUeVOmTEHr1q2xfv167Nixw6ntXrx4EU8//TT69euHO++806Nlpqph28nyxGBt6kQgNTUVqampyMvLQ83IEAxomQgA+GIra52JiIiIiKoyvwicN23ahMzMTKSlpaFdu3YW80eOHAkAWLlypVPbfeKJJ5Cfn4/333/fI+WkqkepcU6OQnp6OtLT0yGEAACM75ICAFj+xzlk5hfrVkYiIiIiInKPXwTOu3fvBgC0b99ec748fc+ePQ5vc9WqVfjqq6/w97//HQ0bNnS/kFTlnL2ej/OZBTAaJLSqE2Uxv3P9ODSuFYH84lIs3XlGhxISEREREZEn+EXgfOpU+Xi6SUlJmvPl6enpjjWpzc3NxSOPPIImTZrg2Wef9UwhqcrZfqOZdos6UQgLMlrMlyRJqXWev6WiJpqIiIiIiKoWvwicc3JyAABhYWGa88PDwwEA2dnZDm3vhRdeQHp6Oj788EMEBQU5XZ7CwkJkZWWZ/KOqZ/vJ8mbaHVJirS4zrF1dhAcZcTwjF78du+KtohERERERkQf5ReDsSdu3b8c777yDe+65B7169XJpG6+++iqio6OVf8nJyZ4tJHnF9hv9mzulxlldJjIkEMPb1wVQXutMRERERERVj18EznIW7by8PM35ubm5AIDIyEib2ykpKcEDDzyAmJgYvPnmmy6X57nnnkNmZqby7/Tp0y5vi/SRVVCMwxfKWwp0tFHjDADjbjTXXn3gIi5kFlR62YiIiIiIyLMC9C6AN9SrVw8AcOaMdoImeXpKSorN7Zw5cwa7du1CYmIiRo0aZTLv+vXrAIAdO3YoNdHr1q3T3E5wcDCCg4MdLD35oj9OXUeZAJLjQlEzKgR5eXlo3rw5gPK+zWpNE6PQOTUOv5+8igW/n8JT/RrrUWQiIiIiInKRXwTObdq0AQDs3LlTc748vXXr1g5t78KFC7hw4YLmvOvXr2P9+vUulJKqkh03EoN1Silvph0WFob9+/dbXX5c1xT8fvIqFm47hUf7NESg0S8aexARERERVQt+cfferVs3REdH49ixY9i1a5fF/MWLFwMABg8ebHM7qampEEJo/lu7di0A4C9/+YsyjaovuX9zh1TbzbRlA1okIiEiCBezCrHmwMXKLBoREREREXmYXwTOQUFBePTRRwEAkydPVvo0A8Ds2bOxZ88e9OzZEx06dFCmz5kzB02bNsVzzz3n9fKSbysuLcMfp64DADqmWE8MphYUYMDYTuVdBpgkjIiIiIioavGLwBkoH0LqpptuwubNm9GoUSOMGTMGXbp0wdSpU1GjRg188sknJstfvnwZhw8fxvnz53UqMfmqg+ezkF9ciqiQADSqWZF4rkWLFmjRooXVJHR33lQPBgnYfOwK/rzk2NBnRERERESkP78JnENCQrB27VpMnz4dYWFhWL58OdLT0zFx4kTs3LkTDRo00LuIVEWox282GMoTgQkhcODAARw4cMBqM/26MaH4S7NaAIDPt5zyTmGJiIiIiMhtkmBnXN1lZWUhOjoamZmZiIqK0rs4ZMcjX+zAqr0X8PStTTC5d0MA5UOaycOe5eTkIDw8XHPdX49k4J5PfkdkcAC2Pv8XhAX5RX4+IiIiIiILVSkO8psaZyJPEEKY1Dg7q3vDBKTGhyG7sATf7Drn6eIREREREVElYOBM5IQz1/JxKbsQgUYJbZJinF7fYJAwrkv5eOHzf0tn9nUiIiIioiqAgTORE7anl4/f3KJONEKDjC5tY2SHJAQHGHDgfBZ23sjOTUREREREvouBM5ETtt1opt3RhWbaspiwIAxpUwcA8DmHpiIiIiIi8nkMnImcsEMOnFNNx2+WJAkpKSlISUmBJEl2tzO+a3lz7e/2nMeVnELPF5SIiIiIiDyGgTORgzLzinHkxvjL5onBwsLCcPLkSZw8eRJhYWF2t9U6KQZtkqJRVFqGRdvPVEp5iYiIiIjIMxg4Ezlo56lrEAJIjQ9Djchgt7cnJwn7Yms6SsuYJIyIiIiIyFcxcCZykJwYzLyZtqsGt6mD6NBAnLmWj/VHLnlkm0RERERE5HkMnIkctN1GYrD8/Hx06tQJnTp1Qn5+vkPbCwk0YlSHJADlQ1MREREREZFvYuBM5ICikjLsOn0dANAx1TJwLisrw/bt27F9+3aUlZU5vN27bzTXXnckA6eu5HmkrERERERE5FkMnIkcsP9cJgpLyhATFogGCREe2279hHD0aJQAIYAvfmetMxERERGRL2LgTOSAHekVzbQNBvvDTTlj/I1a50XbTqOguNSj2yYiIiIiIvcxcCZygNy/uUOKZxKDqfVpWhN1okNwLa8Yq/ae9/j2iYiIiIjIPQyciewQQqgyalv2b3ZXgNGAu26qBwCYv4XNtYmIiIiIfA0DZyI70q/k4XJOEYKMBrSqG10p+xjdKRmBRgl/nLqOfWczK2UfRERERETkGgbORHZsv9G/uVVSNEICjVaXS0hIQEJCgkv7qBkZggEtawMAPmetMxERERGRT2HgTGTH9pM3mmlrjN8sCw8PR0ZGBjIyMhAeHu7SfuQkYct3nUVmfrFL2yAiIiIiIs9j4Exkh1zj3DHV84nB1DqlxqJJrUgUFJdhyY4zlbovIiIiIiJyHANnIhuu5xXhz0s5AIAONmqcPUGSJIzrWl7r/PmWdAghKnV/RERERETkGAbORDbI4zc3qBGOuPAgq8vl5+ejV69e6NWrF/Lz813e3/B2dREeZMTxy7nYfOyKy9shIiIiIiLPYeBMZMO2G+M32+rfDABlZWVYv3491q9fj7KyMpf3FxEcgBHtkwAA839jkjAiIiIiIl/AwJnIhh3K+M2V279ZbdyNJGE/HbyI85mu114TEREREZFnMHAmsqKwpBS7z5SPqWyvxtmTmiRGonP9OJSWCSz4/bTX9ktERERERNoYOBNZse9sJopKyhAfHoT6Ca4NMeUqeWiqBb+fQnGp602/iYiIiIjIfQyciazYfqN/c4eUWEiS5NV939oiEQkRwcjILsTq/Re9um8iIiIiIjLFwJnIiorxm73XTFsWFGDAnZ2TAQDzt5z0+v6JiIiIiKgCA2ciDUIIZSiqDimOJQYLCwtDWFiYx8pwZ+d6MEjAluNXcfRitse2S0REREREzmHgTKTh+OVcXM0tQlCAAS3rRtldPjw8HLm5ucjNzUV4uGf6Q9eJCUXfZrUAAJ9v4dBURERERER6YeBMpGHHjf7NbZNiEBxg1K0c47uWJwlbsvMscgtLdCsHEREREZE/Y+BMpGH7jfGbO+jQv1mtW1oC6ieEI6ewBMt3ndW1LERERERE/oqBM5EGOaO2o+M3FxQUYNCgQRg0aBAKCgo8Vg6DQcLdN9UDAMz/LR1CCI9tm4iIiIiIHMPAmcjMlZxCHL+cC6B8KCpHlJaWYtWqVVi1ahVKS0s9Wp5RHZIREmjAoQvZSsIyIiIiIiLyHgbORGbk4LRRzQjEhAXpXBogOiwQQ9rUAQDMZ5IwIiIiIiKvY+BMZEbP8ZutGd8lFQCwau95XM4p1LcwRERERER+hoEzkZntJ8sTg3V0cPxmb2iVFI02yTEoLhX4attpvYtDRERERORXGDgTqRQUl2Lv2UwAvlXjDADju5QPTfXl1lMoLWOSMCIiIiIib2HgTKSy50wmiksFEiKCUS8uTO/imLi9dW3EhAXi7PV8rD10Se/iEBERERH5DQbORCry+M0dU2IhSZLOpTEVEmjE6I7JAJgkjIiIiIjImxg4E6nsOOlaYrDw8HAIISCEQHh4eGUUDQCUMZ3XH8lA+pXcStsPERERERFVYOBMdENZmcCOU3Lg7DuJwdRS4sPRs3ENAMAXW0/pXBoiIiIiIv/AwJnohmMZObieV4yQQANa1InSuzhWyUnCFm0/jYLiUp1LQ0RERERU/TFwJrpBHr+5bXIMAo3OnRoFBQUYNWoURo0ahYKCgsoonqJ305qoGxOK63nF+HbP+UrdFxERERERMXAmUmyX+ze7MH5zaWkpFi9ejMWLF6O0tHJrgY0GCXfd6OvMJGFERERERJWPgTPRDXJG7Q4+Nn6zljGdkhFolLD79HXsPZOpd3GIiIiIiKo1Bs5EADKyC5F+JQ+SBLSv5/uBc0JEMAa2qg0A+Jy1zkRERERElYqBMxGAHTdqm5vUikR0aKDOpXGMnCTsm91nkZlXrHNpiIiIiIiqLwbORAC23ejf3CHF92ubZR1SYtE0MRIFxWVYvPOM3sUhIiIiIqq2GDgToSKjdscq0L9ZJkkSxnctr3X+fEs6ysqEziUiIiIiIqqeGDiT38svKsX+s+UJtlzJqK2nYW3rIiI4ACcu52LzsSt6F4eIiIiIqFpi4Ex+b/eZ6ygpE6gVFYyk2FCXthEWFoacnBzk5OQgLCzMwyW0Ljw4AHe0rwsAmL/lpNf2S0RERETkTxg4k9/bfrI8MVjHlDhIkuTSNiRJQnh4OMLDw13ehqvG3UgS9tOBizifme/VfRMRERER+QMGzuT3qmL/ZrVGtSLRpUEcygSwYOspvYtDRERERFTtMHAmv1ZWJrBDDpzd6N9cWFiIiRMnYuLEiSgsLPRU8Rw2vksqAGDBttMoKinz+v6JiIiIiKozBs7k145cykZ2QQnCgoxoVjvS5e2UlJTg008/xaeffoqSkhIPltAx/VvUQo3IYGRkF2L1gQte3z8RERERUXXGwJn82vYb4ze3qxeDAGPVPR0CjQbc2bkeAGD+b+k6l4aIiIiIqHqpupECkQfIzbQ7VLFhqLTc2TkZRoOErSeu4sjFbL2LQ0RERERUbTBwJr+2TcmoXTUTg6nVjg5Fv2a1AACfb2GtMxERERGRpzBwJr91MasAZ67lwyCVN9WuDsZ3LR+aaunOs8gp9H5fayIiIiKi6oiBM/ktuX9z08QoRIYE6lwaz7g5LR4NaoQjp7AEy/84q3dxiIiIiIiqBQbO5Le2p99opl1Fx2/WIkkSxt1UXuv8+ZZ0CCF0LhERERERUdXHwJn8llzj3MED/ZvDwsJw6dIlXLp0CWFhYW5vzx13dEhCSKABhy5kY/uN5GdEREREROQ6vwqc8/PzMWPGDDRu3BghISGoU6cO7r33Xpw963iT1pKSEsyaNQuDBg1CgwYNEBkZiZCQEDRq1AiPPPII0tOZlKkqyC0swYHzWQCATqnuZ9SWJAk1atRAjRo1IEmS29tzR3RoIIa1rQuAQ1MREREREXmC3wTOBQUF6NOnD15++WXk5ORg6NChSE5Oxty5c9GuXTscP37c4e28+OKL+PXXX1G7dm0MGDAAt956K4qKivDBBx+gdevW2L59eyW/G3LX7tPXUVomUCc6BHViQvUujseN61LeXPv7feeRkV2oc2mIiIiIiKo2vwmcX3nlFWzZsgVdu3bFkSNH8NVXX2Hr1q146623kJGRgXvvvdeh7YSEhGDjxo24du0aNm3ahK+//hrffPMNjh8/jmnTpiErKwsPPfRQJb8bctc2uZm2B2qbAaCwsBCTJ0/G5MmTUViof6Dasm402tWLQXGpwKLtp/UuDhERERFRlSYJP8geVFRUhJo1ayIzMxM7d+5Eu3btTOa3adMGe/bswfbt29GhQweX91NSUoLIyEgUFBTg+vXriI6Odmi9rKwsREdHIzMzE1FRUS7vnxw3/uOt2HD0Ml4c0gITbk51e3u5ubmIiIgAAOTk5CA8PNztbbpr6c4zmLJoN+pEh2DDs31gNOjbhJyIiIiISK0qxUF+UeO8adMmZGZmIi0tzSJoBoCRI0cCAFauXOnWfiRJgtFohCRJCAoKcmtbVHlKywT+OHUdQPXKqG1uYKvaiA0LxLnMAvxy6JLexSEiIiIiqrL8InDevXs3AKB9+/aa8+Xpe/bscXkfQgi89tpryM3NRe/evREaWv36zVYXhy5kIaewBBHBAWia6NtPttwREmjE6E7JAID5W5gkjIiIiIjIVQF6F8AbTp06BQBISkrSnC9PdzYj9rPPPouLFy8iKysLe/bswbFjx9CsWTN89NFH7hWYKtWOG0M0tasXU+2bL9/dOQX//fU4fj2SgZOXc5GaoH8TciIiIiKiqsYvAuecnBwAsDq+rtwfNTs726ntLlmyBMeOHVP+bt26NT7//HPUr1/f5nqFhYUmCaSysrKc2i+5Rx6/uWOKZxKD+bJ68WHo1bgG1h7OwBdb0/H8oOZ6F4mIiIiIqMrxi6baleXPP/+EEAIZGRn44YcfEBgYiA4dOuDTTz+1ud6rr76K6Oho5V9ycrKXSkxARY1zde7frCYPTbVo+xkUFJfqXBoiIiIioqrHLwJnOdtxXl6e5vzc3FwAQGRkpEvbT0hIwK233oqff/4ZiYmJePjhh3H6tPUhgJ577jlkZmYq/2wtS5517no+zl7Ph9EgoW1yjN7F8YpeTWqibkwoMvOLsXL3Ob2LQ0RERERU5fhF4FyvXj0AwJkzZzTny9NTUlLc2k90dDQGDx6M/Px8/PTTT1aXCw4ORlRUlMk/8o7tN2qbm9eOQniw53oqhIaG4sSJEzhx4oTPJYYzGiTc3aX8HPicScKIiIiIiJzmF4FzmzZtAAA7d+7UnC9Pb926tdv7SkhIAABkZGS4vS3yvB0nrwIAOqR4tpm2wWBAamoqUlNTYTD43mk1umMygowG7D6Tid2nr+tdHCIiIiKiKsX37vArQbdu3RAdHY1jx45h165dFvMXL14MABg8eLDb+1q/fj0AIC0tze1tkedtO+lf/ZtlCRHBGNgqEQBrnYmIiIiInOUXgXNQUBAeffRRAMDkyZOVPs0AMHv2bOzZswc9e/ZEhw4dlOlz5sxB06ZN8dxzz5ls67vvvsPmzZst9pGXl4fnn38e69evR2JiIgYMGFBJ74ZclVNYgkMXyjOYezqjdlFREZ5++mk8/fTTKCoq8ui2PWV81/KuCCt2n8P1PN8sIxERERGRL/KL4agA4IUXXsCaNWuwefNmNGrUCD169MD/t3ff4VGUaxvA79nd7G56IQktBQiEXkIKIFJVQIpIFwQpYqMIInrkKILtWACxgA0lICpFFBQbgoJSQwoJUgOBVJKQ3rN1vj82yUckhASSzJb7d125LpjZnbnjmTPss+8775OUlISIiAh4eXlh48aN1V6fnZ2NCxcuID09vdr2yMhIvPLKK2jdujV69eoFV1dXZGRkIDY2Frm5uXB1dcWOHTuqFiQj83EyOQ9GEfBxt0cLV3WDHlun02H16tUAgJUrV0KpVDbo8RtCbz93dG7pgnPphdgZnYq5A9pJHYmIiIiIyCLYxIgzAKjVahw4cADLly+Hg4MDdu/ejaSkJMyaNQsxMTFo165uRcT48eOxZMkStGrVCpGRkdixYwciIyPh7++PZcuW4dy5cxgwYEAj/zZ0O6qmaTfw882WQhAEzKhoTfXV8SQYjaLEiYiIiIiILIMgiiI/PUussLAQrq6uKCgo4Arbjejhz4/jyKUcvPZgt6oCsqGUlJRUzTIoLi6Go6Njgx6/oZRo9Oj7vz9QpNHjyzlhGBjoJXUkIiIiIrJRllQH2cyIM9k2vcGIk8n5AIBQG1sY7HqOKgUmBPsAALZwkTAiIiIiojph4Uw24XxGEUq1BjirFQj0dpY6jqSmV/R0/uNcJtLyyyROQ0RERERk/lg4k02IrOjf3NvPHTKZIHEaabX3dka/ds1gFIGtEclSxyEiIiIiMnssnMkmRCWZFgaz5Wna16tsTbUtMhlavVHiNERERERE5o2FM1k9URQRVTHiHNzA/Zsr2dvb4/Tp0zh9+jTs7e0b5RwN6b4uzeHtrEJ2sRa/ncmQOg4RERERkVlj4UxWLzWvDJmFGihkAnr5ujXKOWQyGbp27YquXbtCJjP//1vZyWWYGmZ61vmrY1wkjIiIiIioNub/CZ/oDkVXTNPu2soF9kq5xGnMx9QwP8hlAk4k5uJ8RqHUcYiIiIiIzBYLZ7J6UUmmadohbRpnmjYAaLVarFy5EitXroS7Uag4AABwx0lEQVRWq2208zSkFq5qDOvSHADwFVtTERERERHdFAtnsnpRiaYR5xD/xlsYTKfT4ZVXXsErr7wCnU7XaOdpaDP6mhYJ2xWThqJyy8lNRERERNSUWDiTVSso0+FCZhEAIJgrat+gX0AztPNyRInWgN0n06SOQ0RERERkllg4k1U7mZwHUQT8mznA21ktdRyzIwhC1ajzluNJEEVR4kREREREROaHhTNZtcqFwYIbcZq2pRvf2wf2dnLEZxbjxJVcqeMQEREREZkdFs5k1SIr+jeHNFL/Zmvgam+HB4NaATCNOhMRERERUXUsnMlq6QxGxKbkAwBC+XxzraZXTNf+7XQGrhWVS5yGiIiIiMi8sHAmq3X2aiHKdUa42tshwMtJ6jhmrWsrV/T2c4PeKGL7iRSp4xARERERmRUWzmS1KqdpB/u7QyYTGvVcarUaJ06cwIkTJ6BWW+YiZDP6mUadt55Iht5glDgNEREREZH5YOFMVqspFwaTy+UIDQ1FaGgo5HJ5o5+vMdzfrSU8HJW4WlCOP89fkzoOEREREZHZYOFMVkkURURVFM6hbbgwWF2o7eSYHOILgIuEERERERFdj4UzWaXk3FJkFWlgJxfQw8e10c+n1WqxatUqrFq1ClqtttHP11ge7uMHQQAOXczGlewSqeMQEREREZkFFs5klaISTaPN3Vq7Qm3X+FOndTodnn/+eTz//PPQ6XSNfr7G4uvhgCEdvQEAX3PUmYiIiIgIAAtnslKcpn37ZlS0pvo2OhVlWoPEaYiIiIiIpMfCmaxSdNL/r6hN9TMw0Au+HvYoKNNhz6mrUschIiIiIpIcC2eyOvmlWsRnFgNg4Xw75DIBD/cxjTp/xenaREREREQsnMn6xCSbpmm383SEp5NK4jSWaXKIL5QKGU6lFiAuJV/qOEREREREkmLhTFancmEwjjbfPg9HJUZ3bwmAramIiIiIiFg4k9WpLJxD2rBwvhPT+5mma++Ju4q8EsttsUVEREREdKdYOJNV0eqNiEvNBwAE+zfditpqtRoHDhzAgQMHoFarm+y8jSnI1w1dW7lAozdiZ3Sq1HGIiIiIiCTDwpmsyumrBdDojXB3sEOAl2OTnVcul2Pw4MEYPHgw5PLG7xvdFARBqGpN9VVEEoxGUeJERERkSbR6I7aeSEZ6QZnUUYiI7hgLZ7IqUYmVbag8IAiCxGks3wO9WsFZrUBSTikOXcqWOg4REVmQdX9exLLv/8GML06gXGeQOg4R0R2RtHB+9dVX8eOPP0oZgayMVM8363Q6rF+/HuvXr4dOp2vSczcmB6UCE4N9AABbjnGRMCIiqpusIg0+P3wFAHDpWjHe+vW8xImIiO6MpIXzypUrsXv3bikjkBURRRHRSabCObSJC2etVosFCxZgwYIF0GqtayGt6RXTtf88n4nUvFKJ0xARkSVYf+ASSrUGtHI1rfux6Wgi/o7PkjgVEdHt41RtshqJOaXIKdFCqZChW2tXqeNYjQAvJ/Rv3wxGEdh6IlnqOEREZOZS80rxTYTp34t3JvbEzIouDUu/jWOXBiKyWCycyWpEVjzf3KO1K1QK61igy1xULhK2PTIFGj2fUyMiopt7b/9FaA1G3BXQDHd38MQL93dGgJcjrhVp8N9d/0AUudgkEVkeFs5kNaIrnm8OZv/mBndv5+Zo7qJCdrEWv53OkDoOERGZqYuZRfg+xtTC8LnhHQEA9ko53n8oCAqZgF9PZ+C7mDQpIxIR3RaF1AGKi4uRnHx70z/9/PwaOA1Zsqgk04hzaBP2b7YVCrkM08L8sXZ/PL46noSxvVpLHYmIiMzQmt/jYRSBYV2aI8jv/7/I7tbaFc/cF4hVey9g5Y9n0KetB3w9HCRMSkRUP5IXzt999x2+++67er9PEATo9fpGSESWKLdEi4SsEgBAsD9HnBvDQ2G++PDPi4hMzMO59EJ0bukidSQiIjIjcSn5+O1MBmQCsLRitPl6Tw4KwIHz1xCVlIclO2Kx7fF+kMvYOpKILIPkU7VFUbytH6PRKHV0MiOVq2kHeDnC3VEpcRrr1NxFjeFdWwAAvjrO1lRERFTdO3tNLafGBfkgsLnzDfvlMgFrp/SCo1KOyMQ8fPp3QlNHJCK6bZIXzrNmzYLRaLytH6JKVdO020gzTVulUuGnn37CTz/9BJVKJUmGplDZmmrXyTQUlVtPv2oiIrozRy5l48ilHNjJBSy+t8NNX+fr4YCVD3QFAKzdF4/TaQVNFZGI6I5IXjgTNYSqhcEkmqatUCgwatQojBo1CgqF5E9ANJq+7TzQ3tsJpVoDdp3k4i5ERGSaPfjO3gsAgIf7+N/y2eWJwT4Y0bUFdAYRi7fHolzHbg1EZP5YOJPFK9cZcCrV9I11iEQjzrZCEISq1lRbjiWxpQgREWHvmUzEpeTDQSnH/CHtb/l6QRDwv/Hd4eWswqVrxXjr1/NNkJKI6M6wcCaLdzqtAFqDEZ5OSrRpJs0KnTqdDps2bcKmTZug01n3FOZxvVvDQSnHxWvFiLiSK3UcIiKSkMEoYs3vptHmOf3bwsu5bo8reTgqsWpiDwDApqOJOHQxq9EyEhE1BBbOZPGikv5/mrYgSLM6p1arxezZszF79mxotVpJMjQVF7UdHgwytaPawkXCiIhs2q6Tabh4rRiu9nZ4bGC7er13cEdvPNLPNItp6bdxyC+17n8/iciySVo4h4eHY+7cuVJGICsQlWga9Qxh/+YmM72P6YPO3tMZuFZYLnEaIiKSgkZvwNp98QCApwYHwNXert7HWHZ/ZwR4OSKzUIMXd53mI0BEZLYkLZw3b96Mw4cP17gvOTkZubmcBkq1E0WxqhVVcBv2b24qXVq5IMTfHXqjiG2RKVLHISIiCWyNSEZafhm8nVWY2a/NbR3DXinHe1OCoJAJ+PmfdC48SURmS9LC+eDBgzh/vuYFIdq2bYvnnnuuiRORpUnIKkFeqQ4qhQzdWrlKHcemzKiYXvdNRDL0BraHIyKyJSUaPdYduAQAePqeDrBXym/7WN19XKtaWK344QxScksbJCMRUUMy22ecRVHkdB26pcpp2j193aBUmO3lbJVGdGuBZo5KZBSWY/+5a1LHISKiJhR+5Aqyi7Xwb+aAKaG+d3y8JwcFINjfHUUaPZ7dEQeDkZ8Bici8sNIgi1a5MFiIRP2bbZlKIa/6sPQVFwkjIrIZ+aVafPr3ZQDAkvsCYSe/84+TCrkMayf3gqNSjhOJufis4vhEROaChTNZtMrnm0PZv1kS0/r4QRCAw5eykZBVLHUcIiJqAh//lYCicj06tXDGmB6tGuy4fs0csGJMVwDAu/su4HRaQYMdm4joTrFwJouVXazBlewSAEBvP2lHnFUqFXbs2IEdO3ZApapbD0tr4OPugHs6eQMAvj6eLHEaIiJqbJmF5dh0JBEA8NzwjpDJGrYN5KQQHwzr0hw6g4hntseiXGdo0OMTEd0uFs5ksaISTaPNgc2d4OpQ/xYYDUmhUGDSpEmYNGkSFAqFpFma2vS+pkXCvo1OQalWL3EaIiJqTB/8cREavRHB/u4YWvHFaUMSBAFvju8OTycVLl4rxtu/1byILBFRU5O8cN68eTPkcvkNP4Ig3HSfXC63ueKEbhSdVNG/mdO0JTWwgxf8PBxQVK7HnrirUschIqJGkphdgu0VLQj/M6ITBKFhR5srNXNSYdWkHgCA8COJOHQxq1HOQ0RUH5IXzpWrZ9f3x2hk+xtbZ04Lg+n1enz77bf49ttvodfb1qirTCbg4T5+AIAvjyVxNXwiIiv17r546I0iBnf0Qljbxv3SekhHb8yomNG09Ns45JdqG/V8RES3ImnhbDQa7+iHbFe5zlC1aEiIv/QjzhqNBpMnT8bkyZOh0WikjtPkJoX4QqmQ4czVQsSm5Esdh4iIGtjZq4X4sWJW0dJhHZvknP8d2RntvByRWajBi7tP84tZIpKU5CPORLcjLiUfOoMIL2cVfD3spY5j8zwclRjdoyUAYAtbUxERWZ3Vv18AAIzu0RLdWrs2yTntlXK8N6UXFDIBP59Kx+7YtCY5LxFRTVg4k0WKqmpD5d5oz1hR/VROqfvpVDpySziljojIWkQl5uLP89cglwl4tolGmyv18HHDons6AABe3n0GqXmlTXp+IqJKkhbOQ4cOxTvvvFPjvuTkZOTm5jZxIrIUUYmmayPYDKZpk0kvXzd0a+0Crd6Ib6NSpI5DREQNQBRFvPObabR5cogP2no6NnmGpwYHoLefG4o0ejy7Iw4GI6dsE1HTk7RwPnjwIM6fr7nNQNu2bfHcc881cSKyBEajiGgzWhiMTARBqBp1/ioiCUZ+sCEisngH47NwIjEXSoUMT1eM/DY1hVyGtVN6wVEpR8SVXHx+6LIkOYjItpntVO3K1bOJ/u1SVjEKy/Wwt5OjSysXqePQdR7o2RrOagVScsvwF9uHEBFZNKNRxKqK0eaZ/fzR0lW6NUX8mzni5TFdAJietz5ztUCyLERkm8y2cCa6mahE02hzL1832Ml5CZsTe6Uck4J9AQBfHeMiYUREluznf9JxNr0QTioFnhrcXuo4mBzii2FdmkNnEPHM9liU6wxSRyIiG8KqgyxO5fPNIW3MZ5q2UqlEeHg4wsPDoVQqpY4jqYf7mno6/3nhGlJyuYgLEZEl0hmMeHdfPADgsQHt4OEo/b9tgiDgzfHd4emkQnxmcdWz10RETYGFM1mcyhW1Q9qYz8JgdnZ2mDVrFmbNmgU7Ozup40gqwMsJd7f3hCgC35xIljoOERHdhp3RqbiSXYJmjko8OqCt1HGqNHNS4Z2J3QEAG49cweGL2RInIiJbYVOFc1lZGV5++WUEBgZCrVajVatWmDNnDtLS6t4XMD8/H9988w2mTp2Ktm3bQqlUwtnZGX369MH7778PnU7XiL8BXSssR3JuKQQBCPJzkzoO3cT0ikXCtkemQKPnVDoiIktSrjPg/f0XAQDzhrSHk0ohcaLqhnZqjof7mGY3Lf02DvmlbIFIRI1P8sJ58+bNkMvlN/wIgnDTfXK5HApF/W7i5eXlGDp0KF577TUUFxdj7Nix8PX1RXh4OIKCgnD5ct1WaFy9ejUefvhhbN++He7u7hg/fjzCwsIQFxeHxYsXY+jQoSgt5fTUxlI52tyxuTNc1OYzsqvX6/Hzzz/j559/hl6vlzqO5O7t7I0WLmrklmjx6z8ZUschIqJ62HIsCRmF5Wjlqq4qUM3Ni6M6o52nIzIKy/HS7tNcUJaIGp3khXPl6tn1/TEajfU6z+uvv47jx4+jX79+iI+Px/bt2xEREYE1a9YgKysLc+bMqdNxHB0d8fzzzyMxMRExMTHYtm0b/vjjD/zzzz/w8/PD4cOH8frrr9/Ofwqqg8qFwczp+WYA0Gg0GD16NEaPHg2NRiN1HMkp5DJMq/iwteU4FwkjIrIUReU6fHTwEgBg8b2BUNvJJU5UMwelAmun9IJCJuCnU+n4Ifaq1JGIyMpJWjgbjcY7+qkrrVaLdevWAQDWr18PJyenqn1LlixBjx498NdffyE6OvqWx1q2bBnefvtt+PlV/wa2Q4cOeOuttwAAW7durXM2qp/oJNPCYKFm9Hwz1eyhUF8oZAKik/Jw9mqh1HGIiKgONhy6grxSHQK8HDG+d2up49Sqp69bVW/p5T+cRlp+mcSJiMiaST7i3BSOHDmCgoICBAQEICgo6Ib9EydOBADs2bPnjs7Ts2dPAMDVq/zWszGUavU4XVGABfub14gz3cjbRY3h3VoAAL6K4KgzEZG5yynW4ItDpkfXlg7rCIUFtHycNzgAQX5uKCrX49kdsTAaOWWbiBqH+d8RG0BcXBwAoHfv3jXur9x+6tSpOzpP5XPSLVq0uKPjUM1iU/JhMIpo4aJGazd7qeNQHcyoWCRs98k0FJZz4TwiInO2/kACSrQG9PBxxYhulvFZRiGX4b0pveCglOP45Vx8frhua9YQEdWXTRTOycmmljg+Pj417q/cnpR0Z6Ni77//PgBg7Nixd3Qcqln0dc83C4IgcRqqiz5tPdDB2wmlWgN2xdR99XoiImpaqXml+KpiTYrnhne0qH9n/Zs54uXRXQAAq/fG41w6Hw8iooZnE4VzcXExAMDBwaHG/Y6OjgCAoqKi2z7HJ598gv3798PNzQ0vvPBCra/VaDQoLCys9kO3VtW/mdO0LYYgCJjRzzTqvOV4Elc9JSIyU+/vvwitwYh+7Zrh7vaeUseptymhvri3c3NoDUYs3haLch1bIRJRw7KJwrmxHTp0CIsWLYIgCNi4cSNatWpV6+vffPNNuLq6Vv34+vo2UVLLZTCKiKksnLkwmEUZF9QaDko5Ll0rxvHLuVLHISKif7l0rQjfxaQCAJ4bYVmjzZUEQcBbE7rD00mJC5lFWL33gtSRiMjK2EThXLmK9s36K5eUlAAAnJ2d633s06dPY+zYsdBqtXj//fcxbty4W75n2bJlKCgoqPpJSUmp93ltTXxmEYo0ejgq5ejUov7/OzU2pVKJdevWYd26dVAqlVLHMSvOajuMCzKtzPoVW1MREZmdNb/HwygC93Vpjt5+ljury9NJhXcm9gAAfH74Co5cypY4ERFZE5sonCtbR6Wmpta4v3K7v79/vY575coVDBs2DHl5eVi5ciUWLlxYp/epVCq4uLhU+6HaVU7TDvJzN8tVPu3s7DB//nzMnz8fdnZ2UscxO9MrFgnbeyYDmYXlEqchIqJKp1Lz8evpDAiCaSVtSze0U3NM62P63PfsjjgUlHJhSiJqGOZXgTSCyjZRMTExNe6v3N6jR486HzM9PR333Xcf0tPTsWjRIqxYseLOg9JNRSWapviyDZVl6tzSBaFt3KE3ith2gjMsiIjMxaqKKc3jerVGRzOc0XU7XhrVGW09HZFRWI6XfjgtdRwishI2UTj3798frq6uSEhIQGxs7A37d+7cCQAYM2ZMnY6Xl5eH4cOHIyEhAbNnz8batWsbMi7VIOq6FbXNkcFgwMGDB3Hw4EEYDFyQpCaVo87fnEiCzmCUOA0RER1NyMahi9mwkwt45r5AqeM0GAelAmun9IJcJmBP3FX8EMuuDkR052yicFYqlViwYAEAYP78+VXPNAPAu+++i1OnTmHQoEEIDg6u2r5u3Tp06tQJy5Ytq3as0tJSjBo1Cv/88w8mT56MDRs2WOQiGpYkvaAMafllkAmmqdrmqLy8HEOGDMGQIUNQXs6pyDUZ0a0FPJ2UyCzU4I9zmVLHMQuiKKJYo0dqXilOpxXgyKVs/HwqHV9HJGH9gUt44+ezeO7bODz2ZRQe/zIKsSn5UkcmIishiiLe+c002jw1zA++HjV3HrFUvXzdsHBoewDAS7tPIy2/TOJERGTpFFIHaCovvfQS9u/fj6NHj6JDhw4YMGAAkpKSEBERAS8vL2zcuLHa67Ozs3HhwgWkp6dX2/7iiy/i2LFjkMvlUCgUePTRR2s836ZNmxrrV7E5laPNnVu6wEllM5es1VEp5JgS6ov1BxKw5XgSRnRrKXWkBmM0iigq1yO/TIv8Uh3yy3TIL9WioExn+nupDvllWhTUsE9vrHuLruOXc7DzqbsQ2Nw6plMSkXT2nc1EbEo+7O3kWFBRYFqbBUPa4+CFLMSm5GPpjjh8PbcPZDIOdhDR7bGZKkStVuPAgQN488038c0332D37t3w8PDArFmz8Nprr8HHx6dOx8nLMxVxBoMB33zzzU1fx8K54USzf7PVmBrmh48PJuDIpRxculaM9t5OUkeqRm8wmgraiqK2oLIQrih4C0q1Vfuu/3tBmQ530qJaqZDB3cEObvZKuDrYwc3eDm4OdnBzUMK14s/fRaciJjkfMzeewPfz7kJLV/uG+8WJyKYYjCJW/24abZ7dvw28ndUSJ2ocCrkM703phZEfHMKxyzn44vAVPDawndSxiMhCCaJ4Jx/3qCEUFhbC1dUVBQUFXGG7BqM/PITTaYX4cGoQxvSsvUe2VEpKSqranhUXF8PR0VHiROZr7uYo7D+Xidn922DFmK6Ncg6N3nDd6K5phNdU6OqqjQpf//eCUh2KNPo7Oq+DUl5R9CorCl87uNpX/Nn+X3+vKJTdHOygtpPf8tj5pVpM+PgoErJK0LG5M3Y82Q+u9lzBnYjq7/uYVCzZEQcXtQKHnh8KVwfrvpdsPZGMZd//A6Vchh8W9EfnlvysRWQuLKkOspkRZ7JMxRo9zl4tBGC+C4NR/czo54/95zKxMzoVzw3vCAdlzbchURRRrjP+f6F7/Qjw9QXx9cVvxfYy3Z0t0OasVlQrbCtHfav//f8LYteKbSrFrQvg2+XmoMTmOWGY8PFRXMgswuNfRmHznLA6Fd1ERJW0eiPW7o8HADw5OMDqi2YAeCjUF3+cu4b95zLxzPZY7J7fn/dOIqo3Fs5k1mKT82EUgdZu9pyaaiUGtPeEfzMHJOWU4r/f/wMvZ1WNI8D5ZTpo9be/+rZMQFWB+/+F77/+fsP0aCVc1Aqz7BUOAD7uDtg0OwyTPzmGiCu5eHZHHD6cGsRn9oiozrZFJiMltwxezirMvqut1HGahCAIeGtCd4x4Lw/nM4qw5vcLeHFUF6ljEZGFYeFMZi0qif2brY1MJmB6H3+88cs57I69esvXK2RC9VHeihHeytFfdwc7uFZsv74QdlYprLKg7NzSBZ/OCMbM8BP4+Z90eDmrsGJMF67uT0S3VKrV44M/LgEAnr6nA+yVtjPq6umkwtsTeuDRzVH4/PAVDOnkjbsCPKWORUQWhIUzmbXKhcFCzXyatp2dHd55552qP1Ptpvf1R1p+GUq1+n+NCP9rarSDEo5KOYvCf7mrvSfWTO6Fp7eexKajiWjpqsYTgwKkjkVEZi78SCKyizXw83DAlBBfqeM0uXs6N8fUMD9sPZGMZ3fE4bdFA21iqjoRNQwWzmS29AYjYioK52B/D4nT1E6pVOK5556TOobFsFfKsfKBxlkYzFY80LMVrhWW4/Wfz+HNX8/D20WFcUF16w5ARLYnv1SLT/5KAAAsuS8QSoV5PpLS2F4a1RnHErKRmFOK5T+cxgdTg6SOREQWwjbvmmQRzmcUoURrgLNKgY4t2LeW6N/mDmiHuXebnlF87ttTOHQxS+JERGSuPvnrMorK9ejUwhkPmGmHiqbgqFJg7ZRekMsE/Bh3FT/EpkkdiYgsBAtnMluV07SD/N0hN/NnVQ0GAyIjIxEZGQmD4c5WdCaqj/+O7IwHeraC3ijiyS3ROJ1WIHUkIjIz1wrLsenoFQDA0mEdrXL9h/oI8nPHgiHtAQAv7T6Nq/llEiciIkvAwpnMVmSiaWGwEAtYGKy8vBxhYWEICwtDeXm51HHIhshkAlZN6oG7ApqhRGvArPBIpOSWSh2LiMzIB39eRLnOiN5+brins7fUcczCgqHt0dPXDUXlejy7Iw5Goyh1JCIycyycyWxVjjhbQuFMJCWVQo5PZgSjUwtnZBdr8MjGE8gt0Uodi4jMQFJOCbadSAEAPD+iExdbrGAnl+G9Kb1gbyfHscs52HjkitSRiMjMsXAms5SWX4b0gnLIZQJ6+blJHYfI7Lmo7bB5Thhau9njSnYJ5myKRKlWL3UsIpLY2n3x0BtFDAz0Qt92zaSOY1baejpi+WhTP+d3fruA8xmFEiciInPGwpnMUlTFNO2urVzgoOTi70R10dxFjc1zwuDmYIfYlHws/OYk9Aaj1LGISCLnMwrxQ9xVAMDzwztKnMY8TQ3zxT2dvKE1GLF4Wyw0eq5TQkQ1Y+FMZikqsbINFadpE9VHe28nfDEzBCqFDH+cv4aXdp+GKPLZPSJbtHrvBYgiMKp7S3Rr7Sp1HLMkCALemtADzRyVOJ9RhDW/x0sdiYjMFAtnMktRVc83m3f/ZiJzFOzvgQ+nBkEmANsiU/De/otSRyKiJhadlIv9565BLhOwZFig1HHMmpezCm9P6AEA2HDoMo4l5EiciIjMEQtnMjuF5TpcqHjOKKQNR5yJbsewri3w2oPdAADv/3ERW08kS5yIiJqKKIp457cLAICJvX0Q4OUkcSLzd2+X5pga5gtRBJ7dEYuCMp3UkYjIzLBwJrNzMjkfRhHw9bBHcxe11HHqxM7ODitWrMCKFStgZ2cndRwiAMDDffzx9FBTr9IXd/2D/WczJU5ERE3h74vZiLiSC6VChkX3dpA6jsV4aVQXtGnmgKsF5Vjxw2mp4xCRmWHhTGYnuqp/s+VM01YqlVi5ciVWrlwJpVIpdRyiKs/cF4jJIT4wisCCrTGISc6TOhIRNSKjUcSqvecBADP6+qOVm73EiSyHo0qBd6f0glwmYHfsVeypWFiNiAhg4UxmqOr5Zk7TJrpjgiDgjXHdMaSjF8p1Rjy6KRIJWcVSxyIzciW7BKfTCqSOQQ3k19MZOJ1WCEelHPMGB0gdx+L09nPH/CH/P1MnvaBM4kREZC5YOJNZ0RmMOJmcD8CyRpyNRiPOnDmDM2fOwGhk+x8yL3ZyGdY/3Bs9fd2QV6rDzI0ncK2wXOpYZAZ2nUzF8LV/Y8y6w9gRlSJ1HLpDeoMRa/aZnm1+bGA7NHNSSZzIMi0c2h49fVxRWK7HszviYDSyMwERsXAmM3MuvRBlOgNc1Ap08LacxUzKysrQrVs3dOvWDWVl/HaazI+DUoGNM0PQppkDUvPKMCs8EkXlXPzGVomiiHf3xeOZ7XHQGowQReA/353CzuhUqaPRHfguJhWXs0rg4ajE3AHtpI5jsezkMqyd0gv2dnIcTcjBxiNXpI5ERGaAhTOZlev7N8tkgsRpiKxLMycVvpzTB55OSpxNL8STX0VDq+cMCVtTrjNg0bZYfPCHqU3Zk4MCMKOvP0QReG5nHHadZPFsicp1hqrWc/MGB8BJpZA4kWVr5+WEF0d1BgC8s/cCLmQUSZyIiKTGwpnMSnTV882WM02byJL4NXNA+KwwOCrlOHIpB8/v5DREW5JdrMG0DcfxY9xVKGQC3pnQAy/c3wmvPNAVD/fxq2jFE4cfYtOkjkr19NXxJKQXlKOVqxrT+/pLHccqPNzHD0M7eUOrN2LRtpPQ6A1SRyIiCbFwJrMhiiIiK1bUDvbnwmBEjaW7jys+nh4MRcXKsW//dl7qSNQELmYW4cH1RxCTnA8XtQJfPhqGyaG+AACZTMBrY7thapgvjCLwzPZY/MgVhS1GUbkO6w9cAgAsurcD1HZyiRNZB0EQ8NaE7vBwVOJ8RhHe/T1e6khEJCEWzmQ2UvPKcK1IA4VMQE8fN6njEFm1gYFeeHtCDwDAp39fxsbDfIbPmh26mIXxHx1Fal4Z/Js5YNf8/rgrwLPaa2QyAW882B1TQkzF8+JtJ/HTKRbPluDzQ1eQV6pDOy9HTOjtI3Ucq+LtrMZb47sDAD47dBnHL+dInIiIpMLCmcxGVJJptLlba1fYK/ltOVFjmxDsg+dHdAQAvPbzWRZJVurriCTTYnAaPcLaeGDXvP4I8Kp58UWZTMCb47tjUrCp9/eibbH45Z/0Jk5M9ZFTrMHnhy4DAJ69ryMUcn60a2jDurbAQ6G+VY8yFHJhRSKbxLsrmY3IioXBQjhNm6jJPDUoADP7mRaGWrI9DscSOJpiLQxGEa//dBYv7joNg1HE+N6tsWVuGDwclbW+TyYT8NaEHpjQ2wcGo4iFW0/iVxbPZuujgwko0RrQrbUL7u/WQuo4Vmv56C7wb+aAtPwyrPjhjNRxiEgCLJzJbERXFs5tLK9wtrOzw9KlS7F06VLY2dlJHYeozgRBwMtjuuL+bi2gNRjx+JYonM8olDoW3aESjR5PbInG5xVT8JcOC8SaST2hUtRtNo9cJuCdiT0wPqh1VfH82+mMxoxMt+Fqfhm2HE8CADw3vBO7UTQiR5UC707uBZkA7DqZxhk6RDaIhTOZhYJSHeKvmVo9BPtb3oraSqUSq1atwqpVq6BU1j6aQ2Ru5DIBa6f0QlgbDxSV6zFz4wmk5bMfuaVKLyjDpE+OYf+5TCgVMqybFoQFQztAEOpXVMllAlZN6omxvVpBbxSx4JsY/H6GxbM5eX//RWj1RvRp64GBHTxv/Qa6I8H+7lgwpD0A4MVdp5FRUC5xIiJqSiycySzEJOdBFIE2zRzg5aySOg6RzVHbybHhkRAENndCZqEGszaeQH6pVupYVE+n0wrw4PojOJteCE8nJbY93heje7S67ePJZQLWTOqJMT1NxfP8b2Kw/2xmAyam25WQVYxvo1MAAM+P6FTvL0bo9iy8pwN6+riioEyHpd+ynR+RLWHhTGahcmEwSxxtBgCj0YjExEQkJibCaDRKHYfotrg62GHT7DC0cFHj4rViPPZlFMp17FtqKX4/k4FJnxxDZqEGgc2dsGtef/T2u/NHXxRyGdZO7olRPVpCZxAx7+sY/HmexbPU3v09HkYRuLezN1s4NiE7uQzvTukFtZ0Mhy9lY9PRRKkjEVETYeFMZiGq4vnmUAt8vhkAysrK0LZtW7Rt2xZlZZziSparlZs9Ns8Jg7NagcjEPCzadhIGjqiYNVEUseHvy3jiq2iU6QwY0METO5+6C74eDg12DoVchven9MLI7qZn4Z/cEoMDF6412PGpfk6nFeDnf9IhCMDS4R2ljmNzAryc8OKoLgCAt347jwsZRRInIqKmwMKZJKfVGxGXmg/AMhcGI7I2HVs4Y8MjIVDKZdh7JhMrfzwDUWTxbI50BiP+u+sfvPHLOYgiML2vH8JnhcJF3fCLFCrkMrz/UBBGdDUVz09sicZf8VkNfh66tXf2XgAAjO3ZCp1auEicxjZN7+OHIR29oNUbsXh7LDR6zs4hsnYsnElyZ64WoFxnhJuDHdp51txblIiaVt92zbB2Si8IArDleBI+OpggdST6l4IyHWaFn8DWEykQBODl0V3w2thujdrH104uw4fTgjC8a3No9UY89mUUDl1k8dyUjl/Owd/xWVDIBCy5j6PNUhEEAW9P7AEPRyXOpRfi3X3xUkciokbGwpkkF51kmqYd7OfOVhpEZmRUj5Z4ebRpOuKqvRewMzpV4kRUKTmnFOM/OoIjl3LgoJRjw4wQzLm7bZMsEGUnl+HDqb1xXxdT8Tx3cxSOXMpu9POSaVr+O7+dBwBMDfODX7OGm45P9eftrMab47sDAD77+zKOX86ROBERNSYWziS5qKr+zZa5MBiRNZvdvy2eGNQOAPCf707hIJ9rlVxUYi4e/OgIErJK0NJVjW+f7Id7uzRv0gxKhQzrp/XGvZ29odEb8ejmSBxl8dzo/jh3DTHJ+VDbybBwaHup4xCA4V1bYHKID0QReHZHHArLdVJHIqJGwsKZJCWKYtWK2ny+mcg8/Wd4J4wLag2D0bSi8qmKNQmo6f0Qm4ZpGyKQW6JF99au2D2/P7q2cpUki1Ihw/qHe2NoJ2+U64yYszkSxxI44tZYDEYRqyqebZ7dvy28XdQSJ6JKL4/pCj8PB6Tll2HlD2ekjkNEjYSFM0kqKacU2cVaKOUydG8tzYc/IqqdTCbg7Qk9MKCDJ0q1BszZFImknBKpY9kUURTx3v54LNoWC63BiGFdmmP7E33RXOLiSaWQ46OHe2NwRy9T8bwpEhGcrtoofoxLw4XMIrioFXhyYIDUceg6TioF1k7pBZkAfH8yDT+fSpc6EhE1AhbOJKmoiuebu/u4Qm0nlzjN7VMoFJg3bx7mzZsHhUIhdRyiBqdUyPDx9GB0beWC7GItHtl4AtnFGqlj2YRynQGLt8fivf0XAQBPDGqHT6YHw0FpHvcatZ0cn0wPxsBAL5TpDJi9KRKRiblSx7IqWr2xavGpJwYFwNWh4VdNpzsT7O+O+UNM0+f/u+sfZBSUS5yIiBoaC2eSVFTFh6sQf8uepq1SqbB+/XqsX78eKpVK6jhEjcJJpUD47FD4etgjKacUj26KRIlGL3Usq5ZTrMHDn0fgh9irUMgEvDW+O5bd39nsFlJU28nx2YzgqlkJszaeqLq/053bHpmMlNwyeDqpMLt/G6nj0E08fU8H9PBxRUGZDs/tjIPRyDZ+RNaEhTNJqnLEOdjCC2ciW+HtrMbm2WFwd7BDXGoB5n8TA53BKHUsq3TpWhEe/OgIopPy4KxWYPOcMDwU5id1rJtS28mx4ZEQ9G/fDCVaA2aFR1Z1TaDbV6rV44M/LwEAnr6nvdnMNKAb2cllWDulF9R2Mhy6mI3NxxKljkREDYiFM0kmv1SLS9eKAVh+4SyKIrKyspCVlQVR5DfMZN3aeTlh46xQqO1kOHghC8u+/4fXfQM7fDEb4z46ipTcMvh5OGDXvP7o395T6li3pLaT4/NHQtGvXTMUa/SYufEETiazeL4Tm44mIqtIA18PezwUar5fnJBJgJcTXhzZGQDw1q/ncTGzSOJERNRQWDiTZCpHItp5OaKZk2VPby4tLYW3tze8vb1RWloqdRyiRhfk547103pDLhOwMzq16vlLunNbTyRjZvgJFJXrEeLvjt3z+6O9t5PUserMXinHF7NC0LedB4o1ejzyxQnEpuRLHcsiFZTq8MnBBADAM/cGQqngxzZLML2vPwZ39IJGbzQt6KfnrBwia8A7MEkmsrJ/s4WPNhPZqns6N8cbD3YDAHz45yV8dTxJ4kSWzWAU8b9fzmHZ9//AYBTxYK9W+PqxPvBwVEodrd4clApsnBWKsLYeKNLoMeOLCLYxuw2f/p2AwnI9Aps7YWyv1lLHoToSBAHvTOgBdwc7nE0vxNr9/GKRyBqwcCbJRFf2b/b3kDgJEd2uh8L8sPjeDgCAl384jb1nMiROZJlKtXo8+VU0Pvv7MgBgyX2BWDulF1QKy+024KBUIHxWKELbuKOoXI/pn0fgdFqB1LEsxrWicoQfSQQALB3WEXIzWxCOauftosab43sAAD75K4Ft2oisAAtnkoRGb0BcqukDVEgbjjgTWbJF93TA1DBfGEXg6a0nuZpyPWUUlGPyp8ew72wmlAoZPpgahKfv6QBBsPxCyVGlQPjsMAT7u6OwXI+HWTzX2bo/L6FMZ0CQnxvu69Jc6jh0G0Z0a4FJwT4QRWDJjjgUluukjkREd4CFM0nidFoBtHojmjkq0dbTUeo4RHQHBEHAa2O74d7OzaHRG/Ho5ihcusYFceridFoBHlx/BKfTCtHMUYmtj/XFAz1bSR2rQTmpFNg0OxS9/dxQUKbD9C8icPZqodSxzFpKbim2nkgGADw3vKNVfIliq1Y80BW+HvZIyy/Dyh/PSB2HiO4AC2eSRFTF8829/d35gYDICijkMnw4NQhBFcXRzI2RyCwslzqWWdt3NhOTPz2GjMJydPB2wu75/S2+w8DNOKvtsGlOGHr5uiG/VIeHPz+Oc+ksnm9m7b546AwiBnTwxF0B5r+aOt2ck0qBtZN7QSYA38ek4Zd/0qWORES3iYUzSaKyf3Mop2kTWQ17pRxfzAxFO09HpOWXYebGE5yaWANRFPH5oct4fEsUSrUGDOjgie/m3QVfDwepozUqF7Udvnw0DD19XJFXqsPDn0fgQgZnJvzbhYwi7IpNA2AabSbLF9LGA08NDgAA/HfXP/xSkchCsXCmJieKYlUrqmArWRhMoVBg5syZmDlzJhQKhdRxiCTj4ajE5jlh8HJW4XxGEZ74MhoavUHqWGZDZzDixd2n8frP5yCKwLQ+ftg4KxQuajupozUJU/HcBz18XJFbosW0DccRzz631az+/QJEERjZvQV6+LhJHYcayKJ7AtG9tSvyS3VY+m0cjEZR6khEVE8snKnJXc4uQW6JFkqFDN1au0gdp0GoVCps2rQJmzZtgkpl2T2pie6Ur4cDNs0OhZNKgWOXc/DsDn5IBICCMh3mbIrENxHJEATgpVGd8caD3WAnt61/il3t7bBlTh90a+2CnIri+SKLZwBATHIe9p3NhEwAltzH0WZrolTIsHZKL6jtZDh0MRtfHkuUOhIR1ZNt/WtNZiG64vnmnj6uFt1qhYhurmsrV3wyPRh2cgE/nUrHG7+ckzqSpFJySzHh46M4dDEbDko5PpsRgrkD2tnsGg+uDnb46tE+6NLSBdnFWkzdEIFL14qljiUpURTxzm/nAQATg33Q3ttJ4kTU0Np7O+G/IzsDAN789Ty/MCKyMCycqclFVfZvbmMd07QB0weekpISlJSUQBQ5skYEAHd38MTqST0BAF8cvoLPD12WOJE0opNy8eD6I7h0rRgtXNTY8UQ/thcC4OagxNdz+6BzSxdkF2swdcNxJGTZbvF86GI2jl/OhVIuw6J7A6WOQ41kRl9/DAr0gkZvxOLtsdDqjVJHIqI6YuFMTa5yRe0QK1o9trS0FE5OTnByckJpaanUcYjMxtherfHfkZ0AAK//fA4/VCx6ZCt+iE3D1A0RyCnRomsrF+ye3x/dWrtKHctsuDuaiudOLZyRVaTB1M+O47INFs+iKGLV3gsAgOl9/dHazV7iRNRYBEHAqok94O5ghzNXC/He/nipIxFRHbFwpiaVU6zB5ewSALDatitEVN1jA9phdv82AICl38bhyKVsaQM1AVEU8f7+i1i0zTSidF+X5vj2yX5o4aqWOprZ8agonjs2d8a1ItPIc2LFvxO24tfTGfgnrQCOSjnmDwmQOg41Mm8XNd4c3x0A8MlfCYhMzJU4ERHVBQtnalKVq2l38HaCm4NS4jRE1BQEQcDyUV0wqkdL6AwintgSjbNXrbeHr0ZvwJIdcVhbMZL0+MB2+GR6MByUXHH/Zpo5qfD1Y33QwdsJmYWm4jkpxzaKZ73BiNW/m0abHx3QDs2cuMCkLRjRrSUmBvvAKALPbI9FEVv3EZk9Fs7UpCr7N4ewfzORTZHJBLw7uSf6tvNAsUaPWeEnkJJrfY815JZoMf3zCOw6mQa5TMD/xnXHf0d2hlxmm4uA1YenkwrfPNYX7b2dkF5QjqmfHUdyjvVdI//2fUwaLmeVwN3BDo8NaCt1HGpCK8Z0gY+7PVLzyvDKnrNSxyGiW2DhTE0qqmI6krX0byaiulMp5Ph0Rgg6tTBNyZ0ZfgJ5JVqpYzWYS9eK8eD6I4hMzIOzWoHNs8MwrY+f1LEsipezCt881gcBXo64WlCOqRuOW+UXLJXKdYaqZ1znDW4PZxvp500mzmo7rJ3SCzIB2Bmdil//SZc6EhHVgoUzNZlynQGn00zTM0M54kxkk1zt7bBpdhhauapxOasEj26ORLnOIHWsO3bkUjbGf3QEybml8PWwx655d+HuDp5Sx7JI3s5qbH2sL9p5OiItvwwPfXYcqXnWWTx/HZGMqwXlaOGixox+/lLHIQmEtvHAk4NMz7Uv2/UPMgvLJU5ERDfDwpmazD9pBdAajPB0UsHPw0HqOEQkkRauamyeEwZXezvEJOdj4daT0BsstyXLthPJmLnxBArL9Qj2d8fuef3R3ttZ6lgWzdtFja2P90XbiuJ56objSMsvkzpWgyrW6LH+wCUAwKJ7O0BtJ5c4EUll8b2B6NbaBfmlOjy38xTbWhKZKRbO1GQqV40M8XeHIFjX835yuRwTJ07ExIkTIZfzww/RrXRo7ozPZ4ZAqZBh39lMvPzjGYv7sGg0injzl3N44ft/oDeKGNurFb6e24eLOzWQ5i6mkec2zRyQkluGqZ8dx1UrKp6/OHQFuSVatPV0xKRgH6njkISUChnem9ILKoUMf8dnYdn3/0Cjt/yZOETWhoUzNZnoROtdGEytVuPbb7/Ft99+C7Wa7WaI6iK0jQc+eKgXBAH4JiIZH/55SepIdVaq1eOpr6Px6d+XAQCL7+2A96b04qhhA2vhahp59vNwQHJuKaZuOI6MAsufyppbosWGQ6ZrZ8l9gVDI+XHM1rX3dsZrY7tBEIBtkSmY/Mkxq5tlQWTpeKemJmE0iohOriycuTAYEZmM6NYSrz7QFQDw7r547IhMkTjRrWUWlmPKp8ex90wmlHLTSNHiewOtbiaNuWjpao+tj/eFr4c9knJMxbOlPwf68cFLKNbo0aWlC0Z1byl1HDITk0N9sWl2GNwc7BCXWoDRHxzC3/FZUsciogo2VTiXlZXh5ZdfRmBgINRqNVq1aoU5c+YgLS2tXsf566+/8Morr2DUqFHw8vKCIAho06ZN44S2EglZxcgv1UFtJ0PXVi5SxyEiMzKjXxvMH/L/i+P8eT5T4kQ3d/ZqIR5cfwT/pBXAw1GJbx7rgweDWksdy+q1drPH1sf6wsfdHleySzD1s+O4ZqHFc3pBGTYfSwIAPD+iI2RsVUbXGRTohT0L7kb31q7IK9VhZvgJfPjHRRiNlvUoC5E1spnCuby8HEOHDsVrr72G4uJijB07Fr6+vggPD0dQUBAuX75c52MtWrQIK1euxC+//ILs7OxGTG09Kvs39/Rxg50VTkkrKSmBIAgQBAElJSVSxyGyOEuHdcSE3j4wGEXM//okYlPypY50g/1nMzHxk6NILyhHgJcjds/rzxk0TcjH3QFbH+uL1m72uJxdgoc2HMe1Issrnj/44yK0eiPC2npgUKCX1HHIDPl6OODbJ/thapgvRBFYsy8ej30ZhYIyndTRiGya9VUwN/H666/j+PHj6NevH+Lj47F9+3ZERERgzZo1yMrKwpw5c+p8rGHDhuH111/H3r17cebMmUZMbT2iKp5vDuWHTCKqgSAIeGtCdwwK9EKZzoA5myJxJds8voQSRRFfHL6Cx7ZEoVRrwN3tPfH9vP7wa8buAE3N18MB2x7vW9XObNqGCGQVaaSOVWeXs4qxIyoVAPCfER05vZ9uSm0nx5vje+CdCT2gVMjwx/lrGPPhYZy9Wih1NCKbJYiWtozpbdBqtfD29kZBQQFiYmIQFBRUbX/Pnj1x6tQpREVFITg4uF7HzsjIQMuWLeHv74/ExMTbyldYWAhXV1cUFBTAxcU6pzEPWnUASTmlCJ8diiEdvaWO0+BKSkrg5OQEACguLoajo6PEiYgsU4lGj6kbjuNUagF8Pezx/VP94eUs3SrVeoMRK/ecwVfHkwEAU8P88OrYrlY5c8aSJOWU4KHPjiO9oBwdvJ2w9fG+8LSA1cwXfBODn06l455O3vhiVqjUcchCnE4rwJNfRSM1rwwqhQz/G9cdE7gSO1kJS6qDbOJf/iNHjqCgoAABAQE3FM0AMHHiRADAnj17mjqaTcgq0iAppxSCAPT2s74VtYmo4TiqFNg4KxT+FS2IZm86gWKNXpIsheU6zN4Uia+OJ0MQgJdGdcb/xnVj0WwG/Js5YutjfdHCRY2L14rx8IYI5BSb98jz6bQC/HQqHYIALB3eUeo4ZEG6tXbFTwvvxuCOXtDojXj22zi8uIstq4iamk386x8XFwcA6N27d437K7efOnWqyTLZkugkU//mjs2d4WpvJ3EaIjJ3nk4qbJ4dhmaOSpxOK8RTX0VDqzc2aYaU3FJM+OgoDl3Mhr2dHJ9OD8bcAe04tdaMtPF0xNbH+8LbWYULmUV4+PMI5JZopY51U6v2XgAAPNCzFTq3NO9RFTI/bg5KbJwZisX3doAgAF9HJGPyp8fZsoqoCdlE4ZycbJpi5+NT87SWyu1JSUlNlsmWVD7fHOzP0WYiqps2no7YOCsU9nZyHLqYjRe+O4WmerIoJjkP4z46govXitHcRYVvn+yHYV1bNMm5qX7aVhTPXs4qnM8owrQNx5FnhsVzxOUc/BWfBYVMwJL7AqWOQxZKJhOw+N5AhM8Khau9HeJS8jH6g0M4fJEL1dKtGYyiRS6oaE5sonAuLi4GADg41LyQS+XzqEVFRU2SR6PRoLCwsNqPNYtMquzfzMKZiOqup68bPpreG3KZgO9PpuGdihG7xrQn7ioe+uw4sou16NrKBT/MvxvdWrs2+nnp9gV4OWHrY6ZnnM9nmEae80vNp3gWRbHq2p0S6gv/ZlwDg+7M4I7e+Gnh3ejW2gV5pTo8sjEC6w9cYssquqnzGYUY//FRzNkUCb2haWdwWRObKJzNzZtvvglXV9eqH19fX6kjNZoyrQFn0goAACH+1ruitlwux8iRIzFy5EjI5XKp4xBZjSEdvfHW+O4AgI8PJmDz0cRGOY8oivjwj4tYuPUktHoj7u3cHDue6IcWrupGOR81rPbeTtj2eB94OilxNr0QD38egYJS82jd8+f5a4hOyoPaToan7+kgdRyyEr4eDtj55F2YEuILo2h6FODxLWxZRdVp9Aas+f0CRn9wGHEp+UjKLkV8ZrHUsSyWTRTOlasdl5aW1ri/su+us7Nzk+RZtmwZCgoKqn5SUlKa5LxSiEvNh94oormLCj7u9lLHaTRqtRo///wzfv75Z6jV/KBN1JAmhfhi6TDT9NaVe87g13/SG/T4Gr0Bz+6Iw5p98QCAuXe3xaczguGoUjToeahxtfd2xjeP9UUzRyXOXC3E9C8iJC8ijEax6tnmmXe1QXMX/vtADUdtJ8fbE3vg7QndoVTIsP/cNTywji2ryCQqMRcj3z+ED/+8BL1RxLAuzbH/2UHo0oprLNwumyic/fz8AACpqak17q/c7u/v3yR5VCoVXFxcqv1Yq6hE08JgIf4eXFSHiG7b/CHtMaOvP0QRWLQ9Fieu5DbIcXNLtJjx+Ql8fzINcpmAN8Z1w0uju0Au4/3KEgU2NxXPHo5K/JNWgEckLp73nLqK8xlFcFYr8NSgAMlykHWbEuqH7568Cz7u9kjKKcX4j4/gu+iaP/OS9Ssq12H57tOY+MkxJGSVwNNJhY8f7o1PZwTzy7s7ZBOFc8+ePQEAMTExNe6v3N6jR48my2QropK4MBgR3TlBELDyga4Y1qU5tHoj5m6ORHzmna1LkZBVjHEfHcGJxFw4qxTYNDsUD/dpmi9QqfF0bOGMr+f2gbuDHeJSCzBz4wkUljd98awzGPFuxSyGJwa2g5uDsskzkO3o7mNqWTUo0AvlOlPLqpd2s2WVrfnzfCaGrf0bW46bFjyeHOKDP5YMwv3dW3IAqwHYROHcv39/uLq6IiEhAbGxsTfs37lzJwBgzJgxTZzMuhmNIqIrCufQNtb7fDNgmu7v6OgIR0fHqqn/RNSw5DIBH0wNQoi/OwrL9Zi58QTSC26vFcvRhGyMW38ESTml8HG3x/fz7sKADl4NnJik0rmlC76e2xduDnaITcnHzI0nUNTExfP2yBQk5ZTC00mJ2f3bNum5yTa5OSgRPisUi+4xtaz66ripZdVVtqyyetnFGizcehJzNkUhvaAcfh4O+HpuH7wzsSdcHdgKtqHYROGsVCqxYMECAMD8+fOrFTbvvvsuTp06hUGDBiE4OLhq+7p169CpUycsW7asyfNai/hrRSgq18NBKUfnlk3z/LiUSktLb/ocPRE1DLWdHJ/PDEF7byekF5Rj1sbIek/F3RGZgke+OIHCcj16+7lh9/z+6NDc+u9RtqZLKxd89WgfuNrb4WRyPmaFR6JYo2+Sc5dpDfjgj4sAgAVD2vN5eWoyMpmAZ+4LxMaZ17Ws+vAwjlxiyyprJIoivo9Jxb3v/oU9cVchE4DHB7bD3sUD0b+9p9TxrI5NFM4A8NJLL6FPnz44evQoOnTogClTpqBv37549tln4eXlhY0bN1Z7fXZ2Ni5cuID09BsXofn888/Rt29f9O3bF6NGjQIApKenV23r27fvTaeF25LK/s29fN2gkNvMpUZEjczNQYnNc8LQ3EWFC5lFeOzLKJTrbj0d0WgU8dav5/H8d6egN4oY07MVvqloY0TWqVtrV3w9tw9c1ApEJ+VhdvgJlDRB8bz5WCKuFWnQ2s0eU/v4Nfr5iP5tSCdTy6qurVxMazl8wZZV1iYltxQzwyOxZEcc8kt16NzS1ELxvyM7w17JDi+NwWaqGbVajQMHDmD58uVwcHDA7t27kZSUhFmzZiEmJgbt2rWr87FSU1MRERGBiIiIqgJZq9VWbYuIiLD63sx1EV3Vv9m6p2kTUdNr7WaPTbPD4KxS4MSVXCzZEQtDLR8Iy7QGzPs6Bp/8lQAAePqeDvjgoV5Q2/HDhbXr1toVX83tA2e1ApGJeZgdHtmoxXNBmQ4fHzRdZ0vuC4RKwWuMpOHr4YDvnroLk0N8rmtZFS35avN0ZwxGERsPX8Hw9/7G3/FZUCpkeG54R/y4oD+6+7hKHc+qCaIo8qsniRUWFsLV1RUFBQVWtcL2gHf+REpuGb6cE4aBgdb97GBJSUlV27Pi4mI4OjpKnIjINhxNyMasjZHQGoyYdVcbrBjT5YYFUK4VlmPul1E4lVoApVyGtyd2x7ggH4kSk1TiUvIx/fMIFGn06NPWA+GzQ+GgbPgp1Kv3XsC6A5cQ2NwJvy4ayBXaySxsO5GMl388A63eiDbNHPDx9GB0bmk9nzltxYWMIvznu1OITckHAIS19cBb47ujnZeTtMHugCXVQTYz4kxNK7OwHCm5ZZAJQJCfm9RxiMhK3RXgiTWTTZ0TNh1NxKd/X662/+zVQjy4/ghOpRbA3cEOXz/Wh0Wzjerp64YvHw2Dk0qBiCu5eHRTFMq0DbvicFaRBhuPXAEAPDusI4tmMhsPhflh55P90NrNHok5pRj30RHsOsmWVZZCozfg3d8vYPSHhxCbkg9nlQJvjOuGbY/1teii2dKwcKZGUfl8c8cWLnBWczU/Imo8Y3q2wkujOgMA3vr1PL6PMX0Y/PN8JiZ9chRXC8rRzssRu+f3t/oV/ql2QX7u2DzHVDwfu5yDuV9G1un5+Lpaf+ASSrUG9PJ1w7AuzRvsuEQNoYePG35aeDcGVrSsemZ7HJbvPg2t3ih1NKpFVGIuRr5/CB/8eQk6g4j7ujTHviWD8HAff8j45VyTYuFMjSIqKRcAENrGNvo3y2QyDBo0CIMGDYJMxv9bETW1uQPa4bEBppY/z+88heW7T2Pu5iiUaA24K6AZdj3VH/7N+AgFAcH+7tg8JxSOSjmOXMqp8+Jyt5KSW4qvI0y9U58f3pE9U8ksuTuaWlY9fU8HAMCW40mY/Omx227tR42nWKPHyz+cxqRPjyEhqwSeTip89HBvfDYjGC1c1VLHs0n8hE+NonLEOdjfNgpne3t7HDx4EAcPHoS9vb3UcYhs0rL7O2Nsr1bQG0VsOZ4EowhMCfHF5jlh7GNJ1QT7e2DTnDA4KOU4dDG7QYrntfvjoTOIuLu9J+5iGxgyY3KZgCX3BSJ8Vihc1ArEpuRj9AeHcZQtq8zGn+czcd+7f+HLY0kQRWByiA/2LxmIkd1b8ks5CbFwpgZXotHjbLppVXGuqE1ETUUmE7BqYk8MCvSCTACW3d8Jb03oDju2w6MahLbxQPisUNjbmYrnJ7ZE33bxHJ9ZhF0n0wAAzw3v2JAxiRqNqWXVAHRp6YKcEi2mfxGBjw5eAtcNlk5OsQZPbz2JOZuikF5QDj8PB3w9tw/emdgTbg5KqePZPH6aoAYXl5IPg1FEK1c1Wrtx9JWImo5SIcOm2aE4+fIwPDEogN/MU636tGuG8Nmm4vmv+Cw89VU0NPr6F8+r916AKAIjurZAT1+3hg9K1Ej8mjng+3l3YWKwqWXVO7+ZWlYVlrNlVVMSRRHfx6Ti3nf/wo9xVyETgMcHtsPexQPRnzNYzAYLZ2pwkZXTtG1otLmkpAReXl7w8vJCSUmJ1HGIbJogCHC159Rsqpu+7Zrhi1khUNvJcOBCFuZ9FVOv4vlkch5+P5sJmQAsHR7YiEmJGofaTo5VE3vgf+O6QymXYd/ZTDzw4WGczyiUOppNSM0rxczwSCzZEYe8Uh06tXDG7vn98d+RnWGvZB94c8LCmRpc5cJgITbyfHOl7OxsZGfz+SAiIktzV4AnvpgZCpVChj/OX8P8r0/WeaXhVXsvAADG9/ZBe2/nxoxJ1GgEQcC0Pn749rqWVQ+uP4LdFY8gUMMzGEVsPHwFw9b+jb/js6BUyPDc8I7Ys/Bu9PBxkzoe1YCFMzUog1HEyeR8ALazMBgREVm+/u098fnMECgVMuw/l4kF38RAZ6i9eD58MRtHE3KglMuw+N4OTZSUqPH09HXDnoV3Y0AHT5TrjFi8PRYv/8CWVQ0tPrMIEz4+ild/OotSrQFhbTzw66IBmD+kPdflMGP8X4Ya1IWMIhRr9HBSKdCpBb95JyIiyzGggxc2PGIqnn8/m4mF35y8afEsiiJW7T0PAJjWxw8+7g5NGZWo0Xg4KrFpdhgWDm0PAPjyWBKmfMaWVQ1Bozfg3X3xGPXBIcSm5MNZpcAb47ph2+N9EeDlJHU8ugUWztSgKqdpB/m5QcFvzIiIyMIMCvTCZzOCoZTL8NuZDCzaVnPxvPdMBuJSC+CglGNBRYFBZC3kMgHPDuuIL2aGwEWtwMnkipZVCXwk7XZFJ+Vi1AeH8cEfF6EziLi3c3PsWzIID/fxh0zGhSwtASsbalC21r+ZiIisz+CO3vi0onj+5Z8MLN4eC/11xbPBKGL17/EAgEfvbgtPJ5VUUYka1T2dm+OnhQPQubJl1ecR+OSvBLasqodijR4rfjiNiZ8cw6VrxfB0UmL9tN7Y8EgwWriqpY5H9cDCmRpUdJKpcA61oRW1iYjI+gzp5I2Pp/eGnVzAz6fS8cyOuKri+fuYVFy6Vgw3Bzs8NrCdxEmJGpdfMwfsmncXJvQ2tax669fzePIrtqyqiwPnr2HYu39h87EkiCIwMdgH+5cMwqgeLdku0QIppA5A1uNqfhnS8ssglwnoZWN9LGUyGUJCQqr+TERElu+ezs3x0cPBmPd1NPZU9FZ9a3wPvLf/IgBg3uAAuKjZ+oysn9pOjtWTeqC3vxte+fEs9p7JRHzmEXwyPRgduabNDXKKNXj1p7P4IfYqAMDXwx5vjuuBuzuwJ7MlE0TOtZBcYWEhXF1dUVBQABcXF6nj3LYf467i6a0n0a21C35aOEDqOERERA1i75kMzP86BnqjiHaejricXYIWLmocfG4w1Hbss0q2JS4lH099FY2rBeWwt5PjrQndMbZXa6ljmQVRFLE7Ng2v7jmLvFIdZILpcY5n7guEg5LjlTWxpDqIQ2PUYKITK/s3c5o2ERFZj+FdW2DdtCDIZQIuZ5cAAJ6+pwOLZrJJPX3d8NPTAzCggyfKdAYs2haLlT+esfmWVal5pZgVHolntschr1SHTi2csWtef7w4qguLZivBwpkaTGTFwmAhbbgwGBERWZcR3Vriw6lBUMgEdPB2wqQQH6kjEUmmsmXVgiGmFeU3HU3EQ58dQ0ZBucTJmp7BKCL8yBUMW/s3/orPglIhw3PDO2LPwrvR08YeXbR2/PqDGkSxRo/zGYUAbHPEubS0FF26dAEAnD17Fg4O7OdJRGRtRnZviV6+bnBSK2DHlotk4+QyAUuHd0QvXzc8syMWMcn5GP3hIXwwNQh3BdjGs7zxmUX4z3encDI5HwAQ1sYDb07ozp7MVop3fWoQJ5PzYBSB1m72Nrm0viiKSEpKQlJSEls0EBFZsVZu9lwQjOg693Zpjj0L7kanFs7ILraNllUavQFr98Vj1AeHcDI5H04qBV5/sBu2Pd6XRbMVY+FMDaKyf3Mop2kTERER2ZQ2no7YNa8/xvduXdWy6qmvYlBkhS2ropPyMPqDw3j/j4vQGUTc29kb+5YMxPS+/pDJ2GLKmnGqNjWIqCTTwmDB7N9MREREZHPslXKsmdQTvf3c8cqeM/jtTAbiM4vwyYxgBDa3/JZVxRo9Vu+9gM3HEiGKgKeTEisf6IpR3dmT2VZwxJnumN5grHq2I8SfI85EREREtkgQBEzv648dT/RDS1c1LmeXYOy6I/ghNk3qaHfkwIVrGL72b2w6aiqaJwb7YN8zgzC6RysWzTaEhTPdsfMZRSjVGuCsVljFN4pEREREdPuC/Nzx08K7cXd7y25ZlVOsweJtJzE7PBJp+WXwcbfHlkfDsHpST7g7KqWOR02MhTPdsciK/s29/dwh57MdRERERDavmZMKm+eEYf6QAACmllVTNxxHZqH5t6wSRRG7T6bhvrV/Y3fsVcgEYO7dbfH7MwMxoIOX1PFIInzGme5YVFJF/2YbnqYtCEJVOypO2SEiIiIytax6bngn9PJ1x5IdsYhOysOoDw5j3bQg9G3XTOp4NUrNK8VLu0/j4IUsAECnFs54a0IP9GJPZpvHEWe6I6IoIqpixDnEhhcGc3BwwJkzZ3DmzBn2cCYiIiK6zn3VWlZp8PDnEfjsb/NqWWUwith05AqGrf0bBy9kQSmXYemwQPy44G4WzQSAhTPdodS8MmQWaqCQCbypEBEREVGNKltWjQtqDYNRxP9+OY95X5tHy6qLmUWY9MlRrNxzFqVaA0LbuOOXRQOwYGgHKBUsl8iEU7XpjkRXTNPu2soF9kq5xGmIiIiIyFzZK+V4d3JP9PZzw6s/ncWvpzNwIbMIn0yXpmWVVm/ERwcvYf2BS9AZRDipFPjP/Z3wcJgfezLTDfgVCt2Rqv7N/rY7TRsASktL0bVrV3Tt2hWlpaVSxyEiIiIyS4IgYEa/Nthe2bIqqwQPrj+CPXFXmzRHdFIeRn94CO/tvwidQcS9nb2xb8lAzOjrz6KZasQRZ7ojUYmmEefQNra7MBhgetb77NmzVX8mIiIiopvrXdGyauHWkziakIOFW08iJjkP/x3ZGXbyxhvbK9HosWrvBWw+ZurJ3MxRiZUPdMXoHi25wCvViiPOdNsKynS4kFkEAAi28cKZiIiIiOqnmZMKX84Jw1ODTS2rwo8kYupnjdey6uCFaxi29m9sOmoqmif09sH+JYMwpmcrFs10Syyc6badTM6DKAJ+Hg7wdlZLHYeIiIiILIxCLsN/RnTCZzOC4axSIKqiZdXxyzkNdo7cEi0WbzuJWeGRSMsvg4+7Pb6cE4Y1k3vC3VHZYOch68bCmW5b5cJgIRxtJiIiIqI7MKxrC/y4sHrLqg1/X76jR+BEUcQPsWm4992/sDv2KmQC8OjdbfH7MwMxMNCrAdOTLWDhTLctsrJ/s40vDEZEREREd66tpyO+n3dXVcuqN345h/nfxKBYo6/3sdLyyzBnUyQWbYtFbokWHZs74/t5/bF8dBc4KLnME9Ufrxq6LTqDEbEp+QA44kxEREREDcNBqcC7k3siyM8Nr/10Fr/8k4ELGUX4dEYw2nvfumWV0Shiy/EkvPPbeZRoDVDKZVg4tD2eGBTAnsx0R1g40205e7UQ5TojXO3t0N7LSeo4khMEAf7+/lV/JiIiIqLbIwgCHunXBt1au2LeVzFIyCrBA+uO4J2JPTC6R6ubvu9iZhH+890pxCTnAwBC/N3x1oTudSq4iW6FhTPdlspp2sH+7ux1B8DBwQGJiYlSxyAiIiKyGr393PHT03dj4TcncexyDhZ8cxIxSflYNrJTtZZVWr0RHx9MwPoDl6A1GOGolOOF+zvh4T7syUwNh/MV6LZULgwW7M9p2kRERETUODydVNjyaBieHGRqWbXxyBVM23Ac1ypaVsUk52H0h4ewdn88tAYjhnbyxr4lgzCjXxsWzdSgOOJM9SaKIqIqV9Rm4UxEREREjUghl+GF+zshyM8NS3fEITIxD6M+PIyhHb2xIzoFogg0c1RixQNdMaZHSz42R42CI85Ubym5Zcgq0sBOLqCnr5vUccxCWVkZQkNDERoairKyMqnjEBEREVmd4V1b4IcF/dGxuTOyijTYHmUqmsf3bo39SwbhgZ6tWDRTo+GIM9Vb5fPN3Vq7Qm0nlziNeTAajYiKiqr6MxERERE1vHZeTtg1/y68uucsTl8twHPDO2EQezJTE2DhTPXGadpEREREJBUHpQJvTeghdQyyMZyqTfUWnWQacQ5p4yFxEiIiIiIiosbHwpnqJb9Ui/jMYgBcUZuIiIiIiGwDC2eql5hk0zTttp6O8HRSSZyGiIiIiIio8bFwpnqJSuTzzUREREREZFu4OBjVS1Xh3IaF8795enpKHYGIiIiIiBoBC2eqM63eiLjUfABAsD8XBrueo6MjsrKypI5BRERERESNgFO1qc5OXy2ARm+Eu4MdArwcpY5DRERERETUJFg4U51FV0zTDvb3gCAIEqchIiIiIiJqGiycqc4iEyv7N/P55n8rKyvD4MGDMXjwYJSVlUkdh4iIiIiIGhCfcaY6EUUR0UlcUftmjEYj/vrrr6o/ExERERGR9eCIM9VJYk4pckq0UCpk6O7jKnUcIiIiIiKiJsPCmeqkcpp2j9auUCnkEqchIiIiIiJqOiycqU6qFgbj881ERERERGRjWDhTnUQlVSwMxv7NRERERERkY1g40y3llmiRkFUCAAjmwmBERERERGRjuKo23VLlatoBXo7wcFRKnMZ8OTg4SB2BiIiIiIgaAQtnuiVO0741R0dHlJSUSB2DiIiIiIgaAadq0y1VLgwWwoXBiIiIiIjIBrFwplqV6ww4lVoAAAhpwxFnIiIiIiKyPTZVOJeVleHll19GYGAg1Go1WrVqhTlz5iAtLa3ex8rLy8OiRYvg7+8PlUoFf39/LF68GPn5+Q0fXEKn0wqgNRjRzFGJNs34DO/NlJeXY9SoURg1ahTKy8uljkNERERERA3IZgrn8vJyDB06FK+99hqKi4sxduxY+Pr6Ijw8HEFBQbh8+XKdj5WdnY2wsDB88MEHUCgUePDBB+Hs7Iz3338fffr0QW5ubiP+Jk0rKun/p2kLgiBxGvNlMBjwyy+/4JdffoHBYJA6DhERERERNSCbKZxff/11HD9+HP369UN8fDy2b9+OiIgIrFmzBllZWZgzZ06dj7V48WJcunQJ48ePx4ULF7B9+3acPn0aCxcuRHx8PJYsWdKIv0nTikrkwmBERERERGTbBFEURalDNDatVgtvb28UFBQgJiYGQUFB1fb37NkTp06dQlRUFIKDg2s9Vnp6Onx8fKBQKJCcnIzmzZtX7dNoNPD19UVubi6uXr0Kb2/vOuUrLCyEq6srCgoK4OLiUv9fsJGIoojer+1DXqkO38+7C739uDjYzZSUlMDJyQkAUFxcDEdHR4kTERERERGZN3Otg2piEyPOR44cQUFBAQICAm4omgFg4sSJAIA9e/bc8li//fYbjEYjBgwYUK1oBgCVSoUxY8ZUTdu1dAlZJcgr1UGlkKFbK1ep4xAREREREUnCJgrnuLg4AEDv3r1r3F+5/dSpU016LHMXXdG/uaevG5QKm7hUiIiIiIiIbmAT1VBycjIAwMfHp8b9lduTkpKa9FjmLrKyf7M/p2gTEREREZHtUkgdoCkUFxcDABwcam6nVPk8alFRUZMcS6PRQKPRVP29sLDwlueVQvR1K2oTERERERHZKpsYcTY3b775JlxdXat+fH19pY50A73BiJ4+rvBxt+eiYHXg6OgIURQhiiIXBiMiIiIisjI2MeJcudpxaWlpjftLSkoAAM7Ozk1yrGXLllVrWVVYWGh2xbNCLsN7D924kBoREREREZGtsYnC2c/PDwCQmppa4/7K7f7+/k1yLJVKBZVKdctzERERERERkfRsYqp2z549AQAxMTE17q/c3qNHjyY9FhEREREREZk/myic+/fvD1dXVyQkJCA2NvaG/Tt37gQAjBkz5pbHGjFiBGQyGQ4dOoRr165V26fRaLBnzx7I5XKMHDmyQbITERERERGRtGyicFYqlViwYAEAYP78+VXPIQPAu+++i1OnTmHQoEEIDg6u2r5u3Tp06tQJy5Ytq3asli1bYurUqdBqtZg3bx70en3Vvueffx5ZWVmYPn06vL29G/m3IiIiIiIioqZgE884A8BLL72E/fv34+jRo+jQoQMGDBiApKQkREREwMvLCxs3bqz2+uzsbFy4cAHp6ek3HOu9997D8ePH8d1336FTp04ICQnBmTNncPr0aXTo0AHvvvtuU/1aRERERERE1MhsYsQZANRqNQ4cOIDly5fDwcEBu3fvRlJSEmbNmoWYmBi0a9euzsfy9PTEiRMnsHDhQmi1WuzatQsFBQV4+umnceLECXh4eDTib0JERERERERNSRBFUZQ6hK0rLCyEq6srCgoK4OLiInUcIiIiIiKiRmdJdZDNjDgTERERERER3Q4WzkRERERERES1YOFMREREREREVAsWzkRERERERES1YOFMREREREREVAsWzkRERERERES1YOFMREREREREVAsWzkRERERERES1UEgdgABRFAGYGoATERERERHZgsr6p7IeMmcsnM1AUVERAMDX11fiJERERERERE0rJycHrq6uUseolSBaQnlv5YxGI65evQpnZ2cIgiB1nCqFhYXw9fVFSkoKXFxcpI5DNoTXHkmF1x5JidcfSYXXHkmloKAAfn5+yMvLg5ubm9RxasURZzMgk8ng4+MjdYybcnFx4U2UJMFrj6TCa4+kxOuPpMJrj6Qik5n/0lvmn5CIiIiIiIhIQiyciYiIiIiIiGrBwpluSqVSYcWKFVCpVFJHIRvDa4+kwmuPpMTrj6TCa4+kYknXHhcHIyIiIiIiIqoFR5yJiIiIiIiIasHCmYiIiIiIiKgWLJzpBmVlZXj55ZcRGBgItVqNVq1aYc6cOUhLS5M6GpmRwYMHQxCEm/789ttvNb5v06ZNCAsLg5OTEzw8PDBy5EgcPXq01nMdOXIEI0eOhIeHB5ycnBAWFoYvv/yy1vekpqZi9uzZaNWqFdRqNQIDA7FixQqUl5ff9u9MTSs6OhpvvfUWxo8fDx8fn6pr61bM+Rrj/dUy1PfaW7lyZa33wxdeeOGm7+W1R5VKS0uxe/duPProo+jYsSPUajUcHR3Rs2dPvPrqqyguLr7pe3nfoztxO9eeTd73RKLrlJWViX379hUBiC1bthQnT54shoWFiQBELy8vMSEhQeqIZCYGDRokAhAnTJggzpw584afU6dO3fCeRYsWiQBEe3t7cezYseLw4cNFhUIhyuVycdeuXTWeZ+fOnaJcLhcFQRAHDRokTpgwQXRzcxMBiM8++2yN77l48aLo6ekpAhC7desmTp48WWzXrp0IQOzfv79YXl7ekP8pqJGMHTtWBHDDT23M+Rrj/dVy1PfaW7FiRdX/9jXdD3fs2FHj+3jt0fU2bNhQda117txZnDRpkjh8+HDR2dlZBCB26tRJzMzMvOF9vO/Rnbqda88W73ssnKmaF198UQQg9uvXTywqKqravmbNGhGAOGjQIOnCkVmpLJyvXLlSp9fv27dPBCA2a9ZMjI+Pr9p+9OhRUalUim5ubmJeXl619+Tk5IguLi4iAPG7776r2p6RkSG2b99eBCAeOHDghnP1799fBCA+/fTTVdt0Op04btw4EYC4YsWK+vyqJJG33npLXL58ufjjjz+K6enpokqlqrV4MfdrjPdXy1Hfa6/yA2R4eHidz8Frj/5t06ZN4uOPPy6ePXu22varV6+KQUFBIgBx6tSp1fbxvkcN4XauPVu877FwpioajUZ0dXUVAYgxMTE37O/Ro4cIQIyKipIgHZmb+hbO999/vwhAXLt27Q37nn76aRGAuHr16mrb3377bRGAOHbs2Bve8/3334sAxNGjR1fbHhERIQIQvb29b/gGMiMjQ7SzsxPd3d1FnU5Xp9xkPm5VvJjzNcb7q2VrjMKZ1x7Vx9GjR0UAokqlEjUaTdV23veosd3s2rPF+x6fcaYqR44cQUFBAQICAhAUFHTD/okTJwIA9uzZ09TRyMKVlZXhzz//BPD/19H1bnZt/fzzzzd9z6hRo6BWq7F///5qz7dUvmfMmDE39ARs3rw5BgwYgLy8PBw+fPgOfiMyN+Z+jfH+Sv/Ga4/qo2fPngAAjUaDnJwcALzvUdOo6dq7XZZ+7bFwpipxcXEAgN69e9e4v3L7qVOnmiwTmb8vvvgC8+bNw4IFC/DBBx8gOTn5htdcuHABGo0GXl5e8PHxuWH/za6t2q5JpVKJbt26oby8HPHx8XV6T23nIstm7tcYr0vb8Oeff2Lx4sV48skn8frrryM6Ovqmr+W1R/Vx+fJlAICdnR08PDwA8L5HTaOma+96tnTfY+FMVSoLnppuvtdvT0pKarJMZP5ef/11fPzxx1i/fj0WLVqE9u3b47XXXqv2mltdW46OjnBzc0NeXh6KiooAAIWFhSgoKKj1fTVdk7yObZO5X2O8Lm3Dli1b8P777+PTTz/F8uXLERISgokTJ96wIi2vPaqv999/HwAwYsSIqlE33veoKdR07V3Plu57LJypSuUF7uDgUON+R0dHAKi6+ZJtGzhwILZs2YKEhASUlpbiwoULeOONN6BQKPDyyy9X3WiBW19bwI3X1/U33Ppck7yObZO5X2O8Lq1b+/btsXr1apw5cwbFxcVISUnB119/jdatW+O7777DjBkzqr2e1x7Vxy+//IIvvvgCdnZ21b6Y5n2PGtvNrj3ANu97inq9moiowquvvlrt74GBgfjvf/+LkJAQDB8+HCtXrsTjjz8Oe3t7iRISETWN6dOnV/u7o6Mjpk2bhiFDhqB79+7YvXs3jh8/jr59+0qUkCzV+fPnMX36dIiiiFWrVlU9b0rU2G517dnifY8jzlTFyckJgKkJek1KSkoAAM7Ozk2WiSzPsGHDEBISgvz8fERERAC49bUF3Hh9Vb6ntvfVdE3yOrZN5n6N8bq0TS1btsTs2bMBAL/99lvVdl57VBdpaWkYMWIE8vLysGTJEixatKjaft73qLHc6tqrjTXf91g4UxU/Pz8AQGpqao37K7f7+/s3WSayTB06dAAApKenA7j1tVVSUoL8/Hy4u7tX3cRcXFzg6upa6/tquiZ5Hdsmc7/GeF3arn/fDwFee3Rrubm5GDZsGJKSkjB79mysXr36htfwvkeNoS7X3q1Y632PhTNVqZyCERMTU+P+yu09evRoskxkmfLy8gD8/zMkHTt2hEqlQlZWFtLS0m54/c2urdquSZ1Oh9OnT0OtViMwMLBO76ntXGTZzP0a43Vpu/59P6zEa49upri4GPfffz/Onj2L8ePHY8OGDRAE4YbX8b5HDa2u196tWO19r15dn8mqXd8s/OTJkzfsZ6N6qotr166Jjo6OIgAxJSWlavv9998vAhDXrl17w3uefvppEYC4evXqatvffvttEYA4duzYG97z/fffiwDE0aNHV9seEREhAhC9vb3F8vLyavsyMjJEOzs70d3dXdRqtbf/S5IkVCqVWNs/W+Z8jfH+atlude3djNFoFPv06SMCELds2VJtH689qkl5ebk4dOhQEYA4fPhwUaPR1Pp63veoodT32rsZa77vsXCmal588UURgHjXXXeJxcXFVdvXrFkjAhAHDRokXTgyG0eOHBF37dol6vX6atuvXLki9u/fXwQgPvDAA9X27du3TwQgNmvWTIyPj6/afvToUVGlUolubm5iXl5etffk5OSILi4uIgDxu+++q9qemZkptm/fXgQgHjhw4IZ8lRkWLVpUtU2n04njx48XAYgrVqy47d+dpHOr4sXcrzHeXy1XbdfetWvXxHXr1omFhYXVthcVFYlPPPGECEBs0aKFWFJSUm0/rz36N71eL44bN04EIA4YMOCGa6YmvO9RQ6jvtWer9z0WzlRNWVlZ1bdELVu2FCdPnlz1dy8vLzEhIUHqiGQGwsPDq26KI0eOFKdNmyb2799fVKvVIgCxa9euYmZm5g3vW7RokQhAdHBwEMeOHSvef//9okKhEOVyubhr164az7Vz505RJpOJgiCIQ4YMESdOnCi6ubmJAMQlS5bU+J74+HixWbNmIgCxe/fu4pQpU8R27dpV3UD//Y0lmaeffvpJ7NOnT9WPIAgigGrbfvrpp2rvMedrjPdXy1Gfa+/KlSsiANHJyUkcMmSIOG3aNPG+++6ruj7c3NzEw4cP13geXnt0vffee08EIAIQx40bJ86cObPGn6ysrGrv432P7lR9rz1bve+xcKYblJaWisuXLxcDAgJEpVIptmjRQpw1a1a1abdk286ePSs+9dRTYu/evUUvLy9RoVCIrq6uYt++fcU1a9aIpaWlN31veHi4GBwcLDo4OIhubm7iiBEjxCNHjtR6vsOHD4sjRowQ3dzcRAcHBzEkJETctGlTre9JTk4WZ82aJbZo0UJUKpVi+/btxeXLl4tlZWW39TtT06v8gqa2n/Dw8BrfZ67XGO+vlqE+115hYaH4n//8Rxw0aJDYunVrUaVSiQ4ODmLXrl3FZ599VkxNTa31XLz2qNKKFStued0BEK9cuXLDe3nfoztR32vPVu97giiKIoiIiIiIiIioRlxVm4iIiIiIiKgWLJyJiIiIiIiIasHCmYiIiIiIiKgWLJyJiIiIiIiIasHCmYiIiIiIiKgWLJyJiIiIiIiIasHCmYiIiIiIiKgWLJyJiIiIiIiIasHCmYiIiIiIiKgWLJyJiMjqCIJQ68/gwYOljkj1cOnSJSiVSjz33HPVtq9cuRKCIGDlypW1vr9NmzYQBAGJiYm3df709HTY29tj3rx5t/V+IiKyfAqpAxARETWWmTNn1ri9U6dOTZyE7sSyZcugVCrx/PPPS3L+li1b4vHHH8dHH32ExYsXIzAwUJIcREQkHRbORERktTZt2iR1BLpDMTEx2LlzJ55++ml4eXlJluP555/HunXrsHz5cmzfvl2yHEREJA1O1SYiIiKz9fHHHwMAHnnkEUlztG7dGkOGDMGuXbuQmZkpaRYiImp6LJyJiMhmzZo1C4Ig4ODBg9i7dy+GDBkCNzc3CIKA/Pz8qtf99ttvGDVqFLy8vKBSqdCuXTssWbIEOTk5NR43NzcXCxYsQKtWraBWq9GlSxe8//77EEURgiCgTZs21V5f+azuzUbIK5/Rrcm5c+cwa9Ys+Pr6QqVSoXnz5njooYdw5syZG167adOmqmeCk5OTMW3aNHh5ecHe3h4hISHYs2fPTf9bnTt3Do8++ijatGkDlUoFb29v9O/fH6tXr4ZerwcAdOvWDYIg4MKFCzUeIyUlBXK5HG3btoUoijc9V6Xi4mJs27YNHTp0QHBw8C1fXx+DBw++5bPw/34metq0adDpdJzJQERkg1g4ExGRzfvmm29w//33o6SkBPfffz9CQ0OrCtUXXngB999/P/bv34+OHTvigQcegEKhwNq1a9GnT58bRh/z8vJw9913Y/369RBFEWPHjkXr1q2xdOlSPP300w2ae/fu3QgKCsLmzZvh6emJBx54AG3btsWOHTsQFhaGv//+u8b3JSYmIjQ0FCdOnMA999yDoKAgREdH48EHH8Tvv/9+w+u//fZbBAUFYePGjXBwcMC4ceMQHByMlJQUPPfccyguLgYAPPHEEwCAzz//vMbzbty4EUajEXPnzr3pFwHX++uvv1BcXNwoi7mNGDECM2fOvOFnwoQJVa+Ry+XV3lOZ4+eff27wPEREZOZEIiIiKwNArMs/cTNnzqx67bZt227Yv2PHDhGA2K1bN/HixYtV241Go/jyyy+LAMQpU6ZUe8+TTz4pAhBHjBghlpSUVG2PiIgQnZycRACiv79/tfesWLFCBCCGh4fXmNPf3/+G3+fKlSuio6Oj6OTkJO7bt6/avl9//VW0s7MTfX19RY1GU7U9PDy86vd99tlnRYPBULVv7dq1IgBxwIAB1Y4VHx8vqtVqUaFQiF9//XW1fUajUdy7d69YXl4uiqIo5ufniw4ODqKXl1e184qiKBoMBtHPz0+Uy+ViWlpajb/nv/3nP/8RAYifffZZjfsr/7utWLGi1uNU/ve7cuVKra8zGo3igw8+KAIQ582bV+NrPD09RZVKJZaVldXlVyAiIivBEWciIrJadZ2CO2rUKEyZMuWG97/xxhsAgK1bt6J9+/bVjrty5Ur06tULO3fuRHZ2NgCgpKQEmzdvhkwmw7p16+Dg4FD1nrCwMMyfP7/Bfrf33nsPJSUlePPNN3HvvfdW2zdixAg89dRTSElJqXF0tG3btvjf//4Hmez/PwYsWLAA7u7uOH78OLRabdX2tWvXory8HHPnzsW0adOqHUcQBAwbNgwqlQoA4OrqioceeghZWVn44Ycfqr32999/R3JyMkaNGoVWrVrV6Xc8deoUAKBjx461vu6VV16pdcp1UlJSnc63fPly7N69G0OGDMH7779f42s6duwIjUaDc+fO1emYRERkHbiqNhERWa2btaNycnKq9vcHHnjghtdcu3YNcXFx6NChA7p163bDfkEQ0L9/f8TGxiI6OhrDhw9HdHQ0ysrKEBYWhoCAgBveM3XqVLz99tu3+dtUVzmlevz48TXuHzBgAD744AOcOHEC48aNq7Zv8ODBUCqV1bYpFAq0bdsWMTExyMnJQcuWLQEA+/fvB/D/07Bv5cknn8TGjRuxYcMGTJo0qWr7hg0bAACPP/54nY4DmP43AAB3d/daX9ezZ0/06tXrpvt37tyJkpKSWo+xbds2vPHGGwgICMDOnTuhUNT8EcnDwwMAkJWVVevxiIjIurBwJiIiq1XXRZz8/Pxu2FY5Kn3x4sVbPo9bOeJ89epVAIC/v3+Nr/v3omB3ojJf69at65Ttej4+PjW+1tnZGQCg0WiqtqWkpABAjV8E1CQ0NBS9e/fG/v37ceXKFbRt2xaZmZnYs2cPfHx8MGLEiDodBwAKCgqq5bqZBx98ECtXrrzp/oMHD9ZaOEdHR2POnDlwdnbGjz/+WFUc18TFxQUAqi0eR0RE1o+FMxER2Ty1Wn3DNqPRCABo0aIFhg8fXuv7b1YoN5TKLDVtu9moeqU+ffrcsO36KdqN4cknn8Tjjz+OL774Aq+//jo2b94MnU6HOXPm3LDgVm1cXV0BAEVFRY0VFRkZGRg7diw0Gg127NiBLl261Pr6ymLezc2t0TIREZH5YeFMRERUg8pRWU9PzzqPXFdOb77ZM7U32145bbpyderrGQwGZGRk1JgvISEBa9asQbNmzeqU73b4+vri4sWLSEhIqHU69PWmTZuGpUuXIjw8HCtXrsTnn38OmUyGRx99tF7n9vb2BmBq79UYNBoNHnzwQaSlpeHtt9/G6NGjb/mevLw8AICXl1ejZCIiIvPExcGIiIhq4OPjg06dOuHs2bOIj4+v03uCg4Nhb2+P6OhoXL58+Yb927Ztq/F9lQV3Tec5cOAAdDrdDdvvu+8+AMCuXbvqlO12VS489tlnn9X5PY6Ojpg+fTquXr2K559/HhcvXsTw4cNrnBJfm549ewLATftC36nHHnsMERERmD59Op5//vk6vef8+fNQqVTo3Llzo2QiIiLzxMKZiIjoJpYvXw6j0YgJEyYgNjb2hv05OTlVi14BpkXHZsyYAYPBgIULF6KsrKxqX1RUFNatW1fjeQYOHAgA+Oqrr6qt+H3lypWb9n5+9tlnYW9vj6VLl+L777+/Yb9Go8HOnTuRmppal1/1phYvXgy1Wo0NGzZg+/bt1faJooh9+/ZVeya60pNPPgnAtCo3YCpS62vAgAEAgMjIyHq/91beeecdbNmyBX369Kn2v2FtEhISkJOTg7CwsBqn9xMRkfXiVG0iIqKbmDZtGs6cOYP//e9/CA4ORq9evRAQEABRFJGQkIBTp07BycmpWlH45ptv4q+//sIvv/yCgIAADBw4EHl5efjzzz/xxBNPYP369TecJyAgAI888gi+/PJL9OrVCwMHDkRpaSmOHz+OkSNHorS09IZp3u3bt8fWrVsxbdo0TJgwAe3bt0fnzp3h6OiItLQ0xMTEoKSkBCdPnrzpYmB1ERgYiPDwcDzyyCN46KGH8Oqrr6JHjx4oKCjA6dOnkZKSgry8vKqWVJW6d++Ou+66C0ePHkWLFi0wZsyYep974MCBcHJywsGDB287/80sW7YMgGnF7soi/99Wr14NT0/Pqr9X5hg1alSD5yEiIvPGEWciIqJavPHGG/jrr78wYcIEZGRkYPfu3Thw4AAMBgOeeuop/Pjjj9Ve7+HhgSNHjuCpp56CKIrYvXs3kpOT8dZbb+HDDz+86Xk2bNiAF154AS4uLti7dy8SExOxbNkybN269abvGTt2LE6dOoV58+ZBEATs27cPP//8M65du4YxY8bUabGrunjooYcQFRWF6dOno6CgAN999x2io6Ph5+eHNWvW3NDeq9LQoUMBALNnz75pe6faODk5YerUqbh06VKDjzpXLq7222+/YfPmzTX+/PuZ82+++QZ2dnaYNWtWg2YhIiLzJ4iiKEodgoiIyFYIggB/f/9qU7KtkSiK6Ny5M+Lj43Hp0iW0a9futo4TGxuLoKAgLFiwoNYvHhpbamoq/P39MXHixBumrBMRkfXjiDMRERE1uJ07d+LChQsYOXLkbRfNANCrVy9MmjQJGzduxLVr1xowYf2sWrUKMpkMr776qmQZiIhIOiyciYiIqMHMnTsXEydOxIwZMyCXy/HKK6/c8THffPNN6HQ6vPPOOw2QsP7S09Px2Wef4bHHHkPHjh0lyUBERNLiVG0iIqImZO1TtQVBgEKhQIcOHfDqq69i4sSJUkciIiK6YyyciYiIiIiIiGrBqdpEREREREREtWDhTERERERERFQLFs5EREREREREtWDhTERERERERFQLFs5EREREREREtWDhTERERERERFQLFs5EREREREREtWDhTERERERERFQLFs5EREREREREtfg/uDqApVbs9v8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240330012827_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:-1]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            guess = [(2*4.43)**-1,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            if(i==3): optimum_duration = fine[np.argmax(data_fit)]\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$\\Delta T_{\\pi/2} = %.3f$ ms \"%(optimum_duration)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin_rabi.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962781a4-c69b-458c-ab73-025efa807b53",
   "metadata": {},
   "source": [
    "### Rabi on a (b up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04cf5ba-d194-41d5-b471-0eb4711b7154",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2 - 10e3)\n",
    "nuclear_spin_freq_a  = int(808.771e3)\n",
    "nuclear_spin_freq_b  = int(810.469e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.73e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.44e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,5,6))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[True, False,True,False])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7f28763-d4f9-41cd-badd-5f1ca953deb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "state_to_fit = 0\n",
    "\n",
    "p_data = (data[:,:,state_to_fit]>80)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1e07e5-58f0-4e9f-a166-7d03d9555e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.79e3)\n",
    "nuclear_spin_freq_b = int(810.656e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,5,11))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            #click_acc = nuclear_spin_RO(\n",
    "            #        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "            #        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            #)\n",
    "\n",
    "            #raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            # nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[True, False, True, False])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "770c4682-ba2a-4002-8153-2e198d3a9f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*300)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "state_to_fit = 0\n",
    "threshold = 80\n",
    "\n",
    "\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,state_to_fit]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "#p_data = (data[:,:,0]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try:\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except:pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "# bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            # 'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8379442-8f6e-4e98-bbb2-99f1286b3086",
   "metadata": {},
   "outputs": [],
   "source": [
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "\n",
    "delta_timing=((timing[:,1:]-timing[:,:-1])*1e-6).T\n",
    "label=['conditional prep','Ramsey tracking','Pulse sequence','RO']\n",
    "\n",
    "print(delta_timing.mean(1))\n",
    "bins=np.linspace(0,np.max(delta_timing),101)\n",
    "fig,ax=plt.subplots(5,1, figsize=(5,20))\n",
    "for i,detla_time_ in enumerate(delta_timing):\n",
    "    print(label[i],delta_timing.mean(1)[i])\n",
    "    ax[i].hist(detla_time_, bins=51, label=label[i])\n",
    "    ax[i].legend()\n",
    "# ax[0].set_yscale('log')\n",
    "\n",
    "sizes = delta_timing.mean(1)\n",
    "\n",
    "fig,ax=plt.subplots(1,1)\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    return f\"{pct:.1f}%\\n({absolute:d} ms)\"\n",
    "\n",
    "#ax.bar(label,sizes, width = 0.2)\n",
    "#ax.set_xticklabels(label, rotation = 90)\n",
    "#ax.set_ylabel(\"Time (ms)\")\n",
    "# ax.set_yscale(\"log\")\n",
    "ax.pie(sizes, labels=label, autopct=lambda pct: func(pct, sizes))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c02f571-4527-4fd9-b141-660ca13138a1",
   "metadata": {},
   "source": [
    "### Rabi on b (a down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4d34e4-55fc-4301-b673-2d07a23c4f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.concatenate((sinhspace(0,4,11, nonlinearity = 3), sinhspace(4,6,6, nonlinearity = 2)[1:]))\n",
    "y = np.ones_like(x)\n",
    "plt.plot(x,y,\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2124,
   "id": "5d141c70-ea6b-4845-9fbd-94855a495293",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T13:04:45.889463Z",
     "iopub.status.busy": "2024-04-01T13:04:45.888458Z",
     "iopub.status.idle": "2024-04-01T13:04:49.624700Z",
     "shell.execute_reply": "2024-04-01T13:04:49.622766Z",
     "shell.execute_reply.started": "2024-04-01T13:04:45.889463Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[250, 262750, 525250, 787750, 1050250, 1312750, 1575250, 1837750, 2100250, 2362750, 2625250, 2887750, 3150250, 3412750, 3675250, 3937750, 4200250, 4462750, 4725250, 4987750, 5250250]\n",
      "0.05 0.05\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "#raman_pulse_durations = (1e3+1e6*np.concatenate((sinhspace(0,3,11, nonlinearity = 3), sinhspace(3,6,11, nonlinearity = 2)[1:]))) #(1e3+1e6*np.linspace(0,8,11))//4\n",
    "raman_pulse_durations = 1e3+np.linspace(0, 21, 11)*1e6\n",
    "raman_pulse_durations = [int(duration//4) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_b_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n",
    "print(detuned_sideband_amplitude_b_prep,detuned_electron_amplitude_b_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2137,
   "id": "77a12e93-671f-43f6-b6b3-8e5852d306ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:17:59.085401Z",
     "iopub.status.busy": "2024-04-01T14:17:59.084401Z",
     "iopub.status.idle": "2024-04-01T14:17:59.897424Z",
     "shell.execute_reply": "2024-04-01T14:17:59.895422Z",
     "shell.execute_reply.started": "2024-04-01T14:17:59.085401Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 11, 2, 4)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 2 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\171154674.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4e-6\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraman_pulse_durations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mp_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[0mfft_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrfftfreq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mfft_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrfft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mp_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 3 is out of bounds for axis 2 with size 2"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "state_to_fit = 2\n",
    "threshold = 80\n",
    "\n",
    "\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,3]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "#p_data = (data[:,:,0]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            # guess = [1e-3*rabi_freq,30,0.5,1,0.5*(1-(-1)**(i))*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            guess = [0.05,100,0.4,1,i*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            if plot_guess: ax[1].plot(fine+0.04*i,rabi_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "try:\n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi          = fine[np.argmin(data_fit)]\n",
    "    T_pio2        = T_pi-T_half_period/2\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except:pass\n",
    "#ax[1].set_ylim(0.95,0.97)\n",
    "#ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "# bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            # 'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec5c97e0-5fe8-4673-966a-870c425e0b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "raman_pi_duration_b_prep*4e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8398f332-92c6-4a2d-91f2-6222f5b8aa45",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            # guess = [1e-3*rabi_freq,30,0.5,1,0.5*(1-(-1)**(i))*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            if plot_guess: plt.plot(fine+0.04*i,rabi_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "            plt.plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        plt.plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    plt.errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "plt.grid()\n",
    "plt.ylim(0.4,0.6)\n",
    "plt.xlim(1.4,2.2)\n",
    "\n",
    "# plt.ylim(0.9,1)\n",
    "# plt.xlim(4.2,5.5)\n",
    "\n",
    "plt.xlabel(\"Rabi duration (ms)\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "plt.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "plt.tight_layout()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d0f18a-4a82-4500-8fc9-e9383468e219",
   "metadata": {},
   "source": [
    "### 10 pi B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2000,
   "id": "106d1baf-e0c1-458b-8ee5-50aff1b65826",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T02:29:40.495411Z",
     "iopub.status.busy": "2024-03-30T02:29:40.494411Z",
     "iopub.status.idle": "2024-03-30T02:29:43.967605Z",
     "shell.execute_reply": "2024-03-30T02:29:43.965604Z",
     "shell.execute_reply.started": "2024-03-30T02:29:40.495411Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-75000, -65000, -55000, -45000, -35000, -25000, -15000, -5000, 5000, 15000, 25000, 35000, 45000, 55000, 65000, 75000, 85000, 95000, 105000, 115000, 125000]\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "#raman_pulse_durations = (1e3+1e6*np.sinhspace(1, 5.4, 15, 5))//4 \n",
    "raman_pulse_durations = np.linspace(-0.3e6,0.5e6,21)//4 \n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    kk = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    delta_duration_shift  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(delta_duration_shift, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "            with for_(kk, 0, kk < 10, kk + 1):\n",
    "                Pauli_swept( 'bX', delta_freq, pulse_duration_adj=delta_duration_shift)\n",
    "                align()\n",
    "                wait(int(1000e3/4))\n",
    "                align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #play('ON',fsv_trigger)\n",
    "            align()\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2001,
   "id": "647dd875-fd80-4a11-8b03-0045a276cefb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T02:29:43.969601Z",
     "iopub.status.busy": "2024-03-30T02:29:43.968605Z",
     "iopub.status.idle": "2024-03-30T04:30:53.981416Z",
     "shell.execute_reply": "2024-03-30T04:30:53.979415Z",
     "shell.execute_reply.started": "2024-03-30T02:29:43.969601Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 7200 s                                                       \n",
      "\n",
      "(186, 21, 4)\n",
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n",
      "-0.2848029163818646 -0.1605369729308608 0.4823587561278563\n"
     ]
    },
    {
     "data": {
      "image/png": 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DWRk0ycnXQvKxoyhLOVvtFE7Ktm3NulqroqMhU9a/a5C+qAj/9DEO8BX+xefwuvlmjoDtQpzp9e8Iv6tnC87inf3vYOfFnQCAIPcgzO45G+Pbja+x1aW2eUgrGIQBp/NOY2/GXuzL3IcDWQeqtDwHuQehb/O+xhbksL5o5dOqUW/qnWVQHaobrUGLg1kHsTltMzanbTYb9VwhKdAnrA+Ghg/F4qOLkVuSW23QqOjNsGr8KpwrPIfkK8lIzktG8pVk/JP3j6lb6vXCvMIQHRB9LSgHRiPcJ9xiAaWuv6tn88/ih1M/YE3KGlMrqY+bD25vfzvu6ngXwn2cq1t0Y5XqSrH70m5sTN2Irelbazx/lfkoffBgtwchk2SoiDDX/65cv9z0tZrtq2xbw2OvfREwCAOWnlx6w943SpkSHw7/0OGu+XYmDMROjIHYshzhTaYjEGVlKN67zxiCNydCn51jWicplfDq398YguPioAgMrHV/lvq56q9eRemx4yg9dhQl5SFZd6nqIAiSmxtUHTuahWRlmzaQZDW/GWlocKfaOUvQdJbXv71/V4vKivDZ4c/ww8kfoBM6KGQK3NvpXszqNgveSvPeGpa6Lk9n0OFE7glT9+q/L/9tdo0gADT3am5qPe4b1hehXqH1OoYzBWJnqtWW1Fo19lzag8S0RGy7sA2FZYWmdR4KDwxsORBxreMwqOUgUytuQ0eZFkLg4tWLZiE5OS8ZF69erLY2D4UHOgR0MLUidwjogA4BHRp07m70utIb9KZu0Xsy9pgeE+UXhcmdJmNM2zH8fYFxKqJvj3+LRX8vsncpFvP1yK/RJ6yPvctwWgzEToyB2HLs/SbT3gzFxbi6YweKNiXi6rZtMBQVmdbJvL3hPWQIfBLi4TVwEOTe9Zt6wZqBSJeTY2xFPnoUJUePofTIEegLCqpsJ/P2hnuXLqau1h7dYqAIC4MkSSjcsMHYtbua1mfgWtduR+IsIRNwjqDpLK9/e/6u6g16rDqzCov+XoQrpVcAAENaDcHTvZ9GpF9ktY+xVnAr05fhcPZh7Mvch30Z+3Ak+wh0Qme2TaRvJPqG9UXf5n3RJ6wPAt1v/OGdswyqAzAQV5ZXmodtF7YhMS0Rey7tMfugJEAVgKHhQzG89XD0a96vxlHMG9Ob4XpFZUX4J+8fs6B8Jv9MlQ9wAGPobu3bukqX61DP0Bpb+Wq6jvSJnk8grzQPP576EReuXjDtf2j4UEzpNAV9w/qy5fA6f5z9A8/ueLbW7bqHdEdLb2MPuIqfYcUHJqav1/1sr19e+X7F96Zta9lnamEq/sr4q9Y63xr0Fm5te2ut21H1GIgbYf/+/XjnnXewc+dOZGdnw8vLCzExMZgxYwamTZtW5QWi1+uxaNEifP311zhz5gy8vb0xbNgwvPLKK+jUqVO9j19x8vIyMuAfFmapp+VynDEQWYLuyhVc3bKl2pGh5SHB8IkbDp/4eHj16wupAV2Q7UEIAW16eqWu1sdQevw4RGlplW3lIcFw79IVJQcOwHC1hq5TkgRFaCjaJW5yqBDnLIG4cMMGZL7+OvSXrw2Y5GhB05Ff/8JggNBoYCgthUGtxvm77oY+J6f6ja34u3oo6xDe3PcmTl45CcAYNp/t+ywGthxo0eM0lFqrxt+X/zZef5yxHyeunIBBGMy26RDQwdSC3Cu0F3yUPqZ1zjKoTgVnCu/WcOnqJWNX6PTNOJh10Oxct/RuibjWcYgLj0OPZj3q/HOx5s9UZ9AhrTANp66cMobk8qCcU1L9a9lP5Vely3WUXxS2XdhWp+tIfZQ+uKP9Hbgr+i608mllkefQFNV1lHF7t7w6S53OjoG4gX755Rfcdddd0Ov16NmzJ9q1a4fs7Gzs2LEDOp0OkydPxrJly0zbGwwGTJw4EStXroS/vz+GDx+OnJwcbN++HR4eHtiyZQv69u1brxoqTt7FP/5A8xEjHOoNu7MQej3ODI+HLjOz+g2aWCDSXrxo7Aq9cRPUhw6ZjQztFtEaPvHx8BkeD4/Y7jfsYuxMhE4HTUoKSo4cMYVkzT//AHp9nfcR8vTT8LrpJsj9/aEI8Ifk6WnXT9udpdXVUYNmhfq+/oVOB0OpBkJTClFaCoNGY/xavsxQWmoKsMK0rNLX0vJtKj9Wc922ldbVd/oyAGj97bfw6le//0tqklmcifcPvo9159YBALzdvPFw94dxT6d7HHp03cKywmsjWGfuxem802brZZIMXYK6oG9YXyhkCnx+5PMq+3C0QXUqOFN4t1RLthACp/NPIzEtEVvStpg+mKkQHRCN4a2HI651HDoEdGjQ32Z7tLrnluSadbdOvpKMcwXnoBdV/2+SQw5Jkqr0hKhMISnwXL/nMLZt0xgt2tr0Bj1G/jISl9WXb3gN+fo71tv1AydnqdPZMRA3gE6nQ8uWLXH58mUsW7YMkydPNq07efIkBg4ciCtXrmDz5s0YNmwYAODLL7/EzJkz0b59e+zYsQOhocbrm3755RdMnDgR7dq1w8mTJ6FQ1H149IqTt69de/i3bOlQrS6OzlBWBm16Ogo3bkTOgoW1bu9z6y3w6BoDRUgwFEFBkAcFQxEcBLm/v12CSF0DUeWRoYs2bYLmhPkbCVXnTsYQHB8PVfv2LtOlylBSgtKTp5D3448oXLOm3o+XlErI/f0hDwio9NUP8oAAKCrum63zh8zLyyI/X2fo3lsRNLWZmajuGQsAbmFhNX7QJIQAdDoIrdZ4Kyur+n2lZYZqllX/mPL72jKIMi20GRlQ79lTtcDrSO7uEDodoKv5zajVyWRmH2DVxGvIEATPfAAePXo0+G9Tqa4U3xz/Bl8f/Rql+lJIkHB7+9vxeI/HEeQR1KB92lNuSS72Z+3Hvox92Je5zzQidl0EewRj6a1L4aXwgrvCHSq5ym5/Jxt6vau9NCZk6g16HM4+bGoJrhgdGTB+oNGjWQ8Mbz0cw8KHNalWUI1eg5T8FNPAXRWtykVlRbU/GGwlrC9neU05S53OjIG4AY4dO4aYmBhER0fj1KlTVdbPnj0bixYtwltvvYVnnnkGANC5c2ecPHkSK1euxIQJE8y2Hz9+PNasWYMVK1bgjjvuqHMdlQOxd3mQdoRWl+vZq3unKCtD2YULKDufirK0VJSlpkKbmoqy86nQZmRUbblqCJkM8qBAKIKMQVkRXBGWywNzUJDx+6AgyAMCLBKeawtEwmBASVKlkaHTrhsZulev8kGxhlc7MrQrKd67D2n33Vfrdm7h4RAaDfR5edVOB1Unbm6Q+/tB4V8pKJtCs3956/N1IdrHx+zNtzVbXYXBYAyOZWXGVk5N+fdlGgiNBqKsrHyZptI2GmPI1GggyjTGYKopQ1laGq5u2lTrMRVhYZAUimrDq0Ven1YkqVSQ3N0hq+Gr5K6CTFX5qztk7ipIqmtfJXcVZO7ukFQVX8vXVd5P+TL1wUN1+l2tIA8IgPfQofAZHgevm2+GzMOj1scIIbAxdSPeO/AeLhVfAgD0bNYTz/Z9Fp2DOjf4Z+VoMoszsS9zH34/+7vZfKx15aHwgEqugrvCHe5yd3goPEzfuyuMNw+FB9zl7lApVPCQe5iWm21f6X7F/irWKWVKs9d+RStR5dGSK3PEVqL6dkPW6DXYm7EXm9M2Y0v6FtO16oBxFN0BLQYgrnUchoQPqfWa8KZECIHlp5bjjX1v1LotryOtP0teQ25NzlKns7JlIG4yM0OrVKo6bRcUZPwk/dy5czh58iQ8PDwwevToKttNnDgRa9aswdq1a+sViM0IAUgSsv77BnyGD3eo7pOiUtdU9YEDFu3eaQy9F1GWVh52ywNvWVoatJcu3bBFRebpCXlIsPlUQjXwGTkSkkIBXW4u9Lk50OXkQp+XBxgM0GfnQJ+dg6pDZlx/QBnkgYHG4BwUBHlwkDFIm8Kz8asiKAjywMBqf0Y1BSJdVhYuPjEbeTffDM0/yVVHhh4wwBiChw2r08jQrsKzdy8owsKgy8qqPoCVd5mNWr/O2GVWCAi1Gvr8fOjy8qHPz4c+L894y8+HPj+vfF0e9PkFpnVCowG0WtPvSp0pFJD7+RkDs58/So8dq77O8mWXnnveeD24VguhqRRuyyoF12rum0KojdXYVbkaklJpvLm5GW8V39d1mdl6N0huSmgzMpC/fHmtx27x9lvw7NfvWkhVKm1+SUGtv6sA5P7+8Bw4EMXbt0Ofl4eClStRsHIlJJXK+DdgeJzxb0BQ1Vbe5CvJeGv/W9ifuR+AceqYp3o9hZGRI5tcz5EwrzCMixoHhaQwBWLJINApXSDgKpDnDZwMlyBkxuctl+Rm3VhLdCXGaWxq/aPfcBIks2AtIGoMw4BxepZMdSa+O/4d+jTvAz+lH3xVvvBR+thlPtKKN+8VHkl8pNqu3UVlRdhxYQcS0xKx8+JOs3lifZQ+GNJqCOJax+HmFje7bDdgSZLQPqB9nbYN8QyxcjVNT3xEPPqF9XP46/KdpU6qXZNpIdbr9YiOjkZKSkqNXaaFEDhz5gwCAwOxatUq3HbbbejTpw/27dtXZX/Hjx9H165d0aNHDxw6dKjOdZi1EFcKT3533AGPrl0g9/ODzM8Pcj9/Y3dOPz/IvL1t+ubGEt07hVYL7cWLxrBbKfCWpaYaQ+8NrgWVPD2hjIiAsnVr49eICCgjjfflwcGAwWC8hrCWQFRd106h1UJ3Jc8YkHNzjSE5Nwe67PL7uTnQ5+QaQ3ReXv1avGQyY/fbSq3O8sBAFPzyS80DQFV+uLe3sXUoPh7egwZC5lW/kaFdielDBgiY9Uay4LWuhpISU3jWVYTnyoG60lddvjFMC7W69h1biyQZW0CVSkgqJWTKiu9VxvtuFd+X31cqISlVpsdkpZ+C28baW95KH5uCjoPG1hJelYBcbpW/W6ZriBvw+reHuv6uCq0W6oMHUZS4GVcTE41/Jytt6xEbawzHccOhbuGPj/7+CCtOr4BBGKCSqzC963TM6DoDHoraW5WdWcVgNX2TDZi20YDgSr1Sc3yAJQky7IuW4euRX6NHsx7Q6DUo0ZWgVFcKjV6DUl2p8b6+tMr3le9rdBqU6ktNjzXbvtL9Un0pdAbLdsuXIMFH6QM/lR/8lH7wU/nBV+kLX5WvaZmvyte0rmK9n8oPSnnDBlOsrXvnvP7zoBd6bE7bjL2Ze82eczOPZhjWehiGtx6O3mG9HfpadVvidaRE1sUu0w20a9cujBkzBvn5+ejZsyfat2+Py5cvY8eOHejcuTOWLFmCHj16AAAWLVqE2bNn47bbbsOvv/5aZV8FBQXw9/dHYGAgcnNz61xDTYH4huRyyH18jOG4PCTL/fzLv/pB7udbKUhXCtO+vpDqcX0zUL/unUKnqxR6066F39RUaC9evHHo9fCoNvC6RURAERJS6xtpWwQiodNBd+UK9OXB2RiWc8q/v9bqrMvJqX94vk7I008j6F/3Os3I0I7AEa/LNZSWlrc6G8Ny0ebNyPt+aa2P80lIgHvXrtfCrEplDKtm9ysCbHmYrQi3ShVkSjfAza1RAXTtP6vhP+U5BBYB1bVNGQBc8QF2LpqKqV3/hVberezWCmmL178l1fd3VQgBTXIyihITcTVxM0pPnDBbnxEkw772AvvbyxDZfwSe6vs0Wni3sPrzcAR6gx7PvDoY9/9o7Jpb+TfQUH7/q7uD8PZ/ttksZGgN2qoBWleKpOwkvL3/7Vof38q7FbQGLQrLCo2t2I3gofC4Fp6rCczVrfNSeGHi2onIUmfdsNW9srZ+bRHXOg7DWw9H56DOdmnRdga8jpTIehiIG+HIkSO47bbbcPbsWdMypVKJxx9/HC+//DL8/IwTv//3v//Fiy++iClTpmDp0qpvaHU6Hdzc3ODm5oayG4wqqtFooNFc66NVWFiI8PDwKoHYa/BgyFRKY5fNwkLoCwqgLyiAKGncf44yL6/qg7Sv77UWaL+KlmgfpD/4IPTZ2TXuT/L0hEevntClpaPs4sUbDlgjubtfC72REXAzBeBIKJrVHnpr40iBSOh0xpbEivCckw19bi6K9+1H8bZttT6+xbvvwm9M1a75dGOOPnJzXa93tuQow/V1pfQKfvnnF3x34jt0OHIFT/1qgIB5KK4IGu/dbmx9A4zTjHQJ6oIuQV3QNbgrugR1QahXqM3qdqTXf1005ndVm5GBIyu/wsV1K9EmRQ1FpatK5EFB8B42FD5xw+E1oD9k7tXP29pUCL0ex4YMhDwnv9rB3wwADCH+6Lp1p93/FjSkhbBMX4bCskIUaApMX6//vqDMeL9Qc227wrLCKtNW1Vdtre5t/dpiXNQ4xLWOQxu/No06livhdaRE1sFA3EDLly/H9OnTcdNNN+Htt99Gly5dcOnSJbz77rv44osv0LNnT+zevRsqlcpigXj+/Pl45ZVXqiw3BeJauvcZNBroCwpgKA/IxltFYM6/ti6/fF15mDYUFjbuh1VHkkplDL2RxpZeY+iNhDKyvKXXytfsMRC5Nkef29eRu/eezD2JH079gD/O/oEyg/FvmAQJfZL1Nb4pPtrFC2182+Cf/H+q7SYa4hGCLsFd0DWoqykk+7v7W+05OPrr3xLSC9PxzoF3sCV9CwCgufDFU9o4dDlZjOLtO8wux5A8POB18wD4xA2H97ChUAQE2KlqyzGUll673CY1FcX796N42/ZaHxf4wAPwHZEAZdsoyL3td/mJrVoIDcKAq9qrZuG5UFNYNUhrClFQVmC2vlRfir7JBjz1q6G8tkr7xbUPw+544B0O/tRArj4PNZE1MBA3wOnTp9GlSxc0a9YMp06dgre3t9n6sWPH4rfffsMnn3yChx9+2GJdpm/YQmzFUaaFXg99YeF1QboiTFcTpAsKoM3OhqjDta5+EyfCb8wYY+ht1qzJzH1rDY4ciMg2HKl7r86gQ2JaIn44+QMOXb429kHnoM6Y2mkq3GRueGb7M4DBYNZt8lS4DEImmd68l+nLcDrvNI7lHMOx3GM4lnMMZwvOVttC1dK7JboGd0XXoK7oEtwFnYM6w8vNMgHFHvOQ2opaq8bio4vx7fFvoTVoIZfkuLvj3Xi4+8PwUxl7MomyMhTv34+riZtRtHmz+YBnMhk8evaAT9xw+AyPgzIiok7HtceHTAaNBtr09CrjTZSlpkKXkdHo/SvCwqCKioIyqi1UUe2gimoLZVSUzT4wcPQWwl1pO2C4YxaCilBjq/sVH8B91RL0adnP1uU1CU35bxWRvXCU6Qb48ccfodVqMWrUqCphGAAmTZqE3377Ddu3b8fDDz+M1q1bAwAuXLhQ7f4qlkfU8iZDpVLVOMK1IjTUat37JLnc+J99Pf7Dr2trpt/YsWzNrCNJLkfoC88bA5GEagNR6AvPMww3Yb4jRgALF1Tt3mvF1//18krz8MvpX/DjqR9No94qJAUSIhIwudNkdA/pbrqEQSFT4I2//osTEdcunbj+zbtSrkSX4C7oEtwFd+EuAMY3fKeunDKF5OM5x5FWlIaLVy/i4tWL+PP8nwCMLWNt/NqYWpC7BHdBx8COUMnrNhNAZXrDtXEKDmYdbBKtLgZhwO9nf8cHBz9AdonxHNzU/CY82+dZtAtoZ7atpFTC++ab4X3zzQh9+SWUnjhhCseaU6dQcuAgSg4cxOW334ayXZQpHLvHxNT4Qaa1ZhgwTalXeayJOk6pJ/PxMY03AYUChatX13o8VadOxktXsnOgy8yELjMTxbt2mW0jDwqCqm1bKNtFQdU2Cqp2UVC2jbLIJT2VxUfEo49fN2ycNBR53kD024twc9thdvldFULAUFgIbWYWdFmZ0GZkIuLgARTdYMpcGYDgIqBlqg5w7Vn/GszTzRNH7ztq7zLIDhy9JxvVTZMJxBUBtuIa4etVLM/LywMAdO/eHYBx/mKtVgs3N/NREytGlu7WrVuD6gl+/21E3jrWoYJQXaez8ezdy/bF1cAZPnV1hEBE9uU7YgS8+ve3effeU1dO4YeTP+D3s7+bukUHugfizg53YlL0JDTzbFblMQ2dJsLTzRM9Q3uiZ2hP07ICTQFO5J7A8dzjxqCccwxZ6iycLTiLswVnsSZlDQBjOG8f0N7U3bpLcBdE+UfdcLTauk4R4yjq8rfqaPZRvLn/TRzJPgIACPcJx797/xtDw4fWGtAkSYJHly7w6NIFIU88jrILF3F1szEcq/fvR9mZFOSeSUHuF19AERIC72HD4DM8Dp433QRZ+Ye2FddlV0if9WC9rsuudnaBivBbhyn13MovvakYa6Ji/Al5QIDp+Qu9Huq9e2v9f6rNip8hyeXQFxRAk3IWZWdToDmTAs3ZFJSdSYH20iXoc3Ohzs2Fev9+81p8fKppUW4HtxbNG9wjSmYAuqQZ6w1JE5BZ4RJcIQQMBQXQZmZCm5kJXWYWtFmZ0GVkGr9mZkGbldXg0fAvPfwo8mJj4dEtBu7dusGjWze4hdpu3ABnxlDkuqw5jSnZTpMJxGFhYQCAAwcOVLt+f/l/iJGRkQCANm3aoFOnTjh58iR+//13TJgwwWz7FStWADB2tW4IjwE3OdwLoqI188ITs433K60TACAEWzMbyHfECEi9Y3FhwBAAQMgnixA0JI4/y0Zyhg9EKsh9fNDp1EmrH0dn0GFL+hYsO7kMB7MOmpZ3CuyEqZ2nYmTkyFpbYyuH316hvRrckuWn8kP/Fv3Rv0V/07Kckhwczzlu6mp9POc48jR5OHnlJE5eOYkVMP5tVclV6BjY0awlOdI3EjJJVuN1mZfVlzF361yHHLn1Rq3Z2epsLDy0EKtTjC2fngpPzOo2C/d2vrfB0+goW7VE4L/uReC/7oW+oABXt29HUeJmFG/fDl12NvJ/+gn5P/0EydMT3gMHQhHarNoR0XVZWcYeLhXTQ+l00F66VCXw1ml2AU9P89kFKmYYiIiAPCioTq2y9e11I/fzg2fPHvDs2cNsP4biYmjOnUdZyhloUs5Ck5KCsjNnUJaeDkNREUqSklCSlGR+bA8PqNq0MW9RjoqCMjz8hjM6FG7YgMzXXzfdz37kCeTVdypDIYxTvJnCbqaxlTczE9qsLOgyMoxht7S0TvuT+/tD0bw53EJDISQJxVu21F6DRgP13r1Q791rWqZo1gzu3WLgEdMNHt27wb1rV8ir6YXn6hiKXFNjP2Qkx9FkriE+dOgQevUyfjpXcZ1whb/++gvx8fEoLi7Gxo0bER9vfCP15ZdfYubMmWjfvj127tyJZs2MrSm//vor7rjjDrRr1w4nT56Eoh5TG1X0d193Yh0SohMcrnvfptRN+PHzObhvo77KoDrfJshx94MLHOqNpjMNVOFM4c1Z8Gd6TX5pvrFbdPKPyCw2XkuqkBSIj4jHlE5TzLpFOxIhBDKKM8y6Wp/IPYGr2qrjGXi7eaNTYCcczz0Ota76Vi5HnNuzumtIQz1D8XTvp3Gp+BI+P/y56fmMixqH2T1nV9t6bwmGsjKo9+41Tum0eYtZr5UbkVQqKMLCjC29Wm3N2103u0BF8K3rlHp1Za1Rxg1lZSg7d75Ki7Lm/Pkan7fk5gZlZCSUUVFQRV3req1sE4mrW7fWOpWhT0KCcZaC8rBrat3NzLjWypuZBVFpPJIbkQcGQhEWCrfQMLg1D4MiNAxuYaHGr83DoAgNNRuNvK5jXbT65BOUHj+G0iNHUXL0KDT//FO11V+SoGzbFh4xMcag3K073Du0d+kpBZ1tRHyyjPpMY0oNw0G1Gujf//433n33XQBAly5d0LlzZ1y6dAl79uyBwWDArFmz8Pnnn5u2NxgMmDhxIlauXImAgAAMHz4cOTk52LZtG9zd3bFlyxb061e/ASYqTl6nTzuhRVALu3fv0xv0prkTi8qKMH39dOSW5tY4F2GIRwhWjF0BH6UP3OQ1d2e0hZreZNr7Z1oTZwrvzoI/UyD5SjJ+OGXsFq3RG98wB7oHYmKHiZjUYZJNp0KyFIMwILUw1diCnHscx3OO49SVUyjV1631CwAmRE1AdGA0vNy84OnmCS83L/ObwvjV2n/HamrNvl5McAye6/scuoU07DKchhAGA0qPH8eV775H4dq1dX6cpFRCGWGcN14ZEQFl62utvbYcaNGWo4wLnQ5l6ekoS0kpb1E+YwzK587VPD2iJAEy2Q1bzSGXG7e5wYcMZpsHBcEtNNTUuqsIuxZy3cLKw24N45bcSEPevBvUapSePImSw0dQcvQISo8cNfYSuI6kVMK9UydTN2uPbjFwa9260R+OOEM3ZIYi63Hk82/6kKnyQIeVcUBVi2AgboSVK1fis88+w8GDB1FQUAAfHx/ExsZi5syZuOeee6psr9frsXDhQnz99ddISUmBl5cXhg0bhldeeQWdO3eu9/ErB2KFh7FlubbufTqDDiW6kqo3rfGrWqeusk6trbqsulvFG+iGUMgU8FB4wFPhafzqZvxa3bJq77tdW155nbvcvdb/KJ1tsntnC+/OwJV/pjqDDlvTt2LZyWU4kHXtMpBOgZ0wpdMUjGozqkGDVDkynUGHlPwULD+1HL+c/sVi+3WTuVUJy55unqbAfMN1Si+z7TwUHmZ/uyrmoa0YyKw6Msjwys2vYFzUOMgk+4zYX/Db77j09NO1bhf04CwE3HUXFGFhnF2gnDAYoL2UUbVFOSUFhhuNVFUNeUgw3ELDqm/dbd4cimbNILNiS6slWjJ1ubkoOXoUpUeOoKS8JdlQUFBlO5mfHzxiYuDRrVt5S3I3KAID61Wvo0+7xlBkXY50/oUQMBSroc+7Av2VK7j611/I+WBBrY/jlJuNw0DsxCoHYrmH8YWrkqvQO7S3qaX2+tBbMRiONUmQ4CZzs8mx6lLLjcK0h9wDiemJKNHV8Kk8gABVAN4Z8g7cFe5QyVWmm1KuNH3vJnOzSRdSZwvvgON3RXbGn6klFGgKTKNFZxQbp6ORS3JTt+jYkFiH7BZtSfsz92PGnzNq3W5wy8HwdPNEsbYYxdpiqHVqXC27CrVOjWJtcaM+DKyJBMmsNVoIgfOF52t93Ncjv0afsD4Wr6euOF+65QkhkLd8ObJefa3WbZu98AIC777LIboVWzpkCCGgTUtDSXlALj1yBKUnT0KUVX2v4daypambtUe3GLh37gyZh0e1+3WEbsjCYIChqAj6oiLjVJaFhdAXFkFfaPy+9FRynXpe8HVVf9Y+/8JgME5JmpcH/ZUr0F25An1ePvR55d9fKV9evl6fl1ft73RtWrz7LvzGjG50va6K0y41MRq9Brsu7ap1O5kkM7Wm3uhWuaW2cqCscnO7tl4lV+FA1oE6vdH8Iv4LdA7ubNY6XdEirdapq7Rcm62rpmW7Yn1Fd0gBAbVOXeM1gnWRp8nDAxseuOE2EiSzkKyUK+EudzcLzUq5Eu6Ka8uUsvJ1iupDdnWh+/W/Xq+2u6SAgAQJb+17C8PC7TMFhzPSG/R4c9+bLvUzTb6SjOWnluP3s7+bXicBqgBjt+joSQjzCrNzhbbTs1lPhHqG4rL6crW/AxXXEC+KW3TD8681aKHWqqHWqnFVe9UYmrVqFOuKTSG68k2tNQbpYl0xisuKTdtVLBfl/yq2r49sdXbtG1mRM84w4OgkSYIqql3tGwJwj452iDAMWH7wP0mSTNeR+5UPQirKylD6z2mUHDlsuh65LCUF2osXob14EUXr1pcXI4eqfXtTN2v3mG5QtYtCUWJitd2Qrx8Ari5EWVl5oC2EobDA9L0x1BZBX1jz94aiohtOF1ZXeT/+CEiAR9euDtXt11HV1A39RudfaLXG8Foeao0htyLUXgu4+vw84/L8/Btf6lADyd0d8sAASEoVtOfP17p9ybFj8BmRYNWeH2QZbCG2sOpaiAFgYvuJ6NeiX7XhtaJ7sVKmtGrrT0X3vtreaFprsJrK1zNfH7Arh+dDlw/hj3N/1Lq/Zh7N4CZ3Q5m+DBq9xnRzRPGt49E5qDOCPIIQ5B6EII8gBLoHIsgjyC5dX+15ba4Qxg9EcktykVuaa/xakouc0hzkluTiTN4Z/J39d637ubfTvRjUahDCfcIR5hUGhcy+n+/Vt9Vdb9Abu0WfWob9mdemhekY2BFTOk3BLW1uaXLdouvK0XoICCFMf6+ull1Fsc4YlJMuJ2HR34tqfby9W4iBSm8yIaoduZnXOtZfXQerYpdZY8t06bFj5d2sj6D08BHosqv5oMjDA5JOB3GDa67lAQEIefppiOKr5eG2sLz1tur3NV7/XQ+Suzvkvr6Q+fpA7utn+t6gLsHVTZvqviO5HO4dO8IjNhYePXrAIzYWbi1bNPleP/VRazd0GEeD9+zfH4a8a624hsLCBh1P5uMDeWAAFAGBkAcGQh7gD0VgIOQBgcbl5d8rAgMgDww09Wio9bVfiSIkBIHTpyPgrkmQeXk1qE5XxS7TTqymQOwIb4gAx3ujWZ26dpms7mcqhIDWoDULyBq95lpo1l13v7ptarhfeVmZvgylulIUagpRqG3YH+IK3m7eVYNy+ffXh2dPhWej//O0xrW5FSE3pyTHPOiW5lZZdqX0yg27wzeEQlKghXcLhPuGI9w7HOE+126tfFrBXeFe+04aqa6BuEBTgF9P/4ofT/2IS8WXABi7RQ9vPRxTOk1Bj2Y9+AYJ1f+ehnmG4dm+z9r9b1QFe3/IWF+O0A21qeEHDQ2nzcpCyeHDKD161Njd+uhRGBo4h3JtZD4+5UHWF/Lymyng+vmWr6/0vZ+fafuaWvfqEopkPj7wHDAApUlJxu2uowgJMQbk8pDs3qVzgwZNc2aGkhJozp5F2ZkzuLp9Bwp//71hO5LJIPf3hzwgAIoAY4CtHGqNwbd8eUAgFAH+jeq5Udtr3++221C8e7cp3Mv8/BA4dSoCpk6BIiCgwcd1JQzETuz6QOxob4gAx3+j6UxvMusa3m+JvAVKudIsFOaW5kJn0NXreO5yd1NQrgjJFV9NAbo8RPsqfasEq/p8ICKEsXtoRc05JTlmQbeiZbfi+/qMEAwAHgoPBLoHItgj+NoHAB5BuFp2FUtPVp0v9Xrdg7ujUFuIi0UXa702vplnM7OQHO4TjtY+rdHKpxX8VH71qrsmtbW6n847jR9O/YDfUn4z/az8Vf6Y2GEi7oq+y6W6RdeVM4wy7gwfMlbmSAPVNBX8oMEyhF6PK0uW4PI779a6rSo6GqqoqBuEWl/I/cqDr7e31X7H6/OBiDYjAyV//w11UhJKkg6j9MQJQGf+HkByc4N7587XWpF7xMIt1PlmEqiO/mrxtcHpKkZxL+9KX9+u6X533A7vwUNMLbfywEDIfX1t/restte+KCtDwdq1yF38JcrKu1hLnp4IuPNOBM6Y3mTOrbUwEDuxhowybQ+O/kbTWd5kNia8CyFQWFZoFjKvlFyp9n5DWlUVMoWptTnQIxCBqkAkpiXe8NptlVyFDv4djDWU5ta7C7qHwsMUbs2CbjXLampBre/P1CAMuKy+jPSidKQXpSOtMM30/YWiCyjS3ngkWF+lb5WwXHEL8Qyp08jANbW6P9PnGchlcvxw8gfsy9xnWhcdEG3qFm2L1mtn5eiDv1Vw9A8Zyfr4QYNlOOMAcA39QMRQWorS48evheS/k6DPza2ynaJFc3jGxsIj1hiQ3Tt2hOTWsOnkbDGVkb6wEJqUFOMUZmdSoDlzBpqUFOgyMmp8jDwwEKqoKEg+3ijevKXWYzjS+a/La1/o9SjauBE5X3wBzYnya/jd3OA/YTyCHngAyogIW5ftFBiInVjlQNwyqKXDviFyhjeazvIm01bhXa2tdN3tdS21V0qvmH2tLQTWh4fCo0q4DfYINgu6tYXc+rLUz1QIgQJNgTEoF5kH5fSidGSX3HiwI5VcZep2fX1YbuHdAm4ytzrPQyuX5IhrHYcpnaagZ7Oe7BbdxDj6h4xEzsBZr8u2xAciQghoL1xAyd9/oyQpCeq/k6BJTgYMBrPtJHd3uHftAs/y65A9YmOhCAqyWZ0VdHl510JvSgrKUs5AcybF7IOB68lDgqGKagdVVBRU7Y1flVFRpim5nPX815UQAsU7dyL38y+gPlA+paJMBt9RIxE0axbcO3a0b4EOhoHYiVWcvHUn1iEhOoFviBrJWd5kOlp41+g1Zq3LuSW52H1pN9afX1/rY+/tfC9GRIwwBV17fVhii5+pWqvGhasXzEJyRQtzRnEG9KLmUSjlkhyhnqHIKcm5YZdtCRKmdZmGezreg+bezS1SNzkeZ/iQkcgZ8LrsawzFxSg5erQ8IP+N0qTD0Fcz77Nb69bw7HHtWmRV+/ZVAmNDWrKFENBfuVKlm7MmJQX6nJwa61aEhRlDbztj4FW1awdV27aQ+/vX+pxd5fyrDx1C7udf4Oq2baZlXkMGI/jBB+HZs6cdK3McDMROzJYnjxyLo4f3xgxWZi/2/JlqDVpkXs281hX7uhbm+lwz7Ug/UyIiR8frsqsnhEDZufOmVuSSpL+hOZNSpTVV5ukJ9+7d4BEbC8/YWOiu5CHjhReqtrpWhMwFC+DRI9bY4nv6THnoNQZgfX5+jfW4tWgBZbsoqNq1vxaA27aF3MenUc/Tlc5/6alTyP1iMQrXrzf1BvDo3QvBDz4Ir4EDXbY3mUGtxsHuseh75jQDsTNiIHZdjt5C5EyDlVVw1J+pEAI5JTn4KfknfHbks1q3f2vQW7i17a02qIyIqGngddl1oy8sRMnhI8aA/PffKDlyBIarV+u3E0mqeWArSYJbeLh5i29UO6jatrHqNEKudv7LUlOR++VXyF+1CiifdkzVuROCZ86Ez4gRTfq5V0dfVIRDPXsxEDsrBmJyZM4yWJmzcMZWdyIiarqEXg/NmRRTQC7es6faKZ+qkCQoIyKgat/uWuhtFwVlmzaQuXPwR1vRZmXhyjdLkPfTTxDl05ApIyIQNPMB+I0b16ipopxFRQ+B/IwMBmJnxUBMjs7Rrnd2Zs7Y6k5ERK6j4Lffcenpp2vdrvmbb8J/wngbVER1ocvLQ97SZbiydCkM5deNK8LCEDR9GvzvvNMqI4Q7AtM15ELgql5vs0Bc+3wiRNSkxEfEY9W4Vab7nwz/BOvvWM8w3ABymRzP9X2u2nUVre7P9n2WYZiIiOxCERJSp+3cmnPQR0eiCAhAyOOPoV1iIpo98wwUISHQZWYi6403cSZuOLI/+aTaAdacmdDrkfXfN+o9L7UlMBATuaDKAa1XaC8GtkaIj4jH+0PfRzPPZmbLQz1D2QWdiIjsyrN3LyjCwkwDaFUhSVCEhcGzdy/bFkZ1Ivf2QtCM6YhK3ISwV16BW3g49Pn5yFn0Ic4Mi0PWO+9Ae4OprpyF0OtRuG4ddJmZdjk+u0xbGLtME7kmRx9lnIiIXJOrTGXkCoROh8L1fyL3iy+g+ecfAICkVMLv9tsQdP/9UIaH27nCmgkhoLt8GWXnU1F2/rzxllr+fXq6aTCxCrbsMs1AbGEMxERERETkSFxpKiNXIITA1a1bkfv5FyhJSjIulMvhe+utCJr5ANw7dKjyGINajeSexp4A0YcOWu06ZF1ennnYPZ9q/D411TRQWLUUCkCnM91lIHZiDMRERERE5GhcbSojVyCEgHr/fuR+sRjFO3ealnvHxSF41kx4xMaallny/BuKi68F3vKvmvPnoT2feuNrm+VyuLVqCWVEBJSRkcZbRARUkZGQN2uGlIQRxlHRbTyoFgOxhTEQExERERGRLZUcO47cxYtRtGGDaWAqz379EDRrJgxXi5H1f/XrIWAoK4M2Pf1a9+ZKXZ112dk3rEURFmYKu8bgGwFlRCSUrVrecOqoyt37r+oYiJ0WAzEREREREdmD5uxZ5H75FQrWrDHrglxF+TXkoS+/DGV4eJXrerWXLgEGQ40PlwcEmLXyKiMjoWwTCWXr1pB5eDS4fs5D3AQwEBMRERERkT1pL11CzldfI3/ZsgbvQ+blZd69OTLCFIDlfn4WrNacvqgIh3r2slkgVlh170RERERERGRTbi1awHfEiDoFYreWLaDq1Amq61p85cHBkGqassuK5D4+iD54ALBi6K6MgZiIiIiIiKiJqe1a3wohT86F35jRVq7GccnsXQARERERERFZliIkxKLbNVUMxERERERERE2MZ+9eUISFmQbQqkKSoAgLg2fvXrYtzMEwEBMRERERETUxklyO0BeeL79z/cryUaZfeN7l56NmICYiIiIiImqCfEeMQMuFC6AIaWa2XBEaipYLF9Q4D7Er4aBaRERERERETZTviBHwGT4c6gMHocvOhiIkBJ69e7l8y3AFBmIiIiIiIqImTJLL4dWvr73LcEjsMk1EREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiIiJySQzERERERERE5JIYiImIiIiIiMglMRATERERERGRS2IgJiIiIiIiIpfEQExEREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiIiJySQzERERERERE5JIYiImIiIiIiMglMRATERERERGRS2IgJiIiIiIiIpfEQExEREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiIiJySQzERERERERE5JIYiImIiIiIiMglMRATERERERGRS2IgJiIiIiIiIpfEQExEREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiIiJySQzERERERERE5JIYiImIiIiIiMglMRATERERERGRS2IgJiIiIiIiIpeksHcBRERERETk3LRaLfR6vb3LIAckl8vh5uZm7zJqxEBMREREREQNUlhYiJycHGg0GnuXQg5MpVIhODgYvr6+9i6lCgZiIiIiIiKqt8LCQly8eBHe3t4IDg6Gm5sbJEmyd1nkQIQQ0Gq1KCgowMWLFwHA4UIxAzEREREREdVbTk4OvL290apVKwZhqpGHhwd8fHxw4cIF5OTkOFwg5qBaRERERERUL1qtFhqNBn5+fgzDVCtJkuDn5weNRgOtVmvvcsw0yUCcnZ2Np59+GtHR0fDw8EBgYCB69uyJf//739Vuv3btWgwZMgS+vr7w9fXF0KFD8fvvv9u4aiIiIiIi51AxgJYjD5ZEjqXid8XRBl9rcoH44MGD6NSpE9577z24ublh/PjxuOmmm3DlyhV88MEHVbZfsGABxo0bh927d+Pmm29GXFwc9u3bhzFjxuCjjz6ywzMgIiIiInIObB2munLU35UmdQ1xdnY2Ro0ahZKSEqxevRrjxo0zW79v3z6z+8nJyXj66aehUqmwZcsW9O/fHwDwzz//YMCAAXjyyScxatQotGvXzmbPgYiIiIiIiGyjSbUQz5s3Dzk5OXjnnXeqhGEA6Nu3r9n9hQsXQq/X46GHHjKFYQDo0KEDXnzxReh0OixcuNDqdRMREREREZHtNZlAXFJSgqVLl8LLywvTp0+v02MqrhOeOHFilXUVy9auXWu5IomIiIiIqN7UZTpEPvc7Ip/7Heoynb3LoSbEZl2m1Wo1cnJyEBQUBC8vL9PyvLw8vPXWWzh27Bhat26Np556ClFRUfXe/4EDB1BUVISBAwfCw8MD69atw8aNG1FaWooOHTpg0qRJaNGihWn7/Px8pKWlAQB69OhRZX/h4eEIDg5GamoqCgsLHW54cCIiIiIiImocm7UQv/baa2jTpg1OnTplWqbRaHDTTTfhnXfewR9//IHPPvsM/fv3R0ZGRr33f+LECQBAs2bNMGHCBNx666344IMP8Omnn+LJJ59Eu3btsHz5ctP2FWE4ICDALKBX1qpVKwBAampqveshIiIiIiLXsmTJEkiShCVLljTo8UII/Pzzzzh9+rRlC7PgMY4ePQpJkvDkk09auDL7sFkg3rx5M6KiotCrVy/TsqVLl+L06dMYNmwY/vzzTzzxxBPIycmpdjTo2uTl5QEA1qxZg/Xr1+Pjjz/G5cuXcf78eTz99NMoKSnBfffdh6SkJADA1atXAQCenp417rMiKBcVFdW4jUajQWFhodmNiIiIiIioPoQQePTRRzFp0iQMGjQIJ0+edMhjxMTEoE2bNlizZo3F67MHmwXitLQ0tG/f3mzZmjVrIEkSvvnmGyQkJGDBggXo0KED1q1bV+/9GwwGAIBOp8Orr76KRx55BCEhIYiIiMA777yDO++8E1qtFu+8845Fnk+FN954A35+fqZbeHi4RfdPRERERERN32OPPYZPP/0UAJCVlYW4uDiz3rWOdIzx48fj7NmzOHbsmEXrswebBeK8vDz4+/ub7gshsHPnTnTr1s0sRHbv3h3p6en13r+3t7fp++oG1apYtm3bNrPt1Wp1jfssLi4GAPj4+NS4zfPPP4+CggLTrSG1ExERERGR63rsscfwySefICIiAgDQunVrU2BNTk52uGOMHz8eAJpEK7HNAnFYWBjOnTtnun/w4EHk5eVhyJAhZts1dMLmihPr6emJkJCQKusjIyMBAJcvXwZg/AUAjEG9Ivhe78KFC2b7ro5KpYKvr6/ZjYiIiIiILEdvEKbv9527Ynbf2b399tv4+OOP0aNHD/z6668AgISEBHz88cfIyMhAfHz8DRvx7HGMgQMHIiAgAKtXr25UXY7AZqNMx8bG4rfffsOqVaswfPhwvPbaa5AkCWPGjDHb7vTp02ajQddVxUjRJSUl0Gg0UKlUZuuvXLkC4FrLsL+/P1q3bo20tDT8/fffGDhwoNn26enpyMnJQUREBEMuEREREZGdrD+WgXlrjpvuT/tmP5r7uWPe2M4Y1bW5HSuzjBkzZuDo0aNYuHCh2XhEDz/8MAwGA3x8fG447pE9jqFQKDB69GgsW7YMGRkZaN7cec+DzVqIn3nmGQDAHXfcAX9/f6xduxbdu3dHXFycaZusrCwcPnzYbOCtumrdujW6d+8OIYSpW3RlFcsqT7E0evRoAMCKFSuqbF+xbOzYsfWuhYiIiIiIGm/9sQw8vPQQsgo1ZsszC0rx8NJDWH+s/rPTOJrg4GB8//33CAwMrLLu0Ucfxb/+9S+HPMb48eMhhMDatWsbXZ892SwQDxgwACtXrsTAgQPRsWNHTJ06FWvWrIFMdq2E5cuXw8fHB6NGjWrQMSpC99NPP202dVNSUhLee+89AMBDDz1kWj579mzI5XJ89tln+Ouvv0zLT58+jf/7v/+DQqHA7NmzG1QLEREREZErEkJAXaZr9K2oVIt5a46jus7RFcvmrzmBolJto48lRNPpgm0ro0aNgkqlcvpu0zbrMg0YW1tv1OI6Z84czJkzp8H7nzx5MjZs2IBvv/0WnTt3xoABA1BSUoLdu3dDo9Fg5syZuPPOO03bR0dH45133sHcuXMxaNAgJCQkQKlUYsOGDSgpKcGiRYvQrl27BtdDRERERORqSrR6dP7Pn1Y/jgCQWViKmPkbGr2vE6+OhKfSptHI6Xl7e2PYsGFITExEcXGxacpaZ2OzFuLvvvsOu3fvrnW7v/76C999912Dj/PNN9/giy++QFRUFLZu3Yp9+/ahZ8+eWLJkCb744osq2z/55JNYs2YN+vfvjx07diAxMRG9e/fG2rVr8fjjjze4DiIiIiIioqasW7du0Gg0FhsJ2x5s9jHItGnTMG3aNAwYMOCG23311Vf4+uuvG9xXXpIkzJw5EzNnzqzzY2pruSYiIiIiorrxcJPjxKsjG72ffeeuYNo3+2vdbsn0Pujbpuq1sfXh4SZv1ONd1YYNGxASEoLY2Fh7l9JgDtcvwGAwNHjqJSIiIiIisi9JkizS/XhQ+xA093NHZkFptdcRSwDC/NwxqH0I5DLmB1tLS0tDUlISpk2bZjYulLNxuMrPnj3LaY6IiIiIiFycXCZh3tjOAIzht7KK+/PGdmYYtpM1a9YAMI427cys2kL86quvmt1PSkqqsqyCTqdDcnIytm/fjoSEBGuWRURERERETmBU1+b4dGpPzFtz3GzqpbAmNA+xs1q9ejU8PDwwYsQIe5fSKFYNxPPnz4ckSRBCQJIkJCUlISkp6YaPadasGf773/9asywiIiIiInISo7o2x83tgk2jSS+Z3ofdpO2soKAA27Ztw6hRo+Dp6WnvchrFqoH4m2++AWCci2zGjBkYOHAg7r///mq3VSqVaNGiBW666SaoVCprlkVERERERE6kcvjt2yaQYdjO1q1bB61W6/TdpQErB+L77rvP9P23336LW265xWwZERERERERGUVGRkKI6oYQc6xjrF69GjKZDGPGjLFQVfZjs1Gmt2zZYqtDERERERERkRVotVqsW7cO/fr1Q2hoqL3LaTSHm3aJiIiIiIiIHNPZs2cxcOBA3H333fYuxSJsGohPnDiBt99+G9u3b0dGRgbKysqq3U6SJOh0OluWRkREREREDspTqcD5N0fbuwwCEB0djd9++83eZViMzQLxnj17EB8fj5KSEgBAYGAgwsLCbHV4IiIiIiIiIjM2C8TPP/88SkpKMGfOHLz00ksIDAy01aGJiIiIiIisLjY2FvPmzUNsbKy9S6E6slkgPnDgAGJjY/H+++/b6pBEREREREQ2ExsbyzDsZGS2OpBSqUTHjh1tdTgiIiIiIiKiG7JZIB44cCBOnDhhq8MRERERERER3ZDNAvF///tfnDlzBh9//LGtDklERERERERUI5tdQ3zo0CFMnz4dTzzxBH766SckJCSgVatWkMmqz+T/+te/bFUaERERERERuSBJCCFscSCZTAZJklD5cJIkVdlOCAFJkqDX621RlsUVFhbCz88PBQUF8PX1tXc5REREREQWV1painPnzqFNmzZwd3e3dznkBOrzO2PLTGWzFuL//Oc/1QZgIiIiIiIiInuwWSCeP3++rQ5FREREREREVCubDapFRERERERE5EgYiImIiIiIiMgl2azLdFxcXJ23lSQJiYmJVqyGiIiIiIiIXJ3NAvHWrVtr3aZiFGoOvkVERERERETWZrNAfO7cuWqXGwwGpKenY8OGDVi4cCEeeeQRPPLII7Yqi4iIiIiIHF1ZMfDfFsbvX7gEKL3sWw81GTYLxBERETWua9OmDQYPHoy4uDiMHDkSN9100w23JyIiIiIiImoshxpUKy4uDr1798abb75p71KIiIiIiIjqZcmSJZAkCUuWLGnQ44UQ+Pnnn3H69GnLFlaJWq3GqlWrkJSU1KDHHz16FJIk4cknn7RsYXbiUIEYAFq1aoXjx4/buwwiIiIiIiKbEULg0UcfxaRJkzBo0CCcPHnSKse5fPkybrvtNnz00UcNenxMTAzatGmDNWvWWLgy+3CoQFxSUoL9+/fD3d3d3qUQERERERHZzGOPPYZPP/0UAJCVlYW4uDicOnXKzlVVb/z48Th79iyOHTtm71IazWaBOC0trcbbiRMnsGrVKowYMQLp6ekYNWqUrcoiIiIiIiJHZ9Bf+z51t/n9JuCxxx7DJ598YhpHqXXr1qZQnJycbOfqqho/fjwANIlWYpsNqhUZGVnrdEpCCERHR+Odd96xUVVEREREROTQTqwB1j1z7f6yiYBvC2DUW0Dncfary0LefvttfPzxx+jRowe+/PJL9OrVCwkJCejVqxceeeQRxMfHIzk5GZ6envYu1WTgwIEICAjA6tWr8cILL9i7nEaxWSAePHhwjYFYqVSiefPmGDJkCO655x52mSYiIiIiImMY/ulfAIT58sIM4/JJ3zl9KJ4xYwaOHj2KhQsXorCw0LT84YcfhsFggI+Pj0OFYQBQKBQYPXo0li1bhoyMDDRv3tzeJTWYzQLx1q1bbXUoIiIiIiKyFyEArbrx+zHoy1uGRTUrBQAJWP8s0HYoIJM37lhunkAtvVmtJTg4GN9//z0AmAViAHj00UftUVKdjB8/HkuXLsXatWsxa9Yse5fTYDYLxERERERE5AK0auC/LWxwIAEUXgLeDG/8rl64BCi9Gr8fFzJq1CioVCqsXr2agbi+ysrKkJSUhIsXLwIAWrZsidjYWCiVSnuUQ0RERERERPXg7e2NYcOGITExEcXFxfDycs4PFGwaiEtLS/Gf//wHn3/+Oa5evWq2ztvbGw899BBeeeUVXkNMREREROSs3DyNLa6NlbrbOIBWbaasACIGNO5Ybo51ja6z6NatG9avX4/k5GT07NnT3uU0iM0CsUajQXx8PPbs2QPA+MOrGHn6/PnzOHz4MN59913s2rULiYmJUKlUtiqNiIiIiIgsRZIs0/04Ks44mnRhBqq/jlgyro+Ka/w1xC5ArVZXOzhXWVkZ5HI55PL6/ww3bNiAkJAQxMbGWqBC+7DZPMQffPABdu/ejZtvvhlJSUn4+++/sXLlSvz66684dOgQDh8+jEGDBmHPnj1YsGCBrcoiIiIiIiJHJJMbp1YCAFw/4FX5/VFvMgzXwdatW9G2bVvs2rXLbLlWq8XEiRMxZcoU6PX1m9s5LS0NSUlJGD16NGQym8VKi7NZ5cuXL0dISAh+//13xMTEVFnftWtX/PbbbwgODsayZctsVRYRERERETmqzuOMUyv5hJkv923RJKZcspXi4mLk5eXhlltuwe7duwEAOp0Od911F9auXYvCwsJ6B+I1a9YAMI427cxsFojPnDmDoUOHwsfHp8ZtvL29MXToUKSkpNiqLCIiIiIicmSdxwGP7rt2f8oKYM5RhuF6GD16NFasWAGNRoNp06YBAH744QesXLkSI0eOxMqVK+s9wPHq1avh4eGBESNGWKFi27FZIFYoFFCra5+PTK1WQ6HgbFBERERERFSucrfoiAHsJt0AY8eOxc8//2y6r9VqkZCQgFWrVtV7/KaCggJs27YN8fHx1V6X7ExsFohjYmKwefNmnD17tsZtzp07h82bN6Nbt262KouIiIiIiMgljBs3Dj/99BPc3NwwfPhwrF69ukEz/Kxbtw5ardbpu0sDNgzEDz74IEpKSjB06FB89dVXKCkpMa0rKSnBN998g6FDh6K0tBQPPfSQrcoiIiIiIiJyCJGRkRBC4Msvv7TaMSZMmIDs7GysX78eHh4eDdrH6tWrIZPJMGbMGAtXZ3s265t87733YufOnVi8eDFmzZqFWbNmITg4GACQk5MDABBC4MEHH8SUKVNsVRYREREREZFL8fPza/BjtVot1q1bh379+iE0NNSCVdmHTS/W/fzzzzFixAgsWrQIe/fuRXZ2NgBAqVTipptuwuOPP4477rjDliUREREREZGjU3oB8wvsXQUBOHv2LAYOHIi7777b3qVYhM1Hr7rjjjtwxx13QKfTITc3FwAQFBTEgbSIiIiIiIgcXHR0NH777Td7l2ExdkuhCoWiSTSxExERERERkXOy6TzEixYtwrFjx2rc5tixY1i0aNENR6ImIiIiIiJyRLGxsZg3bx5iY2PtXQrVkc1aiBcsWIDPP/8cKSkpNW7j4+ODuXPn4uzZs1iwYIGtSiMiIiIiImq02NhYhmEnY7MW4sTERMTGxqJ169Y1bhMREYHY2Fhs3LjRVmURERERERGRi7JZIE5PT0fbtm1r3S4qKgoXLlywQUVERERERETkymwWiOVyOTQaTa3baTQa6PV6G1RERERERERErsxmgbhDhw7YuXMn1Gp1jduo1Wrs3LkT7du3t1VZRERERERE5KJsFognTpyIK1eu4IEHHkBxcXGV9Wq1GjNnzkReXh4mTpxoq7KIiIiIiIjIRdlslOnHH38c33//Pf73v/9hy5YtuOeeexAVFQUASElJwfLly3H58mVER0djzpw5tiqLiIiIiIiIXJTNArGnpyc2bdqEqVOnYvPmzViwYAEkSQIACCEAAMOGDcP3338PLy8vW5VFRERERERELspmgRgAwsLCsGnTJuzfvx+bNm1Ceno6ACA8PBzx8fHo06ePLcshIiIiIiIiF2bTQFyhT58+DL9ERERERERkVzYbVIuIiIiIiIjIkTAQExERERGRQ1Nr1Yj5NgYx38ZAra15Glei+mIgJiIiIiIiIpfEQExERERERGQBS5YsgSRJWLJkSYMer1arsWrVKiQlJVm0rsqEEPj5559x+vTpBj3+6NGjkCQJTz75pIUrsw8GYiIiIiIiIgdw+fJl3Hbbbfjoo4+ssn8hBB599FFMmjQJgwYNwsmTJ+u9j5iYGLRp0wZr1qyxQoW2x0BMRERERETkAh577DF8+umnAICsrCzExcXh1KlT9d7P+PHjcfbsWRw7dszSJdpckw7Eubm5aNasGSRJQrt27W647ZIlS9C3b194e3sjMDAQt956K3bv3m2jSomIiIiIqCZ6g970/cGsg2b3qW4ee+wxfPLJJ4iIiAAAtG7d2hSKk5OT67Wv8ePHA0CTaCVu0oH4qaeeQk5OTq3bzZkzB9OnT8exY8cQHx+Pvn37YuPGjRg8eDBWrVpl/UKJiIiIiKham1I3YcKaCab7jyQ+gpG/jMSm1E32K8rJvP322/j444/Ro0cP/PrrrwCAhIQEfPzxx8jIyEB8fDzU6rqP3j1w4EAEBARg9erV1irZZhS2PNjly5fxySefYPv27cjIyIBGo6l2O0mSkJKS0qhjJSYm4ttvv8WsWbPwxRdf1Ljdpk2bsHDhQgQFBWHPnj1o3749AGDPnj0YOnQopk+fjqFDh8Lf379R9RARERERUf1sSt2EuVvnQkCYLb+svoy5W+fi/aHvIz4i3k7VOY8ZM2bg6NGjWLhwIQoLC03LH374YRgMBvj4+MDT07PO+1MoFBg9ejSWLVuGjIwMNG/e3Bpl24TNAvHJkycxZMgQ5ObmQghR+wMaoaSkBA8++CA6d+6Mp59++oaB+P333wcAvPTSS6YwDAD9+/fHQw89hEWLFuGrr77CU089ZdWaiYiIiIiaAiEESnQljd6P3qDHG/veqBKGAZiWvbnvTfQL6we5TN6oY3koPCBJUqP24ciCg4Px/fffA4BZIAaARx99tEH7HD9+PJYuXYq1a9di1qxZja7RXmwWiP/9738jJycHd9xxB55//nl06NAB3t7eVjnWK6+8grNnz2Lbtm1wc3OrcbuSkhJs3rwZADBx4sQq6ydOnIhFixZh7dq1DMRERERERHVQoitBvx/62eRYWeosDPhxQKP3s3fyXni61b2FlIBRo0ZBpVJh9erVDMR1sWPHDkRHR+Onn36y6qcvR44cwXvvvYfp06dj0KBBOH/+fI3bJicnQ6PRICQkBK1ataqyvmfPnqZ9EhERERERkZG3tzeGDRuGxMREFBcXw8vLy94lNYjNArEQArGxsVYNwwaDAQ888AD8/f3x9ttv17p9WloaAFQbhgHAy8sL/v7+yMvLQ1FREXx8fCxaLxERERFRU+Oh8MDeyXsbvZ+DWQfxSOIjtW73yfBP0Cu0V6OO5aHwaNTjXVW3bt2wfv16JCcnmxoTnY3NAnHv3r2Rmppq1WN8+OGH2L9/P7755hsEBQXVuv3Vq1cB4IYXkHt5eSE/P7/GQKzRaMwGB7u+Tz4RERERkSuRJMki3Y8HtBiAUM9QXFZfrvY6YgkSQj1DMaDFgEZfQ2xvarW62kxSVlYGuVwOudwxn9+GDRsQEhKC2NhYe5fSYDabdmn+/PnYv38/1q5da5X9p6Wl4aWXXsKQIUMwbdo0qxyjOm+88Qb8/PxMt/DwcJsdm4iIiIioqZLL5Hiu73PVrpNg7HX6bN9nnT4Mb926FW3btsWuXbvMlmu1WkycOBFTpkyBXu948y6npaUhKSkJo0ePhkzmvLP52nTapdmzZ+P222/H5MmTkZCQgFatWtX4wxs8eHC99v3oo4+irKwMn332WZ0fUzGo143m3CouLgaAGrtLP//885g7d67pfmFhIUMxEREREZEFxEfE4/2h7+ONfW/gsvqyaXmoZyie7ftsk5hyqbi4GHl5ebjllltMWUan0+Guu+7C2rVrccstt0Cv1ztcK/GaNWsAGEebdmY2C8RDhw6FJEkQQuD777/H0qVLb7h9fT8F+e233+Dv74+HHnrIbHlpaSkA4OLFixg6dCgA4Mcff0RYWBhat24NALhw4UK1+ywuLkZ+fj4CAgJqDMQqlQoqlapetRIRERERUd3ER8SjX1g/02jSnwz/pEl0k64wevRorFixAhMnTjT1dP3hhx+g1WoxcuRIrFy5Ekql0r5FVmP16tXw8PDAiBEj7F1Ko9gsEP/rX/+y+txe+fn52LZtW7XrSktLTesqQnJ0dDRUKhWys7Nx8eJFtGzZ0uwxhw4dAmC8WJyIiIiIiOyjcvjtFdqryYThCmPHjsXPP/9smgpWq9UiISEBq1atcsjGt4KCAmzbtg2jRo264XhMzsBmgXjJkiVW3b8QVS+0B4Dz58+jTZs2iIqKwpkzZ8zWeXh4IC4uDuvWrcPPP/+MOXPmmK1fsWIFAOMvKBERERERkbWMGzcOP/30EyZNmoTBgwdj9erVcHd3t3dZ1Vq3bh20Wq3Td5cGbHwNsSOaO3cu1q1bh9dffx2jR49G+/btAQB79uzB559/Dn9/f9x///12rpKIiIiIiJq6CRMmIDs7G15eXlAorBPVIiMja2xMrKvVq1dDJpNhzJgxFqrKfpx3ODALiY+Px+zZs5Gbm4vY2FhMmDABt956KwYPHgydTodvvvkG/v7+9i6TiIiIiIhcgJ+fn9XCsCVotVqsW7cO/fr1Q2hoqL3LaTSb/6TT0tKwdu1anD59GkVFRdV+OiFJEr766iub1bRgwQLExsbio48+wsaNG6FUKhEfH4+XX34ZAwYMsFkdREREREREjuzs2bMYOHAg7r77bnuXYhE2DcSvvvoqXnvtNRgMBtOyikBcMeCWEMKigbiuXQKmTZtm0/mLiYiIiIiobjzdPHH0vqP2LoNgHJj4t99+s3cZFmOzLtP/+9//MH/+fISHh+OLL75AQkICAODPP//Ep59+iiFDhkAIgblz52Lz5s22KouIiIiIiIhclM1aiD/55BMolUps2bIFERER2LlzJwCYgvGDDz6IDz74AM888wwmTJhgq7KIiIiIiIgsIjY2FvPmzUNsbKy9S6E6slkL8ZEjRzBgwABEREQAMO8iXeHJJ59EdHQ0Xn/9dVuVRUREREREZBGxsbGYP38+A7ETsVkg1mg0CAsLM92vmFMrPz/fbLvu3btj//79tiqLiIiIiIiIXJTNAnHz5s1x+fJl0/2WLVsCAI4fP2623YULF6DX621VFhEREREREbkomwXimJgYJCcnm+4PHToUQgjMmzcPxcXFAICffvoJO3bsQJcuXWxVFhEREREREbkomwXisWPH4uLFi6YRpG+++WYMGzYMW7ZsQUBAAIKDg3HPPfdAkiS8/PLLtiqLiIiIiIiIXJTNAvHUqVNx8uRJswvMV65ciVmzZiEwMBBFRUXo3Lkzvv/+e4waNcpWZREREREREZGLstm0SyqVCtHR0WbLfH198dlnn+Gzzz6zVRlEREREREREAGzYQkxERERERETkSGzWQlxBp9Ph999/x759+5CTk4N+/fphxowZAIBLly4hJycHnTt3hkJh89KIiIiIiIjIhdg0de7cuRNTp05Feno6hBCQJAlardYUiPfs2YNJkybh559/xu23327L0oiIiIiIiMjF2KzL9IkTJzBq1ChkZGTg8ccfx08//QQhhNk2Y8eOhaenJ3755RdblUVEREREREQuymYtxK+99hpKS0vxxx9/YMSIEdVuo1Qq0bNnT/z999+2KouIiIiIiBycQa1Gcs9eAIDoQwch8/S0c0XUVNishXjLli3o27dvjWG4QsuWLXHp0iUbVUVERERERESuymaBOD8/H+Hh4bVuV1xcDK1Wa4OKiIiIiIiIyJXZLBA3a9YMZ86cqXW7kydP1ik4ExEREREROZIlS5ZAkiQsWbKkQY9Xq9VYtWoVkpKSLFqXJY9x9OhRSJKEJ5980rKF2YnNAnFcXBySkpKwZcuWGrdZuXIlzpw5g4SEBFuVRURERERE5BAuX76M2267DR999JHDHiMmJgZt2rTBmjVrLFyZfdgsED/33HNQKpWYMGECPv30U2RmZprW5eXl4euvv8b9998PLy8vzJ0711ZlERERERERUT2MHz8eZ8+exbFjx+xdSqPZLBB37NgRy5cvh8FgwGOPPYaWLVtCkiR8++23CA4OxsyZM6HRaLBs2TK0adPGVmUREREREZGDE3q96Xv1gQNm98n2xo8fDwBNopXYZoEYACZMmIBjx47h8ccfR8eOHeHu7g6lUom2bdviwQcfxJEjRzBu3DhblkRERERERA6scMMGnB09xnQ/fdaDODM8HoUbNtixKtc2cOBABAQEYPXq1fYupdFsNg9xhYiICCxYsMDWhyUiIiIiIidTuGEDLs6eAwhhtlyXlWVcvnABfGuZ1pUsT6FQYPTo0Vi2bBkyMjLQvHlze5fUYDYPxERERERE1HQJISBKShq/H70eWa//X5UwXH4QQAKy/u+/8OrfH5Jc3qhjSR4ekCSpUftwNePHj8fSpUuxdu1azJo1y97lNBgDMRERERERWYwoKUFyz142OJCxpfifPn0bvavoQwcheXpaoCjXMWrUKKhUKqxevZqBuDpt27Zt8GMlSUJKSooFqyEiIiIiIiJL8fb2xrBhw5CYmIji4mJ4eXnZu6QGsVogPn/+PCRJgqiui0Mt2F2BiIiIiMg5SR4eiD50sNH7UR84gPRZD9a6XfgXn8Ozd+9GHUvy8GjU411Vt27dsH79eiQnJ6Nnz572LqdBrN5lulevXpg6dSrGjx8PD/6iERERERE1aZIkWaT7sdfNN0MRFgZdVlb11xFLEhShofC6+eZGX0Nsb2q1Gp7V/MzKysogl8sht8Dzs8YxNmzYgJCQEMTGxja6Pnux2rRLP/74I8aMGYMjR45g7ty56N69O5577jkcO3YMzZo1Q2ho6A1vRERERETkuiS5HKEvPF9+5/qVxgWhLzzv9GF469ataNu2LXbt2mW2XKvVYuLEiZgyZQr0jZx32RrHSEtLQ1JSEkaPHg2ZzKaz+VqU1SqfNGkSVq9ejYyMDHz44Yfo0qULvv32W4wYMQLh4eH497//jaSkJGsdnoiIiIiInJzviBFouXABFCHNzJYrQkPRsolMuVRcXIy8vDzccsst2L17NwBAp9Phrrvuwtq1a1FYWNjoQGyNY6xZswaAcbRpZyaJhlzk20Dnzp3D0qVL8cMPPyA5ORmSJKFTp0649957MXnyZISHh9uqFKspLCyEn58fCgoK4Ovra+9yiIiIiIgsrrS0FOfOnUObNm3g7u5u9ePpi4pMo0mHf/G5w3aTXrJkCaZPn45vvvkG06ZNq/Pj1q5di4kTJ0IIAa1WCzc3N2i1WowcORKrV6+GSqVqdG2WPkZCQgJ27dqFnJycartiX68+vzO2zFQ2bdtu06YNXn75ZZw8eRJ79+7F448/jtzcXLzwwgvo06ePLUshIiIiIiInUTn8evbu7ZBhuDHGjh2Ln3/+2XRfq9UiISEBq1atskgYtvQxCgoKsG3bNsTHx9cpDDsyu3X2joiIQNu2bdGiRQsIIWAwGOxVChERERERkV2NGzcOP/30E9zc3DB8+HCsXr3a4q3vljrGunXroNVqnb67NGCDUaYrU6vV+PXXX7Fs2TIkJiZCr9fDz88PM2fOxL333mvLUoiIiIiIiBzKhAkTkJ2dDS8vLygU1olqljjG6tWrIZPJMGbMGAtXZ3tWD8QGgwF//vknli5dijVr1kCtVkOpVGLcuHGYOnUqbr31ViiVSmuXQURERERE5PD8/Pwc+hharRbr1q1Dv379msTsQFYLxHv37sWyZcvwv//9D9nZ2ZAkCYMHD8bUqVMxceJEm5xoIiIiIiJyfjJPT3Q6ddLeZRCAs2fPYuDAgbj77rvtXYpFWC0Q9+/fH5IkISYmBk8//TQmT56Mli1bWutwREREREREZGXR0dH47bff7F2GxVh9UK0TJ07gxRdfRJs2baBUKut0s9RIakREREREREQ1seo1xEII6HQ6ax6CiIiIiIjIIcTGxmLevHmIjY21dylUR1YLxJxGiYiIiIiIXElsbCzDsJOx2zzERERERERERPbEQExEREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiImoQIYS9SyAn4ai/KwzERERERERUL3K5HACg1WrtXAk5i4rflYrfHUfBQExERERERPXi5uYGlUqFgoICh235I8chhEBBQQFUKhXc3NzsXY4Zq81DTERERERETVdwcDAuXryICxcuwM/PD25ubpAkyd5lkQMRQkCr1aKgoABXr15Fy5Yt7V1SFQzERERERERUb76+vgCAnJwcXLx40c7VkCNTqVRo2bKl6XfGkTAQExERERFRg/j6+sLX1xdarRZ6vd7e5ZADksvlDtdNujIGYiIiIiIiahQ3NzeHDj1ENeGgWkREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFxSkwnEarUaq1atwv3334/o6Gi4u7vDy8sL3bt3x6uvvoqrV6/W+NglS5agb9++8Pb2RmBgIG699Vbs3r3bhtUTERERERGRrUlCCGHvIizhyy+/xMyZMwEAnTp1QteuXVFYWIjdu3ejqKgIHTt2xLZt29CsWTOzx82ZMwcLFy6Eh4cHRowYgdLSUiQmJkIIgRUrVmDChAn1qqOwsBB+fn4oKCiAr6+vpZ4eERERERGRS7BlpmoyLcRubm6YNWsWTpw4gRMnTuCnn37C+vXrkZycjB49euDUqVOYM2eO2WM2bdqEhQsXIigoCIcPH8aqVauwfv16bN++HXK5HNOnT0d+fr5dng8RERERERFZV5MJxPfddx8+//xzdOrUyWx58+bN8fHHHwMAfv31V5SVlZnWvf/++wCAl156Ce3btzct79+/Px566CHk5+fjq6++skH1REREREREZGtNJhDfSPfu3QEAGo0Gubm5AICSkhJs3rwZADBx4sQqj6lYtnbtWhtVSURERERERLbkEoH47NmzAIzdqgMDAwEAycnJ0Gg0CAkJQatWrao8pmfPngCAI0eO2K5QIiIiIiIishmXCMQLFy4EAIwaNQoqlQoAkJaWBgDVhmEA8PLygr+/P/Ly8lBUVGSbQomIiIiIiMhmFPYuwNr++OMPfPXVV3Bzc8Nrr71mWl4xDZOnp2eNj/Xy8kJ+fj6Kiorg4+NT7TYajQYajcZ0v7Cw0EKVExERERERkTU16RbiU6dOYerUqRBC4J133jFdS2xJb7zxBvz8/Ey38PBwix+DiIiIiIiILK/JBuKLFy9i1KhRyMvLw9y5czF79myz9d7e3gAAtVpd4z6Ki4sBoMbWYQB4/vnnUVBQYLqlp6dboHoiIiIiIiKytibZZfrKlSsYMWIEUlNTMX36dLz77rtVtmndujUA4MKFC9Xuo7i4GPn5+QgICLhhIFapVKbrkomIiIiIiMh5NLkW4qtXr+KWW27BiRMncPvtt2Px4sWQJKnKdtHR0VCpVMjOzsbFixerrD906BAAoFu3blavmYiIiIiIiGyvSQVijUaD8ePHY9++fRg5ciSWL18OuVxe7bYeHh6Ii4sDAPz8889V1q9YsQIAMHbsWOsVTERERERERHbTZAKxXq/HPffcg82bN2PQoEH49ddfoVQqb/iYuXPnAgBef/11nD592rR8z549+Pzzz+Hv74/777/fqnUTERERERGRfTSZa4g/+ugjrFy5EgAQHByMRx55pNrt3n33XQQHBwMA4uPjMXv2bCxcuBCxsbFISEhAWVkZNm7cCCEEvvnmG/j7+9vqKRAREREREZENNZlAnJeXZ/q+IhhXZ/78+aZADAALFixAbGwsPvroI2zcuBFKpRLx8fF4+eWXMWDAAKvWTERERERERPYjCSGEvYtoSgoLC+Hn54eCggL4+vrauxwiIiIiIiKnYstM1WSuISYiIiIiIiKqDwZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIiIiIiFwSA3G5kpIS/Oc//0GHDh3g7u6OFi1aYMaMGbh48aK9SyMiIiIiIiIrYCAGUFpairi4OLz22mu4evUqxo8fj/DwcHzzzTfo0aMHzp49a+8SiYiIiIiIyMIYiAG8/vrr+Ouvv9C/f3/8888/+N///oe9e/fivffeQ3Z2NmbMmGHvEomIiIiIiMjCJCGEsHcR9lRWVoZmzZqhoKAAhw4dQo8ePczWd+/eHUeOHMGBAwfQq1evWvdXWFgIPz8//PXnT+gddxvkCoW1Sm8UvU6HU3v/REneRXgEtETHfiMdslZnqRNgrdbgLHUCrNUanKVOwHlqdZY6AdZqDc5SJ8BarcFZ6gScp1ZnqRNwvloPbF6Jm0ZOQkFBAXx9fa16PJcPxFu2bEFcXByioqJw5syZKutfe+01/Oc//8G8efMwf/78WvdXEYgLnvNBiSoYl/rPQ4+R91mh8ob7+89v0WLPKwhFrmlZFoIcrlZnqRNgrdbgLHUCrNUanKVOwHlqdZY6AdZqDc5SJ8BarcFZ6gScp1ZnqRNwzlo9NDnwe7PIJoHYMT8WsKHDhw8DAHr27Fnt+orlR44cqfe+Q0QuQnY/gb8Bh/ll+/vPb9F99xPGO9K15Y5Wq7PUCbBWa3CWOgHWag3OUifgPLU6S50Aa7UGZ6kTYK3W4Cx1As5Tq7PUCThvrVdteFyXbyGeO3cuPvjgAzz55JN4//33q6w/fPgwYmNj0bNnTxw8eLDW/VVuIfZVSTAIIFsKguec/XbvlqDX6VCyoDeCxRXIpKrrHaVWZ6kTYK3W4Cx1AqzVGpylTsB5anWWOgHWag3OUifAWq3BWeoEnKdWZ6kTcO5aCzXCZi3ELh+IZ82ahcWLF+PFF1/E66+/XmX9mTNn0L59e7Rv3x7//PNPlfUajQYajcZ0v7CwEOHh4aZATERERERERHVny0DMUaYb6Y033oCfn5/pFh4ebu+SiIiIiIiIqA5c/hpib29vAIBara52fXFxMQDAx8en2vXPP/885s6da7pf0UJ8vcNDvkT7PiMaW26jnN6/Ad23PVDrdvau1VnqBFirNThLnQBrtQZnqRNwnlqdpU6AtVqDs9QJsFZrcJY6Aeep1VnqBJpmrdbg8l2mFyxYgCeffBJ33nknfvrppyrrf//9d4wZMwa33XYbfv3111r3V901xJelIIS89I9D9M3Peb0DQkRujdcROEKtzlInwFqtwVnqBFirNThLnYDz1OosdQKs1RqcpU6AtVqDs9QJOE+tzlIn4Ny1ssu0DXXv3h0AcOjQoWrXVyzv1q1bvfdtKP+oIaP/PLv/kgGAXKHApf7zAFyrrYIj1eosdQKs1RqcpU6AtVqDs9QJOE+tzlInwFqtwVnqBFirNThLnYDz1OosdQJNp1Zrc/lAfPPNN8PPzw8pKSlISkqqsn7FihUAgLFjx9Z735elIBwesMhhhjIHjMOqHx6wCNlSkNlyR6vVWeoEWKs1OEudAGu1BmepE3CeWp2lToC1WoOz1AmwVmtwljoB56nVWeoEmkat1ubyXaYB4KWXXsL//d//YcCAAdiwYQO8vLwAAO+//z6eeuopDBkyBFu3bq3Tviq6TP/150/oHXebQ3ziUh29TodTe/9ESd5FeAS0RMd+Ix2yVmepE2Ct1uAsdQKs1RqcpU7AeWp1ljoB1moNzlInwFqtwVnqBJynVmepE3C+Wg9sXombRk7itEu2UlpaiqFDh2Lv3r1o3rw5Bg0ahNTUVOzduxchISH466+/0LZt2zrty3QNsQ1OHhERERERUVNjy0zl8l2mAcDd3R1btmzByy+/DE9PT6xatQqpqamYNm0aDh06VOcwTERERERERM6DLcQWxhZiIiIiIiKihmMLMREREREREZGVMRATERERERGRS2IgJiIiIiIiIpfEQExEREREREQuiYGYiIiIiIiIXBIDMREREREREbkkBmIiIiIiIiJySQzERERERERE5JIU9i6gqRFCADBOJk1ERERERET1U5GlKrKVNTEQW1hubi4AIDw83M6VEBEREREROa/c3Fz4+flZ9RgMxBYWGBgIAEhLS7P6yaPGKSwsRHh4ONLT0+Hr62vvcqgGPE/Og+fKOfA8OQ+eK+fBc+UceJ6cR0FBAVq3bm3KVtbEQGxhMpnxsmw/Pz++0JyEr68vz5UT4HlyHjxXzoHnyXnwXDkPnivnwPPkPCqylVWPYfUjEBERERERETkgBmIiIiIiIiJySQzEFqZSqTBv3jyoVCp7l0K14LlyDjxPzoPnyjnwPDkPnivnwXPlHHienIctz5UkbDGWNREREREREZGDYQsxERERERERuSQGYiIiIiIiInJJDMRERERERETkkhiIG2nXrl249dZbERgYCG9vb/Tt2xffffddvfezfft2zJw5Ez179kRoaCiUSiUCAwMxbNgwfP/99+Cl3o1nqXN18OBBzJ8/HwMGDIC/vz+USiXCw8MxdepUHDlyxAqVuxZLnaecnBx89dVXmDVrFmJjY6FQKCBJEpYsWWL5opuokpIS/Oc//0GHDh3g7u6OFi1aYMaMGbh48WK995WXl4fZs2cjIiICKpUKERERmDNnDvLz8y1fuIux1Hnatm0bXnnlFYwePRohISGQJAmRkZHWKdpFWeJc5efn44cffsA999yDNm3aQKlUwsfHB/369cPChQuh1Wqt+AxcgyXOk06nw/z58zF69Gi0bdsWPj4+cHd3R/v27fHII48gNTXVis/AdVjy/6nKTp8+DQ8PD0iShPj4eAtV67osdZ4iIyMhSVKNt1OnTjWsQEENtmLFCiGXy4UkSWLIkCHijjvuEP7+/gKAeOqpp+q1r6eeekoAEB06dBAjR44Ud999txg0aJCQy+UCgLjnnnus9Cxcg6XOlVarFQAEABEYGChuueUWMXHiRBEVFSUACKVSKX7++WcrPpOmzZKvqZUrV5rOVeXbN998Y53im5iSkhJx0003CQCiefPmYtKkSaJv374CgAgJCREpKSl13ld2drZo166dACDatm0rJk2aJLp06WL6m5ebm2vFZ9K0WfI8de/evcrrJSIiwnrFuxhLnasXX3xRABCSJIkePXqIu+66S8TFxQmVSiUAiIEDB4ri4mIrP5umy1LnqaioSAAQ3t7eYsCAAWLixIli3LhxonXr1gKA8PX1Ffv377fys2naLPn373pDhw4VkiQJAGL48OEWrNr1WPI8RURECADivvvuq/Z26dKlBtXIQNxAubm5wtfXVwAQv/zyi2l5Zmam6Y3fli1b6ry/48ePi4sXL1ZZfvr0adG8eXMBQKxdu9YSpbscS54rrVYr+vTpI1atWiV0Op1puV6vN71J8fHxEdnZ2ZZ+Gk2epV9Tu3fvFo888oj4+uuvxdGjR8XMmTMZiOuh4ve5f//+oqioyLT8vffeEwDEkCFD6ryvKVOmCADi9ttvF1qt1rT88ccfN/3HRg1jyfP073//W7z++uvizz//FMePH2cgtjBLnav//ve/4plnnhGpqalmy//55x9T2Hr++ectWbpLsdR50mq1YufOnWZ/84QQQqfTieeee04AEL169bJk6S7Hkn//Kvvyyy8FADFr1iwGYguw5HmqCMSWxkDcQG+99ZYAIMaPH19l3a+//ioAiDFjxljkWK+99poAIJ588kmL7M/V2OpcGQwGER0dLQCIJUuWNHp/rsba5+nBBx9kIK4jjUYj/Pz8BABx6NChKuu7desmAIgDBw7Uuq9Lly4JmUwmlEqlyMzMNFtXWloqQkJChFwuF1lZWRar31VY8jxdLyMjg4HYgqx5rir74YcfBAARGRnZqP24KludJ61WK9zd3QUAkZ+f36h9uSprnavMzEwREBAgEhISxJYtWxiIG8nS58lagZjXEDfQ77//DgCYOHFilXWjR4+Gu7s7Nm3ahNLS0kYfy83NDQCgVCobvS9XZKtzJUkSunXrBgC4dOlSo/blimz5mqIb27VrFwoKChAVFYUePXpUWV9xjtauXVvrvtavXw+DwYBBgwYhNDTUbJ1KpcLYsWOh1+vxxx9/WKZ4F2LJ80TWZatz1b17dwD8P6ihbHWeJEmCXC6HJEl8b9dA1jpXs2fPRklJCT755BOL1OnqnOX/KQbiBjp8+DAAoGfPnlXWKZVKdO3aFaWlpfjnn38adZz09HR89tlnAIBbb721UftyVbY6VwBw9uxZAEBYWFij9+VqbHme6MZudC4qL6/LIHKW3BeZ48/WedjqXPH/oMaxxXkSQuCtt95CcXExhg0bBg8Pjwbvy5VZ41z98ccf+N///ocXXngB7dq1a3yRZLXX1DvvvIOHHnoIs2fPxhdffIHs7OxG1alo1KNdVGFhIQoKCgAArVq1qnabVq1a4cCBA0hNTTW1GtbFnj178Pnnn0Ov1+PSpUvYuXMndDodXn/9dQwePNgi9bsSa56r6+3cuRMHDx6EUqnEqFGjGrwfV2TL80S1S0tLA3DjcwGgTqOkWnJfZI4/W+dhq3O1cOFCAMD48eMbtR9XZa3z9OyzzyIrKwuFhYU4cuQIUlJS0KlTJ3z55ZeNK9iFWfpcFRcX45FHHkF0dDSeffZZyxRJVntNPfPMM2b3n3zySXz44YeYMWNGA6pkIG6Qq1evmr739PSsdhsvLy8AQFFRUb32nZKSgm+//dZ0Xy6X49VXX8XTTz/dgErJmueqssLCQtOL8Mknn0Tz5s0bvC9XZKvzRHVTcT4scS4suS8yx5+t87DFufrss8+wadMm+Pv747nnnmvwflyZtc7TL7/8gpSUFNP9bt26YenSpWjTpk0DKyVLn6uXXnoJqamp2LJlC7uxW5Clz9O4ceMwbNgw9OrVCyEhITh79iy+/vprLFy4EA888ACCgoIa9IGgywbi2267DSdPnqzXY7777jv07dvXShUZTZ06FVOnTkVZWRnOnz+P7777Dq+++irWrl2LdevWISAgwKrHd0SOeq4q6PV6TJkyBadPn0bfvn3x6quv2uS4jsbRzxMRUVO1Y8cOzJ49G5Ik4euvv0aLFi3sXRJVcubMGQBATk4ODh48iBdffBG9evXC4sWLcd9999m5Ojpw4AAWLVqEf/3rXxg6dKi9y6EbWLRokdn9Ll264L333kPHjh0xa9YsPPvsswzE9XHu3DkkJyfX6zFqtRoA4O3tbbbM19e3yrbFxcUAAB8fnwbVp1Qq0aFDB7z++usIDAzEU089hf/85z/48MMPG7Q/Z+bo5+rhhx/Gb7/9hujoaPz+++8u+8mio58nqruK81Fxfq5Xn3NhyX2ROf5snYc1z9WxY8cwfvx4lJWVYdGiRbjtttsaXqiLs/ZrKjg4GCNHjsRNN92EmJgYPPzww4iLi0N4eHjDCnZhljpXOp0OM2fOhL+/P959913LFkk2+3/q/vvvx0svvYTk5GScP38ekZGR9Xq8yw6qlZSUBGGcdqrOt4pPjXx9feHn5wcAuHDhQrX7r1geERHR6FrvvfdeAMDq1asbvS9n5Mjn6rnnnsPixYsRHh6OjRs3Ijg4uGFPsglw5PNE9dO6dWsAljkXltwXmePP1nlY61ydO3cOI0aMQF5eHubPn4/HH3+8cYW6OFu9pvz8/DB27FiUlJRg48aNjdqXq7LUubpw4QKSkpKgVCpx5513YujQoabbnDlzAAAHDx40LaP6sdVrSiaTISoqCgCQkZFR78e7bAtxY3Xv3h3bt2/HoUOH0LlzZ7N1Wq0Wx44dg7u7Ozp06NDoYwUGBkImkzV6BDVXZa1z9fbbb+Ott95Cs2bNsHHjRn7C20i2fE3RjVVM3XLo0KFq11csr8vgZpbcF5njz9Z5WONcZWRkICEhARkZGZg9ezbmzZvX+EJdnC1fUxUfoPO9XcNY+lxlZmYiMzOz2nX5+fnYtm1bA6okW76m8vLyAFy7Lrk+XLaFuLFGjx4NAFixYkWVdb/99htKS0sRHx8Pd3f3Rh9rx44dMBgMpk8+qH6sca4WL16MZ599Fv7+/vjzzz8RHR1tsXpdlS1fU3RjN998M/z8/JCSkoKkpKQq6yvO0dixY2vd16hRoyCTybBjxw5cvnzZbJ1Go8HatWshl8s5rVwDWPI8kXVZ+lzl5eVh5MiRSElJwfTp0/HBBx9YslyXZcvXVEXA4nu7hrHUuYqMjKyxF9uWLVsAAMOHDzcto/qx1Wvq+PHjSE5OhqenJzp27Fj/HQhqkNzcXOHr6ysAiF9++cW0PCsrS7Rr104AEFu2bKnyuOjoaBEdHS0uXLhgtvztt98WV65cqbL9vn37TPt79913Lf48XIGlz9XPP/8sZDKZ8Pb2Frt377Z2+S7D0ufpeg8++KAAIL755hsLV940vfjiiwKAGDBggLh69app+XvvvScAiCFDhpht/+GHH4ro6Gjx3HPPVdnXlClTBABxxx13CK1Wa1r+xBNPCADivvvus9bTaPIseZ4qy8jIEABERESEFap2TZY6V8XFxaJ///4CgJg0aZLQ6XS2KN9lWOo8/fbbb2LXrl1V9l9cXCxeeOEFAUCEhYWJoqIiqzwPV2Ctv38VtmzZIgCI4cOHW7Jsl2Op8/T777+LxMTEKvs/fPiw6NSpkwAgnnjiiQbVyEDcCCtWrBAymUxIkiSGDRsmJk6cKPz9/QUAMXfu3GofA0AAEOfOnauyXKlUin79+om77rpL3HbbbaJ79+6m7SdNmmT2RpLqx1LnKisrSyiVSgFAxMTEiPvuu6/a28qVK23zxJoYS76mhBCiX79+pluzZs0EANG2bVvTsocfftjKz8h5lZSUiH79+gkAonnz5mLSpEmm+yEhISIlJcVs+3nz5tUYbrOzs0VUVJQAIKKiosRdd90lunbtKgCI9u3bi9zcXBs9q6bHkudp8eLFptdGz549zf5fqrgdPHjQRs+s6bHUuZozZ44AIORyuZg8eXKN/w9Rw1jqPFUsb9mypRg9erSYPHmyiIuLE4GBgQKA8PPzE9u3b7fhM2t6LPn3rzoMxJZh6ddURESEGDdunLj77rtF3759hUKhEADE0KFDhVqtblCNDMSNtHPnTjFq1Cjh7+8vPD09Re/evcWSJUtq3L6mN+8ffvihuP3220Xbtm2Fl5eXUCqVomXLlmL8+PEMVxZiiXN17tw50/Ib3ebNm2f9J9REWeo1VXldTbfrP5Ukc2q1Wrz88ssiKipKKJVKERYWJqZNmybS09OrbFvbG43c3Fzx+OOPi/DwcKFUKkV4eLh44oknRF5ennWfhAuw1HmqWHejW3W9NKjuLHGu7rvvvjr9P0QNZ4nzdPjwYTF37lzRp08f0axZM6FQKISPj4/o0aOHeP7558WlS5ds9GyaNkv+P3U9BmLLscR52r17t5gxY4aIiYkRQUFBQqFQiMDAQDF06FCxePHiRvWWkYRgh3giIiIiIiJyPRxUi4iIiIiIiFwSAzERERERERG5JAZiIiIiIiIickkMxEREREREROSSGIiJiIiIiIjIJTEQExERERERkUtiICYiIiIiIiKXxEBMRERERERELomBmIiIrEqSJLObTCaDv78/Bg0ahC+//BJCiEYfY+jQoZAkCefPn298wRYwf/78Ks/bx8cH4eHhGDFiBObPn+8wtTZWZGQkJEmydxk1ys/PR1BQEO688057l2KyatUqSJKEn376yd6lEBG5PIW9CyAiItdw3333AQD0ej1SUlKwa9cu7Ny5E4mJiVi+fLmdq7OO7t27IzY2FgBQWlqKrKws7N27Fxs3bsRrr72G2bNn480334RSqbRvoTU4f/482rRpgyFDhmDr1q32LqdB/u///g95eXmYN2+evUsxGT9+PLp3744XXngBt912G9zc3OxdEhGRy2IgJiIim1iyZInZ/Y0bN+LWW2/Fjz/+iClTpmDMmDH2KcyKJkyYgPnz55st0+l0+PHHHzFnzhx88MEHyMrKwrJly+xToAUkJiZCq9Xau4xqZWRk4MMPP8TYsWPRtWtXe5djIkkSnnvuOdxzzz348ssv8fDDD9u7JCIil8Uu00REZBcJCQm49957ARi7kLoKhUKBqVOnYufOnfD29sYPP/yANWvW2LusBouKikLHjh3tXUa1vv76a2g0GvzrX/+ydylVjB8/Hj4+Pvjss8/sXQoRkUtjICYiIrvp0aMHACA9PR2AsYuuJEkYOnRotdtXXJt7fWtzTVJTU/Hwww+jQ4cO8PT0RGBgILp06YIHH3wQycnJVbZPT0/HY489hqioKLi7uyMwMBBjxozB7t27G/T8bqRjx46YM2cOAGDRokVm6250TXRNP6PKP5t9+/ZhzJgxCAoKgiRJSEpKAgAkJSXhmWeeQa9evRASEgKVSoW2bdvikUcewaVLl6rsr02bNgCAbdu2mV0PPW3aNNN2N7qGeM+ePRg/frzpWJGRkdUeCzD2IJAkCfPnz0daWhomT56MkJAQeHh4oHfv3li7du0NfppVCSHw1VdfwcfHB6NHj66yvvLP6+DBg7jlllvg7++PwMBATJo0CRcuXAAAFBcX45lnnkFkZCTc3d3RtWtXrFixotpj7t69GxMmTEBERARUKhXCwsLQt29fPPfcc7h69arZth4eHpgwYQKOHDmCvXv31uu5ERGR5TAQExGR3RQVFQEAVCqVxfednp6Onj17mlrgbr31VgwZMgQqlQqLFy/Gnj17zLbfs2cPunfvjo8//hhubm4YPXo0unbtij///BODBw/G//73P4vXePfddwMwBqmysjKL7HP79u0YOHAgzp8/jxEjRmDw4MGQyYz/3b/55pv44IMPAOD/2bvv8KiqxH3g752aTBoEk0kgIYHQLLSEKh0CurqIKIu7PxdFdC2LChv9gmBZmgKuC+iC7K5K2bWx4lItkEgVCC0LQUqAAAkljZY2ydT7+2MyQ4ZMyCSZyczNvJ/nyZPMuXfOPTPHwbw5556DgQMH4qGHHoIoili+fDl69erlEFR79OiBxx9/HACg1Wrx9NNP278GDhxYZzs+//xzDBo0CBs3bkTnzp3x2GOPQa1WY/ny5UhMTMSpU6ecPu/ChQvo3bs3Dhw4gBEjRqBnz544fPgwHn30UWzdutXl9+HEiRM4f/48+vXrh4CAgFrP279/PwYMGICioiI88MADaNWqFb755huMGDECxcXFGDZsGFavXo3evXujf//+OHHiBMaPH48tW7Y41LNp0yb7642OjsZjjz2Gnj174vr161i4cCGuXr1a49q2P2p89913Lr8uIiJyM5GIiMiDAIjO/ndjsVjE/v37iwDEN998UxRFUTx//rwIQBwyZIjTuv785z+LAMSVK1c6lA8ZMkQEIJ4/f95e9s4774gAxJdffrlGPTk5OeLZs2ftj4uLi8Xo6GhRLpeLn3/+ucO5Bw8eFFu2bCkGBweLhYWFLr1mWzv//Oc/3/E8s9ksqtVqEYCYlZV1x9djU9t7ZLsmAHHhwoVOr7dt2zYxPz+/Rhtmz54tAhCfeeYZl65VXVxcXI3+zc3NFQMDA0W5XC5u2LDB4VpTp04VAYi9evVyeM7KlSvt7X/ttddEs9lsP7Z48WIRgDho0KBa23G75cuXiwDEmTNnOj1e/f1avny5vdxgMIjJyckiAPGee+4Rhw8fLpaVldmPf/rppyIAcfDgwQ71DR48WAQgrl27tsa1Dhw4IJaUlNQoP3bsmNO6iIio6XCEmIiImpTZbMaZM2cwadIk7Nu3D2q1Gs8884zbr1NUVAQASE5OrnGsbdu2SEhIsD9esWIF8vLyMHXqVDz55JMO5/bq1Qtvv/02ysrK8Pnnn7u1jTKZDC1btgQA3Lhxwy11du3aFf/3f//n9NiwYcOg1WprtOGdd95BmzZt3HYv86effoqKigqMHz8ejzzyiMO1FixYgNatW+PQoUPYs2dPjee2a9cO7733nn1UGwBefvlltGzZEunp6S6PpGdmZgIAOnfufMfzBg4ciBdffNH+WKlU4pVXXgEAnDp1CsuXL0dQUJD9+MSJE3HXXXdh3759DouJ3em/t969eyMkJKRGue3ea9uUdiIianpcZZqIiJqEs/tMQ0JCsHr1aodw6i5JSUkAgJkzZ0IulyM5ObnWqbO2qbiPPfaY0+ODBg0CABw4cMDt7RSr9mF2116+v/71r+9Y17Vr17Bx40b88ssvuHnzJsxmMwDAaDTi2rVruH79OsLDwxvVht27dwNAjT8uANbp8b/5zW/w4YcfYvfu3RgwYIDD8aFDh9bYhkqhUKBdu3bIyMjAtWvXEB0dXWcbCgsLAcD+B4fajBo1qkZZ+/btAVjvj+7UqZPDMblcjri4OBw+fBhXr161tyUpKQknT57EhAkT8PbbbyMpKckh1DujUCgQEhKCkpISGAwGn91+i4ioOWMgJiKiJmHbh1gmkyE0NBRdu3bFY489VmdgaaiJEydi69at+M9//oPRo0cjICAAvXv3xoMPPohJkyYhKirKfq5t8arbw9ntnN0H2hgWi8U+MtzYEGrTtm3bWo999dVXeP7552ss8FRdaWlpo9tiuxc5Pj7e6XFb+eXLl2sci4mJcfoc2wirXq93qQ3FxcUOz6tNmzZtapQFBwfXeqz68eptee+993Ds2DFs2rQJmzZtQsuWLTFw4EA88sgj+P3vf1/rH2NCQ0NRWlqKmzdvIjIysu4XRkREbsVATERETcLVlaHvxGKxuHyuXC7HmjVr8MYbb2DDhg3Ytm0b9u/fj927d2PBggX48ccfcf/99zvUO27cOIfpsbdz9/ZCx48fh8FggEajqTU83q6u96C24JWTk2NfHXrJkiV4+OGH0aZNGwQGBgIA7r//fuzbt88+Yu1JdxrBrmtU1VVhYWEAbi3c1pDr1actsbGxOHToELZt24bNmzdj586d9nD8/vvvY9++fWjVqlWN59mCe4sWLVy+FhERuQ8DMRER+QzblNHaRjBt2zPVR8+ePdGzZ0/MmjULJSUlmDVrFhYvXoypU6fap0DHxMQgKysLb7zxhn2qdVOwrVw9cOBAKBS3/pd8p/ehIe8BAHz//fcwGAx4/fXXMWXKlBrHz50716B6nWndujWysrKQk5ODe++9t8Zx24h8bSOw7mAbbb1+/brHrnE7hUKBUaNG2adh5+TkYNKkSdi2bRsWLlyI999/3+F8o9GIsrIyhIaGcro0EZGXcFEtIiLyGXfddRcUCgXOnz8Pk8nkcMxoNGLnzp2Nqj80NBTz58+HIAj45Zdf7OUjR44EAKxbt65R9dfHqVOnsGTJEgCoEVBt96WePn26xvNSU1MbdD3b1GxnU5J37dqFgoKCGuW2kHZ7X9TFds/1V199VeOYwWDAN99843CeJ3Tv3h0AnO433VTi4uIwffp0AHD4783GtvVUjx49mrJZRERUDQMxERH5DJVKhf79++P69etYtmyZvdxkMuG1117D+fPnXa7r3//+t9MQ8sMPP0AURcTGxtrLXnjhBURGRuL999/HP//5zxrTkk0mE7Zs2eK0vvoymUz44osvMGjQIJSXl+Opp57CQw895HDOkCFDAAB//etfodPp7OXbtm2zh+j6si0O9fnnn6O8vNxefvnyZYdVlqu76667oFQqkZ2dbV98yxXPPvssAgMD8fXXXzvssWuxWDBz5kxcvnwZSUlJdd6z3Ri2sH3w4EGPXaO6xYsXIz8/v0b5999/DwAO/73Z2GYo2PqbiIiaHqdMExGRT3nnnXfwwAMPYOrUqVizZg2ioqJw+PBh6HQ6PP3001i9erVL9Xz77bd46qmnkJCQgK5duyIwMBDnz5/H/v37IZPJMG/ePPu5LVq0wIYNGzB69Gi88MILmDdvHu677z60bNkS+fn5yMjIwM2bN7Fu3Trcd999Lr+W9evX26cHV1ZWoqioCIcOHUJJSQlkMhlee+01zJ8/v8bzfve73+H999/H3r17cffdd6N37964dOkSDh48iJSUFHzwwQcut8HmkUcewb333otDhw6hQ4cOGDBgACorK7F9+3b06NED999/P/bu3evwHJVKhQcffBCbNm1C9+7dkZiYCJVKhQEDBtxxq6y2bdviH//4ByZOnIjRo0djwIABiI2NRUZGBrKysqDVat2+hdXt7r77brRr1w779+9HZWVlrfdWu8vs2bPx+uuvo3v37ujYsSNEUcTRo0dx+vRphIeH4/XXX6/xnB07dgAAHn74YY+2jYiIascRYiIi8inJycnYuHEjevfujYyMDOzcuRP9+vXDwYMHXV54CgBSUlIwefJkhISEYPfu3Vi3bh0KCwvxxBNPYP/+/fjNb37jcH6/fv1w7NgxTJs2DaGhodi5cyfWr1+PnJwcDBkyBKtWrXK6x+ydHD16FKtXr8bq1auxefNmZGVloW/fvpg1axbOnTuHDz74AEqlssbzAgMD8dNPP+F3v/sdSktL8f3338NsNmPNmjWYPHlyvdpgo1KpsHv3brz00ksICAjA5s2bcfLkSbzyyitITU112g7AuqfwhAkTcO3aNXz55Zf47LPPXJq6PmHCBOzevRu//vWvcfLkSaxduxYVFRV46aWXcPjwYbcvUHY7QRDw3HPPobS0FJs2bfLotQDgb3/7G377299Cp9Phhx9+wI8//giFQoGUlBRkZmaiY8eODudXVFRg/fr16NatG/r27evx9hERkXOC2BTLSRIRERE1sfz8fLRr1w7JyclNEorr46uvvsL/+3//Dx9//DFeeuklbzeHiMhvMRATERFRszVt2jR88MEHOHr0KLp27ert5gAARFFEz549UVZWhhMnTnCFaSIiL+KUaSIiImq2Zs6ciZYtW2L27Nnebordhg0bcPToUbz33nsMw0REXsYRYiIiIiIiIvJLHCEmIiIiIiIiv8RATERERERERH6JgZiIiIiIiIj8EgMxERERERER+SUGYiIiIiIiIvJLDMRERERERETklxiIiYiIiIiIyC8xEBMREREREZFfYiAmIiIiIiIiv8RATERERERERH6JgZiIiIiIiIj8EgMxERERERER+SUGYiIiIiIiIvJLDMRERERERETklxiIiYiIiIiIyC8xEBMREREREZFfYiAmIiIiIiIiv8RATERERERERH6JgZiIiIiIiIj8EgMxERERERER+SUGYiIiIiIiIvJLDMRERERERETklxiIiYiIiIiIyC8xEBMREREREZFfYiAmIiIiIiIiv8RATERERERERH6JgZiIiIiIiIj8EgMxERERERER+SUGYiIiIiIiIvJLDMREROS30tPTIQgC5HI5Ll686HDs2rVrEATBpa8nn3zSS68AMJvNWLBgATp06AC1Wo0OHTpgwYIFMJvNdT730KFDmDp1Krp164aQkBBERUVhxIgRSEtLc3r+5cuX8fzzz6N9+/YIDAxEfHw8nnrqKZw+fbpR9RIREXmLIIqi6O1GEBERecNLL72EFStWwGAw4N1338XMmTPtx65du4YffvjB4fwZM2agvLwcH330kUN5t27d0K1btyZp8+3++Mc/Yvny5XjmmWdw//33Y8+ePVi1ahX++Mc/YtmyZXd87rhx47Bz5048/vjjSExMRFlZGVauXIlffvkFH3/8MV566SX7udevX8d9990HvV6Pl156Ce3atcPZs2exfPlyCIKAY8eOISYmpt71EhEReRMDMRER+SW9Xo/o6GiMHDkSFy5cwM2bN5GVlVXr+WazGcHBwbj//vvx008/NWFLa3fs2DF0794dr7zyCj788EN7+ZQpU/C3v/0NR48eRdeuXWt9/p49e9CrVy+o1Wp7WUVFBXr06IGioiIUFhZCoVAAAJYvX44//vGP2LhxI0aPHm0//9tvv8W4ceOwePFiTJ06td71EhEReROnTBMRkV/auHEjbty4gSeffBJPPvkkTp8+jfT09FrPP3XqFCorK9GjR4+ma2Qdvv76a4iiaA+iNlOnToUoilizZs0dnz9gwACH0AoAgYGB+PWvf40bN24gPz/fXl5cXAwAiI6Odji/devWAACNRtOgep1ZtWoVBEHAjz/+iHfeeQcxMTEICgrCAw88gNzcXADA3/72N3To0AEBAQHo27cvjh496lCHTqfDzJkz0bFjRwQGBqJly5bo2bNnnaPmRETkX/jnWSIi8kurV69GeHg4HnzwQdy4cQMpKSlYvXo1+vXr5/R8W+ByRyC2WCy4fv26S+cqlUqEhYU5PXbo0CFotVq0a9fOobxdu3aIjIzEoUOHGtS+K1euQKFQoEWLFvay4cOHAwBeeeUVfPDBB4iPj8fZs2eRkpKCTp064be//W2D6r2TmTNnIiAgANOmTcOlS5ewePFijB07FuPHj8eXX36JyZMno6SkBO+//z4ef/xxZGVlQS6XA7BOJf/yyy/x4osvolu3bigvL8fx48exe/duTJ48ud7vCRERNU8MxERE5HcKCgqwZcsWTJo0CSqVClqtFsnJyVizZg2WLFlSY3QTAI4cOQIA6N69e6Ovn5ubWyPE1mbIkCHYsWOH02NXrlxBmzZtnB5r06YNLl++XO+2nTx5Ev/973/xyCOPIDg42F7ep08ffPzxx3jzzTcxcOBAe/ngwYOxdetWhIaGNqjeO5HL5di1a5d9erXRaMSSJUtw48YNHD9+HIGBgQCso9PTpk3DTz/9hFGjRgEANmzYgD/84Q817vcmIiKqjoGYiIj8zhdffAGTyYT/9//+n73sySefxJYtW7Bx40b85je/qfGcI0eOQK1W4+6772709aOiopCamurSuS1btqz1mE6nQ0hIiNNjAQEBKCkpqVe7iouL8fjjjyMwMBCLFi2qcTwmJgZ9+/bFyJEj0aFDBxw7dgx/+ctfMGbMGGzZsgVBQUENqrc2f/jDHxzuNR4wYACWLFmCCRMm2MMwAPTv3x8AkJ2dbS9r0aIF0tPTkZOTg7i4OJevSURE/oWBmIiI/M7q1asRGxuLwYMH28vGjh2LwMBArF692mkgPnr0KO655x4olcpGXz8gIADJycmNrkej0UCv1zs9VllZ6RAa61JRUYHRo0fj3Llz+OGHH2qEyA0bNmDcuHHIyMiwL9T1yCOPoHfv3njggQewbNkyTJs2rd713snt59qmWrdt29ZpefVp6IsXL8aECRMQHx+P++67D8OHD8e4ceMwaNAgl69PRETNHxfVIiIiv3LkyBFkZmbit7/9LQRBsJcHBwfjkUcewZYtW1BQUODwnLy8PBQWFrptQS2z2Yz8/HyXvu50r3Hr1q1rnRZ9+fLlWqdT385gMGDs2LHYt28f1qxZg2HDhtU4Z8mSJejYsWONVatHjRqFkJAQ7Nq1q0H13ontfmBXy6tvnPHoo4/i/PnzWLlyJZKSkvDNN99g8ODBePHFF+vVBiIiat4YiImIyK+sXr0agHWK9O2efPJJmEwmfPHFFw7ldd0/bDAY0KJFC8hkMgQHByM4OBgKhQIBAQH45ptvapx/8eJFREdHu/T12GOP1fpakpKSUFBQgPPnzzuUnz9/HoWFhUhKSrrjewEAJpMJ48ePR2pqKlatWoUxY8Y4Pe/KlSswm801ykVRhNlshslkalC9nnTXXXdh4sSJWLVqFXJzczF+/Hj84x//wLlz55q8LURE5Js4ZZqIiPyGyWTCl19+iXvuucdpuH3wwQfRqlUrrF69GikpKfZyWyCubYRYpVLhb3/7Gz755BP7SGliYiJmzZqFRx55pMb57rqH+IknnsCCBQuwZMkSh32IlyxZAkEQ8MQTT9jLjEYjsrOzERYWZt86yWKx4Pe//z02bNiAv//9707/SGDTpUsXbNq0Cfv27bPfswtY9yHW6XTo1auXvaw+9XqC2WxGaWmpw2rWCoUCXbt2xX/+8x/cuHGjSdtDRES+i4GYiIj8xg8//IDCwkJ07NgRCxYscHpOdHQ0MjMzceTIEXsArisQA8CxY8fs04lNJhNOnDhRY3qxjbvuIe7evTuef/55fPTRRygtLcWAAQOwZ88erFy5Ei+88AK6detmP/fy5cu4++678fTTT2PVqlUAgNdffx1r1qzB4MGDERQUhM8//9yh/pEjR0Kr1QIApk+fjh9++AGjRo3CH//4RyQkJODYsWP45z//iaioKLz88sv259WnXk8oLS1F69at8eijj6JHjx5o1aoVTp06hWXLluHee+/1qb2kiYjIuxiIiYjIb9imS+/Zswd79uy547mrVq3CkiVLAFgX1IqPj691P2DAGogfffRRAMCpU6egVCoRHx/vjmbf0dKlS9G2bVt8+umn+OKLL9CmTRu8++67The4ul1GRgYAYNeuXU7vAd6+fbs9uN5///04dOgQ5syZgzVr1uDKlSsIDw/H+PHjMW/ePERFRTWoXk/QaDR49dVXkZaWhh9//BE6nQ4xMTF48cUXMWPGjFrvQSYiIv8jiNVXoCAiIqIGiYmJwTfffIP+/fvjiy++wNKlS7Fv3z5vN4uIiIjugItqERERNdKNGzdw5coV3HfffQCsI8SdO3f2cquIiIioLgzEREREjXTs2DHEx8cjJCQEgHU7pO+++w4//fSTl1tGREREd8Ip00REREREROSXOEJMREREREREfomBmIiIiIiIiPwSAzERERERERH5Je5D7GYWiwVXrlxBSEgIBEHwdnOIiIiIiIgkRRRFlJaWonXr1pDJPDuGy0DsZleuXEFsbKy3m0FERERERCRpFy9eRExMjEevwUDsZrYtN86fP4/w8HAvt4buxGg0YuvWrRg1ahSUSqW3m0O1YD9JB/tKGthP0sG+kg72lTSwn6Tj+vXraNeunT1beRIDsZvZpkmHhIQgNDTUy62hOzEajdBoNAgNDeU/ij6M/SQd7CtpYD9JB/tKOthX0sB+kg6j0QgATXILKhfVIiIiIiIiIr/EQExERERERER+iYGYiIiIiIiI/BIDMREREREREfklyQbiiooKvPPOO+jUqRMCAgLQunVrTJo0CZcvX3a5jlWrVkEQhDq//vWvf3nwlRAREREREZE3SHKV6crKSgwfPhzp6emIjo7GmDFjcOHCBaxcuRKbN29Geno62rdvX2c9HTp0wNNPP+30WHFxMdavXw8AGDhwoDubT0RERERE9WA2m+0rDzeU0WiEQqFAZWUlzGazm1pG9aFUKiGXy73dDAeSDMTz5s1Deno6+vfvj61btyI4OBgAsGjRIrz22muYNGkSduzYUWc9AwcOrDXsLl++HOvXr8eAAQNcCtdEREREROReoigiPz8fN2/edEtdUVFRuHjxYpNs50POtWjRAlFRUT7TB5ILxAaDAUuXLgUALFu2zB6GASAlJQWrV6/Gzp07cfjwYSQlJTX4Op9//jkAYMKECY1rMBERERERNYgtDEdGRkKj0TQqRFksFpSVlSE4OBgymWTvHJUsURSh0+lQWFgIAIiOjvZyi6wkF4j37NmD4uJiJCQkoGfPnjWOjxs3DpmZmdi0aVODA/H58+exd+9eqFQqjB8/vrFNJiIiIiKiejKbzfYw3KpVq0bXZ7FYYDAYEBAQwEDsJYGBgQCAwsJCREZG+sT0acn9l3D06FEAQGJiotPjtvLMzMwGX8M2Ovzwww+jZcuWDa6HiIiIiIgaxnbPsEaj8XJLyJ1s/dnYe8LdRXIjxLm5uQCAmJgYp8dt5Tk5OQ2+BqdLExERERH5Bl+511TKRFGEzqSDyWKCQqaARtG46eeN4Wv9KblAXFZWBqD2vxQFBQUBAEpLSxtU/4EDB3D69GmEh4fj4YcfrvN8vV4PvV5vf1xSUgLA+hcPX/mrBzln6x/2k29jP0kH+0oa2E/Swb6SDvaVZxiNRoiiCIvFAovF0uj6RFG0f3dHfVJRaixFfnk+TBaTvUwhUyAqKAohypAmb4/FYoEoijAajbVOmW7Kz5LkArGn2UaHx48fD5VKVef58+fPx+zZs2uUb9++ndM7JCI1NdXbTSAXsJ+kg30lDewn6WBfSQf7yr0UCgWioqJQVlYGg8HgtnobOnAmRRWWCly3XK9RbrKYcKn0EsJl4QiUBTZpmwwGAyoqKrBr1y6YTCan5+h0uiZrj+QCsW1V6drepPLycgBASEj9/9phMpmwZs0aAK5Pl54xYwZSUlLsj0tKShAbG4thw4a55eZ/8hyj0YjU1FSMHDkSSqXS282hWrCfpIN9JQ3sJ+lgX0kH+8ozKisrcfHiRQQHByMgIKDR9YmiiNLSUoSEhPjctF1PKbhZcMfjJSiBNlTbRK2xqqysRGBgIAYPHlxrv167dq3J2iO5QNy2bVsAwKVLl5wet5XHxcXVu+6tW7eisLAQ7du3x/333+/Sc9RqNdRqdY1ypVLJfxAlgn0lDewn6WBfSQP7STrYV9LBvnIvs9kMQRAgk8ncsiq0bZq0rc7mrtxY7jBN2hmTxYQKcwWClEFN1CpAJpNBEIQ7fl6a8nMkuf8SunfvDgDIyMhwetxW3q1bt3rXbZsu/fvf/76BrSMiIiIiIqqfoUOHYujQoW6ts64wXN/zmivJBeIBAwYgLCwM2dnZOHLkSI3ja9euBQCMHj26XvWWlZVhw4YNABiIiYiIiKh5MVvMOJh/EN+f+x4H8w/CbDF7u0nUSGfOnMEnn3xiXyzsdgqZa5OBq59XV53OfPDBBwgJCUFlZaXLz/ElkgvEKpUKL7/8MgBg8uTJ9nuGAWDRokXIzMzEkCFDkJSUZC9funQpunTpghkzZtRa73//+1/odDr069cPHTt29NwLICIiIiJqQmk5aXjg2wcwacskTN89HZO2TMID3z6AtJw0bzeNGujEiRMYPHgwnn/+eTz77LNOV83WKDR1hmKlTAmNQuNync6MHTsWZWVlkl1UTnKBGADeeust9O3bF3v37kXHjh3xxBNPoF+/fnjttdcQERGBFStWOJx/9epVZGVlIS8vr9Y6ufcwERERETU3aTlpSNmRggKd4+JKhbpCpOxI8dtQbLaI2Jd9DRuOXMa+7GswW1wfEfW2zMxMDB06FIWFhQCAVatWYeLEiTCbHUf9BUFAdFD0HeuKCoqCIAgu1+lMQkICunbtinXr1jXwFXmXJANxQEAAtm/fjrfffhsajQbr169HTk4OJk6ciIyMDLRv375e9eXl5WHbtm1QKpV44oknPNRqIiIiIqKmY7aYseDAAoioGfZsZQsPLPS76dM//pKPgQu34XefpGPK10fwu0/SMXDhNvz4S+2DZ77i1KlTGD58OCIjIzFt2jQAwIoVK/DFF19g0qRJNc4PVYciNiS2xkixUqZEbEgsQtWh9a7TmbFjx2Ljxo0uBWhfI8lADACBgYGYM2cOzp49C71ej7y8PKxcuRIxMTE1zp01axZEUcSqVauc1hUdHQ2TyQSDwcCtkoiIiIioWcgozKgxMlydCBH5unxkFDpfrLY5+inrGiZ/+T/kFTve75pfXImXPs/w+VDcrl07TJgwAdu3b4dWa90uaeLEiVi9ejUee+wxp88JVYeiU8tOiA+LR0xIDOLD4tGxZUeEqkMbXOftxo4di2vXrmHXrl1ueJVNS3LbLhERERERUd2KdEVuPU/qzBYR76edczJeDogABACzN53AyHuiIJf55j7FarUaixcvrlFe16LAgiDUurVSQ+usrkePHoiPj8e6deswbNgwl5/nCyQ7QkxERERERLWL0ES49TypO3jhOgpKDbUeFwHkFVfiwPnrTdeoZmTs2LFYv359vVao9gUMxEREREREzVBiZCK0Gi0EOB/tFCAgShOFxMjEJm6ZdxSW6l08T5rbB3nb8OHDcfHiRZw+fdrbTakXBmIiIiIiomZILpPjjT5vAECNUGx7PL3PdMhl8iZvmzdEhqhdPC/Awy1pntLT09GqVSt06NDB202pFwZiIiIiIqJmKjkuGYuGLkKkJtKhXKvRYtHQRUiOS/ZSy5pe7/hwaENUtYyXW+8hjg4LQJ924U3ZrGZj3bp1GD16NORyaf2BhYtqERERERE1Y8lxyRgWOwwZhRko0hUhQhOBxMhEvxkZtpHLBExLbo/X152CADgsrmULyX8efY/PLqjly86cOYMTJ05gwYIF3m5KvTEQExERERE1c3KZHL2jenu7GV43onMrLPt/PTH3u5MOWy9FhQXgz6PvwYP3RXuxddK1bt06BAcHY+TIkd5uSr0xEBMRERERkd948L4oPHBfNA6cv47C0kpEhlinSXNkuOHWrVuHX/3qVwgIkN7917yHmIiIiIiI/IpcJqB/QiuM6dEG/RNaSTIMT5061e1bHDWkzry8POzfvx9jx451a1uaCgMxERERERERNciGDRugVCrx8MMPe7spDcJATERERERERA3y+OOP48yZMwgNDfV2UxqE9xATERERERFRg0RERHi7CY3CQExERERERORFO3bs8HYT/BanTBMREREREZFfYiAmIiIiIiIiv8RATERERERERH6JgZiIiIiIiIj8EgMxERERERER+QSzxYz/Ff6vya7HVaaJiIiIiIjI69Jy0rDgwAJcuXalya7JQExERERERERelZaThpQdKRAhNul1OWWaiIiIiIiIvMZsMWPBgQVNHoYBBmIiIiIiIiLyoozCDBToCrxybQZiIiIiIiIi8poiXZHXrs1ATERERERERF4ToYnw2rUZiImIiIiIiLxo6NChGDp0qLeb4TWJkYnQarQQIDT5tRmIiYiIiMinmC1mHMw/iO/PfY+D+Qdhtpi93SQin3PmzBl88sknEEX3LUR1/PhxfPrpp/V6zgcffICQkBBUVlY2+LpymRxv9HkDAJo8FDMQExEREZHPSMtJwwPfPoBJWyZh+u7pmLRlEh749gGk5aR5u2lEPuPEiRMYPHgwnn/+eTz77LOwWCxuqTc1NRV/+MMf6vWcsWPHoqysDKmpqY26dnJcMhYNXYRITWSj6qkvBmIiIiIi8gm2fUhvX222UFeIlB0pDMXkPhYzcH43cGyt9buEZiFkZmZi6NChKCwsBACsWrUKEydOhNnsndeQkJCArl27Yt26dY2uKzkuGVse34KPhnzkhpa5hoGYiIiIiLzuTvuQ2soWHljI6dN+wDZlfufFndCb9W6dEgwAOLkJWHIfsPrXwLfPWr8vuQ84sdG91/GAU6dOYfjw4YiMjMS0adMAACtWrMAXX3yBSZMmea1dY8eOxcaNG90SyuUyOXpG9nRDq1zDQExEREREXlfXPqQiROTr8pFRmNGEraKmVn3K/F8P/RXXKq7hQvEFlOhL3FK/8uwPEL55Gii54nigJA/4z1M+H4rbtWuHCRMmYPv27dBqtQCAiRMnYvXq1Xjssce81q6xY8fi2rVr2LVrl9fa0FAMxERERETkda7uQ+rN/UrJs2qbMm8STbhYerHxodhiRuCO2YCTWQj2sh/f8Onp02q1GosXL0ZEhOM2Rb///e8xZswYL7UK6NGjB+Lj490ybbqpMRATERERkde5ug+pN/crJc+505R5m/zy/MZNn87dB1lZ3h3WMBaBkstAzt6GX8OPjR07FuvXr3f/FHcPYyAmIiIiIq+rax9SAQKiNFFIjExs4pZRU6hryjwAGC1G6Ey6hl+kLN/F8+7cDnJu+PDhuHjxIk6fPu3tptQLAzERERERed2d9iG1PZ7eZzrkMnmTt408z9Wp8CaLqeEXCY5y8Txtw68hUTpdzT80GAyGei2SlZ6ejlatWqFDhw7ubJrHMRATERERkU+obR9SrUaLRUMXITku2UstI09zdSq8QqZo+EXa9oclOBpirZOmBSC0DRB3f8OvIUEnT55E+/btHe7/raysxKOPPoqJEye6XM+6deswevRoyOXS+qOVZANxRUUF3nnnHXTq1AkBAQFo3bo1Jk2ahMuXLzeovgsXLuDFF19Eu3btoFarcdddd6F///74y1/+4uaWExEREVFtbPuQrnhgBRYOWogVD6zAj4//yDDczNU1ZR4AlDIlNApNwy8ik6Ni6J+rHtx+narHDy4A/GwWQkJCAvr27Yvx48dj06ZNAIAxY8Zg27ZteOKJJ1yq48yZMzhx4oRXV7puKEkG4srKSgwfPhxz585FWVkZxowZg9jYWKxcuRI9e/bEuXPn6lXfDz/8gHvvvRf//Oc/0apVKzz22GNITEzEhQsX8I9//MNDr4KIiIiInJHL5Ogd1RsPtX8IvaN6c5q0H7jTlHmbqKAoCELtgdkVxg6/gvib1UBotOOB0NbA+H8B9zzSqPqlSKVSYe3atfYQDAC7du3C+vXr8etf/9qlOtatW4fg4GCMHDnSk031iEbMOfCeefPmIT09Hf3798fWrVsRHBwMAFi0aBFee+01TJo0CTt27HCprlOnTuGxxx5DSEgIUlNTcf/9t6ZIWCwWZGRwrzsiIiIiIk+zTZlfcGCBwwJbCkGBNiFtEKoOdc+F7h4N3P1r62rSZQXWe4bj7ve7keHqlEolvv76a0yYMAEbNmzAxo0bkZzs+qyMdevW4Ve/+hUCAgI82ErPkFwgNhgMWLp0KQBg2bJl9jAMACkpKVi9ejV27tyJw4cPIykpqc76UlJSUFlZiW+//dYhDAOATCZDr1693PsCiIiIiIjIqeS4ZAyLHYaMwgxcL72OVvpWiA+LR6A60L0XksmBdoPcW2cTmzp1KqZOneq2+hQKBb744gtUVFQgKCjI5efl5eVh//79ePXVV93WlqYkuSnTe/bsQXFxMRISEtCzZ88ax8eNGwcA9vnvd3Lx4kVs2bIF7du3x0MPPeT2thIRERERUf3YpswPiR0CtVzd6GnS5DqZTFavMAwAGzZsgFKpxMMPP+yhVnmW5EaIjx49CgBITHS+B52tPDMzs866duzYAYvFgvvvvx8mkwn//e9/sWfPHpjNZtx333144okn0LJlS/c1noiIiIiIqBl5/PHH8dBDDyE01E1T2puY5AJxbm4uACAmJsbpcVt5Tk5OnXWdOHECABAcHIxBgwYhPT3d4fibb76JtWvXYtiwYY1pMhERERERUbMUEeHallm+SnKBuKysDACg0Thfct02xF9aWlpnXTdu3AAAfPrppwgODsaXX36JBx98EEVFRZg7dy4+//xzjB07FsePH0ebNm2c1qHX66HX6+2PS0pKAABGoxFGo9H1F0ZNztY/7Cffxn6SDvaVNLCfpIN9JR3sK88wGo0QRREWiwUWi6XR9YmiaP/ujvrcyba6s6+1yxMsFgtEUYTRaKx1z+Km/CxJLhC7k+0/OJPJhH/84x8YP348AKBly5b497//jaysLBw8eBAff/wx3n33Xad1zJ8/H7Nnz65Rvn379lpDO/mW1NRUbzeBXMB+kg72lTSwn6SDfSUd7Cv3UigUiIqKQllZGQwGg9vqdWXgjDzHYDCgoqICu3btgslkcnqOTqdrsvZILhDbVpWu7U0qLy8HAISEhLhcV3BwMH7zm9/UOP7MM8/g4MGD2LlzZ611zJgxAykpKfbHJSUliI2NxbBhw9CqVas620DeYzQakZqaipEjR0KpVHq7OVQL9pN0sK+kgf0kHewr6WBfeUZlZSUuXryI4OBgt2znI4oiSktLERISwoW6vKiyshKBgYEYPHhwrf167dq1JmuP5AJx27ZtAQCXLl1yetxWHhcXV2ddtnPatm3r9EMRHx8PACgsLKy1DrVaDbVaXaNcqVTyH0SJYF9JA/tJOthX0sB+kg72lXSwr9zLbDZDEATIZDLIZI3fHMc2O9RWJ3mHTCaDIAh3/Lw05edIcv8ldO/eHQCQkZHh9LitvFu3bnXWZdu2yXYv8e2uX78OAA57HRMREREREVHzILlAPGDAAISFhSE7OxtHjhypcXzt2rUAgNGjR9dZ1/33349WrVohPz8fWVlZNY7bpko72++YiIiIiIiIpE1ygVilUuHll18GAEyePNl+zzAALFq0CJmZmRgyZAiSkpLs5UuXLkWXLl0wY8YMh7oUCgVSUlIgiiImT55sXyEaANLS0rBq1SoIgoAXXnjBw6+KiIjI88wWMw4VHMJRw1EcKjgEs8Xs7SYRERF5leTuIQaAt956C2lpadi7dy86duyIQYMGIScnB/v370dERARWrFjhcP7Vq1eRlZWFvLy8GnX93//9H7Zv3460tDR06tQJ/fr1w9WrV5Geng6z2Yx3330Xffr0aaqXRkRE5BFpOWlYcGABCnQFAIBvfvoGWo0Wb/R5A8lxyV5uHRERkXdIboQYAAICArB9+3a8/fbb0Gg0WL9+PXJycjBx4kRkZGSgffv2LtelVCrx/fffY+HChbjrrruwZcsWHDt2DEOGDMGmTZswc+ZMD74SIiIiz0vLSUPKjhR7GLYp1BUiZUcK0nLSvNQyIiIi75LkCDEABAYGYs6cOZgzZ06d586aNQuzZs2q9bhSqcS0adMwbdo0N7aQiIjI+8wWMxYcWAARYo1jIkQIELDwwEIMix0GuUzuhRYSERF5jyRHiImIiMg1GYUZNUaGqxMhIl+Xj4xC57s3EBERNWcMxERERM1Yka7IrecRERE1JwzEREREzViEJsKt5xERETUnDMRERETNWGJkIrQaLQQITo8LEBCliUJiZGITt4yIiGyGDh2KoUOHersZfomBmIiIqBmTy+R4o88bAFAjFNseT+8znQtqETUQ9/cmbzl+/Dg+/fRTt9Z55swZfPLJJxDFmgsx1uaDDz5ASEgIKisr3dqWpsJATERE1MwlxyVj0dBFiNREOpRrNVosGrqI+xATNVBaThoe+PYBPP/T8/hG9w2e/+l5PPDtA9zKjJpEamoq/vCHP7itvhMnTmDw4MF4/vnn8eyzz8Jisbj0vLFjx6KsrAypqalua0tTkuy2S0REROS65LhkDIsdhgNXDiB1XypG9h+JPq37cGSYqIFs+3vfvqWZbX9v/rHJt5ktZmQUZqBIV4QITQQSIxP9+t/DzMxMJCcn49q1awCAVatWwWQyYeXKlZDL7/y+JCQkoGvXrli3bh1Gjx7dFM11KwZiIj8kms3QHToMU1ERFBER0PRKglDHP3ZEJH1ymRy9tL1QqCpEL20vv/7lz534i7X/4f7e0paWm4b3D77vsCWdVqPFG33eqNcfMURRhM6kg8ligkKmgEahgSA4X6/Bl506dQrDhw9HVFQUnn32WSxYsAArVqzAs88+C0EQsHr16jrrGDt2LJYtWwaz2VxngPY1DMREfqZk61YUvDcfpvx8e5kiKgramTMQOmqUF1tGRCQ9aTlpWHBgQaN/sSZpqc/+3r2jejdhy6guO6/sxNsH3270yH6JvgR55XkwWUz2MoVMgeigaISqQ93ebk9q164dJkyYgJkzZ+KLL74AAEycOBEKhQIhISEu1TF27FjMmTMHu3btwrBhwzzZXLfjPcREfqRk61ZcnjLVIQwDgKmgAJenTEXJ1q1eahkRkfTYpszeHoxsv1j72n2kZosZB/MP4vtz3+Ng/kEu/tQI3N9bmswWMz489mGtI/sAsPDAwjo/GyX6ElwsvegQhgHAZDHhYulFlOhL3NfoJqBWq7F48WJERDhuv/f73/8eY8aMcamOHj16ID4+HuvWrfNEEz2KgZjIT4hmMwremw84WzWwqqzgvfkQzfwFiYioLnVNmQVc+8W6qdgWf5q0ZRKm756OSVsmcfGnRuD+3tKUUZiBosra/0hRfWS/1nNEEXnleXe8Tn55fr1WaW4uxo4di/Xr10vutTMQE/kJ3aHDNUaGHYgiTPn50B063HSNIiKSqPpMmfU2qY1kSwH395amqxVXXTrvTiP7tnuG78RoMUJn0tWrbc3B8OHDcfHiRZw+fdrbTakXBmIiP2Eqcm3alqvnERH5M6lMmZXaSLZUcH9vabor8C6XzrvTyH5dYbi+5zWETlczbBsMBpi9PMsvPT0drVq1QocOHbzajvpiICbyE4oI16ZtuXoeEZE/k8qUWSmNZEsN9/eWnsTIREQERDRqZF8hc21NYlfPq6+TJ0+iffv2DvfqVlZW4tFHH8XEiRM9ck1X2bZd4irTROSTNL2SoIiKgqmgwPl9xIIAhVYLTa+kpm8cEZHE2KbMFuoKnY6+ChCg1Wi9PmVWKiPZUsX9vaVFLpNjStcpePvg2xAgOHx2XR3Z1yg0UMgUdxwBVsqU0Cg07mt4NQkJCejbty/Gjx+PwYMHAwDGjBmDnTt3Yu3atR65pivOnDmDEydOYMGCBV5rQ0NxhJjITwhyObQzZ1Q9uO0vo1WPtTNncD9iIiIXSGXKrFRGsqXMtr93d1V37u8tAUNaD8EHQz5o8Mi+IAiIDoq+4zlRQVEe249YpVJh7dq1GDNmDLZt2wYA2LVrF9avX49f//rXHrmmK9atW4fg4GCMHDnSa21oKI4QE/mR0FGjgA+X1NyHWKvlPsRERPVkmzLrbB/i6X2m+8SUWamMZBM1peS2yRjRdoR11WldESI0EUiMTHT5jxmh6lDEIrbGPsRKmRJRQVEe34dYqVTi66+/xoQJE7BhwwZs3LgRycne/fdm3bp1+NWvfoWAgACvtqMhGIiJ/EzoqFEIGTHCuup0UREUERHQ9EriyDARUQPYpsw29BdrT7ONZKfsSGnwFFGi5kguk6N3VO8GPz9UHYoQVYh91WmFTAGNQuOxkeHbKRQKfPHFF6ioqEBQUJDb6p06dSqmTp1ar+fk5eVh//79ePXVV93WjqbEQEzkhwS5HEF9+3i7GUREtTJbzD4bMm/X2F+sPU0KI9lEUiQIAoKU7guj9SWTydwahhtqw4YNUCqVePjhh73dlAZhICYiIiKfkpaT5jS8vdHnDYa3BvL1kWwikq7HH38cDz30EEJDPTtV3FMYiImIiMhnpOWkIWVHSo37XQt1hUjZkcLtbBrB10eyiUiaIiS+ZScDMREREfkEs8WMBQcWOF38SYQIAQIWHliIYbHDOLJJRM3Kjh07vN0Ev8Vtl4iIiMgnZBRmOEyTvp0IEfm6fGQUZjRhq4iIqDnjCLGfktJiJUTkXvz8ux/fU/co0hW59TwiIqK6MBD7IS5WQuS/+Pl3P76n7hOhce0+NFfPIyIiqgunTPsZ22Ilt09Jsy1WkpaT5qWWEZGn8fPvfnxP3SsxMhFajda+P+7tBAiI0kQhMTKxiVtGRETNFQOxh/yv8H8wW8zeboaDuhYrAYCFBxb6XLuJqPH4+Xc/vqfuJ5fJ8UafNwCgRii2PZ7eZzqnoxMRkdswEHvIqztfxQPfPuBTowNcrITIf/Hz7358Tz0jOS4Zi4YuQqQm0qFcq9FyyyUiInI73kPsQb62ZyIXKyHyX/z8ux/fU89JjkvGsNhhXKiMiIg8joHYg3xtz0QuVuJZotkM3aHDMBUVQRERAU2vJAhy/vLWGKLZDN3Bgwg5cgS6iAiE9u3rs++pr/c/P//ux/fUs+QyOXpH9fZ2M4iIqJljIPaw6lPmvP0/dttiJYW6Qqf3vAkQoNVouVhJA5Rs3YqC9+bDlJ9vL1NERUE7cwZCR43yYsukq/p7Gg3gyldfo9BH31Mp9D8//+7H95SIiEj6eA9xE/GFKXNcrMQzSrZuxeUpUx3CEACYCgpwecpUlGzd6qWWSZeU3lOptJWff/fje0pERCR9DMRNxFemzHGxEvcSzWYUvDcfEGuODtnKCt6bD9HMVWZdJaX3VEptBaT3+TdbzDiYfxDfn/seB/MP+uRqzVJ7T4mIiMgRp0x7mC9OmeNiJe6jO3S4xsigA1GEKT8fukOHEdS3T9M1TMKk9J5Kqa02Uvn8p+WkYcGBBQ6rOGs1WrzR5w2fC5lSeU+JiIioJskG4oqKCsyfPx9ff/01cnNzER4ejgcffBBz585FmzZtXK4nPj4eOTk5tR4/efIkunTp0qA2+vKUOS5W4h6mItemwrt6HknrPZVSW6vz9c9/Wk4aUnak1Lgv19dW7q/O199TIiLybUOHDgUA7Nixw6vt8EeSDMSVlZUYPnw40tPTER0djTFjxuDChQtYuXIlNm/ejPT0dLRv375edT799NNOy8PCwhrcTq1Gi+l9pvvcL27kPooI16bCu3oeSes9lVJbpcJsMWPBgQVOF6nytZX7iYiIvOn48ePYt28fnnvuOa/W+cEHH2D27NkoKipCQECA29rSVCQZiOfNm4f09HT0798fW7duRXBwMABg0aJFeO211zBp0qR6/3Vl1apVbm3jR0M+wrDO/IWtudP0SoIiKgqmggLn95EKAhRaLTS9kpq+cRIlpfdUSm2ViozCDIdp0rfzpZX7iYiIvCk1NRV/+tOf3BqIG1Ln2LFj8X//939ITU3F6NGj3daWpiK5RbUMBgOWLl0KAFi2bJk9DANASkoKunXrhp07d+Lw4cPeaiIAoGdkT4ZhNzAZDcj44V/YuWIeMn74F0xGg7eb5ECQy6GdOaPqgXDbQetj7cwZPrUfra+T0nsqpbZKhasr8vvCyv1ERCRdotmM8v0HULz5O5TvP+AzC2BKUUJCArp27Yp169Z5uykNIrkR4j179qC4uBgJCQno2bNnjePjxo1DZmYmNm3ahKQk743K6LOyUFF9unX1wSOHkSTRebmTn0WHstsuKACCIAAymfUXcUFmLXP2WCYDIECQCdZjVecIguNjCMKt58usfzu59VgOQS4D5HLrL/symfX5bvTzFx9A9uFKtCyxILCq7EDoQlimPIOBT77u1ms1RuioUcCHS2ruQ6vV+tQ+tFIipfdUSm2VAldX5PeVlfuJiEh6SlNTUTh/geP/t6Oi+P/tRhg7diyWLVsGs9kMucQGAiQXiI8ePQoASEx0vmqzrTwzM7Ne9f7lL39BdnY21Go17r33XowdOxYRjbjv7/LTE1Essf8YGk0utwZmheK273IIsqrgLK/tu8zhnJuFlxB+/nKNS7QosQBzP0PGd3vQ+p5eEBQKCEolBKXCej2lEoJC6VBuLbvtuFIBsyAgIDcXlSdOwBwYaC+3nS8olRBUKutXHX0ZOmoUQkaMsK46XFQERUQENL2SODLYCLb3tGT/fhxOTUXSyJEI7dvXJ99T9r/7JEYmQqvRolBX6PQ+Yl9cuZ+IiKSjYvsO3Jw5s8atTqaCAlyeMhX4cAlDcQOMHTsWc+bMwa5duzBs2DBvN6deJBeIc3NzAQAxMTFOj9vK77RytDPTpk1zePynP/0Jf/vb3zBp0qQGtBKQR0RAoVQ6TqN0+Ln6j7Wd4+TnWuqACMBiAUQRomi59dhisf5SaRGtH3z7Obf9bHtssVh/Ba06BovF9RdtNlunmxiNTn6NrR9VLeW2lxyYcQo3Mk418ipAWwCXln1c94kKBQSVCjJbSFarb4VltQoyparaY+ux4o0brM9RVTu36nxredW5ymr1qNUQ1AGQBQZACAiwPq76DqXS7aPwvk6Qy6Hp3RulRUXQ9O7t0wFTkMt9ZmslKZPL5HijzxtI2ZECAYJDKPbllfuJiMj3iWYzShYvdr7uhygCgoCC9+YjZMQIn/6dwxf16NED8fHxWLduHQOxp5WVlQEANBqN0+NBQUEAgNLSUpfqe+SRRzBs2DAkJSUhIiIC586dw4oVK/Dhhx/iueeeQ6tWrTBmzJhan6/X66HX6+2PS0pKAACt//stWrVq5VIbfJ1DYK76WTSbreHZ9t1kBixVgdgWjM0WiGbTrfPMZohmC2A2QbRYAJMZosV6XvWy80d2IvDzTXW2yzCiHyLb3g3RaARMJogmE0SjEaLJZH1c9bNoMllDuskE0WSEaDQBJiMsRhMqSkoQoFQ6Pd9BVblX7y6RySCo1dawrA6whml7aLb9HGD9ueq4LEANISAQQoDaHrZtx2W25wQGQggMdPyuVHrzlTowVvWF8fY+IZ/jrr4a0noI3h/0Pv5y+C8o1BXayyM1kXg96XUMaT2E/z00Aj9T0sG+kg72lWcYjUaIogiLxQJLfQZpaqE7dAiWwsLaTxBFmPLzUX7wIDR9vPNH7vq8TtvtlO54b9xR56OPPopvv/0WixcvvuMgjsVigSiKMBqNtU6vbsrPkuQCsbt99NFHDo/vvfde/PWvf0WXLl3w/PPPY/r06XcMxPPnz8fs2bNrlG/fvr3W0E53dl1/Ff1cOC8jXEB4l86eaUTVHwAEkwmCyQRZ1XfBZIZgMlq/m03247eOVXtsNkFmNFU7r9pzq44L9uNmyExG62OjETKjEYLRaD3P9ldMiwViRQXMFRWeec3VX75cDotKCYtSBVGthkWlgkWlhKhUVf2sgmj/rnQsU9ZSXvVdVDTsn53U1FQ3v0ryFHf11cvKl3Eh6AJKxVKECCGIV8RDf0yP749975b6/R0/U9LBvpIO9pV7KRQKREVFoaysDAZD4xdWrbh40aXzSnNzYerSpdHXqw+TyQTg1uCaKyorK+v9HE/W2bdvXyxZsgQZGRno2LFjrecZDAZUVFRg165d9td9O51OV+/rN5TkArFtVena3qTy8nIAQEhISKOu8+yzz+Ktt95CVlYWLly4gPj4eKfnzZgxAykpKfbHJSUliI2NxbBhw5rNCHFTOyK/Dnyzr87z7us7CD0eeKjB1zEajUhNTcXIkSOh9KER0epEUQSMRlgqKyFWVkLU660/6/UQKyth0eshVuoh6q3HLfaf9dZjVT+L+sqqOmw/62/VUVEBsaIClooKoOofJcFshrzCDHlFpftflEIBWVCQ9UujgaDRVD3WQKYJsv8sBAVBpgmCGKDGsbNn0aN/f6hCQ63lVcdkQRrr/eHkE9z9mRLNZlRkZMBcVAR5RAQCExM5hc0NpPBvH1mxr6SDfeUZlZWVuHjxIoKDg92yv608NhY3XTgvpG1baEJDG329+lBU/T4T6sJ1dTodNBqN/T0JDQ2FwWCAXC5v8IJW7qjz2LFjaNWqFXr06HHH51RWViIwMBCDBw+utV+vXbtW/xfRQJL7TbJt27YAgEuXLjk9biuPi4tr1HVkMhkSEhJQWFiIvLy8WgOxWq2GWq2uUa5UKvkPYgP1fOBJHJjzAcJKLE73BbMAKA6To88DT1rv024kn+8rlQqouhXA00SDARadDpaqgGzRVUCs0Nl/tpbrrAG6+mP7zxVVz7+trKLi1jR0kwmW4mJYiotdbldrAIX//tzpMUGtvhWwq0L2rccayIODIQsOgSwkGPKQEMiCgm/9HBwCeUgwZCEh1inofnaPtqe44zNVsnVrzZW7uQKoW/n8v31kx76SDvaVe5nNZgiCAJlMBpms8bvFanr1giwyEpaiIuf3EQsCFFotgnr3ti4M6wV1vc6TJ09i2LBhWL58uf33FoPBgMceewytWrXCv//973pf0111rl+/HqNHj67zMyCr2p3mTp+XpvwcSS4Qd+/eHQCQkZHh9LitvFu3bo2+1o0bNwDcui+ZmoZCqYJlyjMQ5n4GCxw3y7bAurCW5dWJUChrW3qLGkpQqSBXqSBv0cLtdYtGozUcl5c7fJkdHutu/ayz/mwuK8W1S5cRplJB1N06LlYFbFGvh1mvh/n69cY1UKmEPCgIspCq8BwcAllIiDVQh4RAFhxUI0TLgoOrHQ+xjlgzVDdaydat1pU+uQIoERG5kSCXI/RPf7KuMi0Ijv+fqfr/t3bmDJ+ejZSQkIC+ffti/PjxGDx4MABgzJgx2LlzJ9auXeu1Os+cOYMTJ05gwYIFDWqDN0kuEA8YMABhYWHIzs7GkSNH0KNHD4fjtk4bPXp0o65z/PhxZGVlQaPRoEsT30NAwMAnX8fPgH0fYpviMDksr070qX2IbcwWMzIKM1CkK0KEJgKJkYlcCbcaQamEXKmEvB5TkMwWMw5cOYDUfakY2X8k+rTuY39PRYOhKkzraoTsW6G6HJayMpjLymApLYO5rBSW0jJYSkutZVVfqJqabr55E+abNxv8GkWZAIsmAEJwEAJbRkAeFgZ5aChkoSGQh1p/loeFQhZi/W49Zv0uDw31qcXMvEU0m1Hw3nyuAEpERB4ROGwoNEsW19yHWKuVxCwklUqFtWvX4ne/+x2+/fZbAMCuXbuwfv16PPjgg16rc926dQgODsbIkSMb1AZvklwgVqlUePnll/Huu+9i8uTJ2Lp1q30Ed9GiRcjMzMSQIUOQlJRkf87SpUuxdOlSjB07FvPnz7eXf//99wgICMDw4cMdrpGZmYnf/va3EEURzz33HFQqjkR6w8AnX4dp/KvITPsapXm5CIluiz7Jv/XJkeG0nDQsOLAABboCe5lWo8Ubfd5AclyyF1smXbe/p9/89I3DeyqoVFCoVEDLlo26jmixWEejS0thLi2FpawclrKqn0vLqn62heeqn6sH6tJSmEpLIJgtECwi5GUVQFkF9PlX690WQaOxhuOQEMjCQq0huvrP1cN19UAdFgaZG+6t8gW6Q4cdfkGpoWoFUN2hw9zmioiIGiRk5EiEJidb/59TVARFRAQ0vZIk84dWpVKJr7/+GhMmTMCGDRuwceNGJCc37vfNxta5bt06/OpXv3LLvd5NTXKBGADeeustpKWlYe/evejYsSMGDRqEnJwc7N+/HxEREVixYoXD+VevXkVWVhby8vIcyg8cOIDZs2cjLi4O3bt3h0ajwblz55CRkQGTyYShQ4dKcti/OVEoVUj81VPebsYdpeWkIWVHisN+qQBQqCtEyo4ULBq6iKG4npryPRVkMsirpj4ro6Mb1tbtf4LSJECjBzR6IKgSCK60fn8q9jF0VLaGubgE5pJiWEpKYC4phbmkBJbiYuv3qu3kRJ0OJp3uzoGwttcREGAdkW7Rwvpl+7l6WYvbjoeF+dyotKmoyK3nEREROSPI5ZL+w6pCocAXX3yBiooKt93e2dA68/LysH//frz66qtuaUdTk2QgDggIwPbt2zF//nx8+eWXWL9+PcLDwzFx4kTMnTsXMTExLtXzwAMP4OLFizh48CD27NmD4uJihIaGYuDAgXjyySfxzDPPNHyltowMhA8fLpm/NFHDmC1mLDiwoEZwAwARIgQIWHhgIYbFDuP0aRdJ6T21t1UADEoBBiVwM/jWcQECzmr24cfHf7xjW0Wz2TrqXFICc3EJLKUl9p+tIbrqWEkxLMUlMJeWOvwMsxliZSVMlZUwFRTUeh1nZMHBroXnaj/LQkM9ttiIIiLCrecRERE1VzKZzO1rHTWkzg0bNkCpVOLhhx92a1uaiiQDMQAEBgZizpw5mDNnTp3nzpo1C7NmzapR3r9/f/Tv398DrQPy/zgZlW3aSOJeBGq4jMIMh2nStxMhIl+Xj4zCDPSO6t2ELZMuKb2n7mqrIJfbA2d9iaJoXXzs5k2Yb9yEubjYfi+0ufgmzDeLHX+u+m4pKQFE0X4ftfHyZdcvKpNZQ3J4OOQtW0DRMhzyli0hhIWhRV4eSi0iVBER1mPh1mOuTunW9EqCIirKGuzvsAKopldSzWNERETU5B5//HE89NBDLm0Z5YskG4ilwJdXRBXNZsneN+FLinSuTdt09TyS1nvqC20VBME+5Rsuzo4RzWaUHzgAY24uhIAAKGNirCPU9iB9K1Rbiothsv18sxgWnQ6wWGC+cQPmqpX4q4sEULB5c812ajRQtGhRFaJbQhHeEvIWLW+F6qpyectwREyZgjyJrQDKf1OJiMhfRUh81hYDsSf56Iqo3N/TfSI0rv0D4Op5JK33VEpttbnT57/Fo4/W+XyLwXBrNPrGdZhv3IDp+nWYb9yE8epVXDxxAtrAAFhuFsN8/TpMN28CRiNEnQ5GnQ7GK1dca6htSna1QCwEBiJ4yBCYr19HSWoqFK1aQdGqFeStWkEWFOS1La/4byoRETXWjh07vN0Ev8VA7Gk+tiIq9/d0r8TIRGg1WhTqCp3e8ypAgFajRWJkohdaJ01Sek+l1FbAPZ9/mUoFWWQklJGRNY4ZjUYc/P57JD70EJRVi3WJVdOyzddt4flG1ejydZhu3IC56rHphjVUm69fty4yZrHUqF/U6VD6ww8o/eGHGscEtRryVuFQhN8Kydbv4Q7BWdGqFeQtWkBQuOd/f/w3lYiISNoYiJuIL6yIyv093U8uk+ONPm8gZUcKBAgOoUiAdbRqep/pXl/8SUqk9J5Kqa3e+vwLggB5SAjkISFAXJxLz7EYDDVHoK/fgOnaVZivXYfp2jWYr12D6do1mK5fh6jTQdTrYbqSB9OVvLovIAjWadutwiG/Q4BW3HUX5BERkNWy9R7/TSUiIpI+BuIm4gsronJ/T89IjkvGoqGLnO5DPL3PdG651ABSek+l0lYpff5lKhVk2kgotTVHoZ2x6HTW0GwLyfbAfB3ma1et369fg+nqNZhv3gRE0Tpiff06gLN1tycsDIq77oIiIsL6vepnc0mxZN5TIiIico6B2NN8aEVU7u/pOclxyRgWOwwZhRko0hUhQhOBxMhEnxgZlCrbe3rgygGk7kvFyP4j0ad1H598T6XQ/8358y/TaKDSaFxaVEw0meyjzqarV633OVcL0PZR6KtXYbp2DTAaYSkuhqG4GIbs7Aa1r3jjBlhKS+xBWn7XXZCp1Q2qi4iIiNyLgdiTfGxFVO7v6Vlymdzr2wA1N3KZHL20vVCoKkQvbS+fCpi38/X+5+ffSlAorCO9ERFA5853PFcUResq20VF1oB89SpMhbd+1p89C/3Jk3Ves/jb/6L42/86lMlCQ2uMOCsiIqCIjIRCa71HWxEZCZlG06jXK2VcuZuIbERnt6aQZPlafzIQe5BCq/WpVUa5vyeR/+Lnv/4EQbDvD63u2LHGcdFsxtkRybW/pwAETSA0/frDfO0qzEVXYSoqgmg0wlJSAkNJSZ2jzrLg4BohWRERCYVWC0VkhLUsIgJCLfc5SxVX7iYiAFBULYBoMpm83BJyJ1t/Kty0wGVj+UYrmqGoj5chZvhwn/prtiCXQztzhnXlUwnt70lEjcfPv/u58p62XrDAIcCJoghLSYl1lLkqIFt/LrJ+FRZavwoKYNHpYCkrg6GsDIZz5+7YFnnLlvaQrIisFp4jtVXfI6Bo1UoS/cuVu4nIRi6XQy6Xo6SkBCEhId5uDrlJSUmJvW99AQOxh2gSE33yF4/QUaOAD5fU/Mu7j41mE5H78fPvfvV9TwVBgDwsDPKwMKgTEu5Yt7ms/FZALrKGZGNhoXXadlVoNhUWQjQaq7ayugH9qVO1VyiX21fObm2xoDAjA+ro1lBEaaGMioYySguFVgtZYGCj3pPG4MrdRFSdIAiIjIxEXl4e1Go1ghq557zFYoHBYEBlZSVktv3uqcmIoojy8nKUlJQgOjq6UX3pTgzEfih01CiEjBjBe7OI/BA//+7nqfdUHhwEeXA7qNu3q/UcURRhvnnzVkgutIZke3CuCs2mq1cBs9n6uKAAwQBKTpxwft2wMCiioqxBWXvruzI6CoqoKCi1WsiCghr12mojpdXQiahphIWFoaKiAlevXkVRIxd+FEURFRUVCAwM9Jkw5m8EQUCLFi0QFhbm7abYMRD7KUEu5y8TRH6Kn3/389Z7KggCFC1bQtGyJdC5U63niWazdUuqwiLor1zGke3b0SUiEpaiIpgK8mHML4AxPx+iTgdzcTHMxcXQZ2XVWp8sJKRqRPm24GwbaY6Kgiw4uN6/cDbn1dCJqGEEQUB0dDQiIyNhNBobVZfRaMSuXbswePBgKJVKN7WQ6kOpVPrMVGkbBmIiIqJmTpDLobTdW9y5E4orKtDqoYccfiEURRGWsjKY8q0B2VSQD2NePowF+TDZHucXwFJaCktpKfSlpdCfqX0fZ5lGYx1Rrh6co6KhbB0NZbT16/aRZq6GTkS1ccc9p3K5HCaTCQEBAQzEZMdATERERNb7m0NCIA8Jcbqqto313uYCa3CuFpjtwTk/H+biYlh0OhjOnbvjgmCysDAoW7e2B2RFlBaysDBYiotrayRXQyciIrdiICYiIiKXWe9tbg91+/a1nmOpqIAxP9+6EFh+VWDOz7N+z8uDMS8PlpISWIqLoS8udmk/ZwCAKCJ48GCU79ljDdDRrSEP9sz9zM0R93YmIqqJgZiIiIjcShYYCHW7dlC3q31BMHNZGUx5eTBeuWINyVfyqsLyFRiyz8F844bT5938z39w8z//uXWt0FD7CLOydTQU0dFQRre2T81WREYy9IF7OxMR1YaBmIiIiJqcPDgY8o4da52ebTEYUPrTNhiysyGaTZAFBjqMMBvz8mApLoalpAT6kpLaFwFTKKxhuU0bKNu0hrJNG6jatKl63MYvAjP3diYiqh0DMZEfMlvMyCjMQJGuCBGaCCRGJkIua96/EBKRtMhUKoT96sE7nmMuK4cpP6/aCPOVqlHnqrL8fMBkgvHiRRgvXnReSW2BubX1Z4VWW2dg9uWpyNzbmchzfPmzT65jICbyM2k5aVhwYAEKdAX2Mq1Gizf6vIHkuGQvtoyIqH7kwUGQd+gAdYcOTo+LZjNMRUUwXr5s/zLYf7ZO1YbRWHdgjoqyjyjfPspckZmJggULfXYqMvd2JvIM3obQfDAQE/mRtJw0pOxIgQjHkYJCXSFSdqRg0dBFDMVE1GwIcrk1zEZFAUk1V6Z2OTBfugTjpUsuX9eUn4/Lr06BZd5chD3+eL33Y3Yn7u1M5H68DaF5YSAm8hNmixkLDiyoEYYBQIQIAQIWHliIYbHDOH2aiPxCQwOz6coV6C9dgim3llHlKnlvvY28ufOgio2BKiYWythYqGJjoIyJhTKmDVQxMZBpNJ56eQC4t3NTEM1m6A4eRMiRI9BFRCC0b19Om23GeBtC8+PxQLxjxw7s2rULeXl50Ov1Ts8RBAGfffaZp5tC5NcyCjMcpknfToSIfF0+Mgoz0DuqdxO2jIjIN90pMJfvP4Dcp5+uuxK9Hoaz2TCczXZ6WH7XXVDFxEAZawvJsVDGxkAVGwuxZctGvwZNryQooqJgKihw/gs893ZulOrTZqMBXPnqaxRy2myzxtsQmh+PBeLi4mKMGTMGu3fvhujsH+BqGIiJPK9I59p0OFfPIyLyZ65OMY58YzrUHTrCeOkijJcuwXDxEowXL8Jw8SIspaUwX72KiqtXUXHkSM0nK5WIDwvDlY0boWrb9lZYrgrQ8pCQOq8vyOXQzpxhncYpCI6huGoqt3bmDI5kNQCnzfon3obQ/HgsEE+fPh27du1Chw4d8OKLL6JTp04IceEfbiLyjAiNa9PhXD2PiMifuTrFOODue2odJTIXF8Nw6RKMFy/BeOnirbB86RKMV64ARiNUV69Cd/UqdHv21ni+PCzMOg27bSyUbdtC1TYOqri2UMbGQhERYb93OXTUKODDJTUXANJqOZLZQJw26794G0Lz47FAvGHDBmi1WqSnpyM8PNxTlyEiFyVGJkKr0aJQV+j0PmIBArQaLRIjE73QOiIiaXHHVGR5WBgCw8IQeO+9NY6JJhMqLl/Gz99+i6Q2bWC+kmcdYb50EcaLl2C+fh3m4mKYi4tR+csvNS+v0UAVGwtV27ZQto2Fqm0cot97F+YbNwCzxd42hrWG4bRZ/8XbEJofj06ZfuihhxiGiXyEXCbHG33eQMqOFAgQHEKxAOsowvQ+07mgFhGRCzw9FVlQKKBs3RoVCQkIfeghKJVKh+PmsnIYL1+CITcXxtyLMFzMhTE3F4acXBjz8iDqdNBnZUGflVWzbqWyamT5VlhWxbW1Pm7dGsJt16KaOG3Wf/E2hObHY4G4Y8eOKC8v91T1RNQAyXHJWDR0kdN9iKf3mc4tl4iI6sGbU5HlwUGQd+6MgM6daxwTDQbr9lFVAdlw8SIMuTkw5uTCcPkyRKMRhnPnYDh3zknFcihbt7besxzXFsrYtrfCckwMZAEBHntNUsJps/6NtyE0Lx4LxK+88gpeeeUVnD17Fh06dPDUZYionpLjkjEsdhgyCjNQpCtChCYCiZGJHBkmImqA0FGjEDJihHUKbVERFBERXp+KLKhUULdrB3W7djWOiWYzjHn5MObmwJCbC0NutbB88SLEykoYL16E8eJFlO/ZU+P5iqgoqOLirF/x8VVfcVDFxEBQqZri5fkETpslX/zsU8N4LBA/99xzOHPmDIYMGYJ58+Zh5MiRiImJ8dTliKge5DI5t1Yi8kPcL9UzBLlcMveJCnI5VDFtoIppg6D773c4JooiTIVFzsNybi4sZWUw5edb743dv9+xYpkMyjZtboXkaoFZGR3V7P4747RZAqT12afaeSwQy6v+ARBFEc8999wdzxUEASaTyVNNISIi8nvcL5XqIggClNpIKLWR0PR2/KOpKIow37wJw4ULMObmQn/hAow5OdBfuADDhRyIOt2tkeXdux3rVSqhjGsLVVzVaHK1wFx9NWyp4bRZoubBY4E4NjZWsv/AERERNSfcL5UaSxAEKFq2hKJlS6BnT4djoijCVFQEw4ULMOTkWL9fyIEh5wKMObnWe5bPZsNwNrtGvTKNBsr4OKjj46GMs363BWZ5ixZN9OoazjZttmT/fhxOTUXSyJGceUEkMR4LxBcuXPBU1UREROQi7pdKniYIApSRkVBGRiKoj+P0Uds9y9aQXD0wX4Dx8mVYdDroT5yE/sTJGvXKW7S4NfW6XTuo2reDun17KNu2hcyH7lcW5HJoevdGaVERNL1783NEJDEeC8RERETkfdwvlbyp+j3LGDjA4ZhoMMBw6ZJ1NPm2wGwqKID55k1U3LyJiqNHHSuVyaCMjYE6vh1U7dtD1S4e6vbtoWrXDvLwcM5QJKJ6adJAfOPGDQBAy5Ytm/KyREREfov7pZKvElQqqNu3h7p9+xrHLDqddWEvW1A+fx76c+dhOH8elrIyGHNyYczJBXbudHieLCzMOu26KiCr27ezji7HxvrVKthE5DqPB+Lvv/8eH374Ifbs2YOKigoAQGBgIAYOHIhXX30VDz30kKebQERE5Le4XypJkUyjQUCXLgjo0sWh3H6/8vkLMJw/5xCUjZcvw1JcjIqjR2uOKsvlUMXE1AzK7dtb74smIr/l0UD8pz/9CR999BHEqvuWwsLCIAgCbt68ia1btyI1NRVTpkzBokWL6l13RUUF5s+fj6+//hq5ubkIDw/Hgw8+iLlz56JNmzYNbvOZM2fQrVs3VFZWYsSIEUhLS2twXURERN7G/VKpOXG4X/m2Kf6WykoYcnJrBGXDuXPWEeecHBhycoDt2x2eJw8LcwzKVT+rYmMhKHh3IVFz57FP+Zo1a/Dhhx8iMjISb731FiZMmICwsDAAQElJCf79739j3rx5+PDDD9GvXz+MHz/e5borKysxfPhwpKenIzo6GmPGjMGFCxewcuVKbN68Genp6WjvZPqNK55//nno9foGPZeIiMjXcL9U8heygAAEdO6EgM6dHMpt+ys7C8rGvDyYi4tR8b//oeJ//3N4nqBUWhf0SkiAOiEB6oT2UCV0gKpdvE8t6kVEjeOxQPzxxx8jICAAu3btQqdOjv8whYaGYvLkyRg5ciR69OiBjz/+uF6BeN68eUhPT0f//v2xdetWBAcHAwAWLVqE1157DZMmTcKOHTvq3ebPPvsMO3bswPPPP49//vOf9X4+ERGRL+J+qeTPqu+vHNSvn8MxS0WFdeT4/Hnoz52zTsU+dw768+chVlRAf+YM9GfOoLT6k2yLeiV0qArJCZDHxUHggAqRJHksEB89ehTDhw+vEYar69SpE4YPH46ff/7Z5XoNBgOWLl0KAFi2bJk9DANASkoKVq9ejZ07d+Lw4cNISnJ9+ldBQQH+7//+DyNHjsTvfvc7BmIiImpWuF8qUU2ywEDn9ypbLDBeyYPhXDb02eegzz4LQ/Y56LOzYSkpsS/qVbZtm/05HQFc+Hi5dTS5QwJU7W3feZ8ykS/zWCA2GAwICgqq87ygoCAYDAaX692zZw+Ki4uRkJCAnrdtDA8A48aNQ2ZmJjZt2lSvQDxlyhRUVFTg448/xqVLl1x+HhERkVRwv1Qi1wgymX27qODBg+3loijCfPUq9NnZ0Gdn20OyPjsb5qtXYcrPhyk/H+V79jjUJw8PhzohAaqE9g4jy4rISG4TReRlHgvECQkJ2LlzJ8rLy2sNxjqdDjt37kRCQoLL9R6tWjUwMTHR6XFbeWZmpst1fv/991izZg3mzJmDDh06MBATERERUQ2CIEAREQFFRITD9Guj0Ygf167FkA4dYL5wwRqUz2XDcDYbxitXYL5+Hbrr16E7eNChPllw8K2Q3KED1B07QN2xIxRaLYMyURPxWCAeP348/vznP+PRRx/Fxx9/jI4dOzocz87OxuTJk1FUVISXX37Z5Xpzc3MBADExMU6P28pzcnJcqq+8vBx//OMf0blzZ0yfPt3ldhARERER2Vg0GgT26AFl796O5TqddSGvc9nQn822BuXsczDk5sJSVobKo5moPOo4kCMLDnYIyOoOHaDq0AGKiAgGZSI381ggfv3117Fhwwb89NNPuOeee5CYmIj4+HgA1rB6+PBhmM1m9OrVC6+99prL9ZaVlQEANBqN0+O20ejS0lKnx2/31ltvIScnB9u3b4eqASsG6vV6h1WpS0pKAFj/Umg0GutdHzUdW/+wn3wb+0k62FfSwH6SDvaVdNyxr5RKKDp3gqJzJ1T/7VU0GGDIzYUx+xwMVaPJ+rNnYawKyhVHjqDiyBGHqmRhYVAlJEDVIQGqDh2h6mBdAVseHu65F9eM8DMlHU3ZRx4LxIGBgdixYwdmzJiBFStW4ODBgzhYbZpIYGAgJk2ahPnz5yMwMNBTzbijQ4cO4aOPPsJTTz2FoUOHNqiO+fPnY/bs2TXKt2/fXmtoJ9+Smprq7SaQC9hP0sG+kgb2k3Swr6SjwX0VF2f9GjEcMJmgunoVqoICqPMLrN8LCqC8dg2W4mJUZmSgMiPD4emm4GAYtJHQa7UwaLXQa6Ng0EbCwt9FneJnyvfpdLomu5ZHdxsPDg7G3/72NyxcuBCHDx/GlStXAACtW7dGUlJSgwKjbVXp2t6k8vJyAEBISMgd6zGZTPjDH/6AFi1a4IMPPqh3O2xmzJiBlJQU++OSkhLExsZi2LBhaNWqVYPrJc8zGo1ITU3FyJEjoVQqvd0cqgX7STrYV9LAfpIO9pV0NEVfWfR6GM+ft48kG7LPwnA2G6bLl6EoK4OirAya7HMOz5FHREDVoYN9VFld9bOs2i4t/oSfKem4du1ak13Lo4HYRqPRYNCgQW6pq23btgBQ68JXtvK4uLg71nPp0iUcOXIEUVFR+M1vfuNw7ObNmwCAw4cP20eOa9vXWK1WQ61W1yhXKpX8oEkE+0oa2E/Swb6SBvaTdLCvpMOjfaVUQt21K9C1q0OxRaezbg119iz0Z637JuvPnoXpSh7MRUWoKCpCxb59Ds9RREdD3bEDAjp3hrpTJ+tXu3YQGnD7oBTxM+X7mrJ/miQQu1P37t0BABm3TRWxsZV369bNpfry8/ORn5/v9NjNmzexc+fOBrSSiIiIiMjzZBoNArveh8Cu9zmUm8vKYDh71hqUz9i+n4GpsBCmvDyY8vJQvmv3rScoFFC3i4e6ky0kd0RAp05QtG7NhbyoWXNbIJ4zZw4EQcDkyZMRHh6OOXPmuPxcQRDw9ttvu3TugAEDEBYWhuzsbBw5cgQ9evRwOL527VoAwOjRo+9YT3x8PERRdHpsx44dGDZsGEaMGIG0tDSX2kVERERE5CvkwcEI7NEDgbf9rmwuLraH48qsLOhPn4H+9GlYSkutwfnMWeC77+zny4KD7QFZ3akTAqpGlOWhoU38iog8w22BeNasWRAEAU888QTCw8Ptj2sLndXVJxCrVCq8/PLLePfddzF58mRs3brVvrL0okWLkJmZiSFDhiApKcn+nKVLl2Lp0qUYO3Ys5s+f37AXSEREREQkcfKwMGiSkqCp9ruyKIow5edDf/o0KrNOQ3+66uv8eeuK1xkZqLhtdqYiOto+iuzuadei2QzdocMwFRVBEREBTa8kCHJ5o+slcsZtgXjlypUAgOjoaIfHnvDWW28hLS0Ne/fuRceOHTFo0CDk5ORg//79iIiIwIoVKxzOv3r1KrKyspCXl+exNhERERERSZEgCFBGR0MZHY3gIUPs5aLBAP35C7cC8unTqDxzGqYrebemXe/cdasihQLqdu1uBeROHRHQuTMU0dEuT7su2boVBe/Nh6naLY2KqChoZ85A6KhRbnvNRDZuC8RPP/30HR+7U0BAALZv34758+fjyy+/xPr16xEeHo6JEydi7ty5iImJ8di1iYiIiIj8gaBSIaBzJwR07uRQbi4psS7edfo0Kk+fhr5qVNlSVmYtP3PGcdp1SAjUHTsioEtnqDt3sX7v1Amy27ZeLdm6FZenTAVum2FqKiiwln+4hKGY3M5ji2rl5uYiODgY4XVsFH7jxg2UlpbaV492VWBgIObMmePSvcqzZs3CrFmzXK576NChLk31JiIiIiL345RZ3yYPDXU+7TovzyEg26ddl5bWnHYtCFDFx0PdpTMCOneBqlNH5M+dVyMMV1UOCAIK3puPkBEj+N8CuZXHAnG7du0wceJEfPbZZ3c8b9q0aVi5ciVMJpOnmkJEREREEsEps9IkCAKUrVtD2bo1Qqq2LQWqT7vOQuWpU9CfPIXKrCyYr12D4fx5GM6fR+kPP9Z9gar7nHWHDiOobx/PvRDyOx4LxKIoujzKytFYIiIiIuKU2ean+rTrsGq7wJiKilB5Kgv6rFOoPJUF3aGDMOUX1FnfzW++gWgyIqBLFyhatfJk08lPeH0f4qtXryLwtvsHiIiIiMi/iGYzCt6bzymzfkIREYHgiAgEDxoIACjffwC5LqxBVLJ5M0o2bwYAyCPuQoDtnuQudyOgS2eo4uMhKLwecUhC3Ppfy65duxwe5+fn1yizMZlMyMrKwpYtW3Dvvfe6sxlEREREJDG6Q4cdpknXwCmzzZqmVxIUUVEwFRQ4/6MIrHsia+6/H4asLBhyc2Euuoryop9R/vPP9nMEtRrqDh2s9yZ3uRsB99wNdecukAcHNdVLIYlxayAeOnSow5LqW7ZswZYtW2o9XxRFCIKA1157zZ3NICIiIiKJMRUVufU8khZBLod25gzr1HhBcAzFVfki+r137VPmLeXl0J85g8pTp6z3Jp/KQuXp0xB1OlQeP47K48dRXO35qrZtoerSBS0FAeVhYQju2pVTrgmAmwPxU089ZQ/Eq1evRkJCAgYMGOD0XJVKhdatW2P06NFITEx0ZzOIiIiISGIUERFuPY+kJ3TUKODDJTUXVdNqayyqJgsKQmCPHgjs0cNeJlosMF68eOve5BMnUXnyJEwFBTDk5MCQk4MIAHk/WhfxUkRGIuDuu6G+524E3H03Au65B8o2bVzeM5maB7cG4lWrVtl/Xr16NQYOHIgVK1a48xJERERE1AzVOWVWEKDQaqHplVTzGDUboaNGIWTEiAZtuyXIZFDFxUEVFwc8cCs8m65fR+WJk9D98gvOb9uGViXFMObkwlRYiLLCQpTt3Gk/VxYaioAuXaoC8t1Q33031O3b877kZsxjPWuxWDxVNRERERE1M65MmdXOnMEFtfyAIJe79T5xRXg4ggcOgLpvH+yLjkLiQw9BbjCgMiurahT5BCpPnoT+zFlYSkqgO3AAugMHbrVHrYa6Uyd7SA64+26oO3WCjAsDe4RoNkNXfc9qD+OfOoiIiIjIJ9RnyixRY8iCgqBJTISm2q2bosEAfXa2fap15cmT0J88CYtOh8pjx1B57Fi1CmRQtW+HgLvvcQjK8rAwL7ya5sO2D/nNy5eb7JoeD8S5ubnYtGkTzpw5g9LSUqd7DguCgM8++8zTTSEiIiIiH9eYKbNEjSGoVNZwe/fd9jLRYoExN9cakKsFZfO1azCczYbhbDZKNm2yn6+MiUHAvfdWfd2DgHvugaJlS2+8HMmpbR9yT/NoIJ4zZw7mzp3rMH3aFohtN6vbVppmICYiIiIiwP1TZokaSpDJoIqPhyo+HqG/+hUAa34xFRah8uQJ6KsFZeOlS/av0mo77SjbtKkWkq1BmSHZ0R33IfcwjwXiNWvWYNasWYiPj8ebb76Jb775BqmpqdiyZQvOnTuHNWvWYMeOHUhJScHo0aM91QwiIiIiIiK3EQQBSm0klNpIhAwdai83Fxej8sQJVB4/jorjx1F5/ASMubkwXr4M4+XLKN261X6usnVrx5B8371+HZLr3IfcgzwWiD/++GOoVCps374dcXFx+Llqw+yRI0cCAF544QUsXrwY06ZNw6OPPuqpZhAREREREXmcPCwMQf37I6h/f3uZuaTEHpJtQdmYkwvjlSswXrmC0tRU+7mK6GgE3HsPAqsFZX/ZK9mb+4t7LBBnZmbi/vvvR1xcHICaU6QB4E9/+hM+++wzzJs3Dz9W7QdGRERERETUHMhDQxHUrx+C+vWzl1lD8kl7SK48fhyGnByY8vJQlpeHsrSf7OcqoqLs06xtQVlx113eeCke5c39xT0WiPV6PaKiouyPAwICAAA3b95Ey2rTAbp3784wTEREREREfsEakvsiqF9fe5m5tLRmSL5wAab8fJTl56Psp2ohWau9tWjXvfci8N57vRoo3aHOfcg9yGOBODo6GoWFhfbHbdq0AQAcP34cAwcOtJdfunQJZrPZU80gIiIiIiLyafKQEAT17eOwmJy5rAz6kyft9yNXHj8Ow/nzMBUUoKygAGXbttnPVWi1COh6HwLv61r1/T5JbQFVYx/yJuSxQNy1a1ccPnzY/njo0KEQRRF//vOfsXHjRgQFBeE///kPdu/ejf7V5tkTERERERH5O3lwMDS9e0PTu7e9zFxWDv2pkw4LdxnOnbsVkqtNt1bGtb0VkLt2RcDdd0Om0Xjjpbik+j7kaA77EI8ePRobN27Etm3bMHz4cAwYMADDhg3D9u3b0bJlS4SGhuLGjRsQBAFvv/22p5pBRERERETULMiDg6Dp1QuaXr3sZZbyclSeOIGKY7+g8pdjqDj2C4wXL1oX78rJRcl331lPlMmg7tDhVkC+rysCOnWEoFJ56dXUZNuH/NK2bcCoUU1yTY8F4t///vcYOHAgIqrNZ1+3bh2mTZuG9evX48aNG7jnnnswY8YMPPjgg55qBhERERERUbMlCwqqMZJsunEDlb8ctwfkymPHYCoqgv70aehPn0bxt/8FAAgqFdRduiDwvvsQ0LUrArveB1W7dhDkcm+9HAhyOTSJiU12PY8FYrVajc6dOzuUhYaG4u9//zv+/ve/e+qyREREREREfk3RsiWCBw1E8KBbazcZCwpQeexWQK44fhyW4mJUZmaiMjPTfp5Mo7Eu2lUVkAO6doWyTRv7TkHNjccCMREREREREfkGpVYLpVaLkORkANbtcI25ubcC8i+/oPLECVh0OugOHoTu4EH7c+UtWyLgvvusAfk+a1CW+srWNgzEREREREREfkYQBKji4qCKi0PYrx8GAIgmE/TZ56qmWh9D5bFfUHn6NMw3bqB8926U795tf74iKsoakLt1Q2C37gi4917Ig4O89XIazG2BePjw4Q1+riAI+Kna3lpERERERETUtASFAgGdOyGgcye0ePxxAIDFYID+1Cl7QK745RgM2edgys9HaX4+SlPTrE+WyaBOSEBA924I7NYNgd27Q52QAEHh22Owbmvdjh07Gvzc5jofnYiIiIiISMpkKpU14HbrZi8zl5Wj8sRx61TrzGOoyMyEKS8P+jNnoD9zBsVrvwUACBoNAu+5pyokd0dgt65QREX5VP5zWyA+f/68u6oiIiIiIiIiHyUPDkJQnz4I6tPHXmYsLERlZqY9IFceOwZLeTl0hw5Bd+iQ/TxFRIRDQA64r6tXp1q7LRDHxcW5qyoiIiIiIiKSEGVkJJTJybcW7TKbYTh3DhXVQrL+9GmYiopQlvYTytKqbpkVBKg7JFjvRe7aDYHdu0EMD2+ydvv2hG4iIiIiIiKSHEEuh7pjR6g7drx1P7JOh8oTJ+wBuSLzKExX8qA/cxb6M2ft+yOXq5RN1k4GYiIiIiIiIvI4mUYDTa9e0PTqZS8zFRWh4tgxVBy1BuTKY79ALC5usjZ5LBDL5XKXzxUEASaTyVNNISIiIiIiIh+kiIhAyPDhCKnatUi0WJCfkQH07t001/dUxbGxsU5XD7NYLMjPz4fRaATAe4+JiIiIiDxNNJuhO3QYpqIiKCIioOmVBKEeA1hETUWQyaBq167JruexQHzhwoVaj1ksFmzbtg1TpkxB165d8dVXX3mqGUREREREfq1k61YUvDcfpvx8e5kiKgramTMQOmqUF1tG5H0yr1xUJkNycjI2b96M7777DgsXLvRGM4iIiIiImrWSrVtxecpUhzAMAKaCAlyeMhUlW7d6qWVEvsErgdimXbt26NevH1asWOHNZhARERERNTui2YyC9+YDoujkoLWs4L35EM3mJm4Zke/waiAGAI1Gg4sXL3q7GUREREREzYru0OEaI8MORBGm/HzoDh1uukYR+RivBuKcnBzs2rULWq3Wm80gIiIiImp2TEVFbj2PqDny2KJa//rXv2o9VlZWhtOnT+Pzzz9HSUkJ/vjHP9a7/oqKCsyfPx9ff/01cnNzER4ejgcffBBz585FmzZtXKrDZDJh3rx5OHjwIE6ePImioiIYjUbExsZi5MiRmD59OlfBJiIiIiJJUkREuPU8ki6uMl47jwXiiRMnOt12yUasum/hqaeewuzZs+tVd2VlJYYPH4709HRER0djzJgxuHDhAlauXInNmzcjPT0d7du3d6me2bNnIzg4GN26dUNSUhIMBgOOHDmC5cuX44svvsBPP/2EXtU2jiYiIiIikgJNryQooqJgKihwfh+xIECh1ULTK6npG0dNhquM35nHAvE777xTayBWqVSIjo7G4MGDXQqut5s3bx7S09PRv39/bN26FcHBwQCARYsW4bXXXsOkSZOwY8eOOusJCAjAzz//jL59+0KhuPVWmM1mvPXWW1iwYAFefPFFHDp0qN5tJCIiIiLyJkEuh3bmDFyeMhUQBMdQXPV7unbmDI4UNmO2VcZv/4OIbZVxfLjE70OxxwLxrFmzPFKvwWDA0qVLAQDLli2zh2EASElJwerVq7Fz504cPnwYSUl3/muXQqHAgAEDapTL5XLMnTsXS5YsweHDh1FcXIywsDD3vhAiIiIiIg8LHTUK+HBJzRFCrZYjhM1cnauMCwIK3puPkBEj/PqPIh4LxJ6yZ88eFBcXIyEhAT179qxxfNy4ccjMzMSmTZvqDMR3IggC5HI5BEGASqVqTJOJiIiIiLwmdNQohIwYwXtI/Ux9VhkP6tun6RrmY5okEO/duxe7d+/GlStXAACtW7fGwIEDnY7O1uXo0aMAgMTERKfHbeWZmZkNbK31/uaFCxeivLwcw4cPR2BgYIPrIiIiIiLyNkEu9+vQ44+4yrhrPBqIjx07hokTJ+LIkSMAbi2kZbu3uHv37li1ahW6devmcp25ubkAgJiYGKfHbeU5OTn1auv06dNRUFCAkpISZGZmIjs7G3fffTc+/fTTetVDRERERETkbVxl3DUeC8RZWVkYMmQIbt68iZiYGIwbNw7x8fEArGH122+/xZEjRzB06FDs3bsXXbp0canesrIyAIBGo3F6PCgoCABQWlpar/Z+++23yM7Otj/u1q0bPv/8c7Rr1+6Oz9Pr9dDr9fbHJSUlAACj0Qij0VivNlDTsvUP+8m3sZ+kg30lDewn6WBfSQf7Shr8rZ+U3btBrtXCXFh4x1XGld27+dx70pTt8VggnjlzJm7evIk33ngDc+bMcVjFGQDef/99vPPOO5g/fz7efPNNfPvtt55qikvOnj0LALh69SoOHz6MN998E0lJSfjkk0/w9NNP1/q8+fPnO902avv27bWGdvItqamp3m4CuYD9JB3sK2lgP0kH+0o62FfS4E/9FDxqJKL//TkAoPr+PyIAiCJyRybjxJYt3mjaHel0uia7liCKzv5c0Hjh4eFo06YNjh07dsfzunbtisuXL+P69esu1ZuSkoLFixfjT3/6ExYtWlTj+NGjR9GjRw8kJibi8OHDDWo7ABQXF6Nr1664evUqsrKyEBsb6/Q8ZyPEsbGxyMvLQ6tWrRp8ffI8o9GI1NRUjBw5Ekql0tvNoVqwn6SDfSUN7CfpYF9JB/tKGvy1n8rS0lC0YCHMBQX2MkVUFO6aPg3ByclebFntrl27hujoaBQXFyM0NNSj1/LYCLHRaHTp3uBu3brhwoULLtfbtm1bAMClS5ecHreVx8XFuVynM2FhYRg9ejQ+/vhjpKamYtKkSU7PU6vVUKvVNcqVSqVffdCkjH0lDewn6WBfSQP7STrYV9LBvpIGf+unlr/6FVqMGiWpVcabsn88Foi7d+/ucE9ubbKzs9G9e/d61QsAGRkZTo/byuuzUFdt7rrrLgBAkZ+vvEZERERERNLFVcZrJ/NUxW+++SYOHjyIFStW1HrOypUrcfDgQcycOdPlegcMGICwsDBkZ2fbV6+ubu3atQCA0aNH17vNt9u5cycAICEhodF1ERERERERkW/xWCAOCgrCSy+9hD/84Q8YPHgwli1bhs2bN2Pz5s1YtmwZhgwZgueeew4vvfQSgoODsWvXLoev2qhUKrz88ssAgMmTJ6O8vNx+bNGiRcjMzMSQIUOQlJRkL1+6dCm6dOmCGTNmONT13XffYe/evTWuodPp8Oabb2Lnzp2IiorCgw8+2Ni3g4iIiIiIiHyMx6ZMDx06FIIgQBRF/Pzzz9izZ4/DcdtaXsuXL8fy5ctrPN9sNtda91tvvYW0tDTs3bsXHTt2xKBBg5CTk4P9+/cjIiKixqi0bWGsvLw8h/KDBw9i9uzZaNOmDXr06IGwsDDk5+fjyJEjuH79OsLCwvCf//wHwcHBDX0biIiIiIiIyEd5LBA/9dRTEASh7hMbICAgANu3b8f8+fPx5ZdfYv369QgPD8fEiRMxd+5cxMTEuFTPY489htLSUuzevRsHDx7E9evXERgYiA4dOuCFF17AK6+8gujoaI+8BiIiIiIiIvIujwXiVatWeapqAEBgYCDmzJmDOXPm1HnurFmzMGvWrBrl3bp1w1//+lcPtI6IiIiIiIh8ncfuISYiIiIiIiLyZR4bIa7OYDDgyJEjuHz5MgDY79lVqVRNcXkiIiIiIiKiGjwaiCsrK/HOO+/gH//4B8rKyhyOBQcH48UXX8Ts2bMREBDgyWYQERERERER1eCxQKzX65GcnIx9+/YBsN6vGx8fD0EQcOHCBRw9ehQffPAB9uzZg59++glqtdpTTSEiIiIiIiKqwWP3EC9evBh79+7FgAEDcOTIEfzvf//DunXr8N///hcZGRk4evQoBg0ahH379mHJkiWeagYRERERERGRUx4LxF999RUiIiLw3XffoWvXrjWO33fffdi8eTPuuusufPHFF55qBhEREREREZFTHgvEZ8+exdChQxESElLrOcHBwRg6dCiys7M91QwiIiIiIiIipzwWiBUKBXQ6XZ3n6XQ6KBRNstg1ERERERERkZ3HAnHXrl2xbds2nDt3rtZzzp8/j23btqFbt26eagYRERERERGRUx4LxC+88AIqKiowdOhQfPbZZ6ioqLAfq6iowMqVKzF06FBUVlbixRdf9FQziIiIiIiIiJzy2FzlCRMm4Oeff8Ynn3yC559/Hs8//zzuuusuAMDVq1cBAKIo4oUXXsCTTz7pqWYQEREREREROeWxEWIA+Mc//oFvvvkGAwcOhFKpRFFREYqKiqBUKjFo0CB88803WL58uSebQEREREREROSUx1ezevzxx/H444/DZDLh2rVrAIBWrVpxIS0iIiIiIiLyKren0u+//x7r16/HxYsXoVar0b17dzzzzDOIoSf27wABAABJREFUj4+HVqt19+WIiIiIiIiIGsStgfjJJ5/E119/DcB6fzAAbNq0CX/5y1/w9ddf45FHHnHn5YiIiIiIiIgazG2B+LPPPsNXX30FhUKBCRMmoGfPnigtLcXmzZuxb98+PPXUU8jJyUFYWJi7LklERERERETUYG4LxKtXr4ZMJsMPP/yAESNG2MtnzJiBZ555Bv/617/w3//+F88884y7LklERERERETUYG5bZfrYsWPo16+fQxi2mTlzJkRRxLFjx9x1OSIiIiIiIqJGcVsgLikpQUJCgtNjtvKSkhJ3XY6IiIiIiIioUdwWiEVRhFwud34RmfUyFovFXZcjIiIiIiIiahS3BWIiIiIiIiIiKXFrIF69ejXkcrnTL0EQaj2uULh9O2QiIiIiIiKiO3JrErXtPdxUzyMiIiIiIiJqKLcFYt4fTERERERERFLCe4iJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/xEBMREREREREfomBmIiIiIiIiPwSAzERERERERH5JQZiIiIiIiIi8ksMxEREREREROSXGIiJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/JNlAXFFRgXfeeQedOnVCQEAAWrdujUmTJuHy5csu13Hz5k18+eWX+N3vfod27dpBpVIhJCQEffv2xYcffgij0ejBV0BERERERETepPB2AxqisrISw4cPR3p6OqKjozFmzBhcuHABK1euxObNm5Geno727dvXWc8HH3yAd999F4IgoEePHujbty+KioqwZ88eHDhwAGvXrsWWLVug0Wia4FURERERERFRU5LkCPG8efOQnp6O/v374/Tp01izZg3279+Pv/71rygqKsKkSZNcqicoKAjTpk3DhQsXkJGRga+//ho//fQTjh07hrZt2+Lnn3/GvHnzPPxqiIiIiIiIyBskF4gNBgOWLl0KAFi2bBmCg4Ptx1JSUtCtWzfs3LkThw8frrOuGTNmYOHChWjbtq1DeceOHbFgwQIAwFdffeXG1hMREREREZGvkFwg3rNnD4qLi5GQkICePXvWOD5u3DgAwKZNmxp1ne7duwMArly50qh6iIiIiIiIyDdJLhAfPXoUAJCYmOj0uK08MzOzUdc5d+4cACAqKqpR9RAREREREZFvklwgzs3NBQDExMQ4PW4rz8nJadR1PvzwQwDAmDFjGlUPERERERER+SbJrTJdVlYGALWu/BwUFAQAKC0tbfA1/v73vyMtLQ0tWrTAG2+8ccdz9Xo99Hq9/XFJSQkAwGg0ctsmH2frH/aTb2M/SQf7ShrYT9LBvpIO9pU0sJ+koyn7SHKB2NN2796NKVOmQBAErFixAq1bt77j+fPnz8fs2bNrlG/fvp3bNUlEamqqt5tALmA/SQf7ShrYT9LBvpIO9pU0sJ98n06na7JrSS4Q21aVru1NKi8vBwCEhITUu+5ffvkFY8aMgcFgwEcffYSxY8fW+ZwZM2YgJSXF/rikpASxsbEYNmwYWrVqVe82UNMxGo1ITU3FyJEjoVQqvd0cqgX7STrYV9LAfpIO9pV0sK+kgf0kHdeuXWuya0kuENu2SLp06ZLT47byuLi4etV7/vx5jBo1Cjdu3MCsWbPwyiuvuPQ8tVoNtVpdo1ypVPKDJhHsK2lgP0kH+0oa2E/Swb6SDvaVNLCffF9T9o/kFtWybYeUkZHh9LitvFu3bi7XmZeXh5EjRyIvLw9TpkzBn//858Y3lIiIiIiIiHya5ALxgAEDEBYWhuzsbBw5cqTG8bVr1wIARo8e7VJ9N27cwAMPPIDs7Gw888wzWLx4sTubS0RERERERD5KcoFYpVLh5ZdfBgBMnjzZfs8wACxatAiZmZkYMmQIkpKS7OVLly5Fly5dMGPGDIe6dDodHn74YRw7dgzjx4/HJ598AkEQmuaFEBERERERkVdJ7h5iAHjrrbeQlpaGvXv3omPHjhg0aBBycnKwf/9+REREYMWKFQ7nX716FVlZWcjLy3Mof/PNN7Fv3z7I5XIoFAo8++yzTq+3atUqT70UIiIiIiIi8hJJBuKAgABs374d8+fPx5dffon169cjPDwcEydOxNy5cxETE+NSPTdu3AAAmM1mfPnll7Wex0BMRERERETU/EhuyrRNYGAg5syZg7Nnz0Kv1yMvLw8rV650GoZnzZoFURRrBNtVq1ZBFMU6v4iIiIiIiKj5kWwgJiIiIiIiImoMBmIiIiIiIiLySwzERERERERE5JcYiImIiIiIiMgvMRATERERERGRX2IgJiIiIiIiIr/EQExERERERER+iYGYiIiIiIiI/BIDMREREREREfklBmIiIiIiIiLySwzERERERERE5JcYiImIiIiIiMgvMRATERERERGRX2IgJiIiIiIiIr/EQExERERERER+iYGYiIiIiIiI/BIDMREREREREfklBmIiIiIiIiLySwzERERERERE5JcYiImIiIiIiMgvMRATERERERGRX2IgJiIiIiIiIr/EQExERERERER+iYGYiIiIiIiI/BIDMREREREREfklBmIiIiIiIiLySwzERERERERE5JcYiImIiIiIiMgvMRATERERERGRX2IgJiIiIiIiIr/EQExERERERER+iYGYiIiIiIiI/BIDMREREREREfklBmIiIiIiIiLySwzERERERERE5JckG4grKirwzjvvoFOnTggICEDr1q0xadIkXL58uV717Ny5E7Nnz8bDDz+MiIgICIKA+Ph4zzSaiIiIiIiIfIbC2w1oiMrKSgwfPhzp6emIjo7GmDFjcOHCBaxcuRKbN29Geno62rdv71JdU6ZMwdGjRz3cYiIiIiIiIvI1khwhnjdvHtLT09G/f3+cPn0aa9aswf79+/HXv/4VRUVFmDRpkst1jRo1CvPmzcOWLVtw/PhxD7aaiIiIiIiIfInkRogNBgOWLl0KAFi2bBmCg4Ptx1JSUrB69Wrs3LkThw8fRlJSUp31vf/++/af8/Pz3d9gIiIiIiIi8kmSGyHes2cPiouLkZCQgJ49e9Y4Pm7cOADApk2bmrppREREREREJCGSC8S2+30TExOdHreVZ2ZmNlmbiIiIiIiISHokF4hzc3MBADExMU6P28pzcnKarE1EREREREQkPZK7h7isrAwAoNFonB4PCgoCAJSWljZJe/R6PfR6vf1xSUkJAMBoNMJoNDZJG6hhbP3DfvJt7CfpYF9JA/tJOthX0sG+kgb2k3Q0ZR9JLhD7mvnz52P27Nk1yrdv315raCffkpqa6u0mkAvYT9LBvpIG9pN0sK+kg30lDewn36fT6ZrsWpILxLZVpWt7k8rLywEAISEhTdKeGTNmICUlxf64pKQEsbGxGDZsGFq1atUkbaCGMRqNSE1NxciRI6FUKr3dHKoF+0k62FfSwH6SDvaVdLCvpIH9JB3Xrl1rsmtJLhC3bdsWAHDp0iWnx23lcXFxTdIetVoNtVpdo1ypVPKDJhHsK2lgP0kH+0oa2E/Swb6SDvaVNLCffF9T9o/kFtXq3r07ACAjI8PpcVt5t27dmqxNREREREREJD2SC8QDBgxAWFgYsrOzceTIkRrH165dCwAYPXp0E7eMiIiIiIiIpERygVilUuHll18GAEyePNl+zzAALFq0CJmZmRgyZAiSkpLs5UuXLkWXLl0wY8aMJm8vERERERER+SbJ3UMMAG+99RbS0tKwd+9edOzYEYMGDUJOTg7279+PiIgIrFixwuH8q1evIisrC3l5eTXq+vTTT/Hpp58CuLW8d15eHvr162c/5+OPP0ZiYqIHXxERERERERE1NUkG4oCAAGzfvh3z58/Hl19+ifXr1yM8PBwTJ07E3LlzERMT43Jdly5dwv79+x3KDAaDQ5ltb2EiIiIiIiJqPiQ3ZdomMDAQc+bMwdmzZ6HX65GXl4eVK1c6DcOzZs2CKIpYtWpVrcfu9DV06FDPvyAiIiIiIiJqUpINxERERERERESNwUBMREREREREfomBmIiIiIiIiPwSAzERERERERH5JQZiIiIiIiIi8ksMxEREREREROSXGIiJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/xEBMREREREREfomBmIiIiIiIiPwSAzERERERERH5JQZiIiIiIiIi8ksMxEREREREROSXGIiJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/xEBMREREREREfomBmIiIiIiIiPwSAzERERERERH5JQZiIiIiIiIi8ksMxEREREREROSXGIiJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/xEBMREREREREfomBmIiIiIiIiPwSAzERERERERH5JQZiIiIiIiIi8ksMxEREREREROSXGIiJiIiIiIjILzEQExERERERkV9iICYiIiIiIiK/xEBMREREREREfomBmIiIiIiIiPySZANxRUUF3nnnHXTq1AkBAQFo3bo1Jk2ahMuXL9e7rhs3bmDKlCmIi4uDWq1GXFwcpk6dips3b7q/4UREREREROQTJBmIKysrMXz4cMydOxdlZWUYM2YMYmNjsXLlSvTs2RPnzp1zua6rV6+iT58++Oijj6BQKPDoo48iJCQEH374Ifr27Yvr16978JUQERERERGRt0gyEM+bNw/p6eno378/Tp8+jTVr1mD//v3461//iqKiIkyaNMnluqZOnYqzZ8/iscceQ1ZWFtasWYNffvkFr7zyCk6fPo2UlBQPvhIiIiIiIiLyFoW3G1BfBoMBS5cuBQAsW7YMwcHB9mMpKSlYvXo1du7cicOHDyMpKemOdeXl5eGrr76CSqXCxx9/DIXi1tvxl7/8BV9//TU+//xzvP/++4iMjKxXO08fTEWf5HGQK3zzLTabTDi1fwsqblxGYMs26NL3AZ9sq1TaCbCtnmA2mXAq/QcYctJxKl3APf0f8sl2AtJ5TwHptFUq7QSk01Z+pjxDKm2VSjsBCbZVAp8ryb2nEmirVNoJSK+tpw+mNtn1fPNduIM9e/aguLgYCQkJ6NmzZ43j48aNQ2ZmJjZt2lRnIP7xxx9hsVgwaNAgaLVah2NqtRqjR4/GihUr8P3/Z+++w6Mo9zaO30tCKikEQocAoQlILwIiAUERBESKoByD2MuRomJXQM9RQJq9AnoQ5AiKgoCCdCnSEem9d0hCSELK8/7Bu3tYklCym2w28/1c116aZ56Z+c0+s8veO2Vnz1bfvn1vqM5aix/TqZWv6EizN1X/ztgbmje3rf/1a5VZMVS1dNrRdnxesXxXq7fUKVFrbrDXebNO62ZJ+v1jHf89/9Upec9zKnlPrd5Sp+Q9tfKayh3eUqu31Cl5Z635/XXljc9pfq/VW+qUvLTWlFN5tk6vC8QbN26UJDVo0CDL6fb2TZs2uWVZ48ePv65lZSXSnFbk8me1Xso3O9v6X79W3eXPXvrD9r/2/Fart9QpUWtu8JY6JWrNDd5Sp+Q9tXpLnRK15gZvqVOi1tzgLXVK3lOrt9QpeW+t5/NwvV53DfGBAwckSeXKlctyur19//79ebqsrBT6/52u9IqhSk9Ly9Ey3Ck9LU1lVgyV9L/a7PJTrd5Sp0StucFb6pSoNTd4S52S99TqLXVK1JobvKVOiVpzg7fUKXlPrd5Sp1Rwas1tXneE+Pz5S98XBAUFZTk9ODhYkpSQkJAny0pJSVFKSorj7/j4eKfphWxSKZ3WXytmq8Ytd12zpty0beUc3azTTt8OXS6/1JpXdaampjr9Nye85TmVvKdWb6lTotYr8Zpyll9q9ZY6JWq9Eq8pZ9R647ylTonXVG4oSLXmJq8LxPnNO++8o6FDh16z37b1K7TnjMmDirJ3cf/KS9e3XIOna83rOufNy/lF+97ynEreU6u31ClRa3Z4TTnzdK3eUqdErdnhNeWMWq+ft9Qp8ZrKDQWx1tzgdYHYflfpCxcuZDk9MTFRkhQSEpIny3r55ZedfpopPj5e5cuXz9SvRv1m+eCbF5v0+8fX7OfpWvOqztTUVM2bN0/t2rVT4cKFc7QMb3lOJe+p1VvqlKj1SrymsubpWr2lTolar8RrKmvUev28pU6J11RuKIi15gavC8QVKlSQJB06dCjL6fb2qKioPFmWv7+//P39s52eYaQTtmL54hb8NZt10PHfiynSnM7y3Pz8Umte11m4cOEcvyl6y3MqeU+t3lKnRK3Z4TV1SX6p1VvqlKg1O7ymLqHWG+ctdUq8pnJDQao1N3ndTbXq1q0rSVq3bl2W0+3tderUydNlZSXj/888ONrsTY/vZJLk4+urI83elPS/2uzyU63eUqdErbnBW+qUqDU3eEudkvfU6i11StSaG7ylTolac4O31Cl5T63eUqdUcGrNbV4XiFu0aKGwsDDt3r1bGzZsyDR92rRpkqROnTpdc1nt27dXoUKFtHTpUp04ccJpWkpKimbOnCkfHx916NAhR7WesBXTxubv55tbmUuXbqu+sfn7Omkr5tSe32r1ljolas0N3lKnRK25wVvqlLynVm+pU6LW3OAtdUrUmhu8pU7Je2r1ljqlglFrbrMZYzx7BXUOvPbaa/rXv/6l5s2b67fffnPcDXr06NF67rnn1KpVKy1atMjR/8MPP9SHH36orl276p133nFaVp8+ffTtt9+qW7du+u677+T7/9+Q9O/fX++//75iY2M1ceLE664tPj5eYWFhWj5nipq07Z4vvnHJSnpamrat+lVJZw8rsGhZ1Wh6Z76sNTfrTE1N1ezZs9WhQ4ccnzZzOW95TiXvqTU9LU1bVszWtvUrVKN+s3xxSk92vOU5lXKvVl5T+b9WXlO5g9eU+3lbrd7wuvK255TXlHt5W61/zp+m5nf1VlxcnEJDQ3N1fV4ZiJOTkxUTE6NVq1apdOnSatmypfbv369Vq1YpMjJSK1euVOXKlR39hwwZoqFDh2YZbk+dOqVbbrlFu3fvVnR0tBo1aqS///5bmzdvVtWqVbVy5UpFRERcd232QHzq1CkVK5a3327gxrj7TRG5g3HyHoyVd2CcvAdj5T0YK+/AOHmP06dPq3jx4nkSiL3ulGlJCggI0MKFC/X6668rKChIM2bM0P79+9W3b1+tW7fOKQxfS/HixfXnn3/qn//8py5evKgff/xRcXFxevbZZ/Xnn3/eUBgGAAAAAHiP/Hmc/DoEBgZq2LBhGjZs2DX7DhkyREOGDMl2ekREhN5//329//77bqwQAAAAAJCfeeURYgAAAAAAXEUgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCX5erqAgsYYI0lKSEhQ4cKFPVwNriY1NVUXLlxQfHw8Y5WPMU7eg7HyDoyT92CsvAdj5R0YJ++RkJAg6X/ZKjcRiN3s9OnTkqRKlSp5uBIAAAAA8F6nT59WWFhYrq6DQOxmERERkqQDBw7k+uDBNfHx8SpfvrwOHjyo0NBQT5eDbDBO3oOx8g6Mk/dgrLwHY+UdGCfvERcXpwoVKjiyVW4iELtZoUKXLssOCwvjheYlQkNDGSsvwDh5D8bKOzBO3oOx8h6MlXdgnLyHPVvl6jpyfQ0AAAAAAORDBGIAAAAAgCURiN3M399fb775pvz9/T1dCq6BsfIOjJP3YKy8A+PkPRgr78FYeQfGyXvk5VjZTF7cyxoAAAAAgHyGI8QAAAAAAEsiEAMAAAAALIlADAAAAACwJAKxi/744w916NBBERERKlKkiJo0aaJvvvnmhpezZMkSPfroo2rQoIFKliwpPz8/RUREqHXr1vrPf/4jLvV2nbvGau3atRoyZIiaN2+u8PBw+fn5qXz58urTp482bdqUC5Vbi7vG6dSpU/rqq6/02GOPqV69evL19ZXNZtPEiRPdX3QBlZSUpDfeeEPVqlVTQECAypQpo379+unw4cM3vKyzZ8+qf//+ioqKkr+/v6KiojRgwACdO3fO/YVbjLvGafHixRo6dKg6duyoyMhI2Ww2VaxYMXeKtih3jNW5c+c0efJk9e7dW5UqVZKfn59CQkLUtGlTjRs3Tqmpqbm4BdbgjnFKS0vTkCFD1LFjR1WuXFkhISEKCAhQ1apV9dRTT2n//v25uAXW4c5/py63c+dOBQYGymazqW3btm6q1rrcNU4VK1aUzWbL9rFt27acFWiQY9OmTTM+Pj7GZrOZVq1amW7dupnw8HAjyTz33HM3tKznnnvOSDLVqlUzd955p+nVq5dp2bKl8fHxMZJM7969c2krrMFdY5WammokGUkmIiLC3HXXXaZ79+4mOjraSDJ+fn7m+++/z8UtKdjc+Zr68ccfHWN1+WPChAm5U3wBk5SUZG655RYjyZQuXdr07NnTNGnSxEgykZGRZvfu3de9rJMnT5oqVaoYSaZy5cqmZ8+eplatWo73vNOnT+filhRs7hynunXrZnq9REVF5V7xFuOusXr11VeNJGOz2Uz9+vXNfffdZ9q0aWP8/f2NJHPrrbeaxMTEXN6agstd45SQkGAkmSJFipjmzZub7t27m86dO5sKFSoYSSY0NNSsXr06l7emYHPn+9+VYmJijM1mM5LM7bff7saqrced4xQVFWUkmdjY2CwfR44cyVGNBOIcOn36tAkNDTWSzPTp0x3tx44dc3zwW7hw4XUv7++//zaHDx/O1L5z505TunRpI8nMnDnTHaVbjjvHKjU11TRu3NjMmDHDpKWlOdrT09MdH1JCQkLMyZMn3b0ZBZ67X1PLly83Tz31lBk/frz566+/zKOPPkogvgH2/blZs2YmISHB0T5q1CgjybRq1eq6l/XAAw8YSebee+81qampjvZ//vOfjn/YkDPuHKcXXnjBvP322+bXX381f//9N4HYzdw1Vv/+97/N4MGDzf79+53ad+zY4QhbL7/8sjtLtxR3jVNqaqpZtmyZ03ueMcakpaWZl156yUgyDRs2dGfpluPO97/Lffnll0aSeeyxxwjEbuDOcbIHYncjEOfQ8OHDjSTTpUuXTNN++OEHI8ncfffdblnXW2+9ZSSZgQMHumV5VpNXY5WRkWGqV69uJJmJEye6vDyrye1xevzxxwnE1yklJcWEhYUZSWbdunWZptepU8dIMmvWrLnmso4cOWIKFSpk/Pz8zLFjx5ymJScnm8jISOPj42OOHz/utvqtwp3jdKWjR48SiN0oN8fqcpMnTzaSTMWKFV1ajlXl1TilpqaagIAAI8mcO3fOpWVZVW6N1bFjx0zRokVNu3btzMKFCwnELnL3OOVWIOYa4hz65ZdfJEndu3fPNK1jx44KCAjQ/PnzlZyc7PK6ChcuLEny8/NzeVlWlFdjZbPZVKdOHUnSkSNHXFqWFeXlawpX98cffyguLk7R0dGqX79+pun2MZo5c+Y1lzV37lxlZGSoZcuWKlmypNM0f39/derUSenp6Zo9e7Z7ircQd44TcldejVXdunUl8W9QTuXVONlsNvn4+Mhms/HZLodya6z69++vpKQkffzxx26p0+q85d8pAnEObdy4UZLUoEGDTNP8/PxUu3ZtJScna8eOHS6t5+DBg/r0008lSR06dHBpWVaVV2MlSXv27JEklSpVyuVlWU1ejhOu7mpjcXn79dxEzp3LgjOeW++RV2PFv0GuyYtxMsZo+PDhSkxMVOvWrRUYGJjjZVlZbozV7NmzNXXqVL3yyiuqUqWK60Ui115TI0eO1BNPPKH+/fvr888/18mTJ12q09eluS0qPj5ecXFxkqRy5cpl2adcuXJas2aN9u/f7zhqeD1WrFihzz77TOnp6Tpy5IiWLVumtLQ0vf3227rtttvcUr+V5OZYXWnZsmVau3at/Pz81L59+xwvx4rycpxwbQcOHJB09bGQdF13SXXnsuCM59Z75NVYjRs3TpLUpUsXl5ZjVbk1Ti+++KKOHz+u+Ph4bdq0Sbt379ZNN92kL7/80rWCLczdY5WYmKinnnpK1atX14svvuieIpFrr6nBgwc7/T1w4EB98MEH6tevXw6qJBDnyPnz5x3/HxQUlGWf4OBgSVJCQsINLXv37t36+uuvHX/7+Pho2LBhev7553NQKXJzrC4XHx/veBEOHDhQpUuXzvGyrCivxgnXxz4e7hgLdy4LznhuvUdejNWnn36q+fPnKzw8XC+99FKOl2NluTVO06dP1+7dux1/16lTR5MmTVKlSpVyWCncPVavvfaa9u/fr4ULF3Iauxu5e5w6d+6s1q1bq2HDhoqMjNSePXs0fvx4jRs3To888oiKFSuWoy8ELRuIu3btqq1bt97QPN98842aNGmSSxVd0qdPH/Xp00cXL17Uvn379M0332jYsGGaOXOm5syZo6JFi+bq+vOj/DpWdunp6XrggQe0c+dONWnSRMOGDcuT9eY3+X2cAKCgWrp0qfr37y+bzabx48erTJkyni4Jl9m1a5ck6dSpU1q7dq1effVVNWzYUF988YViY2M9XB3WrFmj999/Xw8++KBiYmI8XQ6u4v3333f6u1atWho1apRq1Kihxx57TC+++CKB+Ebs3btX27dvv6F5Lly4IEkqUqSIU1toaGimvomJiZKkkJCQHNXn5+enatWq6e2331ZERISee+45vfHGG/rggw9ytDxvlt/H6sknn9SsWbNUvXp1/fLLL5b9ZjG/jxOun3087ONzpRsZC3cuC854br1Hbo7V5s2b1aVLF128eFHvv/++unbtmvNCLS63X1PFixfXnXfeqVtuuUU333yznnzySbVp00bly5fPWcEW5q6xSktL06OPPqrw8HC999577i0Sefbv1MMPP6zXXntN27dv1759+1SxYsUbmt+yN9XasGGDzKWfnbruh/1bo9DQUIWFhUmSDh06lOXy7e1RUVEu1/qPf/xDkvTTTz+5vCxvlJ/H6qWXXtIXX3yh8uXLa968eSpevHjONrIAyM/jhBtToUIFSe4ZC3cuC854br1Hbo3V3r17dccdd+js2bMaMmSI/vnPf7pWqMXl1WsqLCxMnTp1UlJSkubNm+fSsqzKXWN16NAhbdiwQX5+furRo4diYmIcjwEDBkiS1q5d62jDjcmr11ShQoUUHR0tSTp69OgNz2/ZI8Suqlu3rpYsWaJ169apZs2aTtNSU1O1efNmBQQEqFq1ai6vKyIiQoUKFXL5DmpWlVtjNWLECA0fPlwlSpTQvHnz+IbXRXn5msLV2X+6Zd26dVlOt7dfz83N3LksOOO59R65MVZHjx5Vu3btdPToUfXv319vvvmm64VaXF6+puxfoPPZLmfcPVbHjh3TsWPHspx27tw5LV68OAdVIi9fU2fPnpX0v+uSb4RljxC7qmPHjpKkadOmZZo2a9YsJScnq23btgoICHB5XUuXLlVGRobjmw/cmNwYqy+++EIvvviiwsPD9euvv6p69epuq9eq8vI1hatr0aKFwsLCtHv3bm3YsCHTdPsYderU6ZrLat++vQoVKqSlS5fqxIkTTtNSUlI0c+ZM+fj48LNyOeDOcULucvdYnT17Vnfeead2796thx56SGPGjHFnuZaVl68pe8Dis13OuGusKlasmO1ZbAsXLpQk3X777Y423Ji8ek39/fff2r59u4KCglSjRo0bX4BBjpw+fdqEhoYaSWb69OmO9uPHj5sqVaoYSWbhwoWZ5qtevbqpXr26OXTokFP7iBEjzJkzZzL1//PPPx3Le++999y+HVbg7rH6/vvvTaFChUyRIkXM8uXLc7t8y3D3OF3p8ccfN5LMhAkT3Fx5wfTqq68aSaZ58+bm/PnzjvZRo0YZSaZVq1ZO/T/44ANTvXp189JLL2Va1gMPPGAkmW7dupnU1FRH+7PPPmskmdjY2NzajALPneN0uaNHjxpJJioqKheqtiZ3jVViYqJp1qyZkWR69uxp0tLS8qJ8y3DXOM2aNcv88ccfmZafmJhoXnnlFSPJlCpVyiQkJOTKdlhBbr3/2S1cuNBIMrfffrs7y7Ycd43TL7/8Yn7//fdMy9+4caO56aabjCTz7LPP5qhGArELpk2bZgoVKmRsNptp3bq16d69uwkPDzeSzKBBg7KcR5KRZPbu3Zup3c/PzzRt2tTcd999pmvXrqZu3bqO/j179nT6IIkb466xOn78uPHz8zOSzM0332xiY2OzfPz44495s2EFjDtfU8YY07RpU8ejRIkSRpKpXLmyo+3JJ5/M5S3yXklJSaZp06ZGkildurTp2bOn4+/IyEize/dup/5vvvlmtuH25MmTJjo62kgy0dHR5r777jO1a9c2kkzVqlXN6dOn82irCh53jtMXX3zheG00aNDA6d8l+2Pt2rV5tGUFj7vGasCAAUaS8fHxMffff3+2/w4hZ9w1Tvb2smXLmo4dO5r777/ftGnTxkRERBhJJiwszCxZsiQPt6zgcef7X1YIxO7h7tdUVFSU6dy5s+nVq5dp0qSJ8fX1NZJMTEyMuXDhQo5qJBC7aNmyZaZ9+/YmPDzcBAUFmUaNGpmJEydm2z+7D+8ffPCBuffee03lypVNcHCw8fPzM2XLljVdunQhXLmJO8Zq7969jvarPd58883c36ACyl2vqcunZfe48ltJOLtw4YJ5/fXXTXR0tPHz8zOlSpUyffv2NQcPHszU91ofNE6fPm3++c9/mvLlyxs/Pz9Tvnx58+yzz5qzZ8/m7kZYgLvGyT7tao+sztLA9XPHWMXGxl7Xv0PIOXeM08aNG82gQYNM48aNTYkSJYyvr68JCQkx9evXNy+//LI5cuRIHm1NwebOf6euRCB2H3eM0/Lly02/fv3MzTffbIoVK2Z8fX1NRESEiYmJMV988YVLZ8vYjOGEeAAAAACA9XBTLQAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgCAR9hsNqdHoUKFFBYWpltuuUVjx45VamqqW9YzZMgQ2Ww2TZw48brn2bdvn2w2m2JiYtxSg7uX5wkxMTGy2Wzat2+fp0vJUlpamqpXr64mTZp4uhSH9evXy2azacSIEZ4uBQCQDV9PFwAAsLbY2FhJUnp6uvbt26fly5dr1apVmjVrlubOnStfX/6pygs2m01RUVH5NvBey2effaYdO3bol19+8XQpDvXr11fnzp31zjvv6JFHHlFERISnSwIAXIFPGQAAj7ryyO2qVasUExOj33//Xd9995369OmT5zWVLVtWW7duVVBQUJ6vO7/65ptvdOHCBZUtW9bTpWSSkpKiYcOGqU6dOurQoYOny3Hy8ssv6+eff9bw4cM1fPhwT5cDALgCp0wDAPKVpk2bqm/fvpKkX3/91SM1FC5cWDVq1FCFChU8sv78qEKFCqpRo4YKFy7s6VIymTZtmk6cOKEHH3zQ06Vkcsstt6hKlSoaP368Ll686OlyAABXIBADAPKdWrVqSZJOnDjh1G6M0ZQpU9SrVy9Vq1ZNwcHBCgkJUZMmTfTxxx8rIyPjqstdtWqV7rzzToWHhys0NFTt2rXTypUrM/XL6TW/Bw8e1D/+8Q9FRkYqKChIDRs21KRJk7Lt37dvX9lsNi1atCjL6TabTRUrVnRqmzhxomw2m4YMGaIdO3aoV69eKlmypAoVKqQZM2ZIknbt2qUhQ4aoWbNmKlWqlPz8/FSuXDk9+OCD2rFjR5bLk6T9+/c7Xdd9+fZf7RriLVu26IEHHlDp0qXl5+ensmXL6sEHH9T27dsz9V20aJFsNpv69u2rM2fO6Mknn1Tp0qXl7++v2rVra/z48dk+X9n58ssvZbPZ1KtXr0zTLn++du/erZ49e6p48eIKDQ3VXXfdpS1btki6dA3yv//9b1WrVk0BAQGqUqWKPvrooyzXt3nzZvXp00eVK1dWQECAIiMjVa9ePQ0YMEBHjx7N1L937946deqUfvzxxxveNgBA7iIQAwDynYSEBElSiRIlnNpTUlJ0//33a/78+SpVqpQ6deqkW265RX///beefvpp9evXL9tlLl++XLfddpsOHTqku+66S9WrV9f8+fPVqlUr/fbbby7XvHfvXjVp0kSTJk1SaGiounTpouDgYD344IMaNWqUy8u/0vbt29W4cWP9+eefat26tdq1a+c4evvll19q2LBhSkxMVOPGjdW5c2eFhobqP//5jxo3bqxNmzY5llOlShXHddzBwcGKjY11PNq3b3/NOn7//Xc1atRIkydPVunSpdWtWzeVKFFC//nPf9SoUSMtXbo0y/nOnTunZs2a6eeff1bLli3VokULbdu2TQ8//LC+/PLL634e4uPjtXTpUlWpUuWqp3Pbx2fz5s1q27atKlasqLlz5yomJkbHjh1T9+7dNWLECNWqVUsxMTE6ePCgnnnmGX3xxRdOy1m7dq0aN26sb7/9ViEhIerSpYtuueUWpaamaty4cVl+CWD/YiE/Xd8MAPh/BgAAD5Bksvtn6LbbbjOSzKRJk5zaU1NTzY8//mguXrzo1H7ixAnTqFEjI8ksXrzYadqbb77pWNerr75qMjIyHNM+/vhjI8mULl3aXLhwwdG+d+9eI8m0atXqurenffv2RpLp16+fSU1NdbT//PPPxsfHJ8vlxcbGGklm4cKFWS5TkomKinJqmzBhgmN7nnnmGZOWlpZpvhUrVpg9e/Zkah8/fryRZFq3bn1d67pcq1atjCSzd+9eR9v58+dNyZIljSTz4YcfOvUfPXq0kWTKlStnkpKSHO0LFy501N+rVy+TnJzsmPbjjz8aSaZChQrZ1nGlOXPmGEnm/vvvz3L65c/XSy+95Bj/jIwM07dvXyPJ1KxZ09SuXducOHHCMd/8+fOzfE4efPBBI8m89957mda1detWc+TIkUztcXFxplChQje0XQCAvMERYgBAvpCRkaHdu3frySef1JIlS9SlSxfdd999Tn18fX11zz33ZLqONTIyUu+8844k6aeffspy+VFRUY6fYLJ78skn1bRpUx09elTTp0/Pce179uzR3LlzFRoaqtGjRzvdGbtTp07q3r17jpedncjISA0fPlw+Pj6Zpt1yyy2qVKlSpvaHHnpILVq00KJFixQXF+dyDf/97391/PhxNWvWTE8//bTTtIEDB6phw4Y6dOhQls9taGioPvzwQ/n7+zva7rnnHtWuXVsHDhy47rtd2492V69e/ar9KleurGHDhjnG32azaeDAgZIunfI9duxYRUZGOvrffvvtql+/vvbv3+9Uy8mTJyVJbdu2zbSOGjVqqHTp0llua+nSpXXgwAGdPXv2urYLAJA3uMs0AMCjLg+odo8++qg+++yzLKdJ0oYNG/Tbb79p//79unDhgowxjtOsd+7cmeU83bp1y/InnHr37q1Vq1Zp6dKlOb6j9bJlyyRJ7du3V1hYWJbrmDp1ao6WnZ22bdte9S7Y58+f18yZM7VhwwadOXPG8bvOR48elTFGu3fvVoMGDVyqwX469AMPPJDl9D59+mjt2rVaunRppj4NGzZUsWLFMs1TrVo1bd68WUePHs10/XRW7NeZFy1a9Kr9YmJiMn2RUrlyZUmXbqKW1fXilStX1vr1651qadiwoebMmaOnn35ab7/9tm699dbr+mmwiIgIHT58WCdPnrxmrQCAvEMgBgB4lP361eTkZG3cuFHbtm3TF198oebNmzvuNm138eJF9e3bV1OmTMl2efZgfKWoqKgs2+1B58iRIzde/P+zz3utdbjT1e6AvWDBAvXq1ctxNDMr2T1PN8K+3dltn7398OHDmaaVK1cuy3lCQkIkXbpe/HrYj3Tb58tOVtcXFylSRJJUqlSpLI+026dfXssLL7ygZcuWadGiRWrdurWKFCmiZs2aqWPHjurbt2+WX4hIl44SS5eunQYA5B+cMg0A8KiJEydq4sSJ+u6777R161aNGDFCkvT0009r//79Tn1Hjx6tKVOm6Oabb9acOXN0/PhxXbx4UcYYx82MjDF5vg254Vp3zA4ICMiy/fz58+rZs6dOnTqlN954Q1u2bFFiYqIyMjJkjFHv3r0l5c3zlN0RfkkqVMg9H0HsAfRaAf9q67uRWkJDQ7VgwQItXbpUgwcPVs2aNbVgwQINGDBA1atXz/YMBXtwDw8Pv+51AQByH4EYAJCvvPDCC7rjjjt04cIFDR061Gma/WdrpkyZovbt26tEiRKO02D37Nlz1eVeGa6vbC9TpkyOa7ZfN3qtdVzJz89P0qUQe6WDBw/mqJalS5fq9OnT6tatm4YOHaqbbrpJQUFBjnB6refpRtifs+y2z37t7dXu/uwq+53Iz5w5k2vruJLNZtOtt96q4cOHa9WqVTpy5Ih69+6t48eP69VXX81yHvu1w5dfpwwA8DwCMQAg33n33XclSf/5z3+cwpY9VGR1uu1///vfqy7zhx9+UHp6eqb27777TpJ066235rhe+7xz585VfHx8tuu4kj1IX/nbwJI0b968HNVytedo165dWrduXZbzFS5cWGlpaTe0rpYtW0pStqew23+D2d4vN9StW1eSsvy5o7xSokQJDRkyRNKl3yi+Unx8vI4cOaIKFSpw/TAA5DMEYgBAvlO/fn3dc889SktLc5xCLV264ZIkffrpp079p02bpm+++eaqy9y3b1+mI86ff/65VqxYoZIlS6pbt245rjc6Olp33HGH4uPj9dxzzzkF79mzZ+v777/Pcr5WrVpJkj755BOdPn3a0b5hwwa98cYbOarF/hz98MMPTtcQnzt3Tg8//LDj5lpXKlOmjI4fP35D17j27NlTJUuW1LJly/T55587TXv//fe1Zs0alS1b1qXn9lqaN28uHx8frV69OtfWcblPP/1Ue/fuzdQ+e/ZsSVL58uUzTVu9erWMMY7xBgDkHwRiAEC+ZP+JpPHjx+vYsWOSpMGDB8vHx0cvvfSSGjVqpPvvv1+NGzdWjx49HD+hk51HH31U7777rmrXrq37779fTZo00eOPP67ChQtr4sSJV71j8/X45JNPVLJkSX355ZeqXr26evfurVatWunuu+/W448/nuU8rVu3VqtWrbRr1y7VrFlT9957r2677TY1bdo02zs3X0ujRo3Url07HThwQNWqVVPXrl3VtWtXVapUSUeOHFGXLl2ynK9z585KS0tTgwYN1KdPHz3yyCMaOXLkVdcVHBysb7/9VoGBgXr88ccdY9KgQQP1799fRYoU0ZQpU7K93tkdQkJC1LJlS+3atUuHDh3KtfXYffrpp6pcubJq1aql7t27q1evXqpXr54GDhyogICALL/IWLRokSSpY8eOuV4fAODGEIgBAPlS3bp11bVrVyUnJ2v06NGSpNtuu03Lli1TmzZttGfPHs2aNUt+fn6aPn16pt/BvVLz5s21ePFilSpVSrNmzdLWrVt1++23a9GiRWrfvr3L9VauXFmrVq3S/fffr3PnzmnGjBmKj4/XhAkT9Pzzz2c5j81m008//aQnnnhCNptNs2fP1pkzZzRu3LhrhtGr+emnn/Tqq68qMjJSc+bM0dq1a9WrVy+tXLky25s6vfPOO3rmmWeUlpamqVOn6quvvtIvv/xyzXXdfvvtWr16tXr37q1Dhw5p2rRpOnbsmPr06aM1a9bk6unSdo8++qik7E/ddqe33npL/fr1k81m0++//66ZM2cqKSlJjzzyiDZs2KAWLVpkmmfKlCkqXry4unbtmuv1AQBujM0UlNtxAgAAS0pJSVFUVJRKlCihTZs2ebocJytWrFDz5s01ePBgDR8+3NPlAACuwBFiAADg1fz9/fXGG2/or7/+0qxZszxdjpN3331X4eHhevHFFz1dCgAgCxwhBgAAXi8tLU21atVSaGhont1g61rWr1+vBg0aaPjw4Ro8eLCnywEAZIFADAAAAACwJE6ZBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYkq+nCyhoMjIydOTIEYWEhMhms3m6HAAAAADwKsYYJSQkqEyZMipUKHeP4RKI3ezIkSMqX768p8sAAAAAAK928OBBlStXLlfXQSB2s5CQEEmXBi80NNTD1QAAAACAd4mPj1f58uUd2So3EYjdzH6adGhoKIEYAAAAAHIoLy5B5aZaAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJn10CAACAZaWlpSktLc3TZQAFlq+vr3x982/szL+VAQAAALnkwoULOnXqlBITEz1dClDgBQcHq3jx4goKCvJ0KZkQiAEAAGApFy9e1MGDB1W4cGGVLl1a/v7+stlsni4LKHCMMUpJSdGZM2d08OBBVapUSX5+fp4uywmBGAAAAJZy4sQJ+fj4KCoqSj4+Pp4uByjQAgMDFRISor179+rEiRMqV66cp0tywk21AAAAYBnGGF24cEFhYWGEYSCP+Pj4KCwsTBcuXJAxxtPlOCEQAwAAwDJSU1OVnp6uwMBAT5cCWEpgYKDS09OVmprq6VKcEIgBAABgGRkZGZLE0WEgj9lfc/bXYH5BIAYAAIDlcBMtIG/l19ccgRgAAAAAYEkEYgAAAACAJfGzSwCQx8bM2+HR9Q9sV82j6wcAAMgvCMQAAADAZTz9xeX14gtO5LVff/1V7du312OPPabPPvvM0+W4BadMAwAAAACuadWqVZKkJk2aeLgS9yEQAwAAAACuyR6IGzdu7OFK3IdADAAAAFjcvn37ZLPZ1KpVK507d04vvPCCKlWqpMDAQNWtW1e//PKLo+93332nW2+9VSEhISpfvrxeeuklpaamZrncLVu26LHHHlOlSpXk7++vEiVK6MEHH9ShQ4ey7L948WINGDBADRs2VIkSJRQUFKQaNWrohRde0JkzZzL137p1qx5++GFVrlxZ/v7+Cg0NVfXq1dW3b19t3rzZ0W/SpEmy2Wx6+eWXs1xvt27dZLPZtGbNmmyflzNnzmjQoEGqWLGifH191b9//xxtZ354rnft2iWbzaZ27dopMTFRQ4cO1U033aSgoCBFR0drxIgRMsY4+o8YMUI2m02zZ8+WJNWtW1c2m002m0233357lvV4CwIxAAAAYHEbNmyQJIWHh6t+/fqaPn26mjZtqho1amjTpk269957tW3bNvXt21ePPPKIihYtqtatW+v48eMaPny4Ro8enWmZn332merVq6evvvpKpUuXVufOnRUeHq7//Oc/uuWWW3T8+PFM8/Tp00fjx4+Xn5+fYmJi1LZtW508eVLvvfeeYmJinMLg3LlzVb9+fU2YMEGRkZG655571LJlS2VkZOjrr792Wv769eslSQ0aNMhy+9evXy8fHx/Vrl07y+clODhYderU0eTJk9WkSRO1a9fOcZT0RrczPzzXGzdulCRFRESobt26+vLLL3XzzTfr1ltv1b59+/Tiiy9qwoQJjv4lSpRQjx49HP8fGxvreDz44INZPqfegptqAQAAABZnD0g///yz3nzzTb3++uvy8fGRMUZdunTRzJkzdffdd6tYsWLauXOnSpcuLUn66aefdM899+iHH37Qiy++6FjeTz/9pCeffFJVq1bV999/rzp16kiS0tPT9eyzz+rjjz/WO++8o7FjxzrmSUtL07hx49ShQwcFBAQ42i9cuKA777xTy5Yt0/z583XXXXcpPT1djzzyiIKCgrRixQrVr1/faXtWrVqlmjVrOv62B+Ir+0nSuXPntHfvXtWsWdNpvZc/L3PmzNGjjz6q999/36lPTrYzPzzX9hr++9//6pVXXtHQoUPl63spGk6cOFEPPfSQfvjhB/Xr10+S1LdvX1WuXFnff/+97rzzTk2cODHT8+itOEIMAAAAWJz9qGWPHj00ZMgQ+fj4SJJsNpvuuusuSdKZM2c0ffp0R0CT5Jh29OhRR9v58+f12GOPqUiRIpo7d64joEmSj4+PRowYoUKFCmn+/PlONfj6+uree+/NFEqDgoIcRycPHjwoSdq5c6cOHz6sli1bZhlymzZtqpCQEKftCw0NVXR0dKa+9rBct27dbJ+X5s2b69NPP3WqLafbmR+ea3sN3bp107/+9S9HGJak9u3bS5JOnDiR5Tz16tXL9Dx5M44QA4DF5IefE+GnQgAgf7GHnTfffDPTtPj4eEmXjhKWK1fOaVpcXJwkqXjx4o62zz//XCdOnNDgwYNVqVKlTMsLDg5WsWLFdPr06SzXNXv2bG3cuFGnT5/WxYsXneqLiIiQJIWFhclms+nXX3/VJ598ogcffFDBwcFZbtv+/ft19uxZ3XbbbbLZbJmmr1u3TlLWQc++3pEjR6pQIedjiTndzvzwXNuPEL/xxhuZ5jl16pQkqUyZMlnWXdACMUeIAQAAAAuLi4vTvn37FB0drVq1amWabg9PnTt3zjTtr7/+kiSn05NnzJgh6X83YsrqcfLkSRUtWtRpWe+//77Kli2r3r17691339UXX3yhr7/+Wl9//bWjhmrVLn2hWrp0aQ0bNkypqal66qmnFBkZqU6dOmnixIlKTk52Wu7VTpe+fPqVR4jtz0upUqXUrFmzTPPlZDvzw3N97tw5HThwQJUqVXI6omy3adMmScp0PbW9tqyOpHszjhADAAAAFmYPOtn9lM6GDRtks9nUsGHDLKdJzmFz48aN8vf3V69eva663ptvvtnx/+PGjdOAAQNUrVo1vfLKK2rZsqXKlCmjgIAApaenq1SpUkpISHAKg6+99pp69+6tqVOnas6cOZo9e7ZmzZqloUOHavny5Y7Tja8ViP/8809JmYOe/XnJ7shyTrYzPzzX9uVk91vC9ufr8iPBaWlp+vvvv1WuXDkVK1bsquvyNgRiAAAAwMKudipsUlKSduzYoejoaKdrcu3sAc8e0i5evKj4+HhVqlTpum+8lJ6ern/9618KDAzUkiVLVLJkSafpCxYs0KlTp9SwYUOna10lKTo6Wq+88opeeeUV7dmzR7169dLq1av11Vdf6bXXXpP0vyOrVx7xtE/buXOnSpYsqVKlSjlNyyqA2uVkOy9fpqee66yWc6WsAvHWrVuVkpJS4I4OS5wyDQAAAFja1QLSpk2blJ6enm14ujLg+fn5KTAwUAcPHtT58+eva/3Hjh3TyZMnValSpUxhODU1Va+//nq29V2ucuXKuv/++yVJGRkZjnb77/Befu2tnT00ZxX0rva85GQ7r7XMvHiur1WDdCkQh4SEON2AbOvWrZKy/lLB2xGIAQAAAAu72pHQax0l3bp1qypUqOC42ZUktWvXTmlpaXr88cd14cIFp3mMMVqwYIGWLl3qaCtevLh8fX21a9cu7djxvxs/JiQkKDY2VqtWrXKq4ddff9WcOXOUnp7utOyDBw/qk08+kc1mU6dOnRztkZGRkqRZs2Y52pKTkzVgwADNnDlT0tXvMJ1dcLzR7bzWMvPiub7Weg4cOKAzZ86oTp06TqeJ23//+cp1FAScMg0AAABYlP3a0LJlyzqC4+Wudv3t33//rdTU1EzThg8frmXLlmny5MmaN2+eGjVqpIiICJ0+fVobN27U0aNHncKpv7+//vGPf2jChAlq2LCh2rZtK0lavHixqlatqpiYGC1atMixnqlTp2rChAmKiIhQ48aNVaxYMR07dkxLly5Vamqq3nnnHaeaunbtqtmzZ+uZZ57Rt99+q6JFi+rPP/9URESEHnjgAU2aNCnTKcz256VMmTIqUaJEls/djW5nfniu09LStGXLlmvWcOXz0bRpUxUuXFgff/yxdu3apRIlSiggIECffvppls+NNyEQAwAAABa1bds2paSkZPtTOle75tV+6u2V02rUqKH169fr3//+t3777Tf9/vvv8vPzU4kSJdSkSRN17NhRrVu3dprno48+UrFixTR16lT9+uuvqlKlil555RU9++yzqlWrlgoVKuS4I3Lv3r3l7++v5cuXa/Xq1YqPj1fx4sXVqVMn9e/fX7fddpvTsh9++GGdOnVKn3/+udavX6+oqCg99dRTGjx4sOP3ja88Qmx/Xq52mvaNbmd+eK6vVUN2gbhKlSqaOnWq3n77bS1ZskSJiYlZ3vjLG9mMMcbTRRQk8fHxCgsLU1xcnEJDQz1dDoB8KD/8DrCn8TvEADwlOTlZe/fuVaVKlRQQEODpcgDLuJHXXl5mKq4hBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAGA5xhhPlwBYSn59zRGIAQAAYBk+Pj6SpNTUVA9XAliL/TVnfw3mFwRiAAAAWEbhwoXl7++vuLi4fHvECihojDGKi4uTv7+/Chcu7OlynPh6ugAAAAAgLxUvXlyHDx/WoUOHFBYWpsKFC8tms3m6LKDAMcYoNTVVcXFxOn/+vMqWLevpkjIhEAMAAMBSQkNDJUmnTp3S4cOHPVwNUPD5+/urbNmyjtdefkIgBgAAgOWEhoYqNDRUqampSk9P93Q5QIHl4+OT706TvhyBGAAAAJZVuHDhfP1hHUDu4qZaAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLyteBePXq1erZs6fKlCmjwoULKzw8XC1bttSECRNkjMnUPz09XWPGjNHNN9+swMBARUZGqmfPntq6detV1zNz5ky1atVKoaGhCg0NVUxMjH755Zfc2iwAAAAAQD6QbwPx9OnT1axZM33//fcqXbq07r33XjVo0EArV65Uv3791KdPH6f+GRkZ6tGjhwYNGqRDhw6pY8eOqlWrlqZNm6ZGjRrpzz//zHI9Y8eOVefOnbV8+XK1aNFCbdq00Z9//qm7775bH374YV5sKgAAAADAA2wmq0OtHpaWlqayZcvqxIkT+vbbb3X//fc7pm3dulW33nqrzpw5owULFqh169aSpC+//FKPPvqoqlatqqVLl6pkyZKSLgXr7t27q0qVKtq6dat8fX0dy9q+fbtq1aolX19fLVy4UM2aNZMk7dixQ82bN1dcXJy2bt2qKlWqXHft8fHxCgsLU1xcnEJDQ93xdAAoYMbM2+HpEjxuYLtqni4BAADkU3mZqfLlEeJt27bpxIkTql69ulMYlqSbbrrJcXR49erVjvbRo0dLkkaMGOEIw5LUrVs3de7cWbt27dJPP/3ktKxx48YpPT1dTzzxhCMMS1K1atX06quvKi0tTePGjXP79gEAAAAAPC9fBmJ/f//r6lesWDFJ0t69e7V161YFBgaqY8eOmfp1795d0qVrhS9nv07YPv165gEAAAAAFAz5MhBXrlxZ0dHR2r59uyZPnuw0bevWrZo0aZKKFi2qrl27SpI2btwoSapdu7YKFy6caXkNGjSQJG3atMnRdu7cOR04cECSVL9+/UzzlC9fXsWLF9f+/fsVHx/vng0DAAAAAOQb+TIQ+/j46Ouvv1Z4eLgeeOABNWzYUL169VKbNm1Up04dlStXTr///rsiIiIkyRFsy5Url+Xy7O379+93tNnnKVq0qIKDg697PgAAAABAweB77S6e0aJFCy1evFhdu3bVunXrtG7dOkmSn5+f2rVrp8qVKzv6nj9/XpIUFBSU5bLsgTchIeG658luviulpKQoJSXF8TdHkwEAAADAO+TLI8SSNGXKFDVp0kTly5fXqlWrdP78ee3YsUN9+/bVqFGj1KZNG6cg6invvPOOwsLCHI/y5ct7uiQAAAAAwHXIl4F4586dio2NVfHixTVr1iw1adJEwcHBqlq1qj777DPdfffdWrduncaPHy9JKlKkiCTpwoULWS4vMTFRkhQSEuJou9Y82c13pZdffllxcXGOx8GDB29gSwEAAAAAnpIvA/F3332n1NRUtW/f3hFcL9ezZ09J0pIlSyRJFSpUkCQdOnQoy+XZ26Oiohxt9nnOnj3rCL7XM9+V/P39FRoa6vQAAAAAAOR/+fIaYnsQDQsLy3K6vf3s2bOSpLp160qSNm/erNTU1Ex3mrZff1ynTh1HW3h4uCpUqKADBw5o/fr1uvXWW53mOXjwoE6dOqWoqChCLgC42Zh5Ozy6/oHtqnl0/QAAIH/Il0eIS5UqJUlas2ZNltNXr14tSapYsaIkqVKlSrrpppuUlJTk+G3hy02bNk2S1KlTJ6d2+28W26dfzzwAAAAAgIIhXwbiLl26SLp0SvQnn3ziNG3lypUaM2aMJKl79+6O9kGDBkmSBg8erBMnTjjaf/jhB/3888+qUqWKY7l2/fv3l4+Pjz799FOtXLnS0b5z507961//kq+vr/r37+/ejQMAAAAA5Av5MhA3aNBAzz//vCTpqaeeUu3atdWzZ0/deuutatGihRITE/XYY4+pbdu2jnn69eunrl27aufOnapRo4Z69Oih1q1bq3v37goMDNSkSZPk6+t8hnj16tU1cuRIpaSkqGXLlurQoYPuuece1a1bV6dPn9bo0aNVpUqVPN12AAAAAEDesBljjKeLyM6PP/6oTz/9VGvXrlVcXJxCQkJUr149Pfroo+rdu3em/unp6Ro3bpzGjx+v3bt3Kzg4WK1bt9bQoUNVs2bNbNczc+ZMjRw5UuvXr5ck1a9fX4MHD9bdd999wzXHx8crLCxMcXFxXHsMIEuevn4WXEMMAEB+lpeZKl8HYm9EIAZwLQRizyMQAwCQf+VlpsqXp0wDAAAAAJDbCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSfD1dAAAAAPLemHk7PLr+ge2qeXT9ACBxhBgAAAAAYFEEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEn5PhCfPHlSzz//vKpXr67AwEBFRESoQYMGeuGFF7LsP3PmTLVq1UqhoaEKDQ1VTEyMfvnll6uu4++//1aPHj0UGRmpwMBA3XzzzRo7dqwyMjJyY5MAAAAAAPlAvg7Ea9eu1U033aRRo0apcOHC6tKli2655RadOXNGY8aMydR/7Nix6ty5s5YvX64WLVqoTZs2+vPPP3X33Xfrww8/zHIdK1asUOPGjTVt2jRVrlxZnTt31qlTpzRw4ED16tVLxpjc3kwAAAAAgAf4erqA7Jw8eVLt27dXUlKSfvrpJ3Xu3Nlp+p9//un09/bt2/X888/L399fCxcuVLNmzSRJO3bsUPPmzTVw4EC1b99eVapUccyTmpqqBx54QElJSRo9erQGDhwoSTp//rzuuOMOff/99+rQoYP69u2buxsLAAAAAMhz+fYI8ZtvvqlTp05p5MiRmcKwJDVp0sTp73Hjxik9PV1PPPGEIwxLUrVq1fTqq68qLS1N48aNc5rnxx9/1N69e1W3bl1HGJakIkWKOI4ojxo1yp2bBQAAAADIJ3I1EJ86dUppaWk3PF9SUpImTZqk4OBgPfTQQ9c1j/064e7du2eaZm+bOXPmdc/ToEEDVa5cWZs3b9a+fftupHwAAAAAgBdwKRCvWbNGw4YN05YtW5zaf/zxR5UqVUolS5ZUsWLFMh2ZvZ7lJiQkqH79+goMDNScOXM0aNAgPfXUUxo7dqyOHDni1P/cuXM6cOCAJKl+/fqZlle+fHkVL15c+/fvV3x8vKN948aNki6F36zY2zdt2nRD9QMAAAAA8j+XAvEHH3ygf//73ypZsqSjbe/everVq5dOnDihUqVKKTExUYMGDdKiRYuue7n2gF2iRAndc8896tChg8aMGaNPPvlEAwcOVJUqVTRlyhRHf3sYLlq0qIKDg7NcZrly5SRJ+/fvzzSffdr1zAMAAAAAKBhcCsQrV65U/fr1VaxYMUfb+PHjlZqaqvfee0+HDx/WqlWrVKhQoRs6Snz27FlJ0s8//6y5c+fqo48+0okTJ7Rv3z49//zzSkpKUmxsrDZs2CDp0k2wJCkoKCjbZdqDckJCgqPtWvNlNc+VUlJSFB8f7/QAAAAAAOR/LgXi48ePq0KFCk5t8+bNU3BwsJ555hlJUsOGDdWyZUvH6cnXw/77v2lpaRo2bJieeuopRUZGKioqSiNHjlSPHj2UmpqqkSNHulK+W7zzzjsKCwtzPMqXL+/pkgAAAAAA18Gln11KT093umnW+fPntW7dOrVp00Z+fn6O9jJlymjlypXXvdwiRYo4/j+rm2o99NBD+v7777V48WKn/hcuXMh2mYmJiZKkkJAQp/WcPXs22/mymudKL7/8sgYNGuT4Oz4+nlAMAABwDWPm7fDo+ge2q+bR9QPIH1wKxBUqVNDatWsdf//yyy9KS0tT27ZtnfrFx8crLCzsupcbFRUl6dKpzJGRkZmmV6xYUZJ04sQJRx3SpVOtExMTs7yO+NChQ07Lts939uxZHTp0SHXq1Lmuea7k7+8vf3//69ksAAAASZ4PgwCAS1w6ZbpTp046cOCA7r33Xn3wwQd6/vnnVahQIXXp0sWp3/r1668aKq9kv1N0UlKSUlJSMk0/c+aMpP8dGQ4PD3eE4vXr12fqf/DgQZ06dUpRUVEKDQ11tNetW1eStG7duizrsLdnFZYBAAAAAN7NpUD8/PPPq2LFipoxY4b69++vw4cPa8CAAapataqjz6pVq3T48GHddttt173cChUqqG7dujLGOE6Lvpy97fKfWOrYsaMkadq0aZn629s6derk1H61edavX689e/aodu3ajiPSAAAAAICCw6VAXLx4cW3atEnjx4/X8OHDNX/+fL333ntOfY4dO6b+/furT58+N7TswYMHS7oUuo8ePepo37Bhg0aNGiVJeuKJJxzt/fv3l4+Pjz799FOn65V37typf/3rX/L19VX//v2d1tG1a1dVqlRJGzdu1JgxYxztiYmJevrppyVJzz333A3VDQAAAADwDjZjjPF0Ednp27evvv76a4WHh6t58+ZKSkrS8uXLlZKSokcffVSff/65U/8xY8Zo0KBB8vX1Vbt27eTn56fffvtNSUlJev/99/XPf/4z0zqWL1+utm3bKikpSU2bNlVUVJSWLl2qo0ePqnv37vrvf/8rm8123TXbr5eOi4tzOj0bAOy4dtDzuJkOPI33Ac/jfQDIv/IyU7l0hPhKKSkpOnr0qOMaX1dNmDBBn3/+uaKjo7Vo0SL9+eefatCggSZOnJgpDEvSwIED9fPPP6tZs2ZaunSpfv/9dzVq1EgzZ87MMgxLUvPmzbV69Wp169ZNu3bt0s8//6yIiAiNHj1aU6dOvaEwDAAAAADwHm45Qvz555/rk08+0V9//SVjjGJjYzV+/HhJ0g8//KBJkyZpxIgRqlKlissF53ccIQZwLRwZ8jyODMHTeB/wPN4HgPzLa44Qp6enq2vXrnryySe1detW3XTTTboyX9etW1czZszQ1KlTXSoUAAAAAAB3cikQf/jhh/rpp5901113af/+/frrr78y9YmOjlaVKlU0Z84cV1YFAAAAAIBb+boy88SJE1WyZElNnTpVwcHB2farWbOm1q5d68qqAMBtOFURAAAAkotHiLdv366mTZteNQxLUnBwsE6ePOnKqgAAAAAAcCuXAnHhwoWVnJx8zX4HDhxQSEiIK6sCAAAAAMCtXArEtWrV0tq1a5WQkJBtnxMnTmjDhg2qV6+eK6sCAAAAAMCtXArE//jHP3T69Gk98cQTunjxYqbp6enpevrpp3XhwgXFxsa6sioAAAAAANzKpZtqPfbYY/r+++81ZcoULV++XHfeeackaePGjerfv79mzZqlvXv36o477tADDzzgloIBAAAAAHAHl44Q+/j4aPbs2XryySd15MgRff7555Kk9evX64MPPtCBAwf06KOPasaMGbLZbG4pGAAAAAAAd3DpCLEkBQQE6KOPPtKQIUO0aNEi7du3TxkZGSpXrpxat26tMmXKuKNOAAAAAADcyuVAbBcZGakePXq4a3EAAAAAAOQql06ZzsjIUHx8vFJTU7Ptk5qaqvj4eGVkZLiyKgAAAAAA3MqlQDxmzBgVLVpUixcvzrbP4sWLVbRoUX3wwQeurAoAAAAAALdyKRD/+OOPKl++vNq2bZttn7Zt26pcuXKaPn26K6sCAAAAAMCtXArEO3fuVK1ata7Zr3bt2tq5c6crqwIAAAAAwK1cCsRxcXEKCwu7Zr+wsDCdPXvWlVUBAAAAAOBWLgXi0qVLa9OmTdfst2nTJpUoUcKVVQEAAAAA4FYuBeI2bdpo69atmjp1arZ9/vvf/2rLli1q3bq1K6sCAAAAAMCtXArEL7zwgvz8/PTggw/qmWee0aZNm5SYmKjExERt2rRJzzzzjP7xj3/Iz89PL7zwgrtqBgAAAADAZb6uzFyjRg198803io2N1SeffKJPPvnEaboxRgEBAZowYYJq167tUqEAAAAAALiTS0eIJalHjx7atGmTHn/8cVWpUkX+/v7y9/dXlSpV9OSTT2rjxo2677773FErAAAAAABu49IRYrsqVaro448/dseiAAAAAADIEy4fIQYAAAAAwBu55QixJKWlpen06dNKSUnJtk+FChXctToAAAAAAFziciCeP3++3n77ba1cuVKpqanZ9rPZbEpLS3N1dQAAAAAAuIVLgXjWrFnq2rWr0tPTVbRoUVWqVEkhISHuqg0AAAAAgFzjUiAeOnSoMjIyNGbMGD3zzDPy8fFxV10AAAAAAOQqlwLx33//rWbNmql///7uqgcAAAAAgDzh0l2mixQpwo2yAAAAAABeyaVA3LZtW61Zs8ZdtQAAAAAAkGdcCsTDhw9XfHy8XnzxRe4gDQAAAADwKi5dQzxhwgTdddddeu+99zR9+nTFxMSoXLlyKlQoc8622Wx6/fXXXVkdAAAAAABu41IgHjJkiGw2m4wx2rNnj/bs2ZNtXwIxAAAAACA/cfkIMQAAAAAA3silQBwbG+uuOgAAAAAAyFMu3VQLAAAAAABvRSAGAAAAAFiSS6dMS5IxRt9++61++ukn7dy5UwkJCTLGZOpns9m0e/duV1cHAAAAAIBbuBSIL168qI4dO2rBggVZhmBJjrtQAwAAAACQn7gUiEeNGqXff/9dnTp10ujRozVs2DBNmjRJycnJ2rNnj6ZOnar33ntPTz75pIYPH+6umgEAAFwyZt4OT5cAAMgHXArEU6dOVUREhCZPnqzg4GAVKnTpkuTChQurevXqeuONN9S6dWu1bt1a1atXV79+/dxSNAAAAAAArnLpplq7du1SkyZNFBwcfGlh/x+I09PTHX1atmypFi1a6OOPP3ZlVQAAAAAAuJVLgdjHx0dhYWGOv+3B+OTJk079ypYtq+3bt7uyKgAAAAAA3MqlQFy2bFkdOnTI8XeVKlUkSStXrnTqt2nTJhUpUsSVVQEAAAAA4FYuBeJbbrlFmzdvVkpKiiSpQ4cOkqQBAwZo7ty5+uuvv/TPf/5TW7duVdOmTV2vFgAAAAAAN3EpEHfr1k0BAQH67bffJF06QjxgwAAdOHBAHTt2VL169fTRRx8pKChII0aMcEvBAAAAAAC4g0t3me7YsaOOHj3q1DZq1Cg1btxYM2bM0NmzZ1WtWjU9++yzqlq1qkuFAgAAAADgTi4F4uz06tVLvXr1yo1FAwAAAADgFi6dMj1s2DD9/PPP1+w3c+ZMDRs2zJVVAQAAAADgVi4F4iFDhmjGjBnX7Pfzzz9r6NChrqwKAAAAAAC3cikQX6/09HQVKpQnqwIAAAAA4LrkSUr9+++/VbRo0bxYFQAAAAAA1+WGb6rVr18/p7+XLVuWqc0uLS1N27dv15o1a3TPPffkqEAAAAAAAHLDDQfiiRMnOv7fZrNp165d2rVr11XnqVOnjkaOHHnDxQEAAAAAkFtuOBAvXLhQkmSMUZs2bdS+fXu9+OKLWfb18/NTmTJlFBUV5VqVAAAAAAC42Q0H4latWjn+PzY2Vi1btnRqAwAAAADAG9xwIL7chAkT3FUHAAAAAAB5yqW7TB8/flxLlizR8ePHndp3796tXr16qXbt2urQoYNWrlzpUpEAAAAAALibS4H43XffVevWrRUXF+doi4+P16233qrvv/9eW7Zs0dy5c3X77bdr586dLhcLAAAAAIC7uBSIFy1apJo1a6patWqOtokTJ+r48ePq3bu3tm/frtGjRyspKUmjRo1yuVgAAAAAANzFpUB8+PBhVa5c2antl19+ka+vr8aOHauqVatqwIABqlu3rhYvXuxSoQAAAAAAuJNLgTghIUFBQUGOv9PT07VixQo1bNhQxYsXd7TXqFFDhw4dcmVVAAAAAAC4lUt3mS5Tpoy2bdvm+HvZsmU6f/68YmJinPqlpaXJz8/PlVUBAOA2Y+bt8Oj6B7ardu1OAAAg17l0hLhZs2batGmTxo4dq7/++kuvvfaabDabOnXq5NRv69atKlu2rEuFAgAAAADgTi4F4pdffln+/v567rnnVK9ePf3xxx+KiYlR8+bNHX327dunLVu2qGnTpi4Vevr0aZUoUUI2m01VqlS5at+JEyeqSZMmKlKkiCIiItShQwctX778qvP88ccf6tChgyIiIlSkSBE1adJE33zzjUs1AwAAAADyL5dOma5Vq5aWLVumcePG6dSpU2rYsKFeeOEFpz6//vqr6tatq3vuuceVVem5557TqVOnrtlvwIABGjdunAIDA3XHHXcoOTlZ8+bN02+//aZp06ZlWcf06dN13333KSMjQ7fddpuKFy+u33//XbGxsdq0aZPee+89l2oHAAAAAOQ/NmOM8XQR1/L777+rbdu2euyxx/T5558rOjpau3btytRv/vz5ateunYoVK6YVK1aoatWqkqQVK1YoJiZGQUFB2rt3r8LDwx3znDlzRpUqVVJ8fLymT5+ue++9V5J0/Phx3Xrrrdq1a5cWLlyY6bro7MTHxyssLExxcXEKDQ11edsBuJ+nrx8FuIbY83gfAK9DIP/Ky0zl0inTeSEpKUmPP/64atasqeeff/6qfUePHi1Jeu211xxhWLp0rfMTTzyhc+fO6auvvnKa58svv1R8fLy6dOniCMOSVLJkSY0YMUKS+A1lAAAAACiAXDpl+nIHDhzQ0aNHlZKSkm2f22677YaXO3ToUO3Zs0eLFy9W4cKFs+2XlJSkBQsWSJK6d++eaXr37t31/vvva+bMmXruuecc7b/88ku283Ts2FEBAQGaP3++kpOTFRAQcMP1AwAAAADyJ5cD8fjx4/XWW2/pwIED1+ybnp5+Q8vetGmTRo0apYceekgtW7bUvn37su27fft2paSkKDIyUuXKlcs0vUGDBo5lXm7jxo1O0y/n5+en2rVra82aNdqxY4fq1KlzQ/UDAAAAAPIvlwLxhAkT9Mgjj0iSateurWrVqikkJMQthWVkZOiRRx5ReHi449Tlq7EH8qzCsCQFBwcrPDxcZ8+eVUJCgkJCQhQfH6+4uLirzleuXDmtWbNG+/fvJxADAAAAQAHiUiAePXq0fH19NW3aNHXu3NldNUmSPvjgA61evVoTJkxQsWLFrtn//PnzkqSgoKBs+wQHB+vcuXOOQGyf52rzBQcHS5ISEhKynJ6SkuJ0mnh8fPw1awUAAAAAeJ5LN9XauXOnbrvtNreH4QMHDui1115Tq1at1LdvX7cu293eeecdhYWFOR7ly5f3dEkAAAAAgOvgUiCOiIhQ8eLF3VWLw9NPP62LFy/q008/ve55ihQpIkm6cOFCtn0SExMlyXFat32eq8135TxXevnllxUXF+d4HDx48LprBgAAAAB4jkunTHfp0kWzZs1SamrqVe8AfaNmzZql8PBwPfHEE07tycnJkqTDhw87fhf4u+++U6lSpVShQgVJ0qFDh7JcZmJios6dO6eiRYs6wm1oaKjj960OHTqkmjVrZprPvryoqKgsl+vv7y9/f/8b30gAAAAAgEe5FIj//e9/a+HChXrooYf0wQcfqGjRou6qS+fOndPixYuznJacnOyYZg/J1atXl7+/v06ePKnDhw+rbNmyTvOsW7dOkjLdGKtu3bpasmSJ1q1blykQp6amavPmzQoICFC1avx4OwAAAAAUJC4F4ueee041a9bUlClT9Msvv6hhw4YqV66cChXKfCa2zWbTV199dV3LNcZk2b5v3z5VqlRJ0dHR2rVrl9O0wMBAtWnTRnPmzNH333+vAQMGOE2fNm2aJKlTp05O7R07dtSSJUs0bdo09enTx2narFmzlJycrLvvvpvfIAYAAACAAsZmskuf1yGr4Jvtimy2G/4d4itdLRBL0vz589WuXTsVK1ZMK1asUNWqVSVJK1asUOvWrRUYGKi9e/cqPDzcMc+ZM2dUqVIlxcfHa/r06br33nslSSdOnFCLFi20a9cuLVy40HGK9rXEx8c7TsMODQ11aXsB5I4x83Z4ugRY3MB2nHXkabwPgNchkH/lZaZy6QjxwoUL3VWHW7Rt21b9+/fXuHHjVK9ePbVr104XL17UvHnzZIzRhAkTnMKwdOnGYOPHj1fPnj3VvXt3xcTEqFixYpo/f77OnTunQYMGXXcYBgAAAAB4D5cCcatWrdxVh9uMHTtW9erV04cffqh58+bJz89Pbdu21euvv67mzZtnOU+3bt20ZMkSvf3221q5cqUuXryomjVr6plnnlFsbGwebwEAAAAAIC+4FIjzWsWKFbO9vvhyffv2veHfL27RooXmzJmTw8oAAAAAAN7Gpd8hBgAAAADAW93QEeLKlSvLZrNp/vz5qlSpkipXrnzd89psNu3evfuGCwQAAAAAIDfcUCDet2+fpEu/z3v53wAAAAAAeJsbCsQZGRlX/RsAAAAAAG/BNcQAAAAAAEsiEAMAAAAALMmrfnYJAAAAcIcx83Z4dP0D21Xz6PoBXMIRYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFiSr6cLAAAA1jNm3g5PlwAAAEeIAQAAAADWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEm+ni4AgLWMmbfD0yUAAAAAkjhCDAAAAACwKI4QAwAAAHksP5wxNbBdNU+XAHgcgRgAgDzm6Q/CfAgGAOASTpkGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFhSvgzEFy5c0IwZM/Twww+revXqCggIUHBwsOrWrathw4bp/Pnz2c47ceJENWnSREWKFFFERIQ6dOig5cuXX3V9f/zxhzp06KCIiAgVKVJETZo00TfffOPuzQIAAAAA5CP5MhBPnjxZXbt21fjx4+Xj46POnTurZcuW2rt3r9588001btxYJ06cyDTfgAED9NBDD2nz5s1q27atmjRponnz5um2227TjBkzslzX9OnT1apVK82dO1d16tRR+/bttXPnTsXGxur555/P5S0FAAAAAHhKvgzEhQsX1mOPPaYtW7Zoy5Yt+u9//6u5c+dq+/btql+/vrZt26YBAwY4zTN//nyNGzdOxYoV08aNGzVjxgzNnTtXS5YskY+Pjx566CGdO3fOaZ4zZ86oX79+Sk9P17Rp07Ro0SJNmzZN27ZtU5UqVTRq1CgtWrQoz7YbAAAAAJB38mUgjo2N1WeffaabbrrJqb106dL66KOPJEk//PCDLl686Jg2evRoSdJrr72mqlWrOtqbNWumJ554QufOndNXX33ltLwvv/xS8fHx6tKli+69915He8mSJTVixAhJ0qhRo9y7cQAAAACAfCFfBuKrqVu3riQpJSVFp0+fliQlJSVpwYIFkqTu3btnmsfeNnPmTKf2X375Jdt5OnbsqICAAM2fP1/Jycnu2wAAAAAAQL7gdYF4z549ki6dVh0RESFJ2r59u1JSUhQZGaly5cplmqdBgwaSpE2bNjm1b9y40Wn65fz8/FS7dm0lJydrx44dbt0GAAAAAIDneV0gHjdunCSpffv28vf3lyQdOHBAkrIMw5IUHBys8PBwnT17VgkJCZKk+Ph4xcXFXXU+e/v+/fvdtwEAAAAAgHzB19MF3IjZs2frq6++UuHChfXWW2852u0/wxQUFJTtvMHBwTp37pwSEhIUEhLi9NNN2c0XHBwsSY4QnZWUlBSlpKQ4/o6Pj7++jQEAAAAAeJTXHCHetm2b+vTpI2OMRo4c6biW2NPeeecdhYWFOR7ly5f3dEkAAAAAgOvgFYH48OHDat++vc6ePatBgwapf//+TtOLFCkiSbpw4UK2y0hMTJQkhYSEOM1ztfmunCcrL7/8suLi4hyPgwcPXscWAQAAAAA8Ld8H4jNnzuiOO+7Q/v379dBDD+m9997L1KdChQqSpEOHDmW5jMTERJ07d05FixZ1hNvQ0FCFhYVddT57e1RUVLb1+fv7KzQ01OkBAAAAAMj/8nUgPn/+vO666y5t2bJF9957r7744gvZbLZM/apXry5/f3+dPHlShw8fzjR93bp1kqQ6deo4tdtPu7ZPv1xqaqo2b96sgIAAVatWzR2bAwAAAADIR/LtTbVSUlLUpUsX/fnnn7rzzjs1ZcoU+fj4ZNk3MDBQbdq00Zw5c/T9999rwIABTtOnTZsmSerUqZNTe8eOHbVkyRJNmzZNffr0cZo2a9YsJScn6+6771ZAQID7NgwAAA8bM4+fEwQAQMqnR4jT09PVu3dvLViwQC1bttQPP/wgPz+/q84zaNAgSdLbb7+tnTt3OtpXrFihzz77TOHh4Xr44Yed5nnkkUcUGhqqn376ST/88IOj/cSJExo8eLAk6bnnnnPXZgEAAAAA8pF8eYT4ww8/1I8//ihJKl68uJ566qks+7333nsqXry4JKlt27bq37+/xo0bp3r16qldu3a6ePGi5s2bJ2OMJkyYoPDwcKf5IyIiNH78ePXs2VPdu3dXTEyMihUrpvnz5+vcuXMaNGiQYmJicnNTAQAAAAAeki8D8dmzZx3/bw/GWRkyZIgjEEvS2LFjVa9ePX344YeaN2+e/Pz81LZtW73++utq3rx5lsvo1q2blixZorffflsrV67UxYsXVbNmTT3zzDOKjY1130YBAAAAAPIVmzHGeLqIgiQ+Pl5hYWGKi4vjjtNAFrh2EQCA/GFgO24ci/wpLzNVvryGGAAAAACA3EYgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCURiAEAAAAAluTr6QIAAAAA5L0x83Z4dP0D21Xz6PoBiSPEAAAAAACLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABLIhADAAAAACyJQAwAAAAAsCQCMQAAAADAkgjEAAAAAABL8vV0AQDy1ph5OzxdAgAAAJAvcIQYAAAAAGBJBGIAAAAAgCURiAEAAAAAlkQgBgAAAABYEoEYAAAAAGBJBGIAAAAAgCXxs0sAAAAA8pynfwpyYLtqHl0/8geOEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEvy9XQB+UVSUpLeeecdfffddzpw4IAiIiLUvn17vfXWWypbtqynywMAAADgRmPm7fB0CRrYrpqnS7A8ArGk5ORktWnTRitXrlTp0qXVpUsX7du3TxMmTNCsWbO0cuVKVa5c2dNlooDID2++AAAAADhlWpL09ttva+XKlWrWrJl27NihqVOnatWqVRo1apROnjypfv36ebpEAAAAAICbWT4QX7x4UR9++KEk6aOPPlKRIkUc0wYNGqQ6depo8eLFWrt2radKBAAAAADkAssH4j/++ENxcXGKjo5W/fr1M03v3r27JGnmzJl5XRoAAAAAIBdZPhBv3LhRktSgQYMsp9vbN23alGc1AQAAAAByn+UD8YEDByRJ5cqVy3K6vX3//v15VhMAAAAAIPdZ/i7T58+flyQFBQVlOT04OFiSlJCQkOX0lJQUpaSkOP6Oi4uTJMXHx7uzTBQgyYnnPV0CAAAA8oF3Zqzz6PqfblPFo+vPjj1LGWNyfV2WD8SueueddzR06NBM7eXLl/dANQAAAABwfV7xdAHXkJCQoLCwsFxdh+UDsf2u0hcuXMhyemJioiQpJCQky+kvv/yyBg0a5Pg7IyNDZ86cUbFixWSz2dxcrWvi4+NVvnx5HTx4UKGhoZ4uBx7APgD2AbAPQGI/APsA8vc+YIxRQkKCypQpk+vrsnwgrlChgiTp0KFDWU63t0dFRWU53d/fX/7+/k5t4eHh7iswF4SGhua7nR55i30A7ANgH4DEfgD2AeTffSC3jwzbWf6mWnXr1pUkrVuX9fn79vY6derkWU0AAAAAgNxn+UDcokULhYWFaffu3dqwYUOm6dOmTZMkderUKY8rAwAAAADkJssHYj8/Pz3zzDOSpKefftpxzbAkjR49Wps2bVKrVq3UsGFDT5XoNv7+/nrzzTczneIN62AfAPsA2AcgsR+AfQDsA3Y2kxf3ss7nkpOTFRMTo1WrVql06dJq2bKl9u/fr1WrVikyMlIrV65U5cqVPV0mAAAAAMCNCMT/LykpSe+8844mT56sgwcPKiIiQu3bt9dbb72lcuXKebo8AAAAAICbEYgBAAAAAJZk+WuIAQAAAADWRCD2chcuXNCMGTP08MMPq3r16goICFBwcLDq1q2rYcOG6fz589nOO3HiRDVp0kRFihRRRESEOnTooOXLl+dh9cgtp0+fVokSJWSz2VSlSpWr9mU/KFhOnjyp559/XtWrV1dgYKAiIiLUoEEDvfDCC1n2nzlzplq1auX4DcKYmBj98ssveVw13Gn16tXq2bOnypQpo8KFCys8PFwtW7bUhAkTlNVJYenp6RozZoxuvvlmBQYGKjIyUj179tTWrVs9UD2ux9q1a/Xuu+/q3nvvVbly5WSz2WSz2a45X07e7//44w916NBBERERKlKkiJo0aaJvvvnGXZuCHLqRfSAjI0NLly7V4MGD1bBhQ4WEhMjf31/R0dF64okntHfv3quui30gf8rp+8Dl2rZt65jv0KFD2fYr8PuAgVf74osvjCQjydx0002mR48e5s477zQhISFGkqlRo4Y5fvx4pvn69+9vJJnAwEDTpUsXc+eddxpfX1/j4+Njfvzxx7zfELhVbGyssdlsRpKJjo7Oth/7QcGyZs0aU6xYMSPJ1KpVy9x3333mrrvuMlFRUcbHxydT/zFjxhhJxtfX17Rv39506dLFBAYGGknmgw8+8MAWwFXTpk0zPj4+RpJp0KCB6dmzp2ndurXx9fU1ksz999/v1D89Pd107drVSDLh4eGmW7duplWrVsZms5mgoCCzatUqD20JrqZLly6Of/svf1xNTt7v7fuTzWYzrVq1Mt26dTPh4eFGknnuuedyYctwvW5kH9i5c6djeqlSpUznzp1N165dTdmyZY0kExISYpYuXZrlvOwD+VdO3gcuN2HCBCPJ8Xnx4MGDWfazwj5AIPZyEydONI899pjZsmWLU/uRI0dM/fr1jSTTu3dvp2nz5s0zkkyxYsXMjh07HO3Lly83fn5+Jjw83Jw9ezYvykcumD9/vpFkHnvssasGYvaDguXEiROmePHiJigoyPz000+Zpl8ZbLZt22Z8fHyMv7+/Wb58uaN9+/btplixYsbX19fs3Lkz1+uG+6SmppoSJUoYSebbb791mrZlyxYTERFhJJkFCxY42u1fqlatWtUcO3bM0T5t2jQjyVSpUsWkpqbm2Tbg+rz77rvm9ddfNz///LM5evSo8ff3v+oH4Zy8358+fdqEhoYaSWb69OmO9mPHjpkqVaoYSWbhwoXu3jRcpxvZB3bt2mXatWtnfv/9d5ORkeFoT05ONn379jWSTIUKFczFixed5mMfyN9u9H3gcidOnDARERHmjjvuMFFRUdkGYqvsAwTiAmz58uVGkvH39zcpKSmO9rvuustIMmPGjMk0z7PPPmskmffeey8PK4W7XLhwwURHR5uaNWuaHTt2XDUQsx8ULE8++aSRZD766KMb6t+/f/9M00aPHm0kmWeeecbNVSI3/fXXX0aSqV69epbT7a/r4cOHO9puuukmIynLI4SdO3c2ksy0adNyq2S4ybU+COfk/X748OFGkunSpUumeX744Qcjydx9992ulg43uZEwdLkLFy6YsLAwI8ksWrTIaRr7gHe5kX3g/vvvNwEBAWbXrl1XDcRW2Qe4hrgAq1u3riQpJSVFp0+flnTp56UWLFggSerevXumeextM2fOzKMq4U5Dhw7Vnj179Omnn6pw4cLZ9mM/KFiSkpI0adIkBQcH66GHHrqueezXCTP+BYe/v/919StWrJgkae/evdq6dasCAwPVsWPHTP3YDwqGnL7fX+09omPHjgoICND8+fOVnJzs7pKRhwIDA1WtWjVJ0pEjR5ymsQ8UTHPnztXkyZP16quvKjo6+qp9rbIPEIgLsD179kiSChcurIiICEnS9u3blZKSosjIyCx/X7lBgwaSpE2bNuVdoXCLTZs2adSoUXrooYfUsmXLq/ZlPyhY1qxZo4SEBNWvX1+BgYGaM2eOBg0apKeeekpjx47N9CHn3LlzOnDggCSpfv36mZZXvnx5FS9eXPv371d8fHyebANcV7lyZUVHR2v79u2aPHmy07StW7dq0qRJKlq0qLp27SpJ2rhxoySpdu3aWX6BxvtAwZDT93v7/mGffjk/Pz/Vrl1bycnJ2rFjRy5UjbySkZGh/fv3S5JKlSrlNI19oOBJTEzUk08+qRo1amjw4MHX7G+VfYBAXICNGzdOktS+fXvHkQP7h+Cs/lGUpODgYIWHh+vs2bNKSEjIm0LhsoyMDD3yyCMKDw/XiBEjrtmf/aBg2bJliySpRIkSuueee9ShQweNGTNGn3zyiQYOHKgqVapoypQpjv728S9atKiCg4OzXKZ937B/UEL+5+Pjo6+//lrh4eF64IEH1LBhQ/Xq1Utt2rRRnTp1VK5cOf3++++OL0iv9T7APlAw5OT9Pj4+XnFxcVedj/2jYJgyZYpOnDihyMhINW/e3NHOPlAwvfHGG9q3b58+/fRT+fn5XbWvlfYBAnEBNXv2bH311VcqXLiw3nrrLUe7/WeYgoKCsp3X/gGZIOQ9PvjgA61evVojR450nA55NewHBcvZs2clST///LPmzp2rjz76SCdOnNC+ffv0/PPPKykpSbGxsdqwYYMkxr8ga9GihRYvXqzKlStr3bp1mjp1qhYuXKhChQqpXbt2qly5sqPvtfYD9oGCISev98t/spH9o+A6ePCgBgwYIEkaNmyY02UX7AMFz7p16zRu3DjFxsaqVatW1+xvpX2AQFwAbdu2TX369JExRiNHjnRcS4yC6cCBA3rttdfUqlUr9e3b19PlwAMyMjIkSWlpaRo2bJieeuopRUZGKioqSiNHjlSPHj2UmpqqkSNHerhS5LYpU6aoSZMmKl++vFatWqXz589rx44d6tu3r0aNGqU2bdooJSXF02UC8LDExETde++9OnXqlO655x498cQTni4JuSg9Pd1xJuF7773n6XLyHQJxAXP48GG1b99eZ8+e1aBBg9S/f3+n6UWKFJEkXbhwIdtlJCYmSpJCQkJyr1C4zdNPP62LFy/q008/ve552A8KFvt4Ssryplr2tsWLFzv1Z/wLlp07dyo2NlbFixfXrFmz1KRJEwUHB6tq1ar67LPPdPfdd2vdunUaP368pGvvB+wDBUNOXu+Xv6ewfxQ8qamp6tGjh9asWaNbb7010z0HJPaBgmbs2LFav369RowYoeLFi1/XPFbaBwjEBciZM2d0xx13aP/+/XrooYey/AaoQoUKkqRDhw5luYzExESdO3dORYsW9fqd2ypmzZqloKAgPfHEE4qJiXE8evXqJenSlyT2tmPHjkliPyhooqKiJF06pSkyMjLT9IoVK0qSTpw4Iel/43/27FnHP2ZXsu8b9mUj//vuu++Umpqq9u3bO32QsevZs6ckacmSJZKu/T7APlAw5OT9PjQ0VGFhYVedj/3DO2VkZCg2NlZz5sxRvXr1NHPmTAUGBmbqxz5QsMycOVM2m01ff/2102fFyz8b9ujRQzExMZo7d64ka+0Dvp4uAO5x/vx53XXXXdqyZYvuvfdeffHFF7LZbJn6Va9eXf7+/jp58qQOHz6ssmXLOk1ft26dJKlOnTp5Ujfc49y5c46jf1dKTk52TLPfFp/9oGCx3yk6KSlJKSkpmX5+58yZM5L+921veHi4KlSooAMHDmj9+vW69dZbnfofPHhQp06dUlRUlEJDQ/NgC+AO9g8m9g8wV7K32685t19Os3nzZqWmpma60zTvAwVDTt/v69atqyVLlmjdunWqWbOm07TU1FRt3rxZAQEBjp/sgXf45z//qSlTpqhatWr69ddfFR4enm1f9oGCxRjj+EI0KytXrpQkp8vvrLIPcIS4AEhJSVGXLl30559/6s4779SUKVPk4+OTZd/AwEC1adNGkvT9999nmj5t2jRJUqdOnXKvYLiVMSbLx969eyVJ0dHRjjb7kUL2g4KlQoUKqlu3rowxWX4xYm+7/CeW7L87ax/ryzH+3sn+kylr1qzJcvrq1asl/e+MgUqVKummm25SUlKS47cmL8d+UDDk9P3+au8Rs2bNUnJystq2bauAgAB3l4xc8tprr+njjz9WhQoVNG/ePJUoUeKq/dkHCo5FixZl+3nRfnT34MGDMsY4BWLL7AMGXi0tLc107drVSDItW7Y0iYmJ15xn3rx5RpIpVqyY2bFjh6N9+fLlxt/f34SHh5uzZ8/mYtXIC3v37jWSTHR0dJbT2Q8Klm+//dZIMjfffLM5cuSIo339+vUmIiLCSDL//e9/He3btm0zPj4+xt/f36xYscLRvmPHDlOsWDHj6+trdu7cmafbANesXbvWSDKSzMcff+w0bcWKFSY4ONhIMvPmzXO0f/HFF0aSqVq1qjl+/Lijffr06UaSqVKliklNTc2zbUDO+Pv7m6t9pMvJ+/3p06dNaGiokWSmT5/uaD9+/LipUqWKkWQWLlzo7k1BDl1rHxg9erSRZEqVKuW0D1wN+4B3udY+kJ2oqCgjyRw8eDDTNKvsAwRiLzd27FjHB6CuXbua2NjYLB8nT550mq9///5GkgkKCjJdunQxd911l/H19TU+Pj7mxx9/9MzGwK2uFYiNYT8oaGJjY40kEx4ebjp06GBat27t+Afy0UcfzdTf/gHJ19fX3HXXXaZLly4mMDDQSDLvv/++B7YArnr++ecd/ybUqlXL9OjRw7Ro0cIUKlTISDKPPfaYU//09HTHl6pFixY13bt3NzExMcZms5nAwECzcuVKD20JrmbWrFmmadOmjofNZjOSnNpmzZrlNE9O3u+nTZtmChUqZGw2m2ndurXp3r27CQ8PN5LMoEGD8mBLkZ0b2QfWr1/vmN6sWbNsPysuXbo003rYB/KvnLwPZOVqgdgYa+wDBGIv9+abbzo+/FztsXfv3kzzTpgwwTRs2NAEBQWZ8PBw0759e/PHH3/k/UYgV1xPIDaG/aAgycjIMJ9//rljPIODg02zZs3MxIkTs53n559/Ni1btjRFihQxRYoUMS1btjQzZ87Mw6rhbj/88IO54447HEf6ixYtalq3bm0mT56cZf+0tDQzatQoU6tWLRMQEGCKFStmunfvbv7+++88rhzXa8KECdf8d3/ChAlZznej7/fLli0z7du3N+Hh4SYoKMg0atToqu8pyBs3sg8sXLjwuj4rZrXPGMM+kF/l9H3gStcKxMYU/H3AZowx131+NQAAAAAABQQ31QIAAAAAWBKBGAAAAABgSQRiAAAAAIAlEYgBAAAAAJZEIAYAAAAAWBKBGAAAAABgSQRiAAAAAIAlEYgBAAAAAJZEIAYAAAAAWBKBGAAA3LC+ffvKZrNp0aJFni4FAIAcIxADAAAAACyJQAwAAAAAsCQCMQAAHnDw4EE9++yzqlatmgIDAxUREaFGjRpp6NChio+Pd/S7cOGC3nrrLdWuXVuBgYEKCwvTbbfdpu+++y7L5dpsNlWsWDHLaRMnTpTNZtOQIUOc2mNiYmSz2bRv3z7NmDFDt9xyi4KDgxUREaHevXvr0KFDmdbx9ddfS5Jat24tm83meOzbt0+SZIzRt99+q1tvvVUlS5ZUQECAypcvr7Zt2+qjjz7K2ZMGAICb+Xq6AAAArGbp0qXq3Lmzzp07p4oVK6pTp05KSkrStm3bNGTIEHXp0kX16tVTQkKCWrdurbVr1yoyMlJ33323EhMTtWDBAi1dulQrVqzQuHHj3FbXxx9/rNGjR6tly5bq0KGDVq1ape+++05r167Vxo0bFRgYKEmKjY3VsmXLtHv3bt15550qVaqUYxlFihSRJA0ePFjvvfee/P39ddttt6l48eI6duyYNm3apF27dunpp592W90AAOQUgRgAgDx05swZdevWTefOndPIkSM1aNAgFSr0vxO2VqxYoTJlykiSXnnlFa1du1atW7fWTz/9pJCQEEnStm3b1KpVK73//vtq166d7r77brfU9tFHH2np0qVq1qyZpEtHp9u1a6fly5drypQp6tevn6RLR5r79u2r3bt366WXXlJMTIzTcpKTk/XBBx8oJCREGzduVKVKlRzT0tLStGLFCrfUCwCAqzhlGgCAPPTll1/q5MmTat++vZ5//nmnMCxJzZo1U4kSJZSYmKivvvpKhQoV0scff+wIw5JUo0YNvfbaa5Lk1iPEAwcOdIRhSQoKCtKgQYMkSUuWLLnu5cTHxyslJUXR0dFOYViSfH191bJlS/cUDACAiwjEAADkofnz50uSHn/88av2W7t2rZKSktSgQQPVqFEj0/R//OMfkqQ//vhDGRkZbqntjjvuyNRWrVo1SdLRo0evezklSpRQuXLltGHDBr300kvas2ePW+oDAMDdCMQAAOShgwcPSpKio6Ov2u/IkSOSlO0NssLDwxUWFqakpCSdPXvWLbWVK1cuU5v9yHRKSsoNLevrr79WZGSkhg8frujoaFWsWFGxsbGaM2eOW2oFAMAdCMQAAHgpm812Q/2vdST5ytO3XdGmTRvt2rVL3377rf7xj38oIyND33zzjTp06KDu3bu7bT0AALiCQAwAQB4qX768JGn37t1X7We/sdb+/fuznB4XF6dz584pMDBQRYsWdbQXLlxY58+fz3Ie+9HpvBIaGqr7779f33zzjQ4cOKAVK1aoXLlymj59umbPnp2ntQAAkBUCMQAAeaht27aSpM8///yq/Ro2bKjAwECtXbtWO3fuzDR90qRJkqQWLVo4HdktXbq0Tp8+rdOnT2eax379sjv4+flJunTX6Ot1yy23OK593rx5s9tqAQAgpwjEAADkoUceeUTFixfXnDlzNHbsWBljnKavXLlSJ06cUHBwsPr166eMjAw9/fTTSkxMdPTZsWOH3n77bUnSs88+6zR/q1atJMkx3W7EiBFatmyZ27bDfgR7+/btmaYdOHBAEydO1IULF5zak5OTtXDhQkn/O1IOAIAn2cyV/xIDAIBctWjRInXu3FkJCQmqVKmSGjdurKSkJG3dulW7du3S+vXrVa9ePSUkJKh169Zau3atSpQooVatWikxMVELFixQcnKynn322Uw/u/T33387llevXj1FR0frr7/+0sGDB/XQQw/p448/1ptvvqkhQ4Y45omJidHixYu1d+/eTDfx2rdvnypVqqRWrVpp0aJFjva1a9eqcePG8vf315133qnixYtLkoYPH66DBw+qfv36CgoKUqNGjVSuXDklJiZq+fLlOnnypBo1aqRly5bJ398/t55iAACuC0eIAQDIYzExMdq4caOeeOIJGWM0Y8YM/fHHHwoLC9OwYcMcd6AOCQnR4sWLNXToUBUvXlw///yzli5dqkaNGmny5MlZ/gZxrVq1tGDBAsXExGjHjh2aN2+eoqOjtWLFCjVu3Nht29CwYUNNmjRJNWvW1G+//aavvvpKX331lRISEhQdHa1Ro0YpJiZGBw4c0A8//KBly5YpKipKY8aM0eLFiwnDAIB8gSPEAAAAAABL4ggxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJAIxAAAAAMCSCMQAAAAAAEsiEAMAAAAALIlADAAAAACwJF9PF1DQZGRk6MiRIwoJCZHNZvN0OQAAAADgVYwxSkhIUJkyZVSoUO4ewyUQu9mRI0dUvnx5T5cBAAAAAF7t4MGDKleuXK6ug0DsZiEhIZIuDV5oaKiHqwEAAAAA7xIfH6/y5cs7slVuIhC7mf006dDQUAIxAAAAAORQXlyCyk21AAAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCAGAAAAAFgSgRgAAAAAYEkEYgAAAACAJRGIAQAAAACWRCBGJgnJqZ4uAQAAAAByHYEYTt77dbtuHvKbFmw77ulSAAAAACBXEYjh5MOFuyRJQ2du8XAlAAAAAJC7CMT/b/Xq1erZs6fKlCmjwoULKzw8XC1bttSECRNkjPF0eXnO35ddAwAAAEDB5uvpAvKD6dOn67777lN6eroaNGigli1b6uTJk1q6dKmWLVum+fPn69tvv/V0mXnK39fH0yUAAAAAQK6y/GHAtLQ0PfXUU0pPT9e3336rtWvXaurUqVqwYIE2bdqkiIgITZ48WQsXLvR0qXnKjyPEAAAAAAo4y6eebdu26cSJE6pevbruv/9+p2k33XST+vTpI+nSKdUF3YWLaY7/55RpAAAAAAWd5VOPv7//dfUrVqxYLlfieS3eXeD4f44QAwAAACjoLJ96KleurOjoaG3fvl2TJ092mrZ161ZNmjRJRYsWVdeuXT1UYd45e+F/vz/MEWIAAAAABZ3lU4+Pj4++/vprhYeH64EHHlDDhg3Vq1cvtWnzf+ydd9wUxf3HP3v3NJ6H3kE6CAIqRUUEFcVesSeW2LumaIwlsZefRmOLNRpj1xhji11BQFGRJiBSpPf+wNPb3e3vj7vZm52dmZ29273yPPN+vZTntszO7s7OzHe+bSL23Xdf9OrVC1OmTEHHjh2zXdWMooNqaTQajUaj0Wg0muaOjjINYPz48Zg+fTpOPfVUzJs3D/PmzQMAFBUV4aijjsKAAQOE5zY0NKChocH6XVlZGXh9M0E4ZGS7ChqNRqPRaDQajUYTKC1eQwwAb775JsaMGYPevXvjhx9+QHV1NX755RdceOGFePjhhzFx4kSb0Etz//33o127dtZ/vXv3znDtg6FNiV4r0Wg0Go1Go9FoNM2bFi8QL1++HBdccAE6d+6Mjz76CGPGjEFZWRn23HNP/OMf/8CJJ56IefPm4V//+hf3/FtuuQUVFRXWf+vXr8/wHaRPdUMET01dYdsWiZlZqo1Go9FoNBqNRqPRZIYWLxD/+9//RlNTE4499li0bt3asf+ss84CAHz99dfc84uLi9G2bVvbf/nG3z5fhoc+X2bbFo1qgVij0Wg0Go1Go9E0b1q8QLxhwwYAQLt27bj7yfZdu3ZlrE6ZZu5a571FTS0QazQajUaj0Wg0muZNixeIu3fvDgCYM2cOd//s2bMBAP369ctUlTJOq0JnROmoNpnWaDQajUaj0Wg0zZwWLxBPmjQJQNwk+plnnrHtmzlzJh599FEAwBlnnJHxumWK4kJnM9A+xBqNRqPRaDQajaa50+IF4tGjR+OGG24AAFx99dXYe++9cdZZZ+Hggw/G+PHjUVNTg8svvxxHHnlklmsaHDzr6GgslvmKaDQajUaj0Wg0Gk0G0bl1ADz00EMYN24cnn32WcydOxfLli1DmzZtMGHCBFx22WU4++yzs13FQGmKOoXfiA6qpdFoNBqNRqPRaJo5WiBOcOqpp+LUU0/NdjWyAs88OqaDamk0Go1Go9FoNJpmTos3mdYAEZ6GWPsQazQajUaj0Wg0mmaOFog13BRLOsq0RqPRaDQajUajae5ogVjDDaqlfYg1Go1Go9FoNBpNc0cLxBouPK2xRqPRaDQajUaj0TQntECs4aJNpjUajUaj0aTDB/M34rNFW7JdDY1Go5GiBWINFxJUq6K2Cf/8ZhW2VdZnuUYajUaj0WjyhfKaRvz+3/Nx5WtzuekdNRqNJlfQArGG60McjcUHr1veW4h7P16Cc/75Q4ZrpdFoNBqNJl+pa4pafzdEtECs0WhyFy0Qa7iQoFpTl24HAKzYVp3N6mg0Go1Go8kjisLJKWYDJRxrNBpNrqEFYg143sKNCfOm4kLvTWTqsm047elvsXK7FqI1Go1Go2np1GsNsUajyWG0QKzhsrWiHqZporgg2URUA2Nc9OJszFu3G79788egqqfRaDQajSaHMSl/rHqtIdZoNDmMFog1XGoao2iIxFBcELa2/WvGak9l7Kpp9LtaGo1Go9Fo8gDa+kwLxBqNJpfRArFGSMw0URA2khsM8bF+Ut8URV2jHjw1Go1Go8lXYpSGuFGbTGs0mhxGC8Qam1kTDZuKOORRIDYM7xK0aZqY8NBU7H3n53pFWaPRaDSaPIWeWsQE8wyNRqPJBbRArBESZSTiUAoCrlcaozFsrWxANGZi+VYdlEuj0Wg0mnyEFoJJ5gqNRqPJRbRArBFimqbNStqrQJyK/EwvIu+obvBegEaj0Wg0mqxDj+fsArtGo9HkElog1ghhBzCvAm66CuWGiDaZ1mg0Go0mH7EJxNpkWqPR5DBaINYIYRd0U/EJ9n5N+qIZiuKl0Wg0Go3GV0wqznREa4g1Gk0OU5DtCmiyR1M0hiWbK4XBLtjtXoNqxVIIKqnHTI1Go9Fo8h96PI/pwV2j0eQwWiBuwdz034V498eNwv27ahuxcnuN9durD/HG3XXYWd2ATq2Llc+hhfAMKKQ1Go1Go9EEAJ3BQmuINRpNLqNNplswMmEYAG54e4Htt1cNMQC88cM6T8ebOlWhRqPRaDR5T0wH1dJoNHmCFog1QhZtrGS28CXimat2YvLirdx9DRG+hFvdEMG2qnrHdpuGWK2aGo1Go9Foco7keK4FYo1Gk8togVijTFjQWn793Exc+socbKt0CriNUb5AvP+9X2LMfVMcqZVsIbW0zbRGo9FoNHmJ1hBrNJp8QQvEGmWKCsKObXSgjB3VjY79jQINcX1TfPtjk3/BTkoopjXEpk7ToNFoNBpNXqLzEGs0mnxBC8QaZQrDTo2tW6AMkck04bWZ63Ds499Yv20Cscf6aTQajUajyQ3o8VwLxBqNJpfRArFGmSKOzbTbIKcSiGt7VVJDTK8oaw2xRqPRaDT5CT2E6yjTGo0ml9ECsUaZQo5AHKGSDfNcfnnbZIKu3WTaW/00Go1Go9HkBjYNsR7QNRpNDqMFYo0yYY66101DbHBiRctWiuldekFZo9FoNJr8R1t8aTSaXEYLxBplYpwBjRZueeMdqyGOxUws3VwlvIZp8yHWA6hGo9FoNPkIPWeI6RVujUaTwxRkuwKa/IEnENMaYt5+Vj/896+W47HJy4XXMLWGWKPRaDSavEeP5xqNJl/QGmKNMjwNMK0h5plPs7mEZcIwoNMuaTQajUbTHLBpiPV4rtFochgtEGuU4Q1n0Whyqx9RJOki9Pip0Wg0Gk1+Qg/hejzXaDS5jBaINcrwNLZ0lGmehriowFsTi2kfYo1Go9Fo8h5Ta4g1Gk2eoAVijTK88SzqYjLtNZCGbQCNSQ7UaDQajUaTs2gfYo1Gky9ogVjjoHPrYu72VHyIveYepA/X46dGo9FoNPmJPY2iHtE1Gk3uogVijYO7Jw3nbneLMh3hqHTd8hQ7r5H8WwfV0mg0Go0mPzF12iWNRpMnaIFY46AgxCZLisMbzlw1xJ4FYnleY41Go9FoNLlPTJtMazSaPEELxBoHhWF+s+D7EMuDank1k/IjqNb68lpU1TeldK5Go9FoNJr0ocdwbTKt0WhyGS0QU2zfvh033HADhgwZglatWqFjx44YPXo0/vSnP2W7ajkBN8p0VK4hpve78c7cDbjt/UXW71RWlNftrMUhD07FAfdN9n6yRqPRaDQ5yPryWuyqacx2NTxhahcojUaTJxQEVXBjYyO+++47TJ8+HfPnz8f27duxe/dutG/fHl26dMHIkSMxYcIEjBs3DkVFRUFVQ5m5c+fimGOOwc6dOzF8+HBMmjQJlZWVWLx4MR599FE89NBD2a5ixjhkz87c7dw8xDYfYhNLNlfa9ysOguvLa/HHtxfYr5fC+Pn9qh0AgPomHaJao9FoNPnP1sp6HPLgVADAmgdOyHJt1NFRpjUaTb7gu0C8dOlSPPvss3jttdewa9cu4argBx98AMMw0L59e5x//vm4/PLLMXToUL+ro8T27dtx7LHHoq6uDh988AFOPvlk2/5Zs2ZlpV6Z5Iz9eqFz62KcNnoPFIRD6Na2GFsrG2zH8PMQ202irv+PXahVDaTxw+pyxzZtYqXRaDSals5PGyqyXYWUiOk8xBqNJk/wTSDesGEDbr31Vrz22muIxWLo06cPTjjhBIwZMwZ77bUXOnbsiLZt26KiogK7du3CkiVLMGvWLEyfPh2PP/44nnjiCfzmN7/BPffcg169evlVLSXuuOMO7NixA0899ZRDGAaAMWPGZLQ+2aBjWRFuPm4v63eH0iKnQMw5z6YhjppojERt+yOKAjEvjpcePjUajUajyU+iNoE4ixXRaDQaF3wTiAcPHgwAuOyyy3Deeedh/Pjx0uOPOOII6+8ZM2bg1Vdfxauvvoq3334b1dXVflXLlbq6Orz22msoKyvDRRddlLHr5hoGI5B2au00Y+cNaGyUaTYgl+qqcIitAFLzOTLAj5Ct0Wg0Go0mc0SpGCLah1ij0eQyvgnEV1xxBW666SZ0797d87kHH3wwDj74YNx555148MEH/aqSEnPmzEFVVRUOPvhgtGrVCp9++im+/PJL1NfXY/DgwTjrrLPQs2fPjNYpG7ACabtWhY5jeAMaHWU6EjNREDaY/YoCMUdFrMdPjUaj0Wjyk6g2mdZoNHmCbwLxo48+mnYZPXr08KUcLyxevBgA0LVrV5xyyin44IMPbPv//Oc/44UXXsDZZ5+d0XplGlYeLeKkXnLNQ2yaKAjZz4sqxrYqr25wbOMNoFX1TfjDv+fj5JE9MWnkHtb26oYIHp/8C2obo45zNBqNRqPRZJZoTJtMazSa/KDFp13atWsXAOB///sfPvvsMzz11FPYtm0b1qxZgxtuuAF1dXW44IILMH/+fO75DQ0NqKystP2Xj7AaYl4uYte0S9EYCkKshtguEbP7CXd+uJhzPedxT361AlOWbsPv/z3ftv1vny/D89+sxus/rOOWr9FoNBpNrnLb+4tw38fOcTCfYYNuajQaTa4SmEB88cUX4/LLL8eKFSukx73wwgu4+OKLg6qGK7GEwBaJRHD33Xfj6quvRpcuXdC3b1889NBDOPPMM9HU1CRMu3T//fejXbt21n+9e/fOZPV9gxWIiwp4ArHzvAiTdinMCsTMOa0Kw8p14g2gG3fXWX//85tVqG+Ka4QXb3YuRKhGuNZoNBqNJlvsqG7AqzPX4vlvVqOqvinb1fENegzW47FGo8llAhOIX3rpJbzwwgsYO3YspkyZIjxuxowZePnll4OqhiutW7e2/uYF1SLbpk+fzj3/lltuQUVFhfXf+vXrg6lowMg0xGVFcSGWJxDTGuCYyQmqxQyCqnmJRRABGADu/XgJnp2+knsdQD3CtUaj0Wg02SJMjb81Dc3H7SeiTaY1Gk2eEKjJ9IABA1BTU4PjjjsOTz75ZJCXSpm+ffsCAEpLS9GlSxfH/n79+gEAtm3bxj2/uLgYbdu2tf2Xj7CWzLRp89WHDwLA19iyGuJ2pfZgXMu2VnGPH9rD/TnxZOdGRuU8e025sG6qAb00Go1Go8kW9EhV3RCRH5tHpsfsgrlGo9HkKoEKxIcccgimTZuGTp064fe//z2uuOIKRCLyzj7TjBo1CkA8/VJDgzOwU3l5XOCiNcnNETbKM236TIRjtzzE0aiJHm1LbPu3VzWgiYqsRY4X+RLT8AZQ9jQy3rKm2fFtegDWaDQaTW5Dj3U1rgJx0LXxD60h1mg0+ULgQbUOPPBAzJ49GyNHjsQ///lPHHnkkdi5c2fQl1WmT58+GDFiBEzT5JpFk21EcG6usGmAaYGY/M31IY7aNcQ83+PGSCxxvmkJxEO6t3GtE2/8DBusj3L8KJ7JdJQnJWs0Go1Gk0PQYyvtFsQjnzSt9Li8aGNFFmui0Wg0cjISZbpXr1749ttvccYZZ+Drr7/GmDFjsGjRokxcWokbb7wRAHDDDTdg8+bN1vb58+fj4YcfBgBceeWVWalbpmAFTdoX2NIQu5glx0yTq5Ulq8S0zHrdUYPx6wPkAch4Az8btIvUiWceHYkp5nzSaDQajSZL0GNrQ0Q+buWTppXWEP+0sQK/MC5UGo1GkytkLO1SSUkJ3nrrLdx1111Ys2YNxo8fj/fffz9Tl5dyzjnn4IILLsBPP/2EYcOG4YQTTsDEiRMxduxYlJeX47LLLsOZZ56Z7WoGChtUq01JMkV1QUI4dstDHImZgsBbTqG1TUkB7jx5uLROvLJYgZgUqX2INRqNRpOP0EOVu0CcP+MaOwbPXbsrSzXRaDQaOQXuh/jLbbfdhr333hvnn38+zjjjDHTr1i3TVeDy4osvYvz48fjHP/6BadOmwTAMjB49GldccQUuuOCCbFfPdzZX1Nl+sybT7UuLrL/lGmK7fzA32nPCh5geHMOG4RDCWXjX69qm2PabCMh8DXH+TBw0Go1G0zKhhVw3k+k8kof1orRGo8kbMi4QA8Cpp56KGTNmYNKkSVi3bh0MF8EoExiGgcsuuwyXXXZZtquSEbZW2gOIscLp4UO6YEi3NtirRxtLQ8wb22ihMxozuavXTYljaBPmcMhwaHtZuNpmZiMR1nmm2now1mg0Gk2uQ4+bD3y6FCeN6Kl0bK7DLkpnf6an0Wg0fAITiKdOnYru3bsL948YMQJz5szBs88+i2i0+eTdyxdY7Ssrm3ZqXYzPrzsUAPDO3A3xczjlRBmBmHDEXl0xZWk8VRXRENMuvQUhw3FNFp48ywq5RKjWeYg1Go1Gk4/Qw/HG3XXiA5FfAjE7XueA7kOj0Wi4BCYQT5gwwfWYzp0749Zbbw2qChoJ7KAq09aSQYxnwmz3IY5ZmubhPdsmBeLEMfd+vNh2PTfLAJMjgrMDLNEQ84VnHVRLo9FoNLmNFxk3n9Z5tZWWRqPJFzIWVEuTW0Si7MqtWDglQq4sYBb5mwjahmGgU1mR7VpvJzTNbtcjuJloA0A4FHLUQ3SsRqPRaDS5Br1AfcReXaXH8hamcxUtEGs0mnzBNw3xxRdfnPK5hmHghRde8KsqGgXYgUoW4MrSEHM0trRgHS8zfnA4ZKAgHP+7KZqippYz8LOm0UkNsbxuGo1Go9HkIvT41egyXuaTjOn0IdY20xqNJjfxTSB+6aWXhPsMQxylmOzXAnFmYQcqmT+vIdEQ04GyIjETIcO0yitIaG9T1dQqaYjD4ijT+eRrpdFoNJqWCT18fbN8h2O/aTs2f8Y17bak0WjyBd8E4hdffJG73TRNXHzxxTj44INxySWX+HU5TZqwUZlDMh/ixL+8gXjV9ppkmTETsVDSZLrAElbFg+IJ+/bAxws3c/fxNNJsHYjGWJtMazQajSY/sY9V0Zhpi+tBj3v5JRAzG7SCWKPR5Ci+CcSyXL0XX3wxBg0a1Czz+eYr0SirIVYJquXcV9UQSZYZM60BMGQYljlzk8x0WbYrsS8SjWHW6nKM7NPeYQZNBGHeJEH7L2k0Go0m12GHqkgshnAobP2mres27a5H1zYlmapaWmgNsUajyRd0UK0WiheTaSuoFm8nNVB/tHCzNXCHQ0lfqF8/NxOXvzKHW7ZMaCW7/j5lOc755w+46rV5DsE3KtMQax9ijUaj0eQ4onEtuT/59+OTf5GWZZom1u6s4aYizDQ6D7FGo8kXtEDcQmEHYKmGOPEvzwecHXNJuSHDwPryZD7FLxZv5ZYtM2smJtOv/7AOADD9l+2O4yOWhth5vtYQazQajSbXYRWp7DhHj9duGRpe+X4tJjw0Dbd9sMi3+qVKPpl3azSalo0WiFsojpVbaVCt+L+8sY318yXFqqRVipcpEYhNYNqybdhZ02htc66cy3yItbmWRqPRaHIbh4Y4KtYQtyoKQ8ZDny8DkFxIziZe0jtqNBpNNtECcQuF9e0Jy4JqSUym6WLaFBdYwbrCiuMeG9yLxjRNXPjibPvxrIY4KvYh1qvTGo1Go8k32AVrerzu1aFVpquTMtpKS6PR5AtaIG6hsCu3MpNpEhxrV22jYx9dSkM0Zml8ZVGraVR8iGXHE4GaV32tINZoNBpNrsMu3rLWTU2R5P69ureRliWzuso0OtODRqPJF7RA3EJx+hCLj92za3wAXrez1rGPHnybojFLYJWZRnUoLbT+PnXUHsLjeOO6QyCWCtR6MNZoNBpNbuOIMs0sWDdS+YvcFnpzadRjLcC0wbRGo8lVfBOIw+Gw8D/DMPDyyy8L9xcU+Jb9SaOI04dYPFSVFoetc9jVZ/qnaSZTLIUl5T14xgjr71NH7WFpoFm4qZQS2ybu1TX+mwjgnKFWC8QajUajyXXYsWrO2nL77zXlwmNZcmnYa4o4pfdpy7bhmjfmobzGaXGm0Wg02cI3gdg0zZT/i2nb1ozDalZlJtOFoWQzYc9jg2rVN0UT5YmvXVKYLM8wDIzu08G1vuz1e7Yv4daHRltraTQajSbXYRea//KePUL0+/M3JY/NSI3S54FPlzqySxgGcOGLs/Hxws14+ItlWaqZRqPROPFNII7FYmn9p8ksrCAZlrSEMBUhy5kOwn5sUiAWS8TsvluO34t7HFdDnLhgUThs+831Ic6lpXKNRuOZuWvLsWp7dbarodEECjtUFRWIB2Q3H2F2kTpbPDt9pXT/9qqGDNVEo9Fo3NE+xC0UViCWmUzTJs2sQMwOzsTXyTCAE/ftwS2PFYhH9emApfcci/362jXFMh9iMmFImkyLj9VoNPnH+vJanP7M95j48PRsV0WjCRR2qCqSrFDn87BGD/1u6aM0Go0mkwQmEE+bNk352LvuuiuoamgEsIKtSpRpAIhE7dp8VmglwUDCIQM3HD2EWx7PnLqkMIy5a3fZtsVME62L7f7lDoHYJY+xRqPJT1bvqMl2FTSajMBaM7WnAk+6HcuSL+OeTOjXaDSaTBNYjzRp0iTMmzfP9bjbbrsNd999d1DV0Ahw+hCLjw3LNMSCckOGwTVjBtRTMpkA2pTwBeLihEC8Yls1ahsj3PO1ybRGk78UUK4auZRKRqPxG3asOnlET+Gxbp9CLn8pdPDLXK6nRqNpeQS6RHf88cdj+fLlwv1/+ctfcN9992Hw4MFBVkPDwUtQLcMwLC0xmw7CmT9R7NObvJZaHU0TKGRWkYlGmF5dfnzKcpvJNyk/n03LNJqWTgEVzK8xquNMaJovrJArWzR2XRzK4XGP9m/WC9YajSaXCEwg/uCDD1BZWYmjjz4aGzdudOy/+eabcf/992OvvfbC1KlTg6qGRoAXk2kgqSWOxOQm002JiWtYMqC7XStZtukYNNcmciHTQUeWb622+RAXJITlmJaINZq8hdYQN3DSt2g0zYUmZsGHHrtYATifhzX6VlZu1y4RGo0mdwhMID7ssMPw5ptvYsOGDTj66KOxc+dOa9+f/vQnPPjggxg2bBimTp2K7t27B1UNjYAoI9i6aW2JptZNQ0ybTIuCZqgLxGKhlhaIf95UYa9r4mb0CrRGk7/Q/URDkxaINc0Xdlyl5eP563fb9rn6EHNUxOvLa/Hn937Kml/+oK6tAdiF+QXMfWk0Gk02CdRketKkSXj++eexdOlSHHfccaiursb111+Phx9+GHvvvTe++uordOvWLcgqaAQ4NMQuEnFSQ8xGmeaXGzKArm1KuGWpCsQx0xSuhtMm01srG2xhpi0NsRlfeb/69bm4/j/zla6p0WhyA9qt46eNu9EQiWaxNhpNcLAaYjpY5AszVtv2pbLOe8G/ZuGNH9bh1899n1L90oXEAmEX4jUajSZXCDzM34UXXoiHHnoIc+bMwZAhQ/DYY49h7733xpQpU9C1a9egL68RwGpe3TTERCN71WtzpeZcybzA8QLPHtPHUZaiPAwT4tVwNk8jXWRhwtQyapqYu3YXPvlpC96dtxHVDfzgWxqNJvegv/2LX5qDc5//IYu10WiCY/2uWttveoz9aOFm+74UokyvSmiGt1ZmJ/cvyRbRSGnC90xojTUajSYXyEjc++uvvx5//vOfsXnzZowYMQJTp05Fly5dMnFpjQBW0yvLQwwkzZCXb6vG9F+2W9vZsdfyIU6UxyvWm4ZYTSDmlW+aJuqbklolNmWUpnlgmiaemrrC1i41+Q8b+G8Ok5ZN486m3XWYuWqn+4GarPJ/nyy1/ZYJvfkYU4tkhWikYgGozgM0Go0mExS4H6LGxIkTXY8pLIzn1jvzzDNt2w3DwJQpU/yqikYBdrIZdhmcNlXUW39X1DVZf7ODs+VDnJBXeZpnWcAtG6Y4gAgrEFfWJ7W/ZKCNxewCdVM0F6cKmnT5z5z1eOjzZQCANQ+ckOXaaPyC7aM03hn3wFcAgHeuGof9+nbIcm00qkQlUq+7hjj3vhuy4E4LxMu2VsE0TdfFeI0m06zdWYO/vLcIlx86AIcO1sq7loJvAvG0adOUjluwYIFjm+4QMw872fTyCugUKOK0S/ECeavAqvJwzDSFk+LisFhDTATuqGkXgtkI2Zr8Z315LW5656dsV0MTAFog9o/Za8q1QJxHyDIkuH0Vmfhq5qwpx32fLMEdJw3HyN7tHfvp+rcpLrDcmGiLLQD4dsVOHLxn50DrqtF45cHPlmHGih2YsWKHXmRvQfgmEK9evdr9IE3O4BCIoS4R0wFA2MGXlGuZTHPOV10A8eJDTEO006Zp2lak2Uiemvxnxood2a6CJiC0QOwfOag01EiQeffkQvaEM56NB+e64e0FmHz9BMd+WsMdChlWTnFWIGZ9pzWaXIBtp5qWgW8Ccd++ff0qSpMBnD7E6ufSQqYoqFbI8iFOR0Msnsip+BDHTNMmvLP3rMl/2LbUGIlJ24Ymf9CTZf/gpeJJlcr6JizaWIGx/Tu5ZifQpEZaPsQZHOZqBYEq6cWsgpBh5RRnBY3dtU3QaHKNQokFoqb5ot96CyUd7QsRMhsjMeyobuQeQ+ZJ6QTVMiUm0zKhxzKZjtm12dX1Osp0c4O1bKhr1Cu7zYXbP/g521XQcDjr2e9xzvM/4JXv12S7Ks2G3h1b2X7LBeLcWdgNh/ljOT1uh0IGChMa4jpGIBalYXr+61U49elvUVWvBWaNRpMZfBOId+zwx3TRr3I0ctIRiImM+eXircJjiOaAJ/yqBtWSRpmWrODRGmI6zcODny8VnaLJV5imJAtGo9G0VPz8LJZuqQIAvDd/k3+FtnD6dSpL/FsKQD4+55Khk8jViu6HaQ1xLbNgSd9LLGZizppy1DREcN8nS/Djut145fu1/ldao3FBhzVqmfgmEPfv3x+33HJLygLttm3bcOONN6J///5+VUkjIZ2gWkSebYyKtXEhqQ+x2nVqG6Mppl2K/2uaJpoo8+5vluvFluYG25Rywb9Oo9HkB79srbJlTcgWpN8ippqyfiwf+rgotRAdDhnWfYlyKpumiX/OWIUznv0e5zw/09rfpFMlajSaDOGbQHzmmWfioYceQq9evXDKKafgrbfewpYtW6TnbN68GW+++SZOOukk9O7dG4888gjOOussv6qkkZBOxGUVgZYIpTwfM1WT6ar6iHLaJZpwwjyLNZnW7m7ND9ZHPR8mixqNaZo5ZfraElm0sQJHP/o1jnh4erarYi1QFxCBWBpUKxM1UkM0lNMa4rhAzD+Q3Msf3ppv5WJesKHC2l+gB21NFtAa4paJb0G1/vWvf+G6667Drbfeio8++ggffvghAKBnz54YMmQIOnTogDZt2qCqqgrl5eVYtmwZNm+OrxaGQiGcdNJJuOeeezB8+HC/qqSRwC68ukWZvnvScMunjxwrO8ewgmo596kKxJV1TZK0S2HhecSamg2qNbRHW6XravIHx3wphyaLGo2Iq1+fh9U7avD+NeNRUijuy/xCC99Ovl+5EwCwo7ohyzVJCsBEAJQGQcuDd8mmjSoQuDiRdvmBwPxeB23TaDSZwjeBGAD22WcffPDBB9iwYQNeeOEFfPTRR5g/fz42btzovHBBAfbff3+ccMIJuPjii9GrVy8/q6JxQRTMQsTgbm2sv4k8O3PVTts2epwmfsI8oTmkaJewfFu1cJ9UQ5yooGmaoF2Wjh3eXe3CmryBXVvJJe2JRiPi00Vx66kvFm/FySN6BnINLQTL6dq22Pq7KRrLamRZYtlCxk1ZP5aJPi4WM5WEUdERbEaHQkFZbk1Ua4g12cBLGlJN88FXgZjQq1cv3HHHHbjjjjtQU1ODxYsXY9u2baioqEC7du3QtWtXDB8+HKWlpUFcXqNAOmmXiPb337PXW9tChmHPPZgoj7f6rqohliETiLu3K8GCDRWImqbNl0lPD5sf7MClTaY1+cTm3XWBlU138fqzcEKPQ9GYiQwo6rlEojHsrIlnayCmxdn0IZ6zphwXvTQbt50wDGcd0Ft6bFOUXxfWskuoIXYZlcOqq+cajUaTJoEIxDRlZWU44IADgr6Mb+zcuRNDhw7F9u3bMXDgQKxYsSLbVQqEdIJqqRxKJhv/nbtBuC8dRJGqbz1hKDZX1AOITwhpk+l0ImtrchOnhpj/juubojAMoLggS7NejYZDfVNwQYPoOBG655OTzbFh0lPfYvWOGgBAQUIAlMm8qlVNVbt67Rs/oqo+ghvfWegqEG/cXYeV26sxsEtr23anQCz3IRahNcSarKCbXYtEL78x/PGPf2wRqZ/SmQDw5FnWPE8m9AY5xh01rJtVPutDrLWHzR/eK65piGDk3V/g1Ke+y3yFNCkzqk972+/mEuiE7ivZvKx+QvfxQXV9N7+zEMc9/g0aIvmX/5tuT6zFVCb5eVOl9TcRHEkb2cSxIHDTqhJaFaW2+Of1O3uHs+hNW4sZgJWHmMVtTFZN0ahpWXy0cBPmrt2V7WpomhlaIKaYMmUKXn75ZVx22WXZrkrgODTELktihuBvAjufkFk6sZGBvfK7I/YU7gsZRjIPcUwLxM0dti3xXvH3K3eivimGxZsrnTs1OQvbpxRL3CTyCfq+6jMkEAfV9/179nos2VyJ6cu2B1J+pmCDQGWLAsaHeNwDXzmOkb3KRirNYGmKArFXC64+HZ2ub86gWvwyecK07TwtEGsYlm6pxLVv/IjTnwlugVu3upZJ85hh+EBdXR2uuOIKDBs2DDfccEO2qxM47Iq4l3FHRaCVDaqiVd+ZtxwBABjQuUxatmxibBjJ+sVMoDGSvE+d0rD5wTYl3sR/a1V9hmqj8RXmXTYXc3daUG0MsFOiu/g5a8sDuw57rXwkmiOLpQUqeYglD7uOiiK5tbIBkWjMc3A1r267vCjp9PzChNiHeI/2reR1aS5mIRrfWLezNttV0DRTtECc4K677sKqVavw7LPPorCwMNvVCRwySLYtKcAhe3bGoK6tXc5Iwhuj2G0yAVu0r3u7Eiy66xh8ft2h0usXSaKBGoZhS7tER9PWGuLmh0pQraaIXgnJR9g32Xw0xHZz0sCgHuC3K3barGU0diE+V+JLWGmXZFmXJOezgv1DXyyz3ee+vdq51sGrEMozN2efpyjKtEhQ1mhEZMKMPl0rRk1+onsjAAsXLsTDDz+Miy66CIcccki2q5MRyMD5+Nmj8OolB6bUAfTumFzdHdGrvW2f3IdYvK91cQEKwyH8XmIWXVwYb7ZPnD2KUzZsJtP0sJwrk5535m7A2P+bgkUbKzJ+7ZXbq/HejxuaTUoWtinx7oqOhNpc7rslwL4q8t3nOzaBOMB5F7s49BaVFaAls3pHDc569ntMW7bN2patsWHOGrvmPqyQh1i2sMvexz+mr/J8b2GPjZKnsaavacA9D7FGo0omclNrcbhl0jxmGGkQi8Vw6aWXon379njwwQezXZ2MQRSnqZgkkTFsfXk84McfjtzTob2RlatyyfalYi090RCfxMnfGTIMm8k0PVbniob4j28vwJbKevz+3z9m/NpHPDwd1721AB8u3JzxawcBOzayEyzTNHHfJ0uo35molcYP2O9VZhmST2RK+GKf37ItVRm5bq7z+3//iFlryvHuvI3WtmwJxLMYgZjkQo5JlPnyCNTOnV4tErwKHDxzc3ZbocCHWJC1SaMRwqZLCwKtIG6ZNI8ZRho88cQTmD17Nh566CF06tTJ8/kNDQ2orKy0/ZcPkEHS62owOZdeFd64q86RF5h0WmykWHqfDNYs5k/HDEmeLxmwDSTvKWaatslArgROIWQzsun8dbuzdm1/YU2m7XtXbq9m9udWG9A4aYhEsXxrlWPi31zenEzY8fU6zAPbUumvL33Uhxv5buUOvP7DWh9qo85WznPIlkDMjoXhUHLsEuFFQ+x2PL9Ong7nX5Padub+va10UixaQ6zxCj1n1W4gGj8JTCB++eWXUV+f28Fs1q1bh1tvvRUTJkzAhRdemFIZ999/P9q1a2f917u3PG9frkAGSdUAGsVU4IxozMS68mRgg4JwyFrZJpByHzpjX0dZKkI4O1G4eHx/pXoahkGlXbIPuLkSOIWQzUVI1dQdqfDL1io8+uUvqG6IBHYNEezkr4lRQeRWC9Dw+M0Ls3DUo187o4I3k5dnT0kTXC/AChvbfBaII9S3lapG5Zznf8Bf3luEOWvKMyYc8WTfbI0N7GMjmlSZfC6rKk849Srse7Ua45VPL/ZecegAYZTphRsqAo20rml+0HNWLRBr/CQwgfiiiy5Cz5498dvf/hYLFiwI6jJpcc0116CxsRHPPvtsymXccsstqKiosP5bvz4//LTIIKY6+I2ggnHc+/ESHPa3adbvgpDhMIki5Q7q2ga3njDUtk/lkmy9aJNs2cQpZCQ1yLGYaZs85Frf2VwDNxz96Nd4fMpyPPzFsgxcjTWRhqffmtxj1mq7GemJ+/YA0Hy0+5kzmbb/bvTZPtXP+zjj2e9x2StzfCtPBs9SKFvWQ+w4l9SkpqYh5ppM0+NemhkieMg0xIO7tUZBOCR1d7j5nYWerqdp2dAKlUhANvfNc2amcSMwgfjSSy9FJBLBU089hdGjR2Ps2LF44YUXUFNTE9QlPfPRRx+htLQUV155JQ477DDrv1//+tcAgI0bN1rbtmzZwi2juLgYbdu2tf2XD5BxUzVin2EYOGWk02cXiK+uOzTEVKd16SEDcMI+PWxluRFhzPFU/ZpseYgZk+k3Z63LKROt5t7pLt4UvPsA+zrdhKbmIlS1JPbq3gZA/qf2IdB9UJDtkS3bDxNne3np1T3CrFBOXrJNcKS/8LTB2XJfYYfCAomGuHVxgXAfwc1kOogxh9eGyfMMJwR8kYYYAN6fv0m4L0hLJk1+QreIpgz4n+TSnFETLIEJxM899xw2b96M5557DgcccABmzZqFyy+/HD179sSVV16JOXMysxrsxu7duzF9+nTbfz/88AMAoL6+3tqW6+bfXiGTAj/yDzdFYo5VZdYU2+vET7ZiLyvKMGAzmWaLqcqCGa+QZi4RlyUmcEHCvl+n36kezPKdkELk3XwiahOIg7sOW7TfQh/tk5zKnLGmITumsjyh8flvVmW8Hu//uBH3frzEtq1A4kN85NCuAOQTdN55dHsLorlxzbRJjJLEPEDkQ+yGlkU0LHQbZ12i/IKe6+o22HIINKhWWVkZLr30UsycORMLFy7ENddcg4KCAjz33HM48MADMWrUKDz77LNZC0Rlmib3v9WrVwMABg4caG3r169fVuoYFJYPsQfzKNGhp47awzHQsuV61SbIjpbtMxgNMTt5SCWqdlDkTk2CoVVR2P2gNGEngFpD3PywguTlmMtDqtB9YZDaB3ZR0W9T7drGpECbyndVlyXfUV5V6YjTmeIPb813bCMaVa6GuCS+wCh71Kt31Dq22dpBAO2Nt9BCrkm+Xdal6qZj91Iqu7lYhWj8gx4HohkIU67nDC2HjEWZ3nvvvfH3v/8dmzZtwmuvvYZDDz0UCxYswDXXXIOePXvikksuwdy5czNVnRZPKmmXRAFgxg3qjH6dSm3b0haIOYePH9QJ4ZCBY4Z3F55n8yE2TY4Pae50btn0IQ7iMcRiJhZu2G39LshAvkD2NrQPcfPDys3aTF6ebUKXwox/R3UDnp62wjVIFvu4gvK3A1LTPrNuMZmC98zZLAnZggiOvLbeobQIgHyC/rs3f3Rss6cedK+D1zfJs+ZKmkzH74fNQ3xAvw5KZTcXqxCNf9AWD0EFw6NnLnpRpuWQ8VGgqakJVVVVqKqK50Q0TRNNTU148cUXMWbMGJxxxhnYvXt3pqvV4ogyA5YKPPltaI+4zzRrHssW63XCxDv61YsPxM93HYOOZUXWtj8cuSdTx2SU6fqmqFODmENapnzXEK8vr8Xjk5ejorYJQNzs8OQnv81oHdiJI/u+2Xanx7b8gyyuNZd3l67J9BWvzsWDny1zDULl/BaC6/yaIt7LzlZfzJtE0zEuskly8ce5r7TI3YeYp3W3tzf3Bud14Yn3PGOsQExNCO48aZhyTBAtjGhY6AWYTAQo1BrilkPGBOKZM2fikksuQY8ePXD11Vdj4cKFOO200/DFF1+gsrISr7/+OvbZZx+89957+N3vfpeparVYyEfuRUnJG8M27a4D4NQGsgOe106FNyiHQgZKCu1muJccbE/HFDKSE+jPf96KBmailkupl3LIejslrnh1Lh6d/At+9++4VoL1w8vEo3aaTNv3s4GE9OCWf6jkZs0n0jWZnrt2FwBgwYYK6XHs89pa2YDGFARXFeoj3s2fs6Uh5mk0c8X6gASnjJmmI6UMGVK9ak2DFiBUNMR00M1wyFC2TMuV96LJHWIeF3hSgmqeugm2HAIViHft2oW///3v2GeffTB+/Hi8+OKL6NixI+666y6sW7cO//3vf3HkkUeiuLgYZ599NubMmYNhw4bhk08+CbJarvTr1w+maWLFihVZrUeQkI7Ek4aYo9OsqItrBw8b0sW2nR3wgjLXcwTzMgzboL92p92nKpcm1UHmIM0EJE/s9F+2AwCKC4L3GWZxM4ln210OvX6NInSQvOYALegEuUDHe15/+m8wKRDrGr0LxNnqi3nXzZWmRQfVevm7NbZ9lqWEx8rS9xvEI+dppdn5BR1l2jAMW+ocGdlKh6XJXaIZ1hDnkhJFEyyBhYE977zz8O6776KhoQGGYeC4447DlVdeieOPPx4hQcTBgoICHHDAAXj55ZeDqpYmAelH0g2qRXyBBnVtY9vOytl++BDzYOtvGC55GnNogM13DTFLMeOHl4kn7Ygyzex3mEzrwS3vIIGGmsuro61Wgu2OnIV/MH8THv/1KN+vVJuCQJytVEe8y+bKsECbTJMFR4JhLQx5NGmmbs6PRQi2D91e1SC8Jvl2C0OMhlhRFZMr70WTO9BtOCiBmFZW5JISRRMsgQnEb7zxBrp3746LL74Yl19+Ofr06aN03qmnnoq+ffsGVS1NAtKReBOInceesV8v6+/fThyEJ75awS3Xq3mcaifEVsmAYaunIxen7twCIxuBaZw+4i4+xPr15x3E2rK5LGbsoAQIt37ONM2Ug+9lUphIRSCurs+dFHjpTnpJ20w3UGIB7UPMVImU7fW90serjH8rt1dL97NCSHkihgSNZTKdeBw2DTHULdO0MKJhsQeJC759mDkUd0YTLIEJxG+//TYmTZqEggJvlzjppJNw0kknBVQrDSGZdkn9HN5Yf8Z+va2/u7Ytsf4OMu0SjdNk2h6sir1utlecd9c2Wn8Tc/PmQpsS+7fulwDTFI1h46469Otc5tzJXIJ9v6yArKdXuU0k6px9EP/DRs6+fORSKhiWzGLljg8WYfKSbfjk94egXatCz9fJpDBRn0IKpVvfX+TYls4CQDqk2lfVNETw47rdeOjzpWhdUoDXLjkwrfoXUD7EbI0sH2KPdfViMv3zpgrX3K5sk+V9s46gWpRAXBAOefAhVjrMpYzstClNMGTCZJpuLnpRpuUQmErn9NNP9ywMazIHO2CpwDvSdj7VcaQdZVrZZJr9bdg6M/a62TaZpgfmmobc0ZD4QZc2xbbffj3pi1+ajcP+Ng2fLdri2McOVm5Rp/XgltvwhN7SRD5rL0LXjuoGVNbn/oKTrD2+/P1abNxdh3fmbkit7AyuH7ABoFRYuqXKsS1bn2eqz+qaN+bhvBd+wIINFfh2xU7UN6X30EnapXjKQPvDUPEhHtS1tWObF5Ppacu2u9bRLZI/vc0KqkXZSNOBL71eyysVdU049KGpuOejxWmVo8kdMhFUy552Sc8ZWgqBCcTz5s3D9ddfj9mzZwuPmTVrFq6//nrMnz8/qGpoBKTiQ5zOsd41xKom004fYho2HUgmgjBIoS5fH1DE12zRtsS7FkuFb5bvAAC88v0axz7WBJB9uw4fYz225TS8KMgk3UxT1Bl5l0dVfRP2v3cy9r3zC9/r5zcq3ZGo23XrjkUTuQqOiWu6+BW9OlufZ6qTXlaA5AWY8oLlL8/ZlwwuJ67rscO7O7Z5ESAKw+5j/IptdpNq3pgqC6plml5MppUOE/LmrHVYX16HF2asTq8gTc5g1xAHcw262WV7yqjJHIEJxE8++SSefvpp9OvXT3hM//798fTTT+Opp54KqhoaAUSQUM0HCLhPwOh+gy3Xq0ldqhpiw7DHbm6I5lbaHfr6QaVAyRZ0ag0AGZndss/QTSPsNWWJJrPwBeJk9HIVgWPNjmRk+Vz3O1axWBEJD4UukYlEtz7ibv8XCryas4sWJrP1vvya9NY2pmf1k9QQc7pPI6k9FsH2b4Zh1367acLZPpw3bp/4xAzbb57JtCOoFlOuaryJdMfrrC+Aa3zH5hMf0Pu1uxnoNtRSCEwg/uabbzB69Gh06dJFeEyXLl0wevRoTJ8+PahqaASQj9yTD7FrmeJ9XlfOTx/dy/0g8IOY0NvYCfbkJVs91cNv6I52aI+2Gb22GXAnnw1hk80z7dQI66Ba+QTP/LKkMGwtxqmk96G7hFz3O1aZ8BNrm6ZoDKt31Fjb3bRsmfwevS7uXfvGPO72bH2efvWH6caFKLAiqpuOvirpQyw+n/18wobhUUNsnxL+Y/oqwZFJeN+sJRAb/HJLC9VS9KX7XrQw0/yIeXABSBlb4K5gLqHJPQITiDdu3CjVDhP69u2LTZs2BVUNjYDkgJVelGlVvOap7N6uxP0gAbJq/t8nS7OqmaX71jGJlFWZgp64BBFkhF2tzcSEvKFJriF25ikOukaadOBNcAyDCqyl8O3SgmKuW2GoTLbI/Vzy8hwc/rdp1vYCF4FYVrbfsRS8+hB/yokHAGTRh9inC3+1ZFta54dDYi1wyEhqj0Ww54VChs2txO0+ixjB9dHJv0iPB/haOiuLReJ+2MWb0mJVgVjpMCFamGl+0O05KA3xRws3W39n26pQkzkCE4iLi4uxe/du1+MqKysRDqt1jhp/ME3TGii8CEbvz9+Y8jXTDTbiBTdfZ5F2IhPYV+sze22SEgtwn0x75c7//ZwV87SGCLPQwlTBEXVaD245De/1GDCshTuV10enjclFgbhjWZH1t4qJLbn3r3+x+6sWuPh7krbO8wv1O/0ca6mRKtlyafCr62pVlN5cpqgg/q6iMae2nLxFqdaT2dUYieHnTcl8xm736SXIJoG3GEKuI1pwZwVvEem+F93fNz9sPsQBvN/6pqjNski3oZZDYALx8OHDMWPGDJSXlwuPKS8vx9dff41hw4YFVQ0NB/r79jIA7k4jGMuNxw4BAFw4rp/yOc/9Zj8AwJ+P38vTtU4ZtYd0/xeLs2c2TT/7THe0f5+y3Pq7QHFCospL361xLHpk4vbYifi7P260ReV1+hBrchmuQGwk+ymVCdC1b/xo/e2XoOYntJA+e80uLONEW6Y1uKI4D2FXH2IiEDuP83vxyregWnmkIeYJgrxn7YWihHIgGouJo0xLzufdx21Ueqt0LQN4ll6yoFqixWnVhfh0x0itIW5+0N9FEFlDWKszLQ+3HAITiM877zxUV1fjjDPOwIYNzrQRGzduxFlnnYXa2lqce+65QVVDw4GeVPqpKJStXF9ycH9Mu+Ew3H6i+uLH0cO7Y9Fdx+DyQwd6qkfr4gKM6NXO0zmZwhasIYPXdfjSBnD1bORVZjXEHy7YhD++vcCqi1taJk1uITIVJf2UV0EuFzXE7D0+8uUyxzG0IC+SsVRNpnlCmt+LcfkevMjL4zBNE3d8sAh7/uVTx750nyvR5keizh6ayJCyazz/zWpp+W71o60XePz1s6WObTIfYt5ijpcnFIQPcS72CRp1RHmI564tx9PTVghN+FNNc6k1xC2HwBIFX3rppXjzzTcxbdo0DB48GMceeywGDowLNitXrsTnn3+Ouro6jB8/HldeeWVQ1dBwoD9wL1Gm08EwDPTrXOb5vNbFqTVR1SiWmYbuqzMpnLGXCmJllV30z8TtiSbiDU1RoFUhHvzMLmzosS23EfkQy3wrZeRiUC32HnZWNzqO+e2bSS23SMvmZt1DvnGe4Oy3AOs1z7yIbH2fdLAyN5Zvq8bL36/l7lOt//ryWu52YrnDe55uPsQ7qhusv8f064hZa5zWeW6vSbZQunpHDV76bo1jeyQqFojTNURKt1mx72Pq0m246KXZuHvScJx/UL/0CtdkBbq50X3p6c98DwDo3LoYZ+3f23bOaU9/iwUbKjD31iPRqXWxtHxnpoo0K6zJGwITiAsKCvDpp5/id7/7HV5++WW8//77tv3hcBgXXXQRHn/8cRQUBFYNDQc69YKX3MJu5FK/UeBiTpgtbBESMzhXZ99NEHICa06XCX9At4n9xt11tt9aIM5teK/HAGUy7XF2kg2rBTfYW6AFGQIdDV9khuuWM5Zchrfo6Xff45eAnS1tzMbddYjFTKUFYpnrkGr9H/jUqWkFaA2x04mYDGmixUz6HYh8md3qJ2sXmyvquNt5wruVh5gzv9i/r3owyfRNpu3nX/TSbADA7R/8nJZAvK2qHv+YvgrnHNgHA7u0TqeKGo/EbBpi5342TzYALNhQAQCYumw7zthPnsGEbc9aQ9xyCFQSLS0txT//+U/cc889mDZtGtavXw8A6N27Nw477DD06NEjyMtrBNAfuJco0/mEW8CZXCCTHa1bfl4/qKy3myRlYmXV6zX04Jbb8KwmDMOwFu68Cl5bK+t9qZefsPe4g6Mhth/P3+6qITYlGmKfvwPfNMS+lJIaTbEYikPuQbFkQ6bqYxX5tpN32hQzHQuKSR9i/kXoaonq6GYZJNsrurcoR4ombY/2FZ5321Eor2nwZCmWbrNavDkZUIy38JQq1701H9+u2Im3Zq/HoruO8a1cjTv0GM7rx9j+9ZetyRgNKhYLjmwZes7QYsiIarZHjx44++yzM3EpjQJ0J+KnPJxL/Ua6wU2CIltRpllBMAifv0pGG5cJv0LhBE/QrnOoiWo48F5nYySWssl0bYO3dG+ZgL1HN59GkfDqZgVDTuNZAfn9bfKEIlXGD+qEb1fsBJDdyWdjJIbigqRAvGp7NVqXFKBrG3sKQHkeYLX69xCkFSTjVjTGy0PssihkOI9lqWuKIhozhYspsvqL9vF9iOP/0tfpWFbk6qPMkm57mLYsGZn98cnLJUd6Y8H6uMaxOkW/VE3q2NKIcdoe22ROf+Y7628Vi8gI05floNeNJiByU2rQBIpJfeBeokyXpplSIpO4BZzJ1sQrV3yIg0hXUFWfGYG4sr4J2xKaP6/3oVd7cxve6ykrDqesIc5WGh8ZzsjnLlo7QZvt5pKr3TJb5ZlMJ/ZFojH85oUf8H+fLJGW5cYvW51miqrs3bOd9Xc23xa9MLG9qgETH56OMfdNwf99sgS3vb8Ixzz6NcprGqV9iGr9u7Xl+zGScau8ptGRq9nyIWYm6BW1Tfjkp81oopwrRRP/mAnslGhK5cI+fzvPh1jW9gjH7d1dfDGmHD94dSbf7zsVmqlhXV5gN5nmCMTM7yrKck0lujlbprYqazkEriGeNm0avv76a2zevBkNDfyO2DAMvPDCC0FXRZOAFiK8mEz/7cwRuPr17OXw9YKbhrgpalo5HzOJXUOcRZPpIDTEjMl0UALxIX+dioq6Jsy59Uixll2wXQfIyG1430TfTmXKGmLnZMa/uvmBaTo1f27dgOg7KnNZoCSnyYJqfbNiB75ZHv/vz8cPlVfEhS0V9ejuIqTzoP12szn3pAXKtTuTQbae+3qV9ffkJVvRu0Op49zDhnTBtGXblft0UdBHmdafDGnsNS54cRbmr9+NU0b2tLbJ1oNnri7HySN6cvdJhX3BPlFU33g9xBVhiysrCqOGiUORa98vIZV8zRp/oNsE32RafK7KfFf7ELdcAhOIKyoqMGnSJHzzzTeuWhktEGcW+gP3stLZrlWh7Xfn1nbzp1zqNtx8iLdV1aMXZ2ITNLYcehl8YA4NcQYuzpoe+QUJlDRz1U6hYJ++DkeTDdjJR4fSeJ+TDKolP3/ldrumMtcmM7zquNVQ9Km63ZqVC5YzeSdaPV5e2VT5ZWtVSgKxbZKaxddFa4jZsY4Q5fj2AskFWNXmJnqnonHrr6fvY2m3WCFg/vrdAID352+ytsnGdZl5eyo+xLx+PqkhlpQn8JHmlZNrNNfYK/kAPXfhjf+yNqOyjuH0IVavmya/CUwgvummm/D1119j0KBBuPLKKzF48GC0adMmqMtpPBCzVm/VTEgI7KFvXDbWz2r5Cr3SXhQOOdKvnP/CLHx1w2EZrpV9IpRNDXEQJtOOa/osD7Ptb3tVg1CwFz3bXNU4aOKI/CZV8xCzk+pMTmZ2VjfARDzthwheu3RbMBYt+rj2H4ndvMn7oQ9Nxc93HeNbMCxAPdVdfZNdCLdpiLMoETdGk/USDYvhkMGVGokWXtXyprqe73sqEoh/dUAfTF68VfkahiiIAuSLSqn4EMfM+EIUHW05loKGmJuzOEf7ay/zJo2/uAXVkqESRZ51AcjVRRmN/wQmEH/wwQfo1q0bZs6ciY4dOwZ1GU0KkE7Ea8ol9vjB3ewLHNnyz3z4zBF4a/Z6PHPeaGsbvTLNy0W6ykPeST+hH1EmHxc7h8qEhniTIE1HqvC03F4FX3YyrsktRJNkoiG+/YNFeOWSMejRrhX3fGcu7Mx8ZJFoDPvdOxkAsOzeY23BmWh47ZJXwzYlBZbvm2jS565ZFmuIAeDLxVttv03TTGuiryoQ//ndn2y/6eoF/bpkwmRjxN16pyBkcPclI0C7Y5omnpy6QlC+zGRanoeYRvYaZc8gFR9iADji4elY88AJ1m+VOQZbHK+Zpvv9lhSGUN/kv6VSjsbsbBG4aYjlZv/eygf0InpLIrDPuqKiAuPHj9fCcA5CPnCV1TIaNwG6d8fMmyADwOn79cJ/rjzIlnA9nKt5iLPkQ8wOEpkQiDfsqgtcIBEKxIL7O/nJb4OsjiZN2PdJuijS9yzfVo0rX50rPN+5uu9v/UTUUNGsy2vEaZT4GmLncSN7t7edw/uOXDXLid2iAIORmIkmarEw3T6hSFFKePfHjbbftAY76Ncl63PphVPRceGQwdVik8WAb5Zvd+xz1kG8TzYkk8ek8p6kJtMpBgXz0pfzokw7y7P/LuC0n3S/36CyTXhVJmjSZ8OuWpTXNNp9iBWCatGofDvs96E1xC2HwDTEe+65J2pqsqOF08ihTaa94Hb80cO64U/HDMGIXu1Tq5iPFCrmIY5E4yldMmUClb2gWuJ6BElVQwRtS/j+eF5xav/EA5wew/ITp9bIriEGgAUbKoTnNzHWICrtvCESxfX/WYBD9+yMXx3QR72yFAY175ZdUrVd0sfFYibXtNltbuemIY7GYrbrRGImBIptJVIVEuxBtYL9cGXCoF3zJC5DlBoMAGav2eWqaZfdo2yR2kvqMdmCsMxMXlY3L8IpKUcuENsL5GWxSHucCqg5aYE4s5TXNOLgv04FAFx+6ABrOyfAubTNqMQ1YX3sdWaKlkNgarTf/va3mDZtGlas4JsGaTJPYySGF2asthKVew0MQQ/yL110AHf/NYcPwsF7dk6voj7glqMTiE+Ex//1K5z1j+8zUKM4toluBvtZtlOv9TGYjozyarG2zCu8cUn0DPWqbn7i1BA7BWIZrECs0gzenbcRHy/cjJve+cn9YAF07VLxw5Qd99ac9fjjfxY4jvlq6TZpGZZQInh0EUbQZp+drEzuPkXpY1Sf9rbf9DB07Rs/WkHz3Kioa8LzX6/CZg+uGRt2iY+lBWLRvZim8xlMGNwFu+uS/ZybFkquIXa+rDP36wUgOV6rtCFZ2sFUTabdBAP6vsnfsinGiSN62H63KuQJxNJLuhOQ3KqjTGcWMmcFVEymxeWoaIhZK6Nb3/9ZmqpM03wITCC+9NJL8bvf/Q4TJkzAiy++iA0bNgR1KY0iz3+zCvd8tBiXvDwHABwpDtygx4Ah3XM7QJqKhnjhhgpsrWzA7DW7MlCjOPRkJpMrj+w4sKWiPiPXrW7gB49JFfaZiX2ItUCcj7Dvl6xrqWpkmlIIiCIKcOQF1daWikC8aGMl/rdgE/e4JZsrJWXE/xVN3qMxkyvEyGCrP6hra+E+EZ3K7EHH6Hf7/aqd+OtnS5XKufmdhbjvkyX49XMz1S4Mvjk7SV9Fa49E9xI3X7dvC4cM2z24BfqRLRzwFqlJ2VaUaYX3JBPYZOfLF3Pk16TjM0StxRhxPU4ZuQeuOXyg9bsVR0Ocq9o5LRBnFvpp2/osniuJpBxezmwW9vtYsrkSf34v9cVSTf4QmMl0OBzv3EzTxKWXXio91jAMRCL+Tpw1Tn5cl57gRw/6uT4guKVdAoAdVZlf9dtambxmNjXElYpamFzCMJigZPAeVEuT24iiTKv2N5EUNMQqfYUbJnXZVAMTpXLctqoGDO3B30fqIVpMYAVidjEBAKYt24aYaWLiXt0S9bIfE42Z6NmuBJsq6j2Yg7NWAPH/SFWWbUlqg7ZXNeCuD3/GuQf2xUEDO1nbN1fU4dNFWwAAa3fWql0YfGGwqCCEmsYoYjHgvR83YOmWKpywD/+hxkynQFtcEFI2twbk7YNn2ES2kW9A5TnLBFGZ0MvuOqBfh+Q+l2Wf2sYoyorjU0qiuZN9t4ZhYOyATnhq6koAIg1xbnbkG3Yl21y6weg07tDP1xZl2qOGWCWqPu+Yz3/eyjlS09wITCDu3bu37iSaMSomydlEpX5XvT7P+jsWMz0HGUuFW95dmLxmFjXEmUi7BPjry2ua9tVfuQ9xbk6kNHLY10km9qruHWxEeRUzXl4wH6/Q10nH7NTrcbKnYvkQC55dJGqiIZLU6rH+dbWNEVz44mwAwM93HYOy4gLH+4nEYtY4r9qfsUcZSMRwSJxPm0zf9v4ifPbzFny0cLMtivFB93+ldC0WXh1JQKxILIbr3oqbpoviHsRM0yHw3njsXrj7w5+l16AR7b71hKHcd7VyezwWC2mmKn23bCzzoiGm53BummlbgDaXxRgCLTDzAmDl6sJmUTiEpkSarpgpdkvQ+APdjOiFO775v7jRyHJwJ4/J0UanCZzABOI1a9YEVbQmS9DdRK5riL0GvYiaJkJBORxR7KB8aoPsd9lV6+oGu0Y4U52+n0J/zHRqhEXjmx7T8hPnhDz+r+r6WypRpgt96Mvo68iEcL81xLJ+LhlUi7//vk+WoENpUvBjnx1tXkz28Xy8TYO/TwQr7BuG2CRyzU5nYM66NOIf8PoLIhDT190piH0Qi9nf7oTBXdC/c5knk2nec7r+qMG49JAB3An+rNXlAOB54UGEpyjT1Aa369LtJ7kYI68LvdDFOzZXNcRHD++O9xLR0qMxM+fnQ/nO81+vsv6O2BZeMqMh1rQMclvNpwmUQwd38XQ8PTjl+gDg1TghG6uCQWkx15fXYsz/TcFTVK7LBz5dZjsmU52+n1eZuarc7oONZJtkg8jk6kRKIyddk+lUUmbQGuJUv0n6PLnJNEdDydWMqdVD9ljcTKYBYFdtcqGMvaaK//+dJw/3lIOXd5xhGLb+mn6WvOegGnSLB28CTTSTdJ8oc8XgjYM2k06XvpW310ovJosyTQRihbS6RRKVpbR+zC76Xt38L2kLAxWTaXZ/yDBw2BD7nET0GSzbUoX15eqm8n5Djzd6rAmeL6ic6W/PTcYjIu1M1v/2bFdi/a2UdknlA9M0SzImEDc0NGDz5s0oLy/P1CU1LvTu0MrT8XRHI4timQt4rV02VgWDGkgf+HQptlc14KHPk0Lwoo32VDVRheASPFZtr7ZSjKiQyj3GYiZe/m6No86Ac7CzgrdogbhZwAqk5L2qWnywk32VZkAH4OP50SpdV1GTxttXVpxGdF3JYyHXUnVdYqtWQwnERC/K1n9wtzaWMKe6mMDeW8iIm00nr5WEN4FlrV28IFuQmExNukUTZzaoVjJPdnKbe5Rp534Vdx3yLahM6onWmwcvmHgs4U/usMChfr8zTx4YlR5DSR3d7ovutw0jHmiLhtemdlY34JjHvsYhD06Vlp0p9FCTPtGYidlryj1bf5AmN23Zdmqb/YV0bZsUiFX6d60hbrkELhA/99xzGDVqFMrKytCrVy/ccMMN1r53330Xp512mk7NlDHsg1Oxx6STJmdlPFfxrCFOcSLsFdvKckALkbRfIIE140yl0//85y2Y+PB0XPzSbNvkUUYqk4UPFmzEHf/7GSc+McOxz2kyzdcQ60lKfsI2SyNNDbGKkEaXzfogq+Km1Uwe5708GYZEIibPUrUrZOtt89Uz7f8SQgZtyqt2HYfJNFNJW+A8TplVaUQF52lHixPCo03zJHj+8bon9/FiVbhGmebsli34/Gr/3gCSY5rKYh/PH1dUP9M0ceoz3+HoR6db40LyWsnjZq6SKzNoH2JSRzfff/t3bTjGKV6b4pnRiwhqlkLfVqbicTRnXpixCmc++z0ueXm2Y5+sLyTPfi3VJtg2Q5+vfYg1MgITiKPRKE499VRcddVVWLJkCYYOHepo2CNGjMD777+Pt956K6hqaCjYsUm2isyD7ie85jDONF4DuqkkbPeDC8b1s/4OSovJWwV1BNVKodN/beZaAMCMFTtw6StzhMddOK4fOrcuApCaCeriTeJUMhe/lBwwTTN5X2xgJK0hzk/YCTGZL6v2N2y7VmnmtFCZ6mTIrtUUH2cJCiEDeyZSFvGumEp6JgDYVdOI295fhJ82VFjfnuraJXtFumhyHYcGnzJ3djMVBoBXZ67FN8t32LaFQnaxnm4DPGEjHR9i3vvlLQyLnv/WygZbm2pT4gzD4h5l2lm2rH0fOSwe4ZsIj+kKxOx7aozGsGD9bqzcXmMJFqQ+XvpvW6TtxJ9uGuIQ40PssADivC+bv77b4oN0b+rQLVaPNenzamJu8d3KnY59ssX7mLWAk3wfHzIp6ujT6UUvEVogbrkEJhA/+eST+OCDD3Dcccdh7dq1+OknZx6vgQMHYtCgQfj000+DqoZGQhkn758MuuPPRETmdPAqr2cqFzEdvdTPcXRbZT3u/WgxVu+osa3Ui0hlAaBUsb1cf/RgtEncZyq3aJuIM4MTq6WICjTE5LRube05TzW5jVMDmTCZVuxvZCafIui+QkWoc7uuPIpv/N+CkIFnztsPAL8fUP082W/9jv/9jFdnrsVJT85Q8iGmkeX4Jn873g+Vg9ftye2obsBt7y9ybDeYOtLVcBOIvMKee+zw7tz8t6Ln/+TUFbb6tW3l7Ofcg2o5t8ksIEjfRoRUFese2WJ3JGZiS0U9fvWP7/HJT5tt+0hzIt+bl0dNL8RaJtMubY9Oecam1QOAmkanNQDdJrZWNuDpaSuwXZBCUfY9z6AWZjbursPuWn4gNR5+9BmaJLL+rkHiohW1BOLkNtbKh+7HVFK0aZPplktgAvFLL72Ebt264a233kK3bt2Exw0bNgxr164NqhoaCa05q9sy8mkhVDQQi4S6K1+bG2R1LHbXJQddlZQwqlz9+jz8c8ZqnPP8TKXk8zHT+0BeWqTWXmitUbptRmp+SuUhFvkQp2gBq8kSjijGJGiR4vns+1Zpf7YIxyk2WFUf4l2JyM0NkZjU91ZV68QKjHQO33R9iOmyb3//Z25dQ4Zh3YdbnUWRm51BtZJ/8/qoVDVy1Q0RPDl1OQBgTL+O+OXe4/DMeaO58TBk7WBbVb319/kH9VWqMw1XQywLppXYV5rI8bu7tgkbd9dJr3HivoLk1Ig/vwc/X4ofVpfj6tfnMQsQ8Q/ICuDl4VlHeCbTLjNM+tmHDMNxvUomgNqr36/Br5+baf2+4tU5ePCzZbj8VafFUn1TFDUSa4KHPl8KIL5QM/6BrzDy7i/llaWwCcR5NC/KVWQL9A1N4nco+k7tLize6qI1xC2XwATiZcuW4cADD0RZWZn0uLKyMmzfvl16jCYYWhd7FYjzp6Ng5xeTrz8U1x4+CP936j7ZqRCALxdvxYvfrrF++9nvzlkb13Bvrqjn+kHyXp3XyT9Pk8IjZCQNylKZvKpqW+Im0/H9rIkgOU2bs+UZDg1x/F9Viw9nUC33908fkaq2R9WH+PlvkulDDMsslVee2nWl5oSWhlitLFmu8s9+3sI9JmwYlvmoW5152j7AmXZJVAdCql/0be8vwqKNcXeMUCiuRTUMgyuMytrBXR8uBgCM6N0efTuR+Y2ahQCQuoaYNs9+8qvlwuNDBqh6OYnGTNRTQkZlfVLoJM+bXNOLIRE3qJbLh1tGzUEMw/luKxl/8ds++Nn2e8GGeODFH9ftdpR97RvzpNcmWvQlm8UuOiLenLXe+lsLUOkjW7iWaYhF3ym9EOJ13qo1xC2XwATiwsJC1NfXux63bt06tGnTJqhqaCjYoanMo0A8tEdb/yoTMGywmUFd2+CGY4agPZV3M9Pc/ZF9MA9KWOOVyxsUvA7kqlq6UCg5ESKXNU0T363cYWnIVHGbkJF7YCeU5H65aVtq+VFqY4lIl7WCibsmeHh5bgF58CgaZ9olb9dMVUPsZuZLoCdb5I54R6vWQ2YNkswFq6ghZgMace7DoSEOQdkapLaBr+mJmXYttpt2J6VFNtO08sYC9mdSXKjuQ0zTmhMdHFAIqsV547KsDcRKojVlodMYEV+jY1ncTeSeScP59YuZNtedI/423baPvqYnDTFHIHYLhkfXo74p5vgYvGQ0YJm8ZJt0PxGIVfsWEfmkKMhVZMGuZG2QfGvsG6THcPZ8t/fVlEab0+Q3gQnEw4cPx9y5c1FVVSU8Ztu2bZg/fz5GjhwZVDU0ErymTupQVoRZfz4CP915dEA18g/RHJAXFTRTsH2+XwuR7MRV9a1OXqIWKdorReEQNUmO1+2jhZtxzvM/4IS/f+N6vk24UPTHE/kQ84STEXd/wS3r5e/X4Mxnv8d5//zBtY6aYOBpIAF1DbEzqJb7R0afwhMwN+2uc13I4fnb8jh8SFfrb/YbEZXHo8jKnSuevJFyVZ8de0mepoT9HuMm02rCU5OgrpFojAmqlYSrAUqh32QDDdKCWgnH31alb964i2+27BroKUUNMe1HP6J3O+Hx5H3/5qB++PDagx37Y6Zp8zGuotJrkVdE6uNF1qNNplX912kXpqr6JsdiQZDaV3Kv6cYH1VGm00cad0Ein1pNjnmJ9Y20+b79HLfXRfKvD++ZPwogjT8EJh385je/wc6dO3HllVeisdE5mYhGo7jmmmtQW1uLCy64IKhquFJbW4v3338fl1xyCYYMGYKSkhKUlZVhxIgRuPvuu1FdXZ21uvkN2/Gnkjqpa9sSK2BSPuImD6/dWeNZi6kKO1Hya2X5yalM2jLOCM+70rVv/OjpOqq1NQxnoJ2pS+Or9Zsq3K1GaNwm2W4+xF5MYN+aHTeDm8cxv9NkBvZ9k6asbDLNfmMK58jMnXfXNmLcA19h1D1y/0K7hlh8HGmmh+zZWRqMyq1rIAINO5Hk+TYq53Bmteucb4cnWJI+1XXxQbA7xNhMuy2IpaIhXrBht+03nWO5hKMhVhFy1lABelTfP3ssQXUsPjoRcVrmF07v4ZUbjZmuJurJtqn+rGOc9+bW9uj7aIjEHM8mSJeX6ganJVAqLhPawjZ95IEIZcJyfN+z01battc1iU2m3doUaRfjB3WWHqdpfgQmEF9++eU47LDD8Oabb2LIkCG48sorAQALFizA73//ewwePBjvvPMOjjrqKJx77rlBVcOVN954A6eeeir+9a9/IRwO4+STT8YhhxyC1atX44477sABBxyAbdvkpjf5Sq5Hik6Hw/fqyt0uS2+xaXcdJjw0zXXymypsn+/XYP/0NPc83ofumX7nnkp1yT22Ly1K6ZqyCUpDJIaahBmmMKiWoNK8cr2m6tL4D5tSx7PJtEcNsWmaNq0we/60ZWrxLeh2Jtfaxv+lBQVeHd3qTU6XmUynqyHmfTusOWHIgw+x6J5ChliIS8e/mvDL1iqc+ez3tm30oldJoXMa5NVsku473H2InftlAjHtD0nMu2WmxHTboqM479G+lXV9UV+XNHUmdRXX8XcTB9l+8yKte1l0b4zEHOJ3kAIx0U6nG1RPR5lOH9lzVzGZZoPM2QVitjx5XUie89bFBZ6tKDX5TWACcTgcxieffIKrrroKmzZtwnPPPQcA+PHHH/HEE09g3bp1uOyyy/D+++9ndSJaWFiIyy+/HIsXL8bixYvxn//8B5999hmWLVuGUaNGYenSpfjDH/6QtfoFSa7nEk6HgV1ac7fTEwSWBet3B1SbOE4fvaAu5OzxUxVImYJdj7jzpGEAnD7Eqimb1u2sVU5h8/cpy7GjOp5ug32vyaBa/HN55pvN92vIH6oYrY01oU7RZFo2vzVNE2c++z3++PYCaxs7+frDW/OFZdPlnPb0d9bvhiZ3fzjDkPveugvy8X/VgmqlH2WawKZ5ChlJrbebNlH0/AzDsC3O0tXlVd2r+DHdZVGjFUdDPG/dLk/XKKZMkF3fHWebzJWnlvomiKm8TCCmnxn97kf2bg9A3qeSgIwkSCF9L306llL1NXDdUYNt5/IsLdyiTNuuzdEQb61sSCvvtIwOpUXx50g9r1RMtH/aWOFjrVomornQ3LW7MPHh6fydEC9G0G3Gayq+hkj83OKCEN67ejwAoEsbnb6xJeAtqpJHSkpK8NRTT+HOO+/EtGnTsGbNGsRiMfTq1QuHH344evbsGeTllbjgggu4Jts9evTAU089hXHjxuHdd99FY2Mjior8ECpyh1RMpvOJTmVF2MmYP8smh89OX2n7+8oJA32tT1AaYlZ7xhsj6iWpC/zkwvH9ASQnZbQA4Mabs9bhlnft+cpV5ydhZkLpZjLdGImhuMA+Ec6ie7kmQQ0jEHtds/MSQKUhErOisxNkAmZjJMaNtF7XFEUFlR6mTvKtkdJDhpGMMs05TjWYnEowGtVu3rFgx3l2bAT7cCh5H6p1ZgkZhm1x1u2de+037/tkiXQ/L6jWDkGKKBGF1IKcm1C1rdLpNkILjt3blmBL4phWhWGMp6x7iKm8VCCm/qY1XO0SASWjMfEzJsJ3WSKAF/2oacuHPh1LHYqMmBnP7dsYjXoO6AbEv0feu73slTl47dIDlctR5bOft+D5b1ZhdJ8O1rZUIgzf8u5POH4fcZorjTsiq5rzX5DH8xBplpuiqfsQk27QMJIL7dpNvGUQqEBM6NKlC84888xMXMpXRowYAQBoaGjAzp070aNHfnd6rODkZbDKR/p3LnMIxLJFAJLCAQAe+HSp7wKxV18WmsmLt+Lejxfj0V+NxChqAAeckxteuazmLWhY/0gVK5BHv/zFsU31GQmDagnOf3LqCtxy3FDbtub+PfjB9F+2Y+aqnbjh6CGBLKjVCkym6XfTu2Mr4fms/6ZsfssTXKTaM4FAzMLeA41lxgxKs8ppo27xBUi7Zn167WXE/1X3IWauwZmjstczPOQhFn2L4ZC9f6Dry6u535NTng+xV+ggVW5mt6dS1gQEekGPtqb58fajbPUjmmhWU09DP0v6uM6t41quaCwmdEEgLiiknduCxbkseDRFYzgvIcD0aFcCwFuf2hR1mkwDwIwVO5TL8MrrM9faBOKo5HsS0cz1ChmB7nt2VjegU6KtyvJIA+L+mhawvWqIrTkL6PzoWiJuCWidiIRVq+I5IwsLC9GxY8cs18Z/mruG+NFfjcQhe3bGKxePsbZlU+hh+24vq9GXvjIHa3bW4rJX5jj2sXfE6++r6tMXiL1MRNkIuipNjfc4lm9VC2rH8yE2TVNY539MX+XY1ry/Bn+44F+z8My0lbYUNn7CavTJa62kNLDry+twwH2TsYhjqshqTLdytHEE3vcnm/Q3RPmTM7aNyTTE5JKGi++tW9cwbmAnAIrBaKiGfd2Rg/kHg7Ngp2AyDSQFMLc6i+o6dkAnoZmvSj3ThedD7BVaIJb5lK7azu/PCgQm46ywTjTRvFzzvPPp1IrtWiU0xKZYQ0wCChEzclp4kPnGA3Z/drIQ7cmHOBrDifv0QHFBCAM6i/Mo8+hYlpr1Xk1j1PYsUvEh1gup/nIzYyUmQyTc0gt3qoHadtU0JuYMSau2kGLfpmkeBKYhvvvuu5WPNQwDt912W1BVSZnHH38cAHDssceiuDj/fQicUaazU49M0btjKV69xG5qJfMh5hGLmVi/qxZ9O3kboLllMR1xKv5K9Rz/RKfpWrLcTbvr0LN9K4cpaip4EoiZc2iNhCkI6sKb6G6u4Kc2YWE1xKZpeh/E9MRGmRveXoBnp6/Ee1eP8zXqPGu2SybU+/ftgOm/JH1Bt1c14IpX5+LbmyfajmflhI8WbsaT5/CvxdUQSxq5yEyVPaNeqiGO/2vzIeYcJ5q03XrCUJy5f2/c+9FiAN59iDuUid/VvHW7bdYnbkG13r16XKJ8ssWbD/EjZ43A6D4d0LdTma1vob9Cbj8hvYp3SgrS1xD3aJe0WpD161//wvdntgUSk1yHNZnm9Zm0e0zP9q3w+K9HokNpkSWMxyRRpmsS+VvLEjmW7dGzBS8pAd1mycKJF2ExEjXRoawIP915DDZXxANc2sqXPNe2JalNZVsVhj0J/bx6sFZomvRYvKlS+dhozMS3HAsCuq06Ay06y5n+y3Zc8K9Z+PUBvbGuPB49Pr5oGUfnmm4ZBCYQ33nnnTAMQ9iQLP+pxOQ41wTiTz75BC+88AIKCwtxzz33CI9raGhAQ0OD9buyUv1jzjYtcWXTayCxWz9YhDd+WIe7Th6OC8b1S+vabHAQWYRYETyBXqYhvu+TJXjqnNEp+UalA6s1IhMtID5A8e6DV0PVNurQEMe8Lzi0vK8hPVZsq8Zbs9fj0kMG+FYm+8pIOyotdg5VtN8uwYuGJ8LRsrmZTPNghVe5kJq0mJDnIeafX1IYRrtWhdb3w7sHAllcYJV0D52xL/7034WO4+/5aDEuObi/9Zv3LIhmcliPtpapqaqGePWOGtvvdq0K0S+hCTRFEjEHFTeKaMxEJBbD1ooG7n4S/A/wx2T6koP746HPl8WvLajfzuoG3PnhYu4+up+TWfMUhRNRphPv4ZnpKx3HsP7Pk0buASCeUhCIPxuRyTUxmSbPRCgQc6D3k/NSsUIrKghx+31RHmsg7n+cCq2KwjarEJUxoyXlHW6KxrBg/W7s26u9zQoiSLxM0aKxuHsbC2nfb81eZ/njE3j97SNfxL/dfydSLwLxbsiL9Utzt7hsCQQmEL/44ovc7bFYDOvXr8eXX36Jb7/9Ftdccw3233//oKqREkuXLsV5550H0zTx0EMPWb7EPO6//37cddddGaydf7TED9hrqqk3flgHAPjbF8tcBeKmaAw/bazAvnu0QwFH/c4O2jI/MBHcNADMJrpcIoSTQeCFC/bHJS8nza4j0Ri3rjy85KRkJ/vPfZ00UW6IxGzBeAgqQXxEOH2ITc/Bd1rg55A2Mh/WVGDfGXknCs0+fr6HRRCe4CqbEIsEXZNporJAVzb/NIgnW6KFZNJnk6jEbJ3ob0rkQzysZ1th/Whk6aDoBS1DcjzNE1/Z08OJJr7uJtPS3QCA057+Fiu31+Cm4/bi7icBpgCgVVH6E/2SwjCGdGuDZVurhPWTpfCiA/oN7dEW26u2ozVnEYjVED/42TLlOpKxL2qaePn7tdxjGhMRdos4UabdFlV5gqKXPpVu87y5iWwBOVWBuLggZE+ZptCfpWLZla/c89FivPL9Wpw9pg/uP22fjFzTi6ImZprcNkbe403vOM2vVV+fPROA+KSHv1iGl75dgw+uHY8BguwmmvwgMIGYF7mZ5vbbb8eDDz6Iu+++G5dffnlQ1fDMxo0bceyxx2LXrl24/vrr8fvf/156/C233ILrr7/e+l1ZWYnevXsHXc2UYPuZlqghThWVSRgZPC45uD9uO3GY6/H0BKOirgnFBSFXbQVvosBuWcVoYgA6DYb96KaoCVWLQS/yJRtUi2bMfZNx0MDO+OcF9oUwXvmyaKo0zijT9onLqaP2cPV71d+Dd7wskiiVxxRH2iv33XA2edMQcxZgJO1NJPA5XSHE17R86kNyTYjoWsTChTwXqQ9xjPjC2S+k2s55ZRNZnxcEy6ucYBPebfWjjuGcx7vM9qoGW2oUEiDxR0H6JLq/8MNkGqAETsGDkC0Y0GmbHjx9XzwzbQXO5yzA0gLxnDXlnupH2o5s0YiMSaR9iVLgqS5GeVmAluU8puvGo4Hx21cVWlsVhW0LWEoa4hYkEL+SWDh5c9a6DArE6sdGYya3I5WZvnO/Q04ZBpzpI3mQhb57PlqMFy8aIz5Qk/Nk1Yv0xhtvRK9evfDnP/85m9WwKC8vx9FHH421a9fioosuwt/+9jfXc4qLi9G2bVvbf/mCV3/a5kCq1k4q2kYyeLwwY7VSmUSTu7miDgc/8BVOfnKG6zm8fJUqEZzJ+MBOhlU1sF6R+d7UNEYxeclWx3bVID48+D7EyfKuOdw9YriWh73jt/Ug2wZI2+a9G55gx2tDotV93qRJGhBL0BTZvuHRyc5o6cm6xP81YI/zS9dR5v8esjTE8X9pM9LdtY1YsjnpspP0IQZOH90LnVsXY9LIPZQFYt6CAS+VkyU8peGiQD9DUQRkAu993vsx3xRZtF5D9xe8tEuEIg+BNsihokUZ2bdCTKEBoHu7Etw1aW8M5GibikhQrUgMd/zvZ+W6Acm288u2KuExpL9NCsTJfTyB9OwxycV/nqDoxUWJXlzjnVbJcZEgsJYqqkJrSUHYtoCl4lokO2bj7jrc/8kSbNqtFvtC48Srhph3tMxyiTuX41k3hJI9UVVDhJubfH3C3xjQvuTNgayHVdpnn30wY4a7IBA01dXVOO6447B48WKcdtppeP7555UEjXzGqz9tc8BLcIQNu5KdnR85g0mUTwIZtL9ZvgNVDRH8srXaVQDkaogVXqMoH7CX5+HlCaisrFbW2yc4vGesagoXZhZ3YqZdgOEtJLC4TcRbOry26XewEXauGbIEYnG7/9eM1fjNCz+gvinK12oKqsib2MoE4ldn8s1MvciB9HdIT/zIY6yobcLBf53K9Y8GkkIXcXOg08Rc+rI9Aj2dC/bhs0bghz8fgXatCpU1MOzzicVMSsimNMQK2moePPNuwF0w4jW5zbvrqf3U4oLg2nT9ZVGmCyWLxmzE7pCLBlZmTaHqn1lEpV3y6vJExvv15WJhjQgS5J3Sz5q+L/Lu7pm0N7q1jWvmed+TlzrS1+LNTVYIInQD8YVd+3tXFIgLQ9IATDxkx1z68hz84+tVuPDFWUrX1zjxMi2tb4pi/vrdju2yd6Q6ZNEaYgA47envHOWe/6/ke27gBDzV5BdZF4hXrlyJSCSzOVJZGhoaMGnSJMyaNQvHHHMM3nzzTYTD/phR5RKOPMQt0GnSy5ztkpeSE0wv8346j+SWinr8Y/pK7K5ttFJaEIgGphWloXCLBs3zIVZ5iyJ/Qi/35Un2SVxG9ryfnWYPCMM7VNVkmudDTGtqlCZmzCG1jdntl3KNjbucE2nfNcRMgUQe4b2+3bVNqG+K4u6PFuOb5Tvw5qx11jvfo30y6q9oMYunAV293eluQHhz1jrlnMGihYJklGnDNvH79XMz8eXirXhrzjpslGiXyPdL2jsthMxZa9dgmMwiGPkGVCecbMCuSCyZkoQWWEhd/Ao25NZH895nYUGyPip+7XR/IXNTKZQIqqeN3sP2m7wbscm0uD6qAnFhYiGkMRrz7OLh5f0UWAKxaf3LE3gLwiHss0f7xDHOcrxUkT6d11+7Lb7R9VO91cJwSGgWLr6OeEwiFhq/KKYL1DjHeC/tevYae59HFrBkigWugph3INNHA/YI7oA9UGAL1C81O7ImEO/atQt//OMfMX/+fIwZkz27+2g0irPPPhtfffUVDjnkELz77rsoKkotp12+0RI1xN3blSgfu2xr0rRMNMA2RmKOgbpXh+Rk/OznZ+L+T5fiD2/Ndwy2xNxRNcIowPcPllkyWBMa8DXEXjTfXvxFyXzGhCnUmGypsEd/5NVF1WS6Q6n9m42Zpu15qwjEy7Yk3/ejX/6CYbd/jsmLnabdLZXt1c6IvT7Lww4sDbFg2eel79ZYf++qbbLa2lHDulnbRZNc3vanpq7gHJmkss75ffKK56VHix+bNDmm72nWmnJc9socW5RTHqQdk3+bojF8v3InV6Ncmwiox04wVS2fmpgbi8RillBFF6HizwzYF/4Au8aR7kPd+iTe7kLKtFkldQ5rUSKiUGIyzfYpPL9bGtltsdZDbteMmcFGtU36hZuJf+376QUF8idP4PZUR+p03mK922ulBSvVYS1m2s/TPsSZxxlIMfV23btDKbdMmi857lo8eLVgBWIaHYMk/wksqNaAAeJUHNXV1di5cydM00SrVq1w//33B1UNV5588km89957AIDOnTvj6quv5h73t7/9DZ07d85k1fyH+V5bYpRp1YkHC6+DXbm9Gif+fQZ+dUBv3HnycGt7x7KkcEZWEHkRRk0zPrjSE4kaBa3klop6T4J9vP7xf9lOO6ixnY6gK/K5evfHjXjkVyOldVHVELNaHtOkAhgZYo0DEQ6Wb62yCRWPT1kOALjtg0U4khKu8pG1O2uwZHMljhnePS03EN5EMGgNMamvqKsqp/y2GiJR61uiNW6iOjZEnJMbejGLe73aRluEYl6dgfhiVyuINY+G9T87qyQaaiC5iEk0Ia//sA6v/7AOA7o486TXJARiWTDFMf06YpYgOBMbLTsiMJkmdXLzv9y/Xwd8szyZM1Rkqmp/nBzBKHFAYdiwtMG0ry+tIZ65aie3LvRiMG1NwFIoGSPZuw1bGmL+8bx28vivRyIaM21jhgx68cHrgvZJ+/bEjZx0WzwKQsn+m1zPtj9MC8TiBZFUfYh5woVbV0Mvnqou3sZM09bvqyympJIusSWzq6YRFXVNVoo1N9KxXCT9vmxcuu39RfjN2L7u9TAMR13qJfMRLQ/nP4EJxGvWrBHuKywsRO/evTFhwgTcdNNNGDbMPSJvUOzalTS5IIIxjzvvvDP/BWKGlmgyDQC3nzgMd38kCMIigDeZuf2DRahriuKl79bYBGI3IWHmLUdg7P1TAMQHYFpTUu2iIQaAqvomm0Cs8hZpf0Kad+dtwOn7xQPuuOJhHkBcdk1GU+u1fNWgXzzNNxGO4imenOdU1kXQqiiMooKQUChoDl/IhIemAQCeP39/m+bUK9yAVT7riMVpl/hvgtYsNjTFLGGE1mCJTEV51hhDmZREAzqX2awydlY3oD8zseP1DaJJM/0dptL9hiwNsV1zyROkiYDAPjtbFGdJHdh7iEaTJtP05Ym21S2oliMaN/Wb/s5dNcSJf4sLwmiKxt8hbdpM9zebGSsUAmsy/eNtR2HUPV86jpOZTLP3G3ILqsXZRnIEqxKmhE+vC9qtisLo3LoYOziWHo7rMNputg+n2x9pQ7w272WOIfMhjkRjru3C3obUrskKxOlqiA3D/0XCfId8V9/dPBE9OYtPjkXQNK5FLDpUX8GnP23Gtir+92AYzrqQNJY8tIY4/wnMZDoWiwn/a2howJo1a/Dyyy9nVRgG4oKuaZqu//Xr1y+r9QyClmgyDQAXH9zf8zm8Dla0mOzmS0Wn2IjF7AMs62fMg51wydO32OvEzk/u/3QpzvrH967XBLyZxxINsWlC6hNJw81DrKghZu+LaN/j+wzuYDXi7i9w1KPTpeU2p8B6XtO0sPAm+v5riO2/3fxe6eMbo8nFJdrUVTSRZoO6AU4hp2tb+0LRGc86vxVeP8D63zrqa6TWtgpczHJpkgKxfbtNuysRWFiNb1Msxl1YU9UQs4IE/axpra4t4jSveonddD9Ka4hFz56Gve8OAg2tzGSatUpxi7btRwA6co1ICgIxACVhGKAE2USVWc0pvaBArJ940dW9zDHo987GQKyqj7g+P7oNqT7raMy0XAuA1KNMk+vxYnw0Z+qborj4pdn4lyCzBq21/2ljBfcY9lUpxL8UQixnSJlk8fLyQ/kWq1e9Pg93/O9nrNjm9Pk24BRy5SbTKVRYk1NkPaiWJnuk0/G0NHjja7EgOinRmq3h+PsCdnMz2i8PUBOInVpkcU+c9AGL/8ubhNPapYUbdmN3LT99gJcJnZXQHib+9vkypXN4pStriJlnEDNNW6op0RNau7NWsKd5sIATgTNVuCbTvpWeKE9oMi3SECf/bmhKfkt0dGBT0IR4GmJ2squiMeKbTIuEovi/sjYpgwgqbN5VHiINMc//lwcrWEZjyW+K7kesoFqclYHHJv+C+z9ZAsC5cCASPujjeLUjz1skEIuePY2qMCkScG46di9b3mPA6XfLwgrKT50zWqkONLTQzVoJ+Am78MK+O/r5ySKze9Ga0U+HFaTDYcNV69sUSU1DTPc5XjTEdNvgteVftorTWzUX3p6zHl8t3Sa0uHvky+QiiShiO/u9LNpYyT1OBctkOtGaeiQs6fbeo530vFqO5tcwnAty0gUTHxbPv1+5Eyc9MYMbOVsTPFokakGwn2tL1RCnSkWtXaNULDCnI33mrNVJjRz9pG3aq5hdCOB1zCx3fsjmnxR30k6BWFzudyt34OQnv8XEh+VaUxXIhDkWk0+YaHgCt0rE2Pj12LJgM5luqeZM9MCarvCqmr8xrWswE2/LZFowUtEm2yINsciEladJZIUWlfbHN5lWCKqVQpMkfbbKN2XVnbkO/WzYBbIV26otqwxHUK2oaau/VSdLIGavH8Njk5fjH1+vwqbddY73IBQcVU2mKQ0tHWU6FQ2xCFH054vG93NsI/WesWKHY198v/33Cfv2UKoDDa0h7tHWWxyJVK5D6izTEMvwIrPbTKa5MR/k59val6pAHLMfqqYhjj8L2kKAZy7++OTlapXIUT5auMn1mF214tzQAPDPb1ZZf4usLfwcQSyT6USh1rwnhbLYTAB0eTz80BBf+vJs/LSxAqc+/W36hWk8E5gP8SuvvJLW+eeff75PNdEQ2MlPSwyqlQ5sZ0gPiLYciJxOM2QY1vlh28pyzDaRdPPDA5wrqLJBnKxm09pSEcT0rVyQYN6byXTyHNXJk8FxwFKZ3FrnUsRM++TdTSAW6evyXY6m65+uySbvVfitIXakXUq0nUP27MI9nj78wwWbMGlkT9t5vDIJvO+GFdrUNMTObW6CtIHUFmnIfakIxBGBhrikgN9vAcCRj0zHwYM647VLD7TlOAZI2iU4ygwLNMT0c2+IxBzPUpyeSP7seEK5Pcq0+ztTyUsevwb/HfEm99+uiAfwenfeRjxy1kjHfj++FTqoVsfWwWXDYDXETh9itbab6hyD7c/NmEr0cb7ZPc2AzmXo0qYYPyQWq6OmPQuCyvhLmnlJYdiy6GqMxoPoxceReBl+x1fIJOvLa3HtGz9y9zVFY/h00Rb8b/4mLN7EN4MmtC4usIRmoUDs42MqYq7B66+8wM4LZHX1Y6pQm+jXtR96dghMIL7wwgtT8pEikV+1QBw8LTWoVqqwk+X2VMTqnzclhVTrKOrx0p9CQSLIE/Fz/fin5EpsKrk8ZZN2dpfslcvycQLeOmnLBc00ldsZ7yiVyS3v3JiZnNyEQwaMFG1h8l8gpkyH0xxkMxFlmi2OTGQ6ty7GwjuPxl3/W4x35m2grm8/gwQ9iVsFJNqBoJK8+2G3qbQ/3kKDUNgjfu0ptkfyPERpnWiIUM5+frSrB89Hn2g4mxgBN2rzIU5up7WWNDaFHbVAlSxPJBBzNzvKpQ+jNbk8bV3XNsU4Y79eeDqR+1xVUBNVJRVBz08f4rj5enCzZtJnkyov2WI3/1UNzueXFVqM036cx9iP53HVYQMxc1W5JRDHqMjpgFcNMR3Z3Pkd5bNQw1sUJ3PzF2asxgOfLlUqh/7GRQvjqt/FIXt2tkWp50GitSfzZ8e3pzTVNXmxScR1VZ2ryCgMh5Tjpmj8JzCB+Pbbb8eqVavw2muvobS0FEcffbQVmGrt2rX44osvUFNTg/POOw8DBw4MqhoaCdpk2k5ByEAkZuKs/XvhP3M2YL++HTB37S5rPzsBoVc8X6byoVp9JnU4vXJsGAbChoGIaeJ/CzZZmgXeNQBg6Ra5T41sYkQGalGUaZrSIrlA7AUiiJlmeu1MRUM8pl9HaZRpUVAtFVLz9Mwd6NqnO1zzJgPpakF2Vjdgc0W95ePlTLuU/LttSaHDb//l79fafhOfc2ImT/uSs/AWn5xCm3v742uI+eclD+VHPnfDi4a4UaAhpl09ZHm+WcGyKZoUHgyOhvg/s9fjD0cOtrazFqxseeJ8vcnt3PQ7if2dy4qt+Af0ZJt3T8N6tsW5Y/t6Foj9lGr8KMqKMm16iN6vwLAebbF4c3KcIdch33dNQhMaMoBnztsPRw5VE4j9CsOZtSAAAQAASURBVEoY9/WVH2NL3SU4JhwyQCsRY6Zp68NUvndyncJwyJozRDjuCSqB73IVnqtAU9REUYGBzxZtUSrjP7PXo5KK0yB6GqqPqU/HUtdjCqigWpt211nZI3jN0E0Qj5kmx/JMfHyDwiKlG/k928h/AhOIzz33XIwZMwbnnXceHnvsMXTs2NG2f9euXfjDH/6ADz/8EDNnzsTgwYMFJWmCQptM2yF9Xf/OrQHAlo4BcHaG9O+hPZLpWmTdLHnk4cRA+i3jb8ZbZXzwM3lQKtnK5I/rdifqSoRxcTm0QGxyBgMvwzutNRrYtQxg3Z458Oqmsur61zP2xYcL7P5OdLqnUEgu1vqhuclV/FzzCiLK9H73TgYAfPTbg7H3Hu2cEUdZNw+XGyKr61YOyZhds7RuZy0ue2UOLjt0gMMkGHAKbaL21xCJoigcgkG5Qqic58WMnweZzI/q3R4fL9wsPZYIhux3TP+WmXbzTJxXbY9HY6XfA4keu4lJcUQ/F56GWPyMkn/TY9Rnizbj2L17WP1Q5zZFVlosumheucUFIZvQnMrYVxQOKQf545GK9Q8LrSEm5XVpU4ztgtQxqozp39EuEDM+xKQtHdi/E44Z3t1zfdMlZrovvtHtSySMsvEkoqa9D1OJGUDaVzhkoDAcQiQWRVMifgGt3cvnYYUnEEdiMRQhpPxOb3zHnvOa7VvX7axFz/YlPi8ckIUc4KQnZiS3GgaG9miLJVQbd7VEAc/yTHxSPSevvSa/CCyo1i233IIOHTrgxRdfdAjDANChQwe88MILaN++PW655ZagqqGhYD/ulhpoCADOP6gvAGB0n/bWNjIBbJcwha5i0rLIgsKwkz8HiUdNnrnIzFDFjJNljw7O3H4spAjZO6dNpnnBvbwIjiSiZCQWQ68O7iu7AF8bK8rnStOmpIBrMk2qG3bREMseb76vGdH3HYjJdHpFWny3Mr4wxE44WDM7ty6L+PSVFIatd0eXedeHP2PZ1irc8PYCPPylM03MD6vLbemYeO2vuiGCsf83Bec8/wO3zvHzBBpiS8OamjaAvM/zD+rneixZDJO1YZmGmN334/rd+GcivQpt8l1Zxw+sQz+VmClPu2TbTmuIqev84a358XItZZyBw/fqCsDeN/O0fPH24F0gpqvoRaB9bPIveOOHdbZtqvEQZNgE4kTlThulnst47z3acrezApAoD3GBIFKwsL4+zTFMiaUHwbZf8KoKQiG7C49pjzKtIpyRZxE2DOt5NEVjDgEwnxUOvJqTxYJU3yn9bD/9aTMOfWgqrn59nvIYonJZK7uFCeykzL5DhoGnzhkFAGhbUuCoDw/TdM6XZOfs26u9ewU1OU1gAvG0adMwduxYhMNiM8yCggKMHTsW06dPD6oaGgq2Q8nnDjtd7p60N9Y8cALevXq8zRcISA76MRPYhwrXz07g6M6R1hzw+kxacwVQArGCGeGBA+wLSnswye1JIIm9urdxXtiqE9FMid85bUqpkv6JpjBs4O9nj6J+x8tqjMTkq6oupp9sdFMeIUE0yCi1ki8bTGULDvmeh9huMp2e+Mp7j34t7pNyHD7vIfUJCQDsrI5Pgtq3KkymwaGakEq7vvN/SXMGXtuYsXwHdtU24ftVO211pxG1Kfo7TKVtkX6jqCCE00arCUIy+wjZ42TvgbbCoOsu6lNs/WMk5hAoO5Yl0xaRBUrA3s/Sk+/6pphd02wkn4ebli9kGLbxLpWhz4uJ8mOTl+PP7/2EBkprpBoxXwZPIBZFwubxwgUHcLezwYgKqOdqmkmTYFleZh6pxm5gMZFsTxMGd8F+fTs4jrEvUPPLIbEF6OPoY1X6M2I+3qoobD23pqiJ/87dYDuulY8uSJmG9/zIgg4bW0AV+vt5ZnrcdeGLxVuVNcQq7kvkCHasCxnUnIRxIxMRM03HAhB7Su+OyblYoQ/z6Tw2KmgWBCYQ19XVYfNmuUkXAGzZsgX19fWux2n8pwXLwzZEZpmmaaJDWTKSp0PDwUz4CDLBg/ZxBJxmNjwlAqslO3q43X+L1KNjmTjqaNL3D3j2PH7+S/p+vArEX994OE4e0dP6XUhNFGQ+zjbtEqc9qkwiwxzhImYmNTqGi3mqbGBcvaMGL3672rUOuYqfViD8KNNqQ7hpmg6LC3s5cdw0xG5Kth3VcdPRdqWF1ndMytxd22gF05Exc2XSp5+3INO+NBlM77q35nPbj0ibaH2HSE1DTOe+HdJNvABGk0o/P3XZNkfaJXrBLKQgENP5n//62VJHP3Ds3knT21tPGIb7T9snfh51GFv2pop6W+RYsp/um3la/fd+3JiSZqsrk2vYKzf9N6k19MPn1xZUy/QupIruhy2DXogyzaQQpJoxgMAK2qnUDbD7EIdDhi22B4H+5kT9UkHIwIH9O9mOo49VEc5I/vK2rQotq6raRud4WVYUmEdi4PCswYg1W/9OZSmVGRXMl3ivind9FQUOrSFmt1uKjsSl3eR60zQd3wXbPob3aCfcp8k/AhOI9913X3zzzTeYPHmy8JgpU6bg66+/xr777htUNTQS8l375RfsUwgzq+MEZ8Cd5N90B6+ygEomZ8SsUXQNwGlWzU4sIwqagqRmCjh27x7oxBGe6fup45lMC0t3khSIY/JAFNRz47VGJQ1xiJeH2LRFmZaNpdGYKdUg3/XhYtc65Cx268C04C5sKJZ51WvzsM+dX+CzRZttflxs3diJECsQuZntk/bUqjBsvVMyEXtMMS9oLWW1wBNiaNeC937cyG3fomZLJt+8HJcq9GhHaSQUhQ23KO83H7eXY9tFL852mPiGbUJS8qZF90H3Zd8s32Hrx1oVhm3lFRWEMG5gJ8d5bN3jbuGJZ4iksE+/J5EGK0xpe1SD5R04oCP+dMwQ4SKiG+/PT2rVU9Ws0dACMVksLAgbytZeojGfDVZHLx6c+vS3yeBlHkymrzpsoGvmAgD437XjcdiQLnj1kgOFx9A+xKJbtY/V/GPCIQMn7tsDZ+7XK3GOdw0xcaloU1KADmXxxbFdtZyozHms75MFCiwtTk3zTcdsoC3quNpozsYD+3fE6D7tMbhba7xz1TjuNQzKh9i2nVo8I/2HStTyQqa9O+PIuLc5L+gZeXYJ1Ic4FovhxBNPxMUXX4zPP/8cS5cuxdKlS/H555/jkksuwQknnADTNHHzzTcHVQ2NxhV20k0mYTtrGm1h/tkOlB6Av6O0Sir9omgC89Dny3DGM9/Z/PdYjcenTJRHy3ROMkFmo8Py5kV2P2hOIR46/CLKt0qmGZH5MAJqPsQhw3BMcONpOuJ/8zTINCq+gekGrckWfg6w3KBaiud+9nO8zV752jwc9/g3Nj/deDnxkthLODXEiuZ1lDntxl11iMVMbK1Us0SqpiKjcnMVS/z+iduCqE1NXbrdql+6C5KFiqayssuYMDGMCghIw9477d5A71ExmQbYKMDO55OcsCa3ybS6IZvJtFxD7LYoJqIgFMI1hw/CsXv3cD32sV+NlO5X6cvcoK0eyIJFYSiUtq8uO3bQ49OCDRV4IeE7zsvffNVh/CwhonbFsm+v9njpojEYInH5odMjib4beiih28OtJwy1/o67zxg4KWHNFI159yEm8TXKisLoUBpfWC6vcVq/+PG+swU/LgIRJOXnVtQ14fRnvnNsp/tEOiIzry/gPbuCcAjvXj0eX1w3Ae1a8bXvVtPgLKySfeoCMS/KNKOcMMX7NPlHYALxySefjKeffhqhUAgvvfQSjj/+eAwfPhzDhw/H8ccfjxdffBGGYeCJJ57AySefHFQ1NBp3WN9qhQEXsHeA89fvtv5WCT4lW9Gfs3YXvv5lu/WbnZhuY4QzFV8yNu0Sb1IxZ03SnDTd1W3aX0c2UNArxbzHriIA8Sa70Vjy3JDLZFgln+fZz890PSYXoYUVFW27DN67SDUX6g6mDSd9iJmJDCsQK046QoaB9onJ6vn/moW7P1qMtiWFLmfFob833sRM5DrRv3OZFZl+KUcLvm5nrRXJ1w9T9iJFbZ3btUT7WQ1xXRPfzFGsIWbKowVizmsk71qmIY5Ek6bCtNbHLajWk2ePsgfVUnx2XjSig11M2Onn+cy5qWmcDWrRgJi0F4S9WRvwxh52ccXLfZ+4L3+xwM8YJXFNrlxDzFvQbVUYtlK6AckFNvK8lm6pwpuz1jvOkxGxNPMhtEpowBs4EYb9yEubLXhjNlm8Fj0jMu957uuVXJN2eqxw0xA3RmNYvaPGts3+3p2NoHPrIsqH2E7cmsS+4OYaZdpFKTBv3S5MXrJVfrwHojHTZjGnyTyBCcQAcOWVV2Lp0qW47bbbcNhhh2Hw4MEYPHgwJkyYgNtuuw1LlizB1VdfHWQVNBR6AYsPO3CLFK3OiTD/OJXn7DZJtU/K5Z3kpt11AIDiArEpU9Lvzv4vYXdtI/4zJxkUhG8dK74x9p7JBKspIs8f2RSJp3I56YkZqOfk8VOZVPCCav31s6WWGVvIRRu3q1bs20pYsa3a9Zhc5IvFSWuCdIPccoNqpVjWxIen4+056x3b2dfNfpuqfVjIMDCoa2vr90vfrUFhAb8NXHHoADx0Bt9th7eIINIQhwxgcyL10P2fLnWct2F3rfW3H6KCssm0y8VE+9lvr6aBzilKCa0iH2LmZdH9GE9YIpvsPsTOOk1evM0qP+lDnDyGF3egd8dSlBSGcd7YPjht9B6OwIQivAh1bscSAfbqwwbiuH3cNc48ks8oqSEuCIds/R/xxRbx/Pn7ObYVOzTE/LbF85XlaY3jdfVPIKYtfkTm7jyBOGSAm25LpQwRUcqf2nIN4ggyfuaJzjS8x0C+K9GCP7ndmgZ+oEx60arJFoSUpyGOYQGlZADsC2+8T+2xX42yxnn22dPxBsg1VfIQy7Zd+vIc1+O9MG3ZtrTO16RP4F7/ffr0wV133RX0ZTQKaJMOPqxZpqoJoEg7pqJd9bICLxtY36d8GD1piJkJAWsSzBssvDQfEnGxKeqMLkvTGI3hrx8stXKZshCBxDDs1//txEF44qsVABICL2eCQ9KeuE3M7vt4CY4Y2lV6TL7y+c/JFWye5swLjZxJXzpdyp+ogEPJ8uwFpmoyHTJgaW+SZfOPbV1cgNNH9+LWh6sVF5gCiwQDAm15UuAxWi+3PFW/URfxW7RYtHCD/ZukF4XopiTW2Nl/0wI2XyB2aojZ4yLRGGYlLFk+/mkzfn/EngAYk2lOOycBwe49RS4sshS6vFMat1dKBNiy4tSnXeQZmWZSU1kYMmyLiSKNLWHC4K44Yd8eaGiKYvKS+CTcsSgsaBO8YIuisczPoJ0mYH3AoldCv/adNfHxrKYxarMyIHVVbbM8/peItl4QClHpBTlCXQ4LxKZpYlNFvXBhiDdXJG4uon40GjMdeZ5prn3jR8xftxu3njjMNV9zJFEWDd2P8a5BBxVlXbHied+Tv9no4jx49aLPoV1r4vvSe9+8VJeazBKohliTW2h5mI9TQ6woELuslMoEYzefL7po2cB6/6dLrL+LOQJxaVEYdY1Rq7Mll3WbrHA1xJL2I4pSGl/ZlwjEkZg09VJEkPuQjrYbDhlYuV2swXUTHHhBnpoj6U7QeCv/fgWOIQLN9mp7cBp24qNqMm0YhiOdmujMUMgQBp5S8SEmx7i1M1pwKEukZDn/oL7o1ja1SMaqPshSH2LTPb8n77bo9y6qB9s26OfGi1bMBkGLX9t+HK39bYqa1nuzBdXiaIhl1jMyvGiIXa1+iKltGpIivWiQNJm2t3M3y4FwyMBT54zG1YcPsraxQq2oCN6iWDr3o8r89btcfYjpcYZOnUbXz3pHCmbXPHbXNmLNzrilRzQWs549r835kXc6KO7+aDHGP/AVXv9hLXc/b6j4438WABD3+UlXBvF1SR7zBheBuCkacwS0ossVXYNsZ9upwcQRcZuXkGNk29qV2l1w0p1f6+l59glcIF68eDGuu+46jB8/HkOGDMGNN95o7fvuu+/w97//HeXl7qkwNOmjNcR82M5TNDlWNZlesa0a1701X3pN94mWmsl0JyqXJ09DPLBLa5z69LfW72RQLfv1t1a6a4h5/O6IPXHlhIE2ARWw++vI/EybojG0ayX27SQTDcdqMVV/wzCwMWE2bqtD4hy3Z12WYtTMlkZ1g7tpearETGDZliqHmRw7UVedZIYMOCLcsmUThAtgMb65P9sPrCuPT5BrOOak9jolr0NylN49aW98/odDpeeJy1M7zk1gcRPkWnM0mnZtTXK7LNIv7Y8t0xCbpj0qPg3PFBJgfYidL81Lrl4aL5Y8bv1MRCDAeiEZGCj5LbCCg0pkZ8D+3lnrBpHJNE8Y9dNXWMR1by1wWDmx0G1g2dYq62/efYqsJtzGvT9Q43pdU9SWTYElEjOxraoe68trHfuySX1TFC9+uwYAcP8nTtcOgD9X3Li7Dvd9vNhyC2GJxEws2pgMwCbDLcBefNwXj/miNkDe6+w1u5hzWQ2xu0DM20u3D7ZfTdcgQHXOpQmOQE2mH3nkEdx8882IROITBcMwsGPHDtsx1113HYqLi3HFFVcEWRUNtEAsgvUhFWlvHaaSkuf53o8bMaZ/R+F+L5MINh8oDS3M8aJMmzCxdAs9OYj/y97ieS/8wJznhDdwXX/UYG69aF832UDhJhATM1/2ebGPj9e2ySFumrQD+nVsEekOurctSev8ao6GuKLOHyHZNON+vizsxIenoeIRMgyHYLCrxpkaBRB/76I0OazAddv7iwAAa3fKJ750G6br1r60CDNuOhwH/3Wq7XjWTYBF1Ucz7CKAuXVFlfXqgj6tcXamh3PzIXaWwx7HvpMwJURb1+EIJykLxB5Mpt3jQvAFWC/QljdJjXNq90Y/WlbwF03OeTUXaaT9nm3QOby51xPUmW5D5O9UTaanLUsGu2yIxNCuVfw75rW5aMzEmPumAAAW3HG0dJzLFK/NXItbE/0VIO5PRc/y+W/Ewm7MNHHiEzNc67CFEajp+QkhEo056kC/MjcN8WLG6ov1IY7F3DW6fA2xvUwavwXauJZcG/FmksCe9scff4wbbrgBvXv3xrvvvott27Y5Gsy4cePQpUsXfPDBB0FVQ0ORwy4tOYVIWGXHPLcOUObv6DZ5ootu4ASbItDCHq/zZKtIJpBu1+dpdb20n2Q0VPlKbDRmoq1koiAymWbrz8ubPD0Rqdtt/tlSBh1Vc+OK2iauGTtv27vzNqZdLwKvSbKr8KpROMMhwzWHJEHVIoSwZmcNd7sb9ONn/Zt7dSjFgM5l1u+Rvdvjv1ceJC1PVSCWaYjjAZm8C2j2ADd2U0QCfb+dWxfZnj9PiGM1OPHrsBYCjIZY0WQ6VYHY16BaaQqwgD3wGFkcoIVZL8pau+aUsRgSpJnjNRXRfacrIOzFpGESWQ0Q6PGZ1gDT90aelYrZtRt1jVHrXfIWrenI0xt3OS2YsgEtDAP2aM80qcwVTUUL8e9X2ZViV7w613FMU9S5kE63V9H7EzX/uA+xN5Npvg+x+Jx0FU7s6YsEcVU0wRHYTPCRRx5BWVkZvvzyS5xyyino3Lkz97iRI0di2bJlQVVDQ6EVxGqoB9WSlyMTiL2Y4tUzKR3o4BH05GAtZ6LOCjHEXM9t4uRmLuSGqsl0zDTRtkRsqEImfWyaFLb+vAjVBLeJaj5HA/VCVCEvZnlNI0bc/QWOeHi6Y59bzuh0MGHi3XkbHNtZYVVVII5rF+3Dm2jCwvWRNU2s2s4XfO/9eAl3uxv0ggQxmaahhbb563e7+r2qCj+y9l+QYn5eWxdJm0xTm23+dsyiF69Odh8/ToFwBswixdDPVhZUyyte/GPd8s4mo0KnriEmgh6tIaYXfrwsbtBtkH0f9YIAPzxTY1HgsT3alyrXhceHvz3Y9jsZOdqbMEv3Icm0g/xrepkj1TVFrcj1vCjTtEVNOu88G6SSTk9VIFRZyGvipGt0izLNHsNup5spHbVchJtSgG32aZtMM51dy5iV5BaBCcRz587F2LFjMWDAAOlxnTt3xpYtW6THaPxB+yioUSRIz8J2kG4DQDoaYpoGRqgVRWHlBQBayUzqkyZjLhpiF3MhN0i13AYetzJFGmJ24nfBuL6SurgIxKZfoaFyG5WgWjNX7QQArk92oAKxyV/UYAWSsQPEbgg0IcNwtBnR3fOEs9d+WKdk/ucFuv8Y2bu9Yz8bfdhtEi1q13t1b4OnzknmuZUJdSFJVFgZIh9iut+g/2bfLa9OvHLYL5MVOsm7s0eZdk6kUw385EWQ6d5O7pJg+RCnFVQr/m/MNK3vkdY4eymazsvNfgMizeEhezoVG6Kcznvv0Va9MhxYyx2rPYnMnQX9m01D7Goy7Sxj2ZYqTHhoKt770b5gV9cYtRYDeH0rnabMzxRUmSAV4c5Pl7xIjCMQU38LfYgl20PMgpub0O+mFGDr4LeGWE/XM09gAnFjYyPatJEnqgeAbdu2oaAg8OxPGmgfYlVEmhnW5DQdgdhNa0mfyWrF6I6cnpiqCCyWNsFtfFYwF3r1kjHC0+kAOW4m0zJBjexjBzp2MDpr/9745HeHoHPrIrComIe3hG9D5R5rOGlVCDxTVL9QFVZ/O3FPnD2mj2t5IcNwRMoVLQjy2seDnDzCXmG/R7o/6MlJd+IQiF1Ma3m7+3YqxWd/OBS9OybLd9cQy7+P34wVLzYBQJc2SUGQfsT0Z13HLOrdPWlvRzmsDzFbHsDTEDtNplmhuShFs3DAm3mzm1l2E5U3OFWSrih0kC5KQ+whGkIbyjKHdTk5a//eXG3bFRMGOrbxBPxwyEj5mYsgr1isIY7/u3F3na290ccnvwWRmbdz25/+uwBrd9biurcW2LbXR5JBtXgLCHRanlywQvKiEElFeeIlA4AbTVFnQENbIE1R2ZLtbB7iVKJM05ucPsTS4lxxnp/9NtPSCEwg7t+/PxYsWCA9prGxEQsXLsTgwfzAPBp/IX3yHu1b4SPGHEmTRGRexw5qjS4CgmiVHVAQiKmiWbNnUcerIiioa4jl2w4e1BmH7NlFeD4d/EU2ULrtJ7BzSPbxGYaBYT3bon2pUyBWMZlWMRGTpYfKB1Q0xKzgQhOkhljUqNl2WlIYxhWHyq2O4uc5TaZFzYy0j9NH9wIAHDSgE6qohYFUNXqRqGnrM0g737Nra+7xRYymrW0r+UIxb2JJnhctnMnaf1xwkV4Gg7vLF7ZvPGaI9Tf9jOlJNf3t/HzXMTiYo2nk+SKzk1Ja2D3nwD6UQEwfY2+nqfoPA/6autJ5g1OFPpV8j7Zgih6KLikMo2+nUnQsK0J/yn8diPt8874X3rN00/b7BbEWYMsmViOkrVzz+jzbfrr9J32I+dfgCUGiwFNj+3eyyuMF1dpSmQweFWjfqYio/+MFRkzJh1jxHNlYO7hbvG+MRJ0Cqz3tksg2mr+5uCDsOQ8xfw5EKyPE+1KBPb8FrNHnHIEJxCeffDLWrFmDRx55RHjMgw8+iO3bt+O0004LqhoaChJk6I9HD8bee7TLcm1yF5GGmO2geH5DNKypM41bHmIa1tzQFriGWkUc0bs9pvxxgrQsYuLlnoeYtzrKvy6PpGmfvGP/bsVOTF26TV4ZODU1IoGet7ItCppEiCr4EwHA36csdz8oh1HxIeYFJyME60PMhzfZLi50H7YMnoZYcCy5xGFD4gs8bNsPhQz0dDGH5fHPb1Zh3zs/x08bKgAkF9REAirbxru2KcFdJw8XRiXmfQMbdsUjXdPmptKgWgoCMfd0alsnyirjL+/9hFdnrgVgf960QFzK8Z8G7BPemGnC5LgyRGLJmAO/GdvXesf0d88GOPK6oNGJitHgZ45dP9Iu0e+cCGp0eV6rO+X6Cfju5om2Mc8wvPkiu/mD+4XIhzhMLb4CwE9MMCL6HZJjRbXjjQOi7/X6owdb36ar/zinYNM08erMtVb/EDSiGn65eKtjWyrCneo59DhCf2tAcsEl7kNsP89uMu0st0+nUqGFRKuisCMPsZvWnjeXsJ1isPvSNJlmfssUKppgCEwgvvHGG7HHHnvgT3/6E371q1/h3//+NwBg69ateO+993D++efjjjvuQP/+/XHttdcGVQ0NBzZHm8aOaMLNdqBuAkKDZL+b5oGeCpLJ5KUH9wdg75TpPrggZGBgl9ZYcMfReOmiA7jlhqwJgbrJNsFLh58MqiUfeN6asx6/bK12L495JXt242vZeFdyU/KomkzTKTfyEVWTNhFuk750EFWNt5jhFmwKcEYVBWRBteLHsRNruqw9u7m7/7A8/OUvqGmM4uZ3FwJI9h/CxRxO671gXD/86oDegno7txGzdlprKMopCwD79+voai3C2y/y53v3x41WGiq7P3H83wKJKS1dzmOTl+PA/5uCTYwvO52KhE6lYguqFeWbVbvxxqUHYtLInrjz5OHWNj9z7EY4UaG9whWIqTp6MZmO1yWEksKwrX/16u9qGIZj4SAIDfGP63YlrmffTo81vGvT7c0tywKvjxAtYJQWFUhNpml4GuTnv1mF295fhDOe/U56rl+I+j9envHUBGK14+jFgd2MdtqK2s0JqkWPBWwf8ux5+6F1cYFwcY9E9SdFrN5RgyMfmS6tp1uUaef4Ii3OHeZ81RSDGv8ITCDu0KEDJk+ejOHDh+Ptt9/GueeeCwD47LPPcMYZZ+C1117D0KFD8dlnnyn5Gmv8g2gRNHGIqSQQn1wI0y6ZJj75aTPu/N/PiMZMV4GY16ERDa5roCeqdyU+xCMSgXhEgWtIvdu1KnTkYGVxm/Nwg2pRt+M2XtrzEHsbKW49YSiOHNrVtm3fXu2tv585dzSG92yH58/fHx//jjH951zKPaiW2mCWRraUnEDFj032qHjtPdXovSwiiwPeu1MxgQ1zvmORQM+6EbBm4wYM/OWEoa7XHNiljLudPHfyHYjT1LhewoasXbtpiCdfPwG3njAUVx820PX74FmzuGkAYzGTG4VfJmDSu16YsRrbqhqwtdKe/idKLV7Rix500+YF1VJh3KDOePzXo2wab15KtlSF5KTJdBo+xNSpOxN5tQvT0BAnz3MKjV5w5on3XyKevGSb9Fqke2LbJs/UVhxlmiMQu6QtA9wXC3nxF75fGQ9gqBo5P13cXEZUjpWhGpmaXhxgxySykLdhVx3mMoobmYa4S5tixzE0SYE4fsQTX61wrSfpZyZff6i1jW4f7LXSDVrLjoE1DeouWqlEBdc4CTSa1eDBgzF//nx8+OGH+OKLL7BmzRrEYjH06tULRx11FE4//XSEw+6r/Rp/6dy6ONtVyCmOHt4N7yRSvhQVhMTmVDETVyf8k0b0bufqQ8wb6AZ2iWs2U/EhJp263U8v+Tc9CUlF62OvgHOTF8HWCv4SS9bxyKFdrUmNjN4dS61ox4SOpUWYe+uRaFUURmlRvNs6alg3pbqQleVPf38INlfU4f8+WYoV25Ja6ZiiD7FX7UuuoSQQU/domqZtcslr7x3LnD7bfsKbZJQoCMQ8rVVDhD/BIN8COZ71qQsZwGAFDfHbV47D6Hu+dGxfuqUKkWjMmrCLvn36+1INHCbCnorHuX9Q19YYlPBldhOieOfT3wuvHlUNEW5/Icv5rSJERaj8pPHUWqSfMW3H2PH23fKDMAH3nbo3/vLeIjx97mjeaULId5QMquWPhphQkGLaJVG5qcjrheGQbbwLMqoyK7Czlh3sQjRv2BL15bwuUiYQE7//SCyGPdq3wsbddWhTUoCqentwQn4qsMzOfUXjN097HaTJtCyWBWnLd3+02LFP5kNMXpGo2RGrv3i7NPH1L+7WXgcN7AQAGNS1DY4a1g1fLt5qT7vks4aYfXw7qvm5wFnWl9fi5Cdn4PyD+uG6o3Q8pnQITOdx/fXX45577kEoFMKkSZPw1FNP4eOPP8ann36K559/HmeddZYWhrPEFRPcg9K0JOgBtlVh2DWKJQBsrWxISUNsXdNlFmpPWZIQiBO+d/RKYg/Kt9FmOpfCJFd0fYKXMZJnMj2sZztP59IUhA10al1sCcMi+CbT8fKG9miLiXt1cwgAUUWT6SDMAFPhs0Wb8cCnS5WEeNocjjcpk8E+kijnfJXn9uBn7hGbvbhLFYRDuO5I+cAfMpzm1qIo2eQ48k02RViTabUX37GsCG9dPpa778vFW119iOnX2bWN+6KlrD0WUosGbq/ITePOu/+axuSEn/d4qhsi3OvK+j3ZYyYCflMsZi2SGIZhvTtblGmmnXqVzej7pfvUcw/si2X3Hotjhnf3VN7ctbsS9UpoiNMSiJ3baI1zqiXT5aYizLLvNciuknUBsOJVcPrDKw4dYOsDif+6l6BavEWcWxMWI8TEl14s/CNHKOFpkFViIWQC1uceSDXtktpxsoVZVf96kdm8aKGDaJ5Vm/ZF4/vZgoaSNkbXnS0r/aBa9t/lCQsQNx798hfsqm3C41OW69SqaRLYF/nkk09i4cKFQRWvSQHSKdD5BzX2wbxLm2Jhp/nGrLXW33QeSBEyUyg3szS6XyPlEDNouuMkg/XVhw20CQBuwltqUaad5tni8pPliHy7RBhwTuJlmiUa3oDgZs4XiakF1QoiUEwqXPnaPDw7fSW+XOIMhsLSrlXyW2+MuN8kfYu0X2ZjJMaNQK0iyD49baXrMV4DdrnlOOXlIRZh+RWGktoeGi+v/cABnbjfRiPlEyeql2gyI5rkyYLFlVIuE24TbzdNFe9+6JQyvL5E5Jcv07YZhjjAFxm7orFkOpa4D3HienRQLUb48PrV0lVkJ+ipaPVIv2HlVE/DZJrrz23w//ZUrm3s8F4Ia1XhV1d5/kF9HdtYDTupL68PCYUMtCoK492rx+G9q8dZY6jYZNq5jafRH9WnvW1fJBqzcrcXchaYeD7GfrmbqJIPGmI20r7tPOq7ZtuoJRALTiftW7Vtj+lnz3fPiy/ht4ZYlqZPBj0vmbmqHOt2apfIVAnsi+zVqxdiHjUSmmAhmsUcmdfnDPRkoGNZkXAC+u2KpBmvaaYZZdpFS2Cf4MWvQwZQXrAaWvCJ4yz/tFF7WH+7Caf8CIvJbWUumtpkHmLTJgg89quROGTPzjh4kDPtinVuyNlGVf32eEMIWxYr2MYU/ZxzRUNM2Eql9RBBD7IVdY2eVpDJMzFNEwf/9SusL69zHOPXivSWCv69iEp368PieYjVXhg5LGxNrFkfVG8vnjeR+f2/51sWI4UF/PLoy6o8VdntFYRD+Mdv9sNlh/TH2P6dpOW4Tcx5t3+KQl/C+6bc3olosSAZfTZpH2NQx9PX+u/cDbZzPWuI6TQ9Pnz0xVTk3HTL5N2LXWuVWtn05N6Pbu6Jc7yZlYvgucWwgsiSLZUAgNs++NnRH5H2MbpPB4zq08HaLhrj+T7Ezu+DPGeyULN4c6W1r4izeMvTENOLLTyh1G8+WriZu51Xt1T6ddVzpBpiyWJRk00gtu8jTcKt7crSCvLKI1jB06g5nzNNUnpj4R3/+9n2WyVNImDvT85+fiYOfWgqvlme3wFAs0VgAvEpp5yC6dOno6qqKqhLaDzilti+pUJPwsIhwxa4REQ0Zrp2WNI8xB40xOQypFM2zWTnm9S+MiZrTPF7tG+FR341UnwAg1seYlHqFLb4mJkMrhMKGThl1B549ZIDpb6nBgyHGWc6k0inhti+XzUPca59Nyp1picfs9fswu/+PV96vC01RSxZxrYqvj9TumZihI9/4k/WRLhN/Gn/UjeS2oP4b/a5+rUQ8u/Z6wDwJ8yAeEJ13N5xE13WjNrtGRwzvDv+csIw17RjbgH42HZ/07F74RzKx5lXj4e/WMbtQ9wsPUTfGDlv+i/bbX0eubfZa3ahIRLF5grnoo1X33+RD3GqkPqSb1HV2oUH7/n07VRK7U+1XOqHD+19wmBxjnov8MbJgpCB4T3jFiLd25bYFurYT0jU9kVyF7/NOssg76EsYY69u7aJOp4jELsoh4KOifTzpgrc+F++xWYT1xXG+zVUz5FZA/G+X+s8qp7sN219F2mO0Xt1b4MRvdtj7AD7IiJ5p/Scj/UT92ssJPBclHjw2vjrM9f5WpeWQmAC8V133YU+ffrg+OOPx48//hjUZTQeIB9sjs3rsw6bckLl8WyrqncdAFgf4hP37WH9TQt4XdsU4+0rD7IdS2vnCPTkjGy2Urm4mAU7fW7kdefpqOgtlxzSX3o2Hf2V1+6k1zeck4o1imZAXL9FQRAWQlxD7F52rgnEKlmQ2MnHhws2AQDu+3gxjn50Omob7YM6fYcxa9FFXL5o3/ry2kDza7q9CS8aYtI+iGDHpqfy673PXFUOQNyWRROqcYM64+PfHezIMe5Xvdw0xOxzPHJoV0d/w1bl/fmb8Ox0p6m82zsR3RJZkPn6l+3WN24Y9mdw038XopaTR9u7D3Hy73QCYBHIJDqIoFoDu5TZFiRSbRNuiybZgtdeQiEDz5+/Py4e3x9vXWH32We/IdHCsziollyTa9UhcTov/R+vDFH8Atk5fkIiWvPgaYhTqY9I8/vn4/dSOg4AFkjGDLqejvlM4hWl24rvPHk4PrhmPNqX2hfsyXyEtgrswixQ+m0Qq6oh5gnOXuOFaOIEFmV60qRJKC4uxrfffov9998fPXr0QJ8+fVBSUuI41jAMTJkyJaiqaBLQvleaJGHbhML+fB4+cwQ++Wkzpiy1R0d+TWEFriYxObvi0AEY0bs9Dt4zaSZMD/S765qwzx7tbOeSrpDuE2khOmaaCMGwJu/sHIt9w6IgFCJ4fTERzh/71UgMdwmQRQaomGlSdaSeszT9iuEQiOsVTZ146Xuck3fntVUmALn22ahoiEWD6vPfrAYAvPfjRpx7YNJPj77HGGOFwK2DYN8hD04FAHx/y0TXOqaCexR1de0eOY4UmWraHlXY3LoE2xyGea68780vGcZNGGJ3l3HylhpwLqF9udjp4+5m6aEyNtELbHQ38f78Tbjm8EHcuqWKzIRTFfKdViY0SumkXWIfnyOYVcoaYurEgLWVXuB9wwUhAz3bt8LtJw1z7GMXs0TKeHFQLee2Qp5QniighONTzutz3UyigxaIZeMnT2ObmoaYf9Kv9u+D//skGVhRVdBjoZ+hVx9iVUQB76ygflQdenVohfnrd6NHuxJsrqgPQEOsVh5vQcMtA4qGT2AC8bRp06y/TdPEpk2bsGnTJu6xuRKspjkjy5/W0qEH3XDIHtglFEp99XxnImz+Hh1a4fh9etj20dcsCodQUhjGvy8fi18/NxOA08wOsGsWBv3lUyy791jrvbpNjJwmRvK68/p2Uqf2pe5B2ZI+xPyFGJnJuAFn5FvVwYFXb/Ze2d+0CbqMXFtIeum7NbjsUHnEeLf8mOxjtWmIE2O/LEUF77HRz3JtmgE+RK9FyYdY8X2RwyyrBodAHN9+ycH98cKM1UplyhBpZL1OqDLVHtnxuXWJc9oQMgyl+rstUqi4RpCrxINq2Y/nTba9zi/oIvwwmY7ETCvgEgCUFqeeXcOZbsZ9sU8F+jarGiLiAzMM7/nL3kl9k124E5pMCwVitfZDNvG0/bw+lxfJmUZ1fEsVWSA33rXpPnxoj7ZYQvlIixB+/swjki0OnDZqD7z740buPvoZiize0k2NKHpOlg8x9W7J/e7bqx02V9SnlLtZhurCAa9tZcInvTkSmMn06tWrlf9btWpVUNXQJJDlT2vp0IOmwUyy6EimXtlZHQ+b7wx4ZR/UyaA6dkAnHNCvA4Dk+6IHaFZb8facDdZg5j5Rsl/fbeDgTQwsX2CF9mPlIabSLtGnuaVfacNMur1GIaZhhWu2/iZMh2aBhw/KIl/ZuLsOizfJJyo8/zAaZ3ASuxUCANz/qThtEq+d0Garbr7mqcK2h/PG2vP2hkKG8kIWaYvkcHYiQrYTX14eVx02UOlaAFAkiFTsdULlZz9+mcAF4qPfHuxYWGjNCainHADGxX+WJ2yzJNMuOZ+BH4IFLQykkyKJEIuZ2F2bTKHSubV7Si0ZshRJqdbWz7bkZ1pH7wKxXROquihG4LWfVkU8k+l4uTwLAt7x7hpi1Rqmhuyz4419pF8/ZM/OeOSsEUrXEC2Isa9L1Fe0Ly3E5ZK2040yURYtBKXbjEULcpYPMfUeiTUaLwK1V7hBTBUbRZ+OrRzb3BbCNXx80xC/8sorGDRoEMaNGwcA6Nu3r8sZmkwiCxff0mFNph37U5SISURDXkRmuuOl/7b8UiwfYnE9quojVtobV9889rfLLdE+zNGYiYJwSBjAiwedDsWKMs0sPIjPNXDVhIH4x/TkQplqB88bk4qYfOfso4rF1CYk6a4+B8HCDbsxrKczBdG0Zduwu7bJVchiJ4z0TzJR8moyTQcbcctx64ZIm8qmkKInpgf2j6fM8Jp2SXR40hxPXN5Nx+4l3Mfim4bYxwWak0b0tMzoCVdOGIi992iHbVXJCOBPnTM6LX9TNw1wm5ICbK6Ql0EHh2T7vTdnOV1ZvA53XtLLqRCJJRcF92jvnLx6hdbGkzZw8fj++Ne3q/Hn44emVKaKZv6s/XsplZWOSTgLVyCWvNA6xodcJec3DSsQz1+/m+t/awnEnAWT8QOdGRRcLXUClohlPsxRng8xtfjdqwO/zV5z+EB8s3wHFib8fkW3oLpo9dAZI7gm6N3bluCyQwdgTP9kKiTHQi7zb6qIAt4VMSbTFXVN+OSnLQCSWuX0BOLk35cd0h/Pf7NaeZGxC2eBzW0hXMPHt57rwgsvxD//+U/r94ABA3DTTTf5VbwmTWzfau7N67MKPWiGDLvJdFE4lPYCgigwCIHuhInQRfrCz35ORt9lJy3FBaGkybRrEC25BpllcyINzoUvzsa4B75CXWOUMn2WnmorPxYTmExLeh4DcAS1UNHgAvGAPywqGuJ89CEGxO5+F744G394a77r+Y5nQRUY4yzKsPDGXTq1Bbv/5BE9XesEAL+dOAgT9+qKQwXRallfLta/HnBPbUYwXARestUvn11RXmDVNk7wc2GzDSc3vWWGSF2nU2txdHgV3ARMXj1YLA0xnM/g9R/8EIiTf/sh3EXNZEYCPwRsm4tP4uZuO3EoZv3lCFs6LC+4ae4/vPZg3HfqPkpl8eI4pIpXDTGbVsfrN0L3K9GYiVOe+hYrt9c4jiNVYMfkEb3bc58lT0Dh9bVBIUsBKdMQy2Ix9OtUhv9dezD6dIxHORcJuipuDUD8WfJyOB82pAsuObi/rR8SWcSl2yWK7rWAMZn+w79/TJ6TOCWdV0i/A5Lr3M3a5ampK3DJS7O52Uy0hjg1fNMQh0IhRCJJzcCaNWuwfbvOhZUr2DXEWaxIDmIzkQ7ZTaYLwqG0JzG8TprWNtKrzORY8r6ue2uBrW40xYUhqyN1DZrlMI2V1/mBT5fiygkDMT3hP/r9qh1UMBt1DfGsNeU4KJHCgK6+VHvH2XXnScNdrwkANx83FC9/v9a2rSjMHzwJtJ+zjGKBqWs2cfPhdYN9DbYc14lxVjbB5ZrW02Uw+1Uj7P7x6CHS/azJNC38zl6zK75NVUNMgmoJ9pP27pcAKkq75FVJ5Gc/zrooAMm2QT/HdE2I3TSRKppK8pwMRXcWr5YdtLbOj+jL0ahpTVD9yGtcGA6hIRHtll7M6drGGbDUC4Yh7gf36dUurbJThfe8ZH2Iw2Ra8LxF90kLEn+fslx4HdEiWlGYH7uAL6Akt3ldDPNKg8Rkm6edphexRc8w2S+Sc/j3wD4OUYAvw+D3LyrdrooVjwqihQkrynTiOU5dlpRt/NAQ0+fyUjzxeOjzZQCA7dXOlIipBi5r6fimIe7atSt++uknv4rLOHV1dbj99tsxePBglJSUoGfPnrj44ouxcSPfwT/foL9VbTJtx2bKC/vEuDDsDNziFd75tg6Q0kLQwahk9QTiES6jAs2DizysdE/2QGyGbdXYDXpg+n5V3OSMnly6RZmmGT+oE9csmEerorBjEsVqiNlb/2F1OdcXdzhzzQ4KwcQyDU9Y9TIWshMIntZCZn3F9zUXC8QqGrdWLnlxAfsE0zT5E2f1KNPxf0XfRMhlv1eEaY6oZ6XyCv0MRskTiEOchQBZcB4V3BZEvNySIdFe0chym/Lwey7ZFI1ZaVD8SONEl+GHxpngV0l+yna8b072HTo0xEKTaX4liSAxb90uPC4RiEWPPWQYXMsU1ofYNE28OWs99Vt4KV9oaBJ34jzhiV78FqeuikOesUijyZ4uirIfDhncxUKVSO9+dYUizSoR1CPRmM2FBEiOH+n0G/T7Lywgz1PN7HkhJ1WVl0VxTRLfNMRHHnkkXnvtNQwcONDyH/7ss88wcaJ72o1sp12qr6/HxIkTMXPmTPTo0QOTJk3CmjVr8OKLL+Kjjz7CzJkzMWCAf4EisgE9cdYCsR36ccxeU257PoUeTaZ/fUBv/Hv2ets218iqEg0xDTuRLi4MWR2pQyBmpjfs5Fnlln5mhERr1VhhEsYbRNlgZSLYPV41s2zRrEDMe7YzVuxwbHv7yoMw7PbPrd9tOcHRsg1v3POSg9DhTy3R7hJOG7UHzty/N85+fiZ3EkBvYydJKgLBu1ePcz2GNhMzTZMrqKkKC24BWci35Fe3KWrP2TSZ5tWJ1QAB6Ws43Sa3Xu4pZKgFTnPLAcvih9BKc9Xr8zC6T3sA6S8oAHYXGz+tBFQjhfN46/Kx+FUiQ4Kf8NqLrA2y2kfRoaLbfGfeBjx81gis2FYtrZdw8cwwuPVjIwETlyRC0FGmZeXzTabj/8pMptmFwp01jfzjmGdF8rHzjivlxVtR+B5JP5Bul9i9Hd/KIqkhNjHmPrus4reGmERKJz7KqaDl4dTwTSB+5JFHsHv3bnz66adYvXo1DMPAli1bsGWL+0vNdtqle++9FzNnzsRBBx2EL774Aq1bx5OtP/LII/jjH/+Iiy++2JZGKh+h+0MtD9uhV093VDfank9hOCT1d2U5bXQvh0DMe962dEqqGmLDwNljelsry8WUhtjpU2M/l60CfXyfjqVYV+5Mj3PiEzNsv71oiHm+QLSQLBNW2AFQqFETYDBZUdlAGaqTD3Yg93ui7Ae8O/EST0P2Hkg57EB/y/FDbW3ANE1udGreuaKgJYReHVphaA93awB7tM80NcQuJtGkGL8EUKIBYKHfm0qbD9r1xfKTpN6Z6Bs4sH9H/LCaP9GlcQ3+5+GeQkYwi7uj+3TAYUO6YEDn1r6VOW/dbgD+mEzLokynV6693/TCgQm3GKRcAh/e+oFsEaSukXGlEDwfmRtIeU2j63MV7Q+H+CbGDg0xsz9oH2Kp24tEQxyPqSLSENsXzMoVBWIRIcPgBmF0GzN4dUqVjmX8GAnJtEvOwZVULy0fYuodeJ3v8PDTj78l4ZvJdOfOnfG///0PtbW1WLNmDUzTxBlnnJHzaZcaGxvx5JNPAgCeeuopSxgGgOuvvx777rsvpk+fjrlz52arir5Ad7haILaz9x72CTg9ABSGxT40PHiHuplM268d/9eE6TB7MQxWkDas1V23oFpOH+Pk3xeM68eti7POpCz35+HmCyQbJNl9ngcIpmhW+6XqX+OoYy6OMZx25KYhpidA7LvkCbPsJcKMnz37OOky2PmDH3logXjwGoJp8stVnaC41Yncq1/mqaK2T1dX5ZvklXP8PuLUUF4h5dPfskjDW1astrb+00aneR+NaELblufjDPVc014Ihwy8dNEY3H7SMN/L9mNRbWtl0mfQ1wWBHJwXeNUQ1zbacyiLhGdZ31BZ1+S62CS0JjH4QjhrpcAeE3RQYNn98tyLydxDZtBAboG0wVvfX8Q/TqmG4gU+lTGBpLZM53NoI+nDCpko0zTkXaanIU7+TQeepM2z35q9Dv9hlC0q5WnU8T2zZmFhIfr06YM+ffqgX79+6Nu3r9J/2eLbb79FRUUFBg4ciFGjRjn2n3HGGQCADz/8MNNV8xWT+o61ybQdWihghdA2JQWeLBgMA468fXyBWHA+FWWaPcYwDNvAGjNNS7hhF1EdJtLMsETXSZRWgSnAU9olni+Q3WRafC47CfJsMs38ZnPhqmuIgXtP2dv6HfQqfiqwNXr1+zX4auk26Tm0iRz7HuhbJMexq81hwy4QswK4rQzWZNonoXJUnw7J68H0VK4o6riwXVsTP291FHHaaH4kYHqhQiXaMm+y/6dj1NM/uZafKJ7W0Iies+qzEWmRCKJX8NivRzqPDckn7NmkW1t+rmG/2j/BTx9iv4ry1YeY837Ze+5N5WF9g0m7lcqCiQl3wYr+9s49MJkDXWTGz/aR7FgSvIZYDM9X1cvit9sxqq+AvFc2p7vom3n+/P1xQL8OeOeqcWidEGbdLnXmfuLUYbKFcjJm8HyMyRa/8hB3a5s0256yJD6WV9Y34aZ3fsKN7yxEdUPE1Uc4F+cq+UBgw8maNWvw4IMPBlW8byxYsAAAMHr0aO5+sn3hwoUZq1MQaB9iNUifeMPRg3HhuH4Y1LWNx0HVcAhgvP6cLpLuusixdP5eGrvvpFhIZS8p0xC3U/SN9ZJ2iWf6RE9uZBM5dhLkNZcte6+tmPehmpIgHDJw3ti+uOLQePyAXFx1pZvITxsqcNsHP+P3/54vPYcWUtmFEvoeyaDL3nc4bNhSBzVGxJM9tg27TZ5SecRiDTG/tKfPsff1li+coJn5FcGUcMpIgUDs2YfYuc1PX8SSRHCzQgWTab+ejaicPh3LHItsBnJ3LOvetgQT93KmgPNTgAX8tfbK1WfJwj7D1y450Pr7x4RpuuhYgjSVnGm6mt7SxR4zPGmVIboeO+aw32nQUaZlApQ87ZL4OZDFeTeLZtW+gRx307F74ehh3aztopRgRw3rhrevHIf9+iYXR90udd5YsfJN9g7IIj3PZDomGCe9cB2VIrEwbFhzMtJO6FzRtQ0Rdyu3HJyr5AM5ur6aOdati68o9urFXzki29euXZuxOgWBzYc4e9XIG66duCfuPDme6serybRDOOUGmEr+bdo0dkZiG3+C3BSx+06SftJrnmG6FXRWzC3qRUPM8/tRDarFaojbtvIW6oCdzLBRi1WFBvLeyPuXCSwNkSiqGyLC/UFBt50tlfWSI5PQg6lDQ0ynAiFpl5j7DhuGzYydjexKP172mQXh9yryIRa9rY6ti2ymxWTBSzQJTvoQ88sjeThV6FhWJJwgigK6iPDiipEKZCFJxWTar/cqKiZkOCNhy1LC0PAiaGcC3vvx4g+pQk5GmfZzNs4pil2g7tupDEcO7eY8EGIBSbYIHI+JIK+WLTUjnT0h8Se7iEsEqUg0hpXbqx2mt0FHBZabiDvHLTqolggSwMyvhRRbfm3qby9uBm4LGbLvRTYvkJlMk9PSeYd0GifDMHDc3vHxaVfCooYuuSESc13U1/JwarR4gbi6Oh5NsLSUP6kpKysDAFRVVXH3NzQ0oLKy0vZfLqJ9iFPHS4dvcCZpvD6YHtTPP6gfdX78X9M0uYNYDeUjZTeZtl+E1uDF68AK6cm/25e6C8QG6FQMrodztbqiAc95nP13pzK++aEItn7sIOYlCjMgD3RGGPt/U7D3HZ+jJgtCMUF1bkwvqiilXWLuOxSKn1eSaGNsSg96YsFOMtwm8JV1TfLKczBNIMwRNETva2Sv9raJEzFxlKVSof+lmTC4C/514f7KdZWZzN5/2j44fEgXm8ZLhluwvnQpLVLXEPs1KRY9HsMwHFHeQ4Y8Jd6nvz8E++zRDn89fV9f6uYJgx9t2G8Nsf9BtXILnm867xmy450bfTqV4p5J/Nz2pgks2cyf7xFs8TBsAnH876Hd29iOJwLMjf9diCMenu4IvClJE+wLsrHrfws2IRYzbf7XpsLiN5sLO11CgmeqkqqP4FYV2X6pQJyYz7DWUEDyWfnZ95LgXuW1cYGYnrPUN0XR5DKH0WmXUqPFC8Tpcv/996Ndu3bWf7179852lbiQ78Mwsh/VO9/wMofhmfFxcylShZ49JtlmyLuJmfxBjF7NtZlMM5UsYfxuZa88LJi82c83qFVj9wfC8yGmFwFklyNpDCaN7ImubYpx+mix3w+3rszvgwZ2sv32OnDRZuwidtXGBbmlW+QTKb+xm9urNdQGalBnJ5f0QJoMquXUEANJk1o21QmvDIJb3zOa8g12Y0i3+KTz+H26K/tm9u9chlDI4AZ486IVIrx88RgM6tqGczQfmYawV4dSvHjRGBy8Z2elsvzWEL952VicsE8P63erwrgwQvctYh9if8YUUfswTdMRXM8w5P3I0B5t8eFvD8bx1D1lEt483g8f4qMoc1I/hVjfivJxLl5WXID/XHGQbRtvUYYd7wiy/uY31EJ0j3YlaJ/IMx8zgWenr5TWy56b2ykQs4s3RKB598eNAIDnvrYHkg3a59Ot/AtenIW97/jcCuKksvhNfI/9WuOxPVNaA+9jdgfZ99K5tXjhvZVgrAOSCgWesJwqlkCc0BDTc5YfVpdjR1UD9zxCLrp35QMtXiAmUaVra51pZwCgpqYGANCmDX/Sc8stt6CiosL6b/16tShwmYZMUrUo7B1vJtPOoBq88+mO2eAIiiIfYrtfmml1lOwlSgrlAjFdNCsgiFBZNSZwTabpiYNMQ5wo/7FfjcR3N09Eu1Jv+X/ZSRCb21A1yjRbnmhSYTd591R02qikUxvTr6PtN29QJ9g0xInxnQj7BNKek5ME1oc4+bcjyjRVyUd/NQJ3MpF8VfLKEt647EA8ctYI3HLcUL4PMWdmTi5vcCZfoslzMrWZWr1kQk/QkzvZpM6NgwZ2wq0nDrV+EwsA+irC9+PTbYmKMeF8dvGUePZtZCL5l+OHItvMZ/xZAfc8zCrss0c7628/+5tcXSgf09/ef/HavVcNMeGNyw5En46leOD0fa3v9r5PlrieJ3L/Ie2xNaPZbozIx5yg8xC7lf7N8h2ImcCnidy3KovfxIfYP+sQ/oK5yIeYh1sbFtV1v74d8NJFBwjPE411ANC1TbzPrU9RIOalvSJuHsTijG4ft76/CBMfni4tU6ddSo0WLxD36ROPELhhwwbufrJdFAm7uLgYbdu2tf2Xi3jR7mnseJmkA85JCu+RiybNSZNpvgB29eHJCIwx6hjWr8qhTXEET0qWXRAyXH1vTNNU8isicE2mBSvAjuPCSQHFy2BIcKuealAtqzxrkYK/3xakKuDvS2YKJfq2WUGC1hA7o53Ctm/FtmpHeeQeSRtriLA+xGKTabrt9O/cGqXMxNGLoqRT62KcNroXWhWFud9TWRHH3NIyf6bqlGhioje3PPEMVPsBmRbYa9uTQVfnxmOH4KWLDrBFKE0F+rsk90Evrom+2+A1xEnLESCZ71WUom3sALtVSKYxADRxOoywDwsiqq4nXvEtyrQ/xQjhLSqkmrt13MDO+PrGwzFhcBer7X39y3aXsxiBjfpBmiMbt8LNlSZoC1fV8km9RQvtNMQVya/UZ2HBgnlZkXqWCbc2LHKXeueqcdibWmhiIfEU2LReACyrmmjMxKbddahrFC848+AF8yJ9LxmrvS6YaIvp1GjxAvGIESMAAPPmzePuJ9v33TcLfkg+QlaMtEDsnQoPfo2RWMwxQPCe+SWH9EfHsiJcPL4/91haAKUpLghbK+ZxoTlxHuu3HLInuZf51RYVhFy1X7SAnmoeYnoeI2uHaQ+wroOiV5Np8k74QTVkQar8hh3oeAHZWNhFBVpDzArYtqBaponvV+0U1oUM2mzkTVogZsun22nrYqcgm6rvE08w2LdXO5w3tg8uOyT5jVnm0Zxz3fpG1XfbVZByBwA27q5TK0QB+jvcr08HHDbEGdU4nTLJc+lYVoQbjh6Mm4/bS5hv2LegWoJyTNOeWov0L2y/Z03kc2Bmw1uk8cNk2h7EKTiLg0xbu6jCe7esRVRK5Xq4X953Ei8j/je7IFxR14Qlm8XxZbIZZZqmJCH4qViDkcW9VGOKsNDPn54DdPJg9UK3A14wvVQXJMlCQQ1H2KXN48c98BUOefArT2XzhF3yvMh8w+ucRQvEqZEDw0Z2GT9+PNq1a4eVK1di/vz5jv3//e9/AQAnnXRShmvmL8m8ctmtRz7y9hx1M/hozHQKp5yH3rVNCeb85UjczpqM2nyI+b0aKc2EaXWYXDMyShBi99IDQ4GCyXQ0lgzglXLaJdokShpUK71G6nZ2qult1u6swZ5/+RT9bv7Yiv4IsAJxcB9YYyTGTfvA1pOlkNlhF4jtx7Jpl2R+UUQgbmImGbxcxsk6JutSWlTgFPCFV5PDazOGYeDeU/bB1YcNcuzjmTym4kPM4x+/2Q/DerT1FGwrFYIQWGyTUurHtRP3xJUTBnLOIOepVebBM+QLy6JSYqZdECQxCtjFM8tiJgekOV4dlroEa/JarlfrJRmO3PUp9mVBB/TxoiFmNbUy3KykaGwWJhzhmLUSqahrwnGPfyMsL5t5iOl7WbalEvd9vBi7E24ybBvoQUXCj8TchWbCf688yPUYui3Tbby9YlpIwP6+nzl3P4zq0x6vXDzG2uY1oKZVbmKhgDcesm1vR7U81zoLb5GdHVt5ZtUydFCt1PAtHwFJX5QqxHQ50xQVFeHaa6/Ffffdh2uuuQZffPGFFVn6kUcewcKFCzFhwgTst99+WamfX5APSgvE3okPCmodTFPUVIoyDcgnMyb4GuJ4feL//n3KcqzdGfd951pp0sUzL57uhFUmPlHTRG1idVRJQ8yZtNhM/SRFpC0Qu9Qv4jGkZ3EiYMt3K5Pa0vs/XYIHz4hbl0R9NIMVEYnGcND9UxytUGXcY59nvc1kWlxgNCYPFEIibzYxx8iiTNMTv7KiAsdEMFVfOplVAf2dWRpwzoTWbWKn2nfu1b0tPvn9IQCA9qWF1uTSb4IQ+kR+fG6oPpu9e4rNEtnr05QVh22m/2TBje1myGTUD01sOozq0x5fLd3m2O4WHVYFXpofP3C4+vhXtK/wxjqRhriTYkpBwJvfpS3tEtUuyWZR2iURXgUer/AEpF4dWmHDrjrbGPDUVHswMVmbIOOoijXGvr3a44B+HTB7zS7hMaIFcy/9HN0OhnRvg/euHm/bzy7eqiJbWDGMeBpC2hWpKRqzhFrTNPH5z1sxpHsb9O9c5jhfpiEm469XDbEOqpUavgnE/fr1S3lF0TAMRCLZS1dy6623YvLkyfjuu++w55574pBDDsHatWvxww8/oEuXLvjXv/6Vtbr5BekPtck0nyOHdsPkJVtt+UkJXiYd0ZiJooLUV9ppDbFo1Zgc88vWasc223UFfwNO/za31fHKuiZqRVh6aPwYXsoRgZmZ47i0BWL5fq+DSwknYMsH8zdZAjG96hzU57WtqgE7a5wrz6yJMw9WW/EN5SPHTgJZH2KZQFwkyM1It1u2DdP+VaXFYam5tRdk3xg34Jbp3C8qgpjepdJ3fvr7Q3DQ/d5M6FQJoi+nv1sv/Zaqds2tSNH+Xh1KbZpBcu+iIFVeU7X5xeTrD8XnP2/FReP7YTrHF7VYEA3ZC3SKMb/8NwHns0+16KCVU2FFDXHn1kUY3lM9pouXdVJhUC2ByfT2LEcF5hXfuXUxNuySu3A40zUmf8us03i45ggWxBjxMh9oRfkb807bR+InLMPNJL+kMGwTiCvqmqwAh9N+2Y4rX5sLAFjzwAmOc3nzkaKw3WTasw+xDqqVEr4JxIceeqhjAG1oaMDMmTMBAB06dLACU61btw7l5eUwDAMHHnggiouzM3gRSkpKMHXqVNx///1444038P7776Njx4648MILcc8996BXL29pX3IR7UMs57Ffj8TUpduYKM5x3J5ZQciwOrWmWAylhr3z9CLfuUWZBvgTFW4ka4kmgdWSjh3Q0ZYcnuXbFTuoslJrQ6qT7aBNpr0KxMWcwTAi0IIGNRkU+1Ym/xYNmmxQrX/OWG39zZ5CD6Qx03QEzKKhfYhrGyP4/OctmDikG1MnoG1JASrrI/jdEXvagssUhkO2NGLpIGuS9OSK1G1deTKrQNuEwCtqk6eN2gOAs91fOK6fa716tGtl+33VYWKzY68E0ZXTn56X4lU/2XS08LRmdFtCwBBF7e5Qpq4Z9JNBXdtYabh4wmqqwZ9oClJctHCDL/yod2iDu7XGL1urcfLInr7ViQf/uTr76O9uPkLJd5XgxcyUrgLPh1gWWI9H0FGmecWrPBvZWDwqkSJPtQ2yhz1z7mhc9fo87v5UA8eV2OKmOM8rKy7AD38+Asc9/o2V0kiFcCImi2iBmP2u6UXiHznR5mnod09cSqyxNcWgWlpDnBq+CcTTpk2z/a6qqsLEiROx995746GHHsIxxxxj2//FF1/gxhtvRFNTEz7//HO/qpEyrVq1wt133427774721UJBAMG2rUq9BSxryXRurgAJ43gD+Sy7jhkxE1zft4UD5gRiZqOwA1eBEjrUFMsXPFWWl01xMz+tiV2v5yHzxqJF79djcP36orTnv7OURa98ioTkmSoDnLpmjvS98qmvwC8Dy681WG6DFo4DsoXTLS6Tl9NdF8yLRI7CbRpiGN8/yYC7ed0z0eL8eas9Tiwf0dcfugAqoxkgaeM7GkTxoFkyork9YMwmU7+TUqfuzZpukeCjgkzClkm1cltnVsX488ppPbZz0OeZTcC0RALNF9+1cVVQyzpbXl9Bq+v6JQlYZiF15K9CGgi7P1o2sVZOARij+d/+NuDsaO6EXu0b+V+cBrw2gFvocHrs/YS2MpmMm3z6RbXR0bgPsSc8lXqyMv9Pe2GwzB//W6cnJgvqQZOZ7/9PTq0wrzbjsLoe7507Kf/9jIdoMdq0Xnd2pbghz8fgT3/8ql6wQBKi8JCgZidI3iZY5D5Q3FBCGft3xtAsu02pGgyrV2IUyOwoFq33XYbVq5cia+++sohDAPA0UcfjcmTJ2PlypX4y1/+ElQ1NAn6dCrFgjuOxne3HJHtquQd0ojIIcPKfQnEI9uynabXPMaAdw0xr462XMfMvsOGdLH97lhWhD8ePQSjBRN2+h68rn7z6iNNu+Sjhnj6nw5z7PcsELtMHGQ+s36RjoZY6qvOnPLZoi3W3zHTlA6shZRZ1wfzNwEAflhd7jC7JmUYhoFubexpgSaN7InDqbaYqoslvWDz6iVjbPtos1qZFkgkjJFnTy+0HDSwU0rCTWtO5NNUCUIg5mm7lOqi+CjcPm3eJU9JaBzpfufIod0A8E2mcyGgFsBva7kcZZrFa9HFBeHAhWFAsDDiQzorL77+9rRtPA2xt/pkKqgWnfddSSDmLAb361yGU0btYd03rw0O6dbGsY3tX0OGYXuXNjeWFBfm6HzUssW1VOYwsufFulWJxmIzYXU1d225FZiTWOvR3zX5m7QL7+1DS8SpEJhA/O6772LixIno3Lmz8JjOnTtj4sSJeO+994KqhkaTNrL+2DAMW77Tbm1LHBpULxMLwxKI7dq63x2xp+MYGn6UXXEdLhrfH78+oDce+9VIpXrRCen7dnIGhuAx//ajcNOxe1FbkjckjTKd5iSPPt1LygYRvEkBjV1DnPbluIgeicxflyCzCqHPWbGtGqt31Fi/o6bcE6mYSg1Bp7iw5SGmyggZwG8O6osz9uuFp84ZDSCunX3xojHcc71QSt3jAf062vbxmhpJXUZbhQiD33E0xKnKNaU+WugEIffJ+gz5eaoaYjeT6eT+x389Eg+esS/uPXUfAPY+rnu7+HcdVGojP+in2E96Jago0307ldp+56p7FW+sy/QiiC3tEkd4yzWTabI4Q9dbZUGPFfR4TYL9pp89bz/s38+5sM7zUS8QCMT0mOrNZDrZvxout3f2mLg29qLx/ZTKls1LWA2xSKN70UuzMeTWz3D6M9/jqEe/th3Lu08yHnpNF6VNplMjMIF4+/btSoGyIpEIduzY4XqcRpMtzj+on3BfyAAuTeQ5Jf7H7Jzem+khrDLIIBYOGbj+qMHWMTzNA3/MoDXE9gOKCkJ44PR9cUrCP9INkqrHS5CS9qVFOP+gvtZvulOXjXHpT/L8nRy5TbBpf+xMm0zHbNpp/rkF4RAW3nk0dx9d3fW7apl9bhrieJ0aIzFbflq6fcZiSUsHA3Frir+dOQIn7NvDtT5eoE3jZYFgSPHPnDsa95+2D+4/bR/qQH7ZZHOq5sT0oW5Rlr3gp/8owWZV4qF41U/W7TB6f6eyYpy1f2/r3dJaQKIZ5vUVbN7tbHHPKXs7tvkhuNmDi6VdnMXfzhyBY4Z3w1uXjwUA3HjMEADAeWOzkwFEBK8/9jO4mAqi9GTkb6/WI6rjRlV9E37Z6j11VzKoanJbkcJ3UsL4ZrMxEdgyye+BXVoL65A8zq4hpp/B6z+stR2nCq0hdnukd528N/575UHKri+y/rYLs/Auiho+jYrTsqM6HgeBLIbQiyh0cFX6GFV02qXUCGzk6NevH6ZMmYL168U5XNevX48pU6agX79+QVVDo0mbyyifSAB456px1t8hw8D+/Tpi5i1H4LnfxFNzTWDMkVOZPNOmpuzAVVnvXGjiTQxtm9KcLxCB2OvKNz0xiKa46uuVyxILFEcP6+ZLeW7vTxRgKxPQfm+iHIvhkOHwGSfQkxB2EHWLukoH1aLbKH1eNEabTMvLY+vjhf6dy9C3Uyn27dVObq6YKL5T62KcPaaPVJC2tnNMA73Mv+lb8lOjZ7uGT+WkLPQrdjBuZdpNUe37aEFI9o5zRUPck2M+7IfW1Z6+zr977dm+Ff7xm/1x4IBOAIALxvXD9D8dhrtPdgr22STnNMSca6sImzT3fLRE6bhjH/sGRz/6tS0GggpWH2TQ35CKhjguEN9wdHxB/pwxzsURtk2HDAO/OagvfjtxEN6m8g+z9kashpgWZun0SF7eLT3OufUDRQUh7N+vY8ouYAAwYXB8rteHsa7w4vNLFAV2y4/4v+S9efFvB7TBdKr459DEcMkll+DGG2/EhAkTcOedd+Kss85CSUncf6yhoQH/+c9/cMcdd6C2thaXXHJJUNXQaNKmHZUYvnPrInSmchuSwaA7lbC+MBxC59bF1gqgtyjT8YNNyoeYPZ83F+RNjGzmj+pV4EKCO3hd+aYHJXqQCNIc77JDBuCggZ2wV3d1bbYMN/80WggOKp+kyHg5ZjPXVvc5t8plgmjZynYZhJN5iE3bM7KbTCd/BykQF4RDmHL9BBiGIV3Jl5UuOosURwtouWpOmi50X+OWaoRG9XG4BtWSCOS05lemBc4VH2Ie/miI6YWZ4O7VMAxl95hMkgsCse3anDbL06TKoF1VZGzcHU+TdPoz32H5fccpC3Nk/KCfUu+OpfyDKcjC07UT98R5Y/uifakzYB377EOh+Bzoj0cPkZYdMgwUhEO49YShqGmICp+Zl762rLgA/zx/f8RM02a15Ae8arx44QEAnMK3l4VxciwvNgBZpI56DK4RdF7r5kpgAvH111+PuXPn4q233sJFF12Eiy66CF26xFdTtm+Pmw2YpokzzzwT119/fVDV0Gh8xrBNFEVdNT1OecrnSTpCJM1l2AGB19e5BtXySUPsdeWbvvf/Z+++w5yo1j+AfyfZ3pctLHWXpffepCNNkCpNRYrlotcuolholuu181OxXQUsCCoKUkVRQIpSRHoRYVl6X7b3Pb8/loRJMpNMsqmb7+d59iFMy0kmU94557yn1E0BsU4noUXNGNX5wQE6kzEDbW7PjhpiZ1yH8otKTZJEAVCN5AzvveTP03jy2z2Ky1jNMn19w/KmzcZ5Qlgdz1A+VqK8Gad5rfONPsS293lFvj8tTWWtNSVTK5+h9tNakrrKQpIkPN6nATLyilAnXnswVKyxj5vNYZesLGtSQ+zCccxdySk1xLKHTxl52oeOqSy0BMTtkmPdVRyT/WF4MNilXpzL3/ebnadwZ8dk2wvC9F7io7vaYt3B8rGy3/r5b6vryX+vSsEwoNQ3WPk3btlkuvzfe7ulWi4sX87OCtw+TmoZZlEOpXssQ+uhCgTEhpZd8t+RfPhNwP4+xN58DvRmLmsyrdPpsGjRIixatAhdu3ZFYGAgLl68iIsXLyIwMBBdu3bFV199ha+//ho6e3/xRB4iSdqaGpk2PbRj+9f/lWeZNj8P7z51zfL9FIpk7ebSXobM2RVp8hkVeuP5mye7+X09uTPaJcdipizjpjVqTa9OXH+qL396a2/TJnM7T1xF4xk/4qWVB02mq201r6j8QYVaMAxY3/fnMwsxe8UBNJ7xI/71xZ8m82w1+zLUHhSXlpk0YTXPfC3sqCH25HNtWzXsjjYndodUO4JXWx7tUx+zhjS1ax214UjsZdoU1XTe78euGF9be/hRkSaQrubs5tyGVkj+REtA7M6abaUaYkmScGB2f3z3QGe11SrsQpb2fS/vttK/aRJeH9USoRpagGg5zSk1mdZCa0WBu/uHq7FWDPPj2q4m08YaYssxlA2bsbflFANix7ishthgzJgxGDNmDEpKSnDlSvkFLS4uDgEBLn9rIpeQn2ocuWBoWbZMllRLSxCqnGXaeTXEhhogR0a3mDOmFY5fyjEZ0smTAUWrWjFY8sBN+Ouktn5YaheXnm9swPZnbzZ5elvRpkqv/ngYAPDJ5jQ8f+uNgF3terhg6wnV8bMNrP1+3l6nXkNQXFqmadilotIy1eQopbK+8Fr2uauTgVjbulr5DNm0TRLSOGE8WWfYMq03cgpKkBgVYnthFyqy1eH8OlvnMtMs16bLHr2YY3ydW6SesNObbwadUTbT5ITe+1ldRSlAMp/mzmci8ofR8sAoPDgAzWo4L4meOfserlg2mdby29G2jOn/1dYwP/da2/ajN9fH//1yFID3HM/WSmH++5PXENt6aKXYh9h4H3i9htjO+wp/PC84g9tOGwEBAahatSqqVq3KYJh8lk6yftNmnC5fx65xiMv/Le9DbJimISC22Ye4YidIQ7MeR060w1rXwBP9GtpMROJuWj+LtbL+czHHtA9xBQM6tTJZa7r8olltsuU2HStLcYnle8qzQ8vHITbpi2zSL1meZdo2pfEr3SU2PAgzbm2C/k2rmow5aQh+5b/fhMiKD+flDDViQtEwyXPfmYHWPm42xyGWvbZWM/TzgQvG15PNkh56S1ItJc5Iqib/rr3hPOpueg1PZUe0qemGkpQzyfpttj9cWbtpz75XupfQ8lvU9MDfvA+xxs9s7e37ypo9u7KfvD2sfS69WRO9fWcyja+/2nbS6naV+xCX/2tMqmVnQOzN50Bv5vKAuKSkBD/88AOee+45TJ48GfPmzTPOO3v2LPbu3atpeCYibyBBsrvm1a4m07IToeEkqGV1pYuGaeCuvQxKDE8xnXVx8oYnmFqLYO3iEhUa6NQs02o3Odbi7PyiUvWZcPymrLC0DJn5xcb/39UpGa+PbGH8vyFQLCoxC4jNhoK6keBUvRwrH+6Ke7rWwfODtDVjd5St5xV3d62Dj+5qhwOz+xunBQdYNi2MCVPO2u2vtOZ8sfVgTmuz9Pt71DW+ntrfNHmPrSR4njSwWVKFt+HJrPbeQOl8Jv9OVj/SDZ1SXd+H10B+yja/VjjzgYV56xl7tm1c12wVZxQv0CwYVN2mRR9i9TeXz/Kahz7WmkybnXNsPaSWM1Q2BOgtz32Gh8n2HueuGs2gsnNpVe3mzZsxbtw4nDp1CkIISJKE4uJi3H333QCA33//HaNHj8a3336LESNGuLIoRE5RNzHc5GR9La9YcTlr2VKtkWQnQuOTQw03eEoXDWcGncXGGmLnbM8bTtiah4qxUtYAveTUGmK1i7+17eYXWw+IHf2ui0vKsPSvM8b/D2pRDWFBlsMUlQnTvtNqTaat/Ryb1Yh2afNCA2s17XLyPqohgZbPje3JwOwPtP7ubdYQy+ZbSy3SRDYeuvkxE+DFOUkGOCEgLvX3gFjhRyQ//zi7xUTHOlUQGxaEHw+cV5xvreWTJEl4c1RLTLGS40Er82az9jzoND6UNJtu6+ejqXWa3vIzK5dB+29Vfm32mj7EVuapfU/zt6TZ3K7hGDavZQYcbzLNYYgd47Irx8GDBzFgwACcO3cODz/8ML755huLJ1yDBw9GWFgYvvvuO1cVg8gplj3YBcNaVccbo1ra3fjY0T7EpQrJFtQoNpmWvQ62o8/jcwoD1ZderyF2VpDtDRc5pSK8OaolAGDiTSnGadZqiMvKypsMG/9fwQuRIzXEeTZqiB3dZ+b9Qi2Tp5T/eyWn0GRcTJPg2E2ZxbVy5EahcTXL4bu0JKTxJ9a+1p7ycdntCYit/F7kDwnLh9q6Mc9rapTMBOqtDwmmVWTIjYdS9mafrQyUzselDo5ba8vkHql47442mpdXKtttbWuiRc2KP+wz39f2POi88VDSvu9Gy+LmGd+1FivaSisbkyHuvOT5ltXzkdmHbpQUibyiEsxeYbum2JCfxaTJ9PXXjjaZruiDeX/lshriF198EQUFBVi9ejX69eunuExQUBDatGmDv/76y1XFIHKKVrViMGdsawBAbqF9TfztuQbJm0yXGJ8caujro5RlWvbG9iQBuq97Kl5efchkWnGZIcGX5s1Y5Q0XOaX9clvbmujdKNGkSay1C6GAcGqNjSMPCvKtJBgCHK/VLzCreTbfjuF7WX/kksl0eRAsf7LtnWGKupUPd8XJq3kmyeAMWENsyloytG3Hrxpf2xx2SWOeAfOHhAE6yXhjGeilTaa/nuycjMM9GyQaX8tryv2FrRpiZ4kMCcAzt5Q/HNZau6n24NkZv8his34J9vQTNQRI9l4LtLSiMq/Z1DoEW1SIlYDYCzP6WyuG+cOJmxsnKubgUFKqcJ9X0T7E/thyxBlcdlu6fv16dOjQQTUYNqhRowbOnj3rqmIQOZ2W87PWmg5zpkm1tAfEtpJq2Tt+sLmS67WFru5D7M7xI9Uu9rHhQSaf01qT9R92nzUbh9j9fYjzbDSZdrTGZKtsmBtAabxJ5fXk30eJrJbZG25s7Nk9zWpEY2Dzaorz4iKUx+T0V9buv+RN+u0bh9h0Xr3ECONr86BXpzGQ9pReDRMUH6w4QqeT8M7trdG/aVU8adZ/urL6dEI7VI0Kxroneiheg1xxbpH/jrSeN1QflDmhfOY1xErDL9pibzG0HErmx6L8OJWz59xbJfzG+dUbWpMBlvcL02WjQJg/nBDC8gGGEiHEjT7EClmmi0rL8PjXu7HjxFXF9dUwIHaMy2qIr127hlq1atlcLjc3F8XFyv0wibyRvRmb7bk/kydTUErHb2s9OfmUig4TY0yqVaGt3KD0mfbM7IeIYPdloNd6nbX2/X/823Hc27WO8f/OTqp16moejpzPRv2qyjcZgO0bDUcfYmQXWK95Vtuu/DswaXbtHfc1FTJrcBOcyshH61oxni6KV2lcLRK/Hr5oczlbPwFrGfzfHNUSQ+duAWA5DrH8uPHGPsTODtiGtKyOITaGW6tMbm5cFdsaV1Wd36NBAtolx6JFzRinvad8n2k9q6tdZ52x90vMurAs/esMbu9QGx3qVLG57o2cWs5vMi0/9hbe2xFJ0RUfAi4+IhifTmiHkEC9V+QbAUy/ixoxobhHdt03v26XCmHSlUpNn7c24p6u5Vny5ec0+XvJ83h0SKmC7RqCY1e0mPAHLrv7TExMxD///GNzuUOHDmkKnIm8hStriA0ElNPx20P+vhUNiIsrMOySEqXtRIe6N3Ov5oDYxoInr+YZX1f0waz5xb/ba+sBlAdijnK0BanF+JIqfYjNyWuIf9x/IxGNN9zXVHSc44ld6theyA891Ks+AGDu+mMW8wJ0kvE3Yc/5w/y4qxkbanxt3m9Rvqw3Zpn2kkquSisoQIclD9zk1G06cq2rVSVMcbrapno2TMCGI5fQunaMzW0XK1xctvxzWVNAbBz6zu6PZHsF+f2JtWBYXvoaMaGqyxncbOUBiCeYJEo1u50yD4iFUB620NyxS7lYez1Zm1INsTmtrV9YQ+wYlz1K7d27N3bv3o3169erLrN06VL8888/6Nu3r6uKQeR0djc7smscYkMN8Y10/FpOgkp9l0ybTFesz2OJMalWhTZj5A3Bkdan5bZqnAJlT3Z3pF3F/607avE0Xyu14HuHLGmVvRx9wl5UYvoZzLeidtGWX4wvZhfeWN8LogJP3CZM6JwMABjYvOIZhr1VaJAeU/s3QhuFG3v58WHrkPt+143aEMskbvLqY9P15L9xb2wyXSmaR/gZ+c/I1nO0Tye0w/ODGqN9inJwqrb3I6/3o9USvyhdU7SezxxNqqXlUJLXbFp7eC9/GPnRXW3tKoc3kH8y8+u0+TmnrEygqNR6VyYD5T7Eyt+j+cM+pYSPhvcn+7ksIJ42bRqCgoIwbNgwfPDBBzh//kZNQUZGBubNm4d77rkH4eHheOKJJ1xVDCKn0xJIOdpn13BOPHYxR7WG+I6OtU3+//2/b1IMeuTlDAyo2A2ZIzU81njDTavWMthqgZlVcKPLx9c7T+HtdX9j0faTTi1TRb4tR/eZeZMvzTXEKklVvGCXe8Rzg5rgi3s64K3RrTxdFJcLV+jyIO9jaOtGTZ4x3Z6Wz6ZNpr3vh+YFz4LITvLfVGGJ9eDm5sZVcW+3VNX5aoGo4UF2cYntB6hak1UpMSQFs/dnqCWA1hLImXPHEHvOZjoknHoLFaD8AUeRnUm1TGuIlZc1vz9IiVNukaClyfSeU9ew5M/TmsroL1wWEDdq1AiLFi1CWVkZHnroIdSoUQOSJOGzzz5DfHw87rvvPhQWFmLhwoWoU4fN0Mh3aLnfcnQsRMMF6OC5LPxyvU+e+Unw5WHNUE3WNEltCBj5OdpZN4nOquXzhoBYazZaWzXESsMeHbuU69BTWvkNxV8nb9QKV+R7d3jYJRs1xOp9iJVv7uztv+YKnuhaFRSgQ7f6CX6RmfrFoc0spkXKsskGK4zprMb8uIsODURiZDDiI4IRFx5sMs8kIK5gAkFnGtGmBgDg3z3rergkZC/5ebOw2LEWPwZqZ76woPJzgvkQd0pKlM6rshPa0r9OY9byA4rXHcMke68FWpaWX0etXdd9vc7SWuZr85rbMo19iAHlGmK1a6V54K12TbGWz+vbnafw/a7TGDp3C578dg+2HrusqZz+wKVXjmHDhmH//v14+OGH0ahRI4SEhCAoKAipqamYPHky9u7diyFDhriyCEROpyU4iQx2rD+s/Hry1bbyWkbzG0NJkkz606nWKrpg6AJnxbHBAZ4PDgI13jjbqqlSyiy9YOsJDHxnk919eeQPLoa/v9X4uiJfu6PxQZFZjYT5T0i1ybRK1OkNtWRah08hx6TEh2Pj1J4m0+RBcFiQ9rQl5sedTidhy7Te2Dqtt8U5z6QPsRc8bDN4c1RL7J3VD62dlGGaXOeju9oiVj7cnuz3l28jk78taue+0OsBsfkQd3Jf7ziJuev/sTrmtBACj3+9Bwu2nsDGo5cUFrBeDjVaWmkEam4ybd97exuTsc5tNJkW9gTEwjBcnCyplsr3bv4+ag/11a7BGblFmLpkL574Zo9x2rGLOZrK6Q9cllQrKysLUVFRSE5Oxpw5c6wuu3v3brRq1cpVRSFyKi3XlLBgxwI+xSElFE6OWsbpk587XT1ckr3kfZ5rVQnFjFubOmW79tCafMdWDbFaTfDh89k4k5GP2irNmpSo9fetyNfu6L4vMmsmaP7UWktSLdNyOFQMp/L1mzJf4KxzhNJxp/YQy5Fmm+4gSZLV8VbJe/RvmoT4iCDc9sHvAEyDHmsBqxZqNX7h1x8QFVipgX76u30AgKQoy4RVhtOZITETAOQrtFhyuMm0hjXk3SSs5auoaEJDT5N/MoshCM2+pzKhPbGVPX2IzZOjmo8Bbb5Nc9fyOaKPNS6rIb711ltRUFBgc7k///wTffr0cVUxiJxOy/2Wo8MHKW17e5plmn0t426aZrp2qDiW7+ukM4a81ujpAY3Qt4n7M0oGavwwtr47Z15k1GpzK1RD7GhAbNGHGFb/b1Cq2ofY84EKs2+6nvx89OE4+5LnzJRlU7fndys/lL2hOwb5JvkDF/n5qlNqXMU2rPKTNDSZVuujLA8ila4zhtl/ypIuGmqdlZaz++GohsXDZa0+rB2zvj4UkKThnsugTAjNI04YWphp6UNs3rLO2u5UelCv9GDHt/eKc7ksIN68eTNGjRqFUiuZ1nbu3Im+ffsiKyvLVcUgcjotFxWlrM9aKAUNSidW+QlZ7SIkf2rpvGDESTXEsqzXnrpOBmrcR7b2d/qVPNV5Wpvobj12GT/sPqN5DFV7htFy9CGGea2FZUCs/L2o1hA7VgynqmhND9kmPzfVrhJm1x1XddlwLHo7hk+SnwO94XdGvkl+/pXXdk67pRGiQhxvUKn2mzQEr2p9lOWJtJRyMxiuL/JWCCHXg6ZreUXGgNrRYZe03DeEyQJwa8estX6tvsCkybR5E2mzk1yZUO5KpcS+GmL1riLmlK7DhRqSt/kzlwXEjzzyCFatWoWJEycqzt++fTv69u2LnJwcfPXVV64qBpFL9WyYoDjd0cQuSk8Gh7SsbjHNNOOh7W056ybRaX2IZTXEislC3MCb+hre8b9teHTxbizYekJxvnnwGa5QC6DGWQ9DLJtMqyXV8t4a4jwGxC5nfm5y9HmXfTXEsoDY8z8z8lHy2lX57y88OAAvKCSM00q1D3Gg9aRa8muj2oNGAEiMupFkTkBg/5lMtHrhZzz01V8m62pNJGkst4ZlalW58RDL2jVVa4DorUybTFv/ZoQQdgfEARrOYZZD0alvV+n9FWuIfXu3OJXL+hDPmTMHV69excKFCxEbG4t33nnHOO+PP/5A//79kZ+fj8WLF2PEiBGuKgaRS3VWaUqlNWGTOaU+O0oZq01qiNXOiiYDydt3IfxwXFss3JaO6tGh+HrnqRvbcdLdpnxYqopm8HSUo/vIHlouNo70rbKrhthFEQL7EJMS09pax3e6PU2fA7y0DzH5FnlXp/NZpl3+hrSsjtMZeWibrDzWsDWqWYNlv1shhEWgZVJDbCWplrw1T2mZwLwtaQCAVfvOYS5ujBhg7zVPy6FUNyECt3eoDUBYTZrn6wGxSTc1G9+LI02m5f2B1c5hWltpAcoPptlCyjqXBcQAMH/+fGRkZGDu3LmoUqUKZs2ahS1btuCWW25BYWEhvvnmGwwbNsyVRSByKbXzkT0Bi63tKd0Y6k1OzlqSatlXjgHNkjCgWRLe/OmI6jYrQh6gX8gqdM5G7eSOvoZarolahtwwD5rtuel31ueMDTdNDqReQ6wy7JIHA5V7utbBp5vT8Ejveh4rg7/Qm9V0OJpMx57frclvkfEwOShS1iw606zPrk4n4aHe9Z36fvIAtUxYBlolsmtDsUKAYzi05H2QS8oEYsOCTJYzZDy2NyDWcp2RJAmvjGhuczlfT99gmpPFrMm02We7nFOkedjFUoXae/VEqabTre2e45dyUatKKAL0OoQG6qHXSVaTt5GLA2K9Xo9vv/0W/fr1w4svvojLly/jiy++MAbDQ4cOdeXbE7mc2pPfIDubJhm3p3CGU2qGZG2Q+Btlky3jYDBivpYzg5q+Tari18MXMbSVZZPwysJWMHDySh76zdloczsWF0I7yuCsuL9adKjJ/1WTannhNffZgY0xul0t1E+M8HRRKj35OcLeWFi+vD2/Wy1jeBLZ4mjuD1vUakfNa4jNz+zy1jZKibcMc+WtrEpLhUVSzxtNpk0/X98mVfHzwQuq5XbmkaQ1QPRW9uRk+fXwRdzZsbam7SqPQ6xWBlPWzq+D39tsfN2xThV8PbmzavI2KufyNoMhISFYuXIlWrRogQ8++ABFRUX47rvvGAxTpVYlPNj2QgpKFKIJWzUlqkm1nJFoxqLPivMukR+Na4u/ZvRFSny407Zpry3TenvsvQHg1R8Pa3pqG2GW0EWSJAxsngTAtA+XEmfss0HNq2nerloNsSfpdRIaJkXa3XWA7Gdyk1+BHKb2PHwzTUjj8FuSn3NVKxa1VkDyrkNKR4p8LFulrkWGgEgea5aUCYuHlcXXm0wHmQXEH41ri7fHtMStLSzP7wCcGhH7epNp+Xdh/v0qfTKt8X+pQpZptZ+h+e9T63e67fpIJXlKQ3L5+n5xIqfVEH/++edW599xxx04cOAAhg0bhqtXr1osP378eGcVhcht1E5cNzdKxJh2tdCsZrRd28tX6ONh3pfJnNpNvnyys2qInXmzqdN5fozOGjHWg8mKsnWp0RowmPfLlSTgnq6pWL3vvM0aMac8xFDYhNpmrSV/ocrP/AGdO34N8t84uxCTt1EaGxgA4iJuNG1WiktKZP2GrfX/lAdGSn1Hi65vJ9AsS7FOJ2F465o4fC5bcbvOfADu68Mu2Xs/pXWIP8PzY3kfYrUHM5IETO3fEK+vPWLXexgoBsR2baFyc1pAPHHiRE2Z17755ht88803JtMkSWJATJWKTifh1ZEt7F5P6YR19pplQCy/tqiOQ4yK3ySar1cZa9ju71EXH248hum3NrG9sJ1s3QNobd5pnlBFJ0nGfWMrqLZn+Bo1SjcA9maZJv9gnvXeHffBWoYsIfIUpQfdAJAQaZod2pw8y7TSNgzryGv5SoWwOOZs9SFuVkP5wb1zm0w7cWMeYNJkWkPbWq01r4Z9HKDhOi1BwoO96hkDYntr3fOLSiym8Xx5g9MC4hkzZng0aQpRZXAuM99imq0Tq3qT6RuvHa8h1p7EwVc9PaAh7uqc7KLaYuv7Tuv3uWb/OZP/66Qb+1Tpgcn4zsn4/Pd0AEB0aHkt/BujWuLJb/dYLNs+JRaXc4qQdjlX9f2VnoPYm2Wa/IP8XCME8GT/hnhk0V+4vUMtl72nlv53RJ6iVkMs7+urdJmXZ5lWHI1Bocm0eZcVIYQxIDZvMm3Qq1Gi4nRn3tPLh1r0RdaTalnuPM1Npq/vLi3DQJovYu9DBqWHKkqVMP7KaQHxrFmznLUpIp/h7IdAd3RIxpd/nLS5XK7sSZ9aRmvzbK+OsEjzXwlvNyVJshkMD2peDav2nbO6jFaFJaUIDtAb31uLrALTJ7uSJBkvjuY1sv8d0Rw1YkONAXGV6xlHR7atiR92n8Gmo5dNltfrJJu/D3seqLCG2L/pzQLiIS2ro2OdKkiMtJ1XISrUsVsSk4eClfGpHfk0tRpiWwno5OtZG41AXlNYYtaaqEzcqCFWa02mZaSKinqkd3089vVudKsf77yNupFptwzTL6ZHgwSL5bU2ETc8wNCSVd98N2mpVZZTCn5f/fEwHuhZ167tVFa+/ciGyMOcfevVpHoUfnq8u83lLmXfGKpI9SLnhGaEruxD7Eseudmx4TbMr4lv/nQEDZ//ETtPlCe5cPT7TLucq7pPzX8PseE3+qkpBeCBep3N34fS/Kx8y+ZXAGuI/Z1St4qqUSGaHv50To3DxJtS8PLwZna9J5NqkTdT6v8bGRJgcn1VajI97bu9xteGsYTllu0+g4LiUrMaYtPtlJYJ4zS1AEo9iZPydEcMbVUd3//7Jnw4rq3zNupG8u/C/GuMMRvmCrCjyXSpZVItNebX4abVozGkZXX8W2NAq9ZSgcq5LCC+cOECli9fjrS0NNVl0tLSsHz5cly8eNFVxSDyOeZjCCqdVrVkJg5wwk2i+QXRHWP3eiP5jYQ934H5vnv3138AAC+uOgTANf13AvQScgtvBKtRsgzVSu8WoLNd76/0kQtUhnDw9eE1qOJa1oxGjZhQ1K9q3zBXkiRh1pCmuLNjsl3r6UyaTPvnOYq8V3Gp5TlRr5MsuheY+/tCjvG1UkB8IasQq/aeMwm+zB9IlpYJY8AcoNL5Vf065LxjSZIktKkdi/Bgl4726hZarttz1h3VtK1CQ+29ljGizVvsScA7t7fGUwMaaXovtZYK1hK2+ROXBcRvvfUWhg8fjoIC9Qy5+fn5GD58OP7v//7PVcUgcilXtM4z32a1qBCHtqMla6Htspiu568BsbxJWeNqkZrXU0t6YdhaRWpT1S7KQgARwTeyd5sMv6WwSoCGGmKl2WoX0RJfz55CFbb0312wcWpP1SQ+ziavsfHTUxT5GL1k2lXFVoKkQpUm078cvmDy4NW8hlje1FpLvhE5Hks3WGsyrcRaTg45Q3N2R2qI7U1YqNadqfULP9u3oUrKZVerNWvWoGnTpmjcuLHqMk2aNEHTpk2xatUqVxWDyKVccb0wP+k97GBzXS3j2tlL7YLqT/Q6HZ7s10DTsmVl1mtMV+w563A51DJdlpYJdKkXhwd71cX7d7Yxmae09wL1jvUhVmulYN6HjfyPTichwE3BMOCcfAlE7qRlLFu5YoUaYsDyGmP+kFVes6w24oDaA1Emyr1BctFDN0NQq6kPcQXfS+2hi1rNsb9x2RUrPT0dDRrYvmmsX78+Tp60nUTIFQ4fPoxXX30VvXr1Qnx8PAIDA5GUlIQRI0Zg06ZNHikT+RZXXDDMtyhv8mqgpX+KvJmvw32IzVarjMMuaSFPkBGok/BQ7/oY0aaGzfWeX7YPHf7zC67mFplMd87QwMobCQnUQ5IkTO3fCAObVzN7X8t19Dqdzd+x0ny1iyj7EJO7mQbE/nmOIt8imdUQ27qkqyXVEhAWWaYvynKMyNdTq4VUu6zzSLrhSs6Na7grujoFakiQZe19+zapanN9awkvtfZ5rsxcFhCXlmp74iBJEgoLC20v6AJ9+vTBtGnTsHPnTrRu3RojRoxAQkICli5dih49emDOnDkeKRf5N/OTnqM3eE7pQ2x2SfTXJtPyC4nhOwjUMBjhrpPXcDmnEAv/SDeZ/tfJa1h38EKFyqS2K8KD9XatE6iTbP4+utSLs5g2rpNpP8+woPL3ZZNpcjfWEJOvkQ+dB8BmFbFSH2KDMrM+xIu236hkktcs21sTzGPpBvmQmK74XvQa7icsWxXc2O9vj2mFd25vbRxm0dygdzZh7QH1ew7Gwy4MiFNTU/H777+jpEQ5EykAlJSU4Pfff0ft2rVdVQyrGjVqhM8//xyXLl3Czz//jK+//hr79u3Dhx9+CCEEnnzySRw8eNAjZSPf4JI+xLKjsiLbd04fYtP/a+nnUhmZJzoD1JssK1H6+u/9fGcFSqS8TxslRaJLPWvDWliuszM9w+RGKdhsGK+YsEAMMqtpBoAaMaEmw1UZ+kLtP5Nlq+hETmXSv4/1WuQDJLNfqlKWaTlDQGwRFAnT2r3SUvU+xPZev11RE+qr5C2fXPG1mO+bEIVxm63dx0UEB2BIy+qIVGhRCAAHzlq/Ltvqw+4PXBYQDx48GOfPn8e0adNUq+KfeeYZnD9/HkOGDHFVMaxat24d7rrrLoSEmCYtmjx5Mvr164fS0lJ8++23Hikb+QZXXC5MLpIq5ygtpy6TPsROKAvgvxfIBNkYqoYLhyevH88ObGRRqzv91iZY82g34xjHSpR2X0lpmcn0SV3qmMzv16Sq6oVY3ixfKZMqkTtw2CXyNTrJNMCx1dPEENgqXYPl66r1IZYk/+3y5AzWmhs7g3nruy1P98a393dGw6o3knia771m1aNtbkcr9nRyYUA8ZcoUVK9eHW+//TZatWqFd955B6tWrcKqVavwzjvvoGXLlnjrrbeQlJSEqVOnuqoYDmvZsiUA4OxZx5PeEDnCWUGn3gV9iP21yTQAxIaVN0Xq2TDRwyUB+jS2DFIDdJLtvsAK0+7vWdckuL+5senns/bbYQIt8gbyZH9++syOfIwkmdUQ23jCamj6rPTzltfuqWWZdiQhJo+lG+QZ813xMNy8hjguIhjtU6qYPewrf/3rlB74/O4OaFkrxmI7jiY+3XniqkPrVSYuGxCsSpUq+OmnnzB8+HDs27cPjz/+uMl8IQQaNGiA7777DvHx1pr4ecbx48cBAElJSR4uCXk1F1wxnBUQB8pPpA4++jJvfphTqN4ForJb82h3/HH8ikWiKgD4ZnJnjP7od7eVJUCns2hiF6AhKYfSTys8KMDkJsp8qBxrSbK0PjX/4p4OmpYjcgTHISZfcXuH2li0/SSe6NvANKmWjfUKTWqIbywtzNYtFco1xFofZnesUwXb0q7K3osAIKgCAfGUvg3w5s9/W11Gbf/Ir+uG3ZGaEIHUBOUx3h2ttLjjk2048d9BDq1bWbh0hOzGjRvjwIED+P7777Fu3TqcOnUKAFCrVi306dMHI0aMgF6v3rzPU44dO4aVK1cCgMeac5NvcEmTaSdtVN6H2Fk1xKcz8pUX9ANJ0SEY1lo5s3SLmpZNl+Sc/URZr5dgnrtKS5Ivpd9BgF4yqWEIMguIl+8+izdGtVTcntaM0l3qet9DT6o8XDHEHJEr/Gd4Mzzcux6qy/IvAJbXCPMaY819iFWaTGvtPxwSeOOeXC1Bkz8KDLjx/dnq7y03vnMyRrStaTMgVhuzXX7N1rIL/bkVX0W5NCAGAL1ej1GjRmHUqFGufiunKCkpwcSJE1FYWIgxY8agbdu2VpcvLCw0yZKdlcWEMlQxWm7otARYWmoM7aWU6MFfyZNPBegkPDewMV5efUh1+cs5zsumH6CTUGz2I9A0jqHKIvLANsgsqZbacB9A+RAfWjBIIVcyqSHmj428mCRJJsGwJFkGtYD6w0bFPsSy07B5NxZDskOt/Yfla8dFWCaT9Ffy792eB9wSTJsx/6t7KlrWjMGXf6Tj9+NXjNNVa4jtPLexVt9xLg+IXWn48OE4dEj9BlTJ559/jg4d1JvvPfLII9i8eTNSU1Px/vvv29zeK6+8gtmzZ9tVBqo86iUqN1upCKf1IVboe2Iv8xOwqxNL+JKw4BunT71Own3dU/H3hWx8++dpi2UlCZj5wwGnvbdOsuwvrKnJtEKbBiGAK7JgXct4iAZaa4gZpJAr6e2sRSGyxfzBoKsYGkCbn0mLVR5EKv2+5S18fjt6yWSevTXEDauW39NUiwqxmqDR38wa3BSTFuxQnf/J+HZY+tcZrNp3zmS6JEkm92KJkcEY1KIavt55ymQ51TGi7Wz9whpix7klIM7OzsaxY8eQnZ2tmjige/fudm83LS0NR44csWudvLw81Xkvv/wyPvjgA1StWhVr165FlSpVbG7vmWeewRNPPGH8f1ZWFmrVqmVXmcj3/PBgFxy/nINOqZbjs1aUs05nAc4IiM3+z0zCN4TJmpYZAj5rX/PF7AKnvXeATkJYUCD0Osn4kEKtyZUJhfIJCGTkFRv/b/5biQhWv0zwAQl5A5NxiD1YDvJ9n93dAbOXH8BrI1u45f10UnmXFfNb4+ISrTXEwiRD8D8Xc0zmGpNqaUwkEqDX4fO7mfPBXM+GCcbXSnumT5Oq6Fo/3iIg1kmSYrBrPklTDbGGsxsziTvOpQHx/v378dhjj2HDhg02M+iVlpbavf3du3c7WDJLH374IZ5//nlER0fjxx9/RL169TStFxwcjODgYNsLUqXSslaMYoY/Z3BWDXGASR9ix7ZhXhS1p9b+KDTI8um5tX3nzH7Eer2E8OAAxIYF4nJOEQBtNQCKGUrNdqn5BdXaz1FeQzyuU218+cdJm2UgcjaTcYjZGoEqoEeDBPz6ZE+3vZ/h52reL1W1q4qNPsTmCo1JtbSVx9EsxZWdpKHJtFJQq5OAQFlrA8NDZPN7hQCVBxb2Dinngp5yfsNlbUKOHj2Krl274tdff0Xnzp1Rp04dAMDYsWPRoUMHBASUx+JDhgzB+PHjXVUMTRYvXowHH3wQYWFhWLVqFVq1auXR8pB/c9b1SN6EVlPtoVJZzP7Ph483hCvUnFprrmTPwPevjGgOABjUwjKjNXAj+JVfVLXsY6XymZfKfBFrxS6TBcR3dUqx+f5EriD/6fMcRd5K6dpuqPWzqCFWbTJtupEruUX4/q8zqu9p2I5awGWxfR5ADlN6IK7TSSb5RoRxWdPl1Lo86e1sMs1m7o5zWQ3xSy+9hOzsbMyfPx8TJkzApEmTkJaWhoULFwIA/vnnH9xzzz04ePAg/vjjD1cVw6bVq1dj/PjxCAgIwNKlS9GlSxePlYUI0FbDoSXLofxE6miCLfOyPNangUPbqYzCFGqI1S5G9tYO396hNrrVL8/MvGrvOYv5hguv/AKsZR8rBsRmhTO/qGttFs2+S+QpJs1BWcNFXkqxBdH1SeYPTLX2Id596prV97R32CXWEGuh1pzdcpoE04fVN5YxryFWCYjlrV80NJmODPHp1FAe5bIa4l9//RWNGzfGhAkTFOfXq1cPP/zwAy5duoTp06e7qhhWbdmyBSNHjoQQAl9//TX69evnkXIQuYL8BOtoDbE586Ei/FmtKmEW06xl4ba3xXTN2DDVp/qG/Sm/hmqpAVAamsm8XOb3Q+bjWqota08yLiJnYg0x+QKln6bh96q1htjeB492B8QcSMImtUuiUmWG+bQbD7NNl1PbP/bWEEeGcKgsR7nsUcLFixfRuXNn4/8DA8t3UkFBAUJCQgAAMTEx6NmzJ1auXIn33nvPVUVRdeuttyI/Px916tTBsmXLsGzZMotlunbtinvvvdftZSMCgJeGNVOc3qx6NC5kXbS6rt4JATGTJqlrUzsWzw1sjFpVbjwkUKshNgytYS/za+T0W5tgWKvqxn0rmTSZtn21VKpFtlVDXGblNyAZc6SyqRZ5jr21KESeoFRDrPZ7Ve1CbGcNbnGpvQExI2JbrF3KezRIwMa/b2T6Nq+xlRRadwFATJjyEFfya7aWfV+RGmIhhF/nYHBZQFylShWT8XkNGZvT09PRsGFDk2UvXrR+Y+8q165dA1CerTotLU11OQbE5Ck1VGpkXx3ZAu/+chS3d6ytum6ALAh2tMm0Pf1e/dF93VNN/m9tqI6ruUV2b9/84lQ1KhhxETeS+MnvXQI0PPRQejAiBNCncSLWHSo/D5v/UqzVEMsXrhoVjMjgAGQXltgsB5Ez2Ts0CZFHKDWpVakhVnsYbe/P295hl1hDXDHmDx4mdUkx+X/U9YDV/DwVE6pcsysfV1rLLgx2cLiwRkmRfh0MAy4MiOvUqYP09HTj/1u1amVsmjxjxgwAwOXLl7FhwwbUrq1+U+9KtjJfE3ma2vkpPiIYs4cq1x4byC+AQQ5e5XiI2EftglVQXIaTV9WHfJP7cFwb1e2Z9+8y6UOs4WqptIyA6dBK5k+uNcbDkCQJwYE6ZBeqLk7kEhyHmHyBWh9TwDIviNrDaHtHoSi8XkOsdT1njXJRmVmLHeQBcevaMQgLKr+2vjC0KX4/dgVDW9UAYPk9qyUz++ngBeNrLXvGWd3j/JHLvrl+/fph//79xqB48ODBiI+PxwsvvICxY8diypQpaN++PTIzMzF69GhXFYPIp1Xk4uSMpFpWawfJglqztDPX8jVvIzUhwvjafP+r9UcCtF0IlWqRzXex+Xs2SopU3V7VqBCr6xK5g17erJBNpslLKf02Def0S2ZPEtUuvfY+8DGMZ6z1HoDJEW2zdldkOm7wDeM7p+CDcW2NrcgcuVRqyQAub6UWrVLrrORaXrH9BapkXBYQ33XXXZg6dSouXCh/uhEeHo7FixcjJiYG33zzDd5++22kp6ejT58+eO6551xVDCKfVpEAwxlJtdhk2jnyi7SPs24y7qDZBdD8ZkX+89Byw6PUz7huQrjpNs1+Kv8b3051ex+Pb4vWtWOw8N6OiuUDHG+dQKSVSR9i3s+Tl1KKZ3KudzEZ+eHvJtPVHkbb26y1qLT82qM10G2XXMWu7fsja7dFpuMGq3/njjRPtreG+OHe9RTeF2ifEmscxcLgfFaB3eWpbFzWZLpu3bp45ZVXTKb17t0b6enp2LRpEzIyMtCgQQO0bdvWVUUg8lmtasXg+KUctE2OdXgbJkm1HEyUYS2hEllSuwBm5mt/+irfV+b3MOZbN6kh1rCPlW6KOqbG4avtJxW3eWfH2orZtA0aJUVh6b9vDFUnX/fernWw48RVm037iSrKNBMrI2LyTvb8NtUeRt/Wtibe+eWo5u1o7UO8ZVpvnL2Wj+Y1ozVv219ZuyvSmhXakYp4Lb8feQ2x0v2IEMA3kztDkiSkTFtlfyEqMZcFxMuXL0dgYCBuueUWk+nh4eEYMGCAq96WqFL4/oGbUFImrCZpskVeMxcY4GhSLYff3i+pXa9y7Eg0pbeSVTKv2LSmWX5R1WuqIVb+PcmbPsu3ae/ul98MdKhTBc/f2sTOLRDZT37jx3CYvM3zgxrjpVWH8ObolprXUeunWj06BA/3rod3f/1H03aKrydlstXarEZMqGoST9JO68M5ra3/5Ikqtawiv2dUaxVgKNfU/g3x+tojmsrhD1zWlm348OF45513XLV5okpNp5MqFAwDZlmmHawh5rBL9lG7yOUXa28yLX+Sb15LUFxiOhaHfPcEOphUCwAe6l0Pg5pXw0d3tXVav3X2JyZ3cUa+BCJXubdbKg6/OAD9myZpXkdt2CWdTkJIoPYh7ow1xDwunMZaUq0Ak2ug+ja0Xh7fGtNKtj0NNcSy/WyrmfyDvSybVPszl9UQJyQkIDbW8eaeRFQx8lOhljFqlbAPsX3Urld5dtQQyy+oYWY3PsVmd0n/XMy5sZ6DSbUAICokEHPvLM9uXViiPXg3Z1JjzeQs5Cbyvvb2BAtE7mLv79JpWaZL7MsyTRUjH8fZWoI/rcn/6sTfyPGhZQ15RUpWARNl2cNlNcQ9e/bE9u3bObQRkYfIjzxnJNV6YWjTCpao8hugUgNgz9i88tr8AL0Of03va/y/eUBssp6Ghx5aLqjyGyd7T99a+08ROZP8IVIoA2KqBNTyd9h7KTc84GRyQ/eQf83WGuZpfV5sb6sr+b1e+mVtQz1SOZcdIS+++CIuX76Mxx9/HAUFzF5G5EmONpcqkV2Ux3dOcVJpKq9Es2GIDOzpQ2y+r2LDg4yvC0vUA2ItSbXMax1u71DLYpmK1CTI12WNBLmLPMt0aBADYvJ9ar2VHK0hZpNp57H2oDhAYw2xfD8+3qeB6nLyc5uWJ9ry99TpJNzTtY7tlQiAC5tML1q0CAMHDsS7776LxYsXo0+fPqhduzZCQixvGCVJwvTp011VFCK/52iW6dJStvBwBntqWq01NbY2T8sNjzwgXvdEd6TGR1gsY/oW9u1/9iEmTzBpMh3AgJh8n1qTab1OsqvlZeH1/BVautSQNsLKdVFrKyn5vPYp6t1L5bdu9l5TA3QSnh/UGJO6pKDrq+vtWtcfOS0gTk1NxahRo/Dqq68CAGbNmgVJKj9wL168iK+++kp1XQbERK6lZUB3JSVMquV2SomvpvRtgHWHL2J0O8saXWvrmZO3uK6XGKm4TEWGrdFrTChC5Ezye/3QIN74k+9TG4dYr3J+rhkbiviIYOw+dc1kekFx+UmfTaadx3oNsQPjEFu5VprWOGsomyxYl6Ty96kZqz50It3gtID4xIkTuHTpkvH/8+fPd9amicgBzui/X1Km3kSXXEOpFvjhm+vj4ZvrW11PSyDr6iRpJsPfsIaY3ET+uwtmDTH5uKu5RZi/5YTiPLXzarf6CYiPCLIMiK/3IdbywJS0sXYZ1TmQR8Nq02qNfZKVMCmqfVzWZHrChAmu2jQRaRBYwWGbAKCETabdzpWBpKuTHMqDeWaZJnfRsQ8xVSKfbDqO3/6+pDivvMm05XSdpJxjovB6DbEz7gfINq01xN/tOm18be2Sr9YiQI38t8H7N/vwCCGqpLrVi0e3+vH4V/dUh7fBJtOVi6t3p15ik2lyP3lLFmaZJl92NbcI7284pjpfreWzTpKMYw7LGbJMaxmnnirOpA+xleXk+8pa4CzfnpYGezFhgcbXE25Ksbn8nR1rAwD6NK5qe+OVnMtqiInIswL0OnxxT8cKbaPEyjA/5Jj37miNh776y6nbjAjWdiovtTMitrdCWd6ki02myV2C9DeCYI5DTL7snV+OWp2vFjzpJOUWQIY+xI4OvUiWrCXVCtDZ323Iag2xbHtaLsdd68Vj4k0pqBkbisbVomwuP/3WJri5cSI6pcZp2Hrl5tSAePfu3XjhhRccWnfGjBnOLAoROQFriJ0rPEiPdslVnL5drQ//3dlkmhUS5C4p8TeSxrCpPvmy/KJSq/PVft+GJLbmDDXEzDLtPNYuo3rZU2GtpyJri5nUEGu4fkuShFlDmmp7Y5Q/QOzdiLXDgJMD4j179mDPnj12rSOEgCRJDIiJvJC9NYpkXUp8OMKD1WuwBjRNcmi7Wm92xt+Ugi+3ncStLao59D42y2FyM8DAhNyjafVoPDuwERIigz1dFKIKiQyxfluulyTFmkKdJKFEIWAyXMIDOQ6x01i7K9KbtJLStj1ry8mvo65+oO3vnBoQ161bF126dHHmJonIg8a0r4U1+8+jTe0YTxelUgjQSarNm3US8MG4Ng5tNz4iSONywdjxXB/NtWj2Xn+zCoqNrxkQkzv9q3tdTxeBqMLCbHR/UWuGW95kWn09Npl2Is01xFqvgRr7EDMedimnBsRdu3bFvHnznLlJIvKgng0Tsf7JnqgeE+LpolQKOp2kekMjSerz1HRKrYI/jl/Ffd20J05zZZPSv05eM762d4gIIiJ/F2wjG7Ta+Vuvk6z3bWUNsdNo7UOsNSC2dkmWJ6rkMEquxVsWIrKqTnw4x/Z0EmtDKDhysZs3sT2+//dNGNm2ZkWKpcrahd8W1hATEdnnxOVck/+P75xs8n+9DrijY22LptXlfYjVtxvEGmK3kD+wCNI41JW1B+HycY2dEQ8/1KtexTdSSfEIISJyE2u1s45c7MKCAtCmdqxXZnRmQExEZJ9v/7wxPu3/jW2F2UOaIkw2trZOkhAfEYy/pvc1WU8nWe/bGsBkcxX2WJ/6kCTg+UFNVJcxCYg1PoTQumcq0od4+UNd8OLQppjSr4HD26jsOOwSEZGb+FMGXD/6qEREThcVGghJkkxaFhmuIeaJFG09fwzUWFtJ6h7r0wAP9qpntT+2YzXE2t6/In2IW9SMQYuaMY5vwA/wCCEichNfC4jtfSAdHRpofO2NtdZERL4iPKi8zkp+KlUfh9h6k+lAJnVwClvJyQI0BsQ1YkKNryWNdcTsQ+xaTjtCysrKmFCLiMgKXwuI7fXW6JbG12yiR0TkuPYpsQBM+5GqBcRq4xAbBAbwfOwO8mu8tQRpc8a2Mr7W+uyY8bBr8ZEREZGbWEuq5Y3sLW5M2I3hn4IDeXkhInKUoZWNUpNpc7auLQGsIXYLrU2mY2XXSu1NphkRuxKPECIiN/G1GmJ7r7/yj8espkRE1qXGhxtf/7D7jOIykklArLwdjkPsHeQto7T2NdbaZJrxsGvxCCEichNfC4jtJU/6ERzIobqIiKxZMKmD8fWji3crLqPT0ofYxjjE8kzV5Dp6WU28tW5D8nmsIfYODIiJiNxE52MBsb2XX/kFmzXERETW1aoSanMZ+YNUtYeqko0a4lAGxG4RoGFfAUCIAw+MGQ67Fu9YiIjcpLInmiqVVREH6iv3ZyUiqigt2fjltcJWs0zL/t+4WpTJ/FC22HELLQnQACAy5Maot/nFpZq2zRpi12JATETkJr6WVMte8us1h10iIqq4C1kFxtdqrYx0EnBnx9oAgJvqxmFgsyST+Y7USJL9tNYQyzNQ5xaWaNo242HXYkBMROQmPtdk2s4LcLuUWDStHoUhLau7pkBERJVM70aJVueXyFreqD1U1UkSWteOxfbnbsYX93S0uNaEBzMgdge9SQ2x+nLyB8YJkcGatm1tWC2quADbixARkTMoNZm+u0sdzNuShn91T/VAiZwrUK/Dyoe7snaYiEgja+PVmlMbPclwzk2MDAFgWTtZJTzIYh1yPvk13tYD8G8md8aZa3lolBRldTmDWlXCKlQ2so4BMRGRmxgukC1rRmPP6UzUiAnF84MaY2TbmmiUFOnh0lmylrVUDYNhIiLtrPU1NRcVEqg4Xd4nFbCsSQ4OYA2xO8iDYFtdpDrUqQKgis1tfnVvR+xMz8DgFmx55UoMiImInCguPAhXcosU5xmeHn90VzvM25KGuzolQ6eT0KS6tifERERUudjzDFGtL/Dw1jVM/i8PzFLiWLPoLvbUEGt1U7143FQv3inbInXsQ0xE5ERL/90Fj95cX3GeoSYgKToEzw5szCZQRER+zp4aYrmfH++OB3vVxZ4Z/RBoNsydPMn/gGbVKlI8soPejhpi8i4MiImInKh2XBge79tAcZ61rJNEROR/HL0s1K8aian9GyE6zLIZtfxaU9mH+/MmAbJO3rze+xYGxEREbsIbEyIiknNF3gWdxmzH5Fx6FzSZJvdgQGzmxRdfhCRJkCQJX375paeLQ0SViM9dIDnKAxGRSynFwxV9eCpvrutz1x0fxibTvosBscyRI0fw8ssvM0sqEbkEL5BERCSn1If4m/s7V2ybDMw8IoA18z6LAfF1Qgj861//QkxMDIYMGeLp4hBRJcQ+RUREJKd0WWhTO9b4umXNaLu3mX4l98b2ed1xGzaZ9l0MiK/75JNP8Ntvv+HNN99ETEyMp4tDRJWQrwXEbDFNRORajmaZtmbPqUzja1+77viyANbM+ywGxADOnz+Pp556CjfffDPuvPNOTxeHiCopX7sxiQzhUPVERK5ks5ueA4FVvcQI42sfu+z4NJOm6vzifQoDYgCPPPII8vPz8cEHH3i6KERUifnKBfLNUS3RKbUKHuujPHwUERE5h83LgrC/rc6kLimy7fvGdacy4EgSvsvvH/+vXLkS3377LWbPno369et7ujhEVIn5ShOq29rWxG1ta3q6GERElZ4rAtbQIL3xta88iK0M5N91oJ51jr7ErwPinJwc/Pvf/0aDBg3w9NNPO7SNwsJCFBYWGv+flZXlrOIRUSUQHxGMyznl5wjemBARkZwrLgsBuhvBGK867iP/3kMCGRD7Ep8OiIcPH45Dhw7Ztc7nn3+ODh06AACeffZZnDp1Cr/88guCg4MdKsMrr7yC2bNnO7QuEVVeX/+rE3amZ+D1tUeM0xgQExGRnCuG+vSV1kiVjSweRkigXn1B8jo+HRCnpaXhyJEjtheUycvLAwBs374dc+fOxV133YXevXs7XIZnnnkGTzzxhPH/WVlZqFWrlsPbI6LKoWNqHDqmxpkExByGgYiI5FzRZFqvv7FNjhbgPvIa4sRIxyrayDN8OiDevXu3w+uuXr0aZWVl2LdvH3r27Gky7/DhwwCAl19+GZ988gkGDBiAadOmKW4nODjY4dplIqr8Zg5ugtkrDgJgwg0iIjJl67LgSEDLGmLP0OskfDiuDfKLS5EYFeLp4pAdfDogdgZrQfXhw4dx+PBhpKSkuK08RFS5NK0ebXzNmxQiIpJzRcshefccB5JUUwUMaFbN00UgB/htj+9Zs2ZBCKH4N2HCBADAF198ASEEFixY4NnCEpHPksfA7ENMRERytp6ThjrQF9UkILZ7bSL/47cBMRGRO+gYEBMRkQpbfYhfGdEc9RIj8NbolnZs88ZrwSpiIpv8vsk0EZEryTOIMqkWERHJ2bospCZEYN0TPezapisyVxNVZqwhJiJyIfltCZNqERGRnCuyTMsFBfBWn8gWHiUKFixYACEExo0b5+miEJGPk9/suPrGh4iIfIurrwtJzHZMZBObTBMRuZD8Zod9iImISM5VNbij29XEPxdz0LNhoku2T1SZMCAmInIh+cN/NpkmIiK5IL1rAuLXRmpPwkXk79hkmojIheQBMZNqERGRHPv4Enkea4iJiFxI3mSaNcRERCQnD4hva1MTD/Ss68HSEPknPpYiInIhkxpiJtUiIiIZeZPp8Z2TUS8xwoOlIfJPDIiJiFyISbWIiEhNcOCNW3FeI4g8gwExEZELyW9veLNDRERy8hpitiIi8gwGxERELiSxhpiIiFTI+xAH6HmNIPIEBsRERC4kj4H1fPpPREQy8oA4JEDvwZIQ+S8GxERELsQaYiIiUhMsC4jl/YmJyH145BERuZBJDTEDYiIiktHrbtyKy/sTE5H78MgjInIhZpkmIiI1Qgjja9YQE3kGjzwiIjdhQExERHJC9po1xESewSOPiMiFdLIgmEm1iIhILjI4wPg6gAExkUcE2F6EiIgcZTIOMYfUICIimfpVI/Fw73pIiAz2dFGI/BYDYiIiFzLpQ8waYiIiMjOlX0NPF4HIr7FtBhGRC8m7Det4xiUiIiLyKrw9IyJyIXnClABGxERERERehXdnREQuVCYbUoNNpomIiIi8CwNiIiIXksXDkHjGJSIiIvIqvD0jInIheQ2xjjXERERERF6FATERkQsFysaVZJNpIiIiIu/CYZeIiFyoalQI7u1aByGBeoQG6T1dHCIiIiKSYUBMRORiz9/axNNFICIiIiIFbDJNREREREREfokBMREREREREfklBsRERERERETklxgQExERERERkV9iQExERERERER+iQExERERERER+SUGxEREREREROSXGBATERERERGRX2JATERERERERH6JATERERERERH5JQbERERERERE5JcYEBMREREREZFfYkBMREREREREfokBMREREREREfklBsRERERERETklwI8XYDKRggBAMjKyvJwSYiIiIiIiHyPIZYyxFauxIDYybKzswEAtWrV8nBJiIiIiIiIfFd2djaio6Nd+h6ScEfY7UfKyspw9uxZREZGQpIkTxfHRFZWFmrVqoVTp04hKirK08Xxa9wX3oP7wntwX3gH7gfvwX3hPbgvvAf3hfdw5b4QQiA7OxvVq1eHTufaXr6sIXYynU6HmjVreroYVkVFRfEE4iW4L7wH94X34L7wDtwP3oP7wntwX3gP7gvv4ap94eqaYQMm1SIiIiIiIiK/xICYiIiIiIiI/BIDYj8SHByMmTNnIjg42NNF8XvcF96D+8J7cF94B+4H78F94T24L7wH94X3qCz7gkm1iIiIiIiIyC+xhpiIiIiIiIj8EgNiIiIiIiIi8ksMiP1Afn4+ZsyYgQYNGiAkJATVq1fH3XffjTNnzni6aD6rZ8+ekCRJ9e/HH39UXG/BggXo0KEDIiIiUKVKFQwcOBBbt261+l5btmzBwIEDUaVKFURERKBDhw74/PPPXfGxvNKff/6J//73vxgxYgRq1qxp/I5tcdd3ffr0aUyaNAnVq1dHSEgIGjRogJkzZ6KgoMCuz+kL7N0Xs2bNsnqcTJs2TXVd7gt1eXl5WLZsGe655x40bNgQISEhCA8PR8uWLfHCCy8gJydHdV0eF87lyL7gceE6b731FkaMGIH69esjOjoawcHBSE5Oxvjx47Fv3z7V9XhcOJ+9+4LHhXtcuXIFiYmJkCQJ9erVs7qsXx0Xgiq1/Px80alTJwFAVKtWTYwePVp06NBBABAJCQni2LFjni6iT+rRo4cAIG677TYxYcIEi7+9e/darPPoo48KACI0NFQMHTpU9O/fXwQEBAi9Xi+WLl2q+D5LliwRer1eSJIkevToIW677TYRExMjAIgpU6a4+FN6h6FDhwoAFn/WuOu7Pnr0qIiPjxcARLNmzcTo0aNFamqqACC6dOkiCgoKKvrxvYq9+2LmzJnG70LpOPnmm28U1+O+sO5///uf8btv3LixGDVqlOjfv7+IjIwUAESjRo3EhQsXLNbjceF8juwLHheuExcXJ0JCQkSHDh3E8OHDxfDhw0WDBg0EABEYGChWrFhhsQ6PC9ewd1/wuHCPCRMmCEmSBABRt25d1eX87bhgQFzJPffccwKA6Ny5s8jOzjZOf/PNNwUA0aNHD88VzocZAuK0tDRNy//8888CgIiLixN///23cfrWrVtFUFCQiImJERkZGSbrXLlyRURFRQkA4rvvvjNOP3/+vKhXr54AINavX++ET+Pd/vvf/4rp06eL5cuXi3Pnzong4GCrQZg7v+suXboIAOKRRx4xTisuLhbDhw8XAMTMmTMd/tzeyN59YbjBmT9/vub34L6wbcGCBeJf//qXOHjwoMn0s2fPitatWwsA4vbbbzeZx+PCNRzZFzwuXGfz5s0iPz/fYvrcuXMFAFG1alVRXFxsnM7jwnXs3Rc8Llxv3bp1AoD417/+ZTUg9sfjggFxJVZYWCiio6MFALFr1y6L+S1atBAAxM6dOz1QOt9mb0B8yy23CADi7bfftpj3yCOPCADijTfeMJn+6quvCgBi6NChFut8//33AoC49dZbHSi9b7MVhLnru962bZsAIBITEy2eYJ4/f14EBgaK2NhYkwt+ZeOKgJj7omK2bt0qAIjg4GBRWFhonM7jwv3U9gWPC8+oW7euACD27NljnMbjwjOU9gWPC9fKy8sTdevWFU2aNBF///231YDYH48L9iGuxLZs2YLMzEzUrVsXrVu3tpg/cuRIAMCKFSvcXTS/kp+fj19//RXAje9cTm0/rFq1SnWdQYMGISQkBOvWrav0/V3s4c7v2rDO4MGDLcbfq1q1Krp164aMjAxs3ry5Ap/I/3BfVEzLli0BAIWFhbhy5QoAHheeorQvHMV9UXGBgYEAgKCgIAA8LjzJfF84ivtCu9mzZ+P48eP48MMPjd+/En89LhgQV2J79uwBALRp00ZxvmH63r173VamyubTTz/Fv//9bzz00EN45513cPLkSYtljhw5gsLCQiQkJKBmzZoW89X2g7X9FxQUhGbNmqGgoAB///23Mz5KpeDO75rHl3a//vorHnvsMdx///146aWX8Oeff6ouy31RMcePHwdQfsNZpUoVADwuPEVpX8jxuHCfL774AkeOHEH9+vVRv359ADwuPEVpX8jxuHC+vXv34s0338SkSZPQrVs3q8v663ER4PJ3II8xBGdKP2j59PT0dLeVqbJ56aWXTP7/5JNPYvr06Zg+fbpxmq39EB4ejpiYGGRkZCA7OxuRkZHIyspCZmam1fVq1qyJnTt3Ij09HS1atHDGx/F57vyueXxp98UXX5j8f/r06bjtttuwYMECREREGKdzX1Tc//3f/wEABgwYYHzizuPCM5T2hRyPC9d5/fXXceDAAeTm5uLQoUM4cOAAqlevjkWLFkGv1wPgceEuWvaFHI8L5yorK8O9996LmJgYvPbaazaX99fjgjXElZhhuIewsDDF+eHh4QCA7Oxst5WpsujevTu++OILHDt2DHl5eThy5AhefvllBAQEYMaMGcYbIcD2fgAs94V8qA7uP+3c+V3z+LKtXr16eOONN3DgwAHk5OTg1KlTWLhwIWrUqIHvvvsOd911l8ny3BcVs3r1anz66acIDAzEiy++aJzO48L91PYFwOPCHdauXYvPPvsMS5YswYEDB5CcnIxFixahbdu2xmV4XLiHln0B8LhwlXfffRc7duzA66+/jri4OJvL++txwYCYyAEvvPACxo0bh9TUVISGhqJBgwZ49tlnsWzZMgDl4+nl5+d7tpBEHjZu3DhMmTIFTZo0QXh4OGrWrIk77rgDO3bsQFxcHJYtW4Y//vjD08WsFA4fPoxx48ZBCIHXX3/d2H+V3M/WvuBx4Xrr1q2DEAIZGRn47bffUL9+ffTo0QMvv/yyp4vmd7TuCx4Xznfy5Ek8//zz6NGjByZOnOjp4ng1BsSVmKFpSV5enuL83NxcAEBkZKTbylTZ9evXD+3atcO1a9ewbds2ALb3A2C5L+TNgrj/tHPnd83jy3HVqlXDpEmTAAA//vijcTr3hWPOnDmDAQMGICMjA0888QQeffRRk/k8LtzH1r6whseF88XExKBbt25YvXo12rZti+nTp2PHjh0AeFy4m7V9YQ2PC8c9+OCDKCoqwocffqh5HX89LhgQV2K1a9cGAJw+fVpxvmF6cnKy28rkDwxJIs6dOwfA9n7Izc3FtWvXEBsbazzoo6KiEB0dbXU97j9L7vyueXxVjPlxAnBfOOLq1avo168f0tPTMWnSJLzxxhsWy/C4cA8t+8IWHheuERgYiDFjxkAIYcyOy+PCM5T2hS08LhyzcuVKhIWF4f7770fPnj2Nf2PHjgVQ/gDPMO38+fMA/Pe4YEBciRmaae3atUtxvmE6EzI5V0ZGBoAbfR8aNmyI4OBgXLp0CWfOnLFYXm0/WNt/xcXF2L9/P0JCQtCgQQOnlt+XufO75vFVMebHiQH3hXY5OTm45ZZbcPDgQYwYMQL/+9//IEmSxXI8LlxP676whceF68THxwMALl26BIDHhSeZ7wtbeFw47tq1a9i4caPJn6EFY0FBgXGaYTgkfz0uGBBXYl26dEF0dDSOHTuG3bt3W8xfsmQJgPLxv8g5Ll26hE2bNgG4kS4+NDQUvXv3BgB8++23Fuuo7YdBgwaZzJdbuXIlCgoK0KdPH4SEhDjvA/g4d37XhnVWrFiBwsJCk3UuXLiATZs2ITY2Fl26dKnAJ6qchBBYunQpAMvhFrgvtCksLMTQoUOxfft29O/fXzVjK8DjwtXs2RfW8LhwrY0bNwIA6tatC4DHhSeZ7wtreFw4Tgih+JeWlgag/Ps3TEtJSQHgx8eFoErtueeeEwDETTfdJHJycozT33zzTQFA9OjRw3OF81FbtmwRS5cuFSUlJSbT09LSRJcuXQQAMWTIEJN5P//8swAg4uLixN9//22cvnXrVhEcHCxiYmJERkaGyTpXrlwRUVFRAoD47rvvjNMvXLgg6tWrJwCI9evXO/3zebvg4GBh7dTlzu/asL8fffRR47Ti4mIxYsQIAUDMnDnT0Y/pE6zti4sXL4r33ntPZGVlmUzPzs4WkydPFgBEUlKSyM3NNZnPfWFbSUmJGD58uAAgunXrZvEdKuFx4Rr27gseF66zefNmsWbNGlFaWmoyvaioSLzzzjtCp9OJ0NBQcfLkSeM8HheuYe++4HHhXmlpaQKAqFu3ruJ8fzwuGBBXcvn5+aJjx44CgKhWrZoYPXq08f8JCQni2LFjni6iz5k/f77x5Dxw4EBxxx13iC5duoiQkBABQDRt2lRcuHDBYr1HH31UABBhYWFi6NCh4pZbbhEBAQFCr9eLpUuXKr7XkiVLhE6nE5IkiV69eomRI0eKmJgYAUA88cQTLv6k3mHlypWiY8eOxj9JkgQAk2krV640Wcdd3/Xff/8t4uLiBADRvHlzMWbMGJGammp8CFVQUODsr8Oj7NkXhgtuRESE6NWrl7jjjjtE3759jd9XTEyM2Lx5s+L7cF9YN2fOHAFAABDDhw8XEyZMUPy7dOmSyXo8LpzP3n3B48J1DNfm+Ph40b9/f3HHHXeIfv36iWrVqgkAIiQkRHz99dcW6/G4cD579wWPC/eyFRAL4X/HBQNiP5CXlyemT58u6tatK4KCgkRSUpKYOHGiOHXqlKeL5pMOHjwoHnjgAdGmTRuRkJAgAgICRHR0tOjUqZN48803RV5enuq68+fPF23bthVhYWEiJiZGDBgwQGzZssXq+23evFkMGDBAxMTEiLCwMNGuXTuxYMECZ38sr2W4sFr7mz9/vuJ67viuT548KSZOnCiSkpJEUFCQqFevnpg+fbrIz8+vyMf2Svbsi6ysLPH000+LHj16iBo1aojg4GARFhYmmjZtKqZMmSJOnz5t9b24L9TNnDnT5n4AINLS0izW5XHhXPbuCx4XrnP8+HHx7LPPii5duohq1aqJwMBAER4eLpo2bSoefvhhcfToUdV1eVw4l737gseFe2kJiIXwr+NCEkIIEBEREREREfkZJtUiIiIiIiIiv8SAmIiIiIiIiPwSA2IiIiIiIiLySwyIiYiIiIiIyC8xICYiIiIiIiK/xICYiIiIiIiI/BIDYiIiIiIiIvJLDIiJiIiIiIjILzEgJiIiIiIiIr/EgJiIiLyOJEkmfzqdDtHR0ejUqRPmzJmD4uJiTxdR0YIFCyBJEmbNmuXpothNkiSkpKR4uhguM3HiREiShA0bNni6KERE5EUYEBMRkdeaMGECJkyYgDvvvBMtWrTAjh078Pjjj+OWW25BSUmJp4vndVJSUiBJktO2t2HDBkiShIkTJzptm67i7M9ORET+IcDTBSAiIlKzYMECk/9v27YNPXv2xC+//ILFixdj3LhxnilYJXTo0CEEBgZ6uhgu88orr2DatGmoXbu2p4tCRERehDXERETkMzp27GisrVy7dq1nC1PJNGrUCHXr1vV0MVymWrVqaNSoEcLCwjxdFCIi8iIMiImIyKc0bdoUAHDx4kWLeUIILFq0CL1790ZsbCxCQkLQuHFjzJo1C3l5eRbL//PPP5g1axY6d+6MpKQkBAUFoWbNmhg/fjz+/vtv1TJs2bIFffr0QWRkJGJiYtC/f39s27bNarmvXLmCqVOnon79+ggJCUGVKlUwYMAA/PTTTxbLnjhxApIkoWfPnorbmjVrFiRJMtagG5o2p6enAzDtg621X7D5shMnTkSvXr0AAJ999pnJNs37SJ86dQoPPfQQ6tata/xst956K7Zu3WrxPvJm2OfPn8e9996LmjVrIiAgAHPmzAEAnDt3Dq+99hp69OiBGjVqICgoCElJSRgxYgR27NihuD1bn91aH+JTp05h8uTJSE5ORnBwMBITExXfCzDdN/n5+Zg2bZpxvXr16uHVV1+FEELDN05ERN6ATaaJiMinZGdnAwASExNNppeVlWHcuHFYtGgRIiIi0K5dO8TGxmLnzp2YPXs21qxZgw0bNiA0NNS4zieffILXXnsNzZo1Q/v27REcHIyDBw/iiy++wA8//IBNmzahRYsWJu+zcuVKDB8+HCUlJejQoQNSU1OxZ88edO/eXbWv7ZkzZ9C9e3ccP34ctWvXxrBhw3Dp0iWsW7cOa9euxVtvvYXHH3/c4e8kKSkJEyZMwJIlS5Cbm4sJEyYY58XHxzu0za5du+L8+fNYu3Yt6tati65duxrntWrVyvj6999/x6BBg5CRkYGGDRti0KBBuHTpEtauXYsff/wRCxcuxJgxYyy2f+nSJbRv3x4lJSXo2rUrCgoKjLW3P/zwA55++mk0bNgQLVq0QFRUFI4ePYqlS5di5cqVWLlyJfr16+eUz75v3z707t0bly9fRsOGDTFixAicPHkSS5cuxYoVK/DVV19h1KhRFusVFRWhX79+OHjwIHr27Inc3Fxs3LgR06ZNQ3Z2Nl566SXN3zUREXmQICIi8jIAhNolqnv37gKA+PLLL02mv/baawKA6Nmzpzh37pxxemFhobjnnnsEAPH000+brPP777+L48ePW7zHvHnzBADRq1cvk+lZWVkiISFBABDz5s0zTi8rKxNPP/20sdwzZ840We/WW28VAMQdd9whCgsLjdM3bdokwsLChF6vF3/99ZdxelpamgAgevToofgdzJw5UwAQ8+fPN5menJys+r3ZAkAkJyebTFu/fr0AICZMmKC4TmZmpqhWrZrQ6/UW+2PHjh0iNjZWREREiIsXL1psE4AYPny4yM/Pt9ju3r17xf79+y2m//jjjyIoKEjUrVtXlJWVmcyz9dknTJggAIj169cbp5WVlYnmzZsLAOKpp54y2eaSJUuETqcTERER4uzZs8bphn1j2D+ZmZkmn1mv14uwsDCRnZ2tWhYiIvIebDJNRERer6ysDMeOHcMDDzyA3377DUOHDjWpdSwpKcFrr72G8PBwLF68GElJScZ5QUFBePfdd5GUlISPP/4YZWVlxnmdOnVCnTp1LN5v0qRJ6NKlCzZs2IDMzEzj9CVLluDSpUvo3r07Jk2aZJwuSRJefPFF1KxZ02Jbx48fx8qVKxEREYF3330XQUFBxnldu3bF/fffj9LSUsydO9fxL8hD5s2bh3PnzuGxxx7DnXfeaTKvXbt2mD59OnJycvDll19arBscHIx3330XISEhFvOaN29ubBov179/f4waNQrHjh3D/v37K1z+DRs2YN++fahduzZeeuklkyzVt912G4YNG4acnBzMmzfPYl2dToePPvoIUVFRxmnt2rXDLbfcgry8POzcubPC5SMiItdjk2kiIvJaSsPo3Hffffjoo49M5u3atQuXL19G3759UbVqVYt1QkND0bZtW6xatQpHjx5Fw4YNjfNycnKwYsUK7N69G1evXjWOcXzu3DkIIXDs2DG0adMGALBp0yYAwNixYy3eIzAwECNHjjT2gzXYvHkzAGDAgAGoUqWKxXp33XUX3nrrLeO2fYmh//OIESMU53fr1g0AsH37dot5bdq0QY0aNVS3XVhYiB9//BHbt2/HpUuXUFRUBKC8iTMAHD16FM2bN69Q+Q3f+ejRoxUzbN911134/vvvFfdNcnKyye/IoEGDBgDKfz9EROT9GBATEZHXMvQHLSgowJ49e3D48GH873//w0033WTSX/fEiRMAgJ9//tnmWLSGvqIA8Ouvv2Ls2LG4dOmS6vKGPssAcPbsWQDlwZASpQRWhnXUklsZpp85c8Zqub2R4Xvv0qWL1eUuX75sMc3a8Ef79u3DkCFDjNtXIt8vjqrIvlFqDQAAkZGRAMoDeiIi8n4MiImIyGuZj0P8+uuv46mnnsKDDz6IXr16GQNTQzPoevXq2QzO4uLiAJTXDI8ePRpXr17FjBkzMHbsWCQnJyM0NBSSJOGOO+7AokWLXJ4x2FYAr0Te7NuTDOUYOXIkwsPDVZdr1KiRxTSlptJAeabw0aNH48SJE7j//vtx//33IzU1FREREZAkCc8++yxeeeUVt2RytrZvdDr2OiMiqgwYEBMRkc+YOnUq1q1bh59++gmzZ8829u001NY1atTIIohWs2nTJly5cgUjR47E7NmzLeYfP37cYlq1atUAwDjEjzml6dWrV7e6jqEWVN582NDPOCcnR3GdU6dOKU53t5o1a+LIkSOYNm0a2rZt65RtHj58GIcPH0a7du3wwQcfWMxX2i+OcmTfEBFR5cLHm0RE5FP++9//AgC++OILYyDTvn17REdHY+PGjbh69aqm7WRkZABQbvr6zz//YNeuXRbTDX1iv/nmG4t5JSUl+O677yymG4Yr+vHHH3Ht2jWL+YaEU4ZtA+XDBQUEBCAtLQ0lJSUmyxcXF2Pjxo2Kn8kQSJuv4yhb2+vbty8AYOnSpU55P8D6fsnIyMDPP/+suJ4jn93wnX/77bcoLS21mK+0b4iIqHJhQExERD6ldevWGDZsmDGzNFCesfipp55CdnY2RowYoViLeObMGXzxxRfG/xuSH33//fcmfYivXbuGe+65x5hcS27UqFGIi4vDhg0b8NlnnxmnCyEwc+ZMnDx50mKd1NRUDBo0CNnZ2Xj00UdNtvv777/jgw8+gF6vx4MPPmicHhQUhM6dO+Pq1asm2adLSkowZcoUpKWlKX43hhrPI0eOKM63l63tTZ48GYmJiXjttdcsMngbyrt27Vq7MkLXq1cPOp0Ov/76K44ePWqcXlBQgPvvv1/1gYcjn71nz55o3rw5Tpw4gRkzZpg0w166dCm+//57RERE4O6779a8TSIi8i0MiImIyOfMmjULkiRh3rx5OH/+PABg2rRpuOuuu7Bx40Y0btwYnTp1wu23347bbrsNzZo1Q61atfDmm28at9GuXTv07dsXJ0+eRIMGDTB8+HAMHz4cderUwdmzZzF06FCL942MjMSnn34KvV6PiRMnolOnTrjjjjvQrFkzvP7667jvvvsUy/vRRx+hTp06+Pzzz1G/fn3cfvvt6NOnD7p164bc3Fy89tpraNWqlck6M2bMgE6nw2OPPYabbroJI0aMQN26dbFo0SJjsjFzQ4YMAQDcfPPNuP3223Hvvfdi2rRpjnzFAMqTSrVo0QI7d+5Ehw4dMGnSJNx7771Yvnw5ACAmJgY//PADoqOjMXnyZKSkpGDgwIG48847cfPNNyMhIQEDBgzAP//8o/k9ExMTcc899yArKwstW7bErbfeilGjRiElJQW//vqrSTK1in52SZKwcOFCxMXF4T//+Q+aNm2KO+64A127dsWIESOg0+nw6aefGpvKExFR5cOAmIiIfE7Lli0xfPhwFBQU4K233gJQnuTo888/xw8//IC+ffsiLS0N3333HTZv3oyQkBBMnTrVYjzZH374Ac899xwSEhKwZs0a/Pnnnxg7diz++OMPxMTEKL730KFDsX79evTq1Qv79+/HqlWrUK1aNWzcuBE33XST4jo1atTAjh07MGXKFAQEBOD777/Hn3/+iZtvvhlr167FE088YbFOnz59sHz5crRv3x67du3Cxo0b0alTJ+zYsUM1K/IjjzyC559/HhEREfjuu+/w6aefYvHixdq/WAXfffcdhg0bhuPHj+Pzzz/Hp59+atKcvFOnTti3bx+eeuopREVFYePGjVi2bBnS09PRo0cPLFiwAH369LHrPT/44AO8+eabqFOnDn755Rds2rQJffr0wc6dO1UzfDv62Zs3b45du3bhvvvuQ05ODpYsWYIjR45g2LBh2LJlC0aPHm1X2YmIyLdIwh1pGomIiIiIiIi8DGuIiYiIiIiIyC8xICYiIiIiIiK/xICYiIiIiIiI/BIDYiIiIiIiIvJLDIiJiIiIiIjILzEgJiIiIiIiIr/EgJiIiIiIiIj8EgNiIiIiIiIi8ksMiImIiIiIiMgvMSAmIiIiIiIiv8SAmIiIiIiIiPwSA2IiIiIiIiLySwyIiYiIiIiIyC8xICYiIiIiIiK/xICYiIiIiIiI/BIDYiIiIiIiIvJLDIiJiIiIiIjILzEgJiIiIiIiIr/EgJiIiIiIiIj8EgNiIiIiIiIi8ksMiImIiIiIiMgvMSAmIiIiIiIiv8SAmIiIiIiIiPwSA2IiIiIiIiLySwyIiYiIiIiIyC8xICYiIiIiIiK/xICYiIgUnTx5EpMmTULt2rURFBQESZIQExPj6WIREREROQ0DYiIiDWbNmgVJkox/ixcvtrnOoEGDTNY5ceKE6wvqJJmZmejSpQsWLFiAU6dOISwsDFWrVkXVqlU9XTTyIyUlJfjll1/w+uuvY+zYsWjQoAF0Oh0kScLEiRNtrp+Xl4c1a9bgpZdewogRI5CcnGw8HmfNmqW5HMuXL8fgwYORlJSEoKAgVKtWDUOHDsWaNWs0rX/s2DFMnjwZderUQUhICBISEtC/f3989913msugVXFxMVq0aGH8nLa+p/z8fLz99tvo1q0b4uLiEBgYiOjoaLRt2xbPPfcczp8/73BZUlJSNO8r+TmWiMidAjxdACIiXzR//nyMHTtWdf7Zs2exdu1aN5bIuRYtWoTTp08jNjYWW7duRaNGjTxdJPJDp0+fRp8+fRxef/v27Rg4cKDD65eWlmLChAlYuHAhABhbSVy6dAnLly/H8uXL8fDDD+Odd95R3cbq1asxatQo5OXlAQCioqJw9epV/PTTT/jpp58wadIkfPrpp04LBF9++WXs27dP07Lp6eno27cvjh49apwWHR2N7Oxs7Nq1C7t27cLcuXOxfPlydO/e3SnlIyLyNqwhJiKyQ3x8PMLDw7Fu3TqcPn1adbnPP/8cpaWlSElJcV/hnMhwQ927d28Gw+RRkZGR6Nq1Kx599FF89tlnaNWqlV3rx8bG4uabb8bUqVOxaNEiJCUlaV73+eefNwbDjz76KC5duoSrV68iMzMTb7zxBgICAvDuu++qBsRpaWkYPXo08vLy0KVLFxw5cgSZmZnIzMzEjBkzAJQ/XHv99dft+kxq9u3bh//85z9ITU3V1Jpj/PjxOHr0KIKCgvDee+8hOzsb165dQ35+Pn744QfUqFEDmZmZGD16NPLz851SRiIib8OAmIjIDuHh4Rg5ciTKysqwYMEC1eXmz58PAJqaCnojQ21WRESEh0tC/qx27drIzMzEpk2bMGfOHIwfPx7R0dGa1+/WrRuuXr2KdevW4bXXXsPYsWMRHBysad3Lly/j7bffBgAMGzYMc+bMQVxcHIDy88CUKVMwZcoUAMDMmTORlZVlsY0ZM2YgNzcXSUlJWLlyJRo0aACg/LiaPXs2/vWvfwEor9XNyMjQ/LmUlJaW4u6770ZxcTE+/PBDhISEWF0+PT0dv/32GwDgmWeewYMPPmg83oOCgjBkyBB89tlnAIALFy4YlyUiqmwYEBMR2WnSpEkAoBoQb968GX///TdSU1NtNjPcv38/Zs2ahd69e6Nu3boIDQ1FVFQUWrdujeeffx6XL19WXO+BBx4wNt9U65v8wQcfQJIkBAQEaL6Z7dmzJyRJMn62zz77zKQftGH6hg0bTPr7/fXXX7jzzjtRs2ZNBAYGomfPnibbLSoqwvvvv49evXohPj4eQUFBSEpK0tQPMz8/Hy+99BKaNGmC0NBQJCYmYuDAgfjll18A3OinaL4/zMuoxrDMhg0bFOeXlZVh4cKFGDhwIKpWrYqgoCAkJCSgX79+WLRoEYQQiuvJy1VUVITXX38dLVu2RHh4OKKjo9G7d2/8+OOPVssGANu2bcOkSZNQr149hIWFISoqCk2aNMHdd99t0ix/2rRpkCQJTZs2tbq9rKwsREREKH5n3sbQX9hRer3e4XV/+eUXFBYWAgCmTp2quMxTTz0FALh27RqWLVtmMi83N9fYR/iBBx5QTEj3zDPPACjfJ+br2+vNN9/Ezp07MX78ePTt29fm8ufOnTO+bteuneIyHTp0ML7OycmpUPkqQn4Osvbnqw8gicjDBBER2TRz5kwBQCQnJ4uysjJRt25dAUBs3LjRYtm7775bABAvvPCCWL9+vQAgAIi0tDSLZZOTk43zQ0JCRJUqVYQkScZpNWrUEIcPH7ZYLy8vTzRt2lQAEJ07dxbFxcUm8/ft2ydCQkIEADFjxgzNn3P48OGiatWqxnVDQkJE1apVjX+LFy8WQgiTz7VkyRIRGBgoAIioqCgREhIievToYdzmiRMnjGUFICRJEtHR0cb/AxD333+/YnmuXLkiWrdubVwuICBAxMTEGLfz/vvvG7/D+fPnm6wrL6M1hmXWr1+v+P7du3c3Kat52YcMGSIKCwst1jWU69133xUdO3YUAERgYKCIiIgw+S4+/fRTxXKVlJSIRx55xOS9wsPDRWxsrPE3Eh0dbVz++PHjxumbNm1S/bwffPCBcd28vDyr34036tGjhwAgJkyY4ND6hv0yc+ZMq8u9+uqrxu89IyNDdbnExEQBQNx5550m03/88Ufj+tu3b1ddv3HjxgKAGDt2rD0fw8SRI0dESEiISEhIEJcvXxZC3Picat/ThQsXjOVT+y7WrVsnAAidTieOHTtmd7lslUHOcI5VOl7l5yClP8Pv3tHfBBH5N9YQExHZSV4TMW/ePJN5ubm5+Oabb6DT6TTVVvTo0QMLFixAeno68vPzceXKFRQUFGDdunXo0KEDzpw5gzvuuMNivdDQUCxevBihoaH4/fffMXPmTOO8/Px8jB07FgUFBejSpYuxr6IW33//Pc6fP48xY8YAAMaMGYPz588b/wzT5SZOnIi+ffvi0KFDyMzMRH5+Pv73v/8Zv48BAwbgwIED6NmzJzZs2ID8/Hxcu3YN165dw1tvvYWIiAh8+OGH+L//+z+Lbd97773466+/EBwcjA8//BDZ2dnIyMjAiRMnMGzYMGO/TlcoLS3FiBEj8Ntvv6FVq1ZYsWIFcnNzce3aNeTk5OCzzz5DYmIili9fjqefflp1OzNmzMDp06exbNky5ObmIjs7G4cPH0anTp0ghMCjjz6KzMxMi/WeffZZY9/Uu+++G0eOHEFOTg6uXr2KjIwMLFu2DAMGDDAuX6dOHfTv3x8AjN+/EsO8u+66C6GhoQ59N/6mtLTU5jzzRFb79+83vm7WrJnq+oZ5Bw4ccKhsQgjcc889KCgowNtvv21s1m1LYmIiRowYAQB45ZVXMHfuXGMtcHFxMZYvX44JEyYAAKZMmYLU1FSHyucM8nOQ+d8LL7xgbKVRkQRqROTHPByQExH5BHkNsRBCnDx5Uuh0OhEeHi6ys7ONy82bN08AEH379hVCCJs1xNZkZ2eLqlWrWq3xM9T26XQ68euvvwohhJg8ebIAIGJiYkR6err9H1YIMWHCBKs1LvLP1aFDB1FSUqK43AsvvCAAiB49eoiioiLFZb7//nsBQMTHx5vUdG/bts34Hkq1qCUlJaJr167GZZxdQ/z5558LAKJRo0bi2rVriuvu3LlTSJIkgoKCxIULF0zmGWrHgoODxaFDhyzWvXjxorEm/ssvvzSZd+TIEaHT6QQA8dRTT1ktv9yyZcsEABEaGqpYq7lz507j5927d6/m7QohRFpamklttb1/tmpktXJXDfHXX39tLPuGDRsUlzl37pxxmfj4eJN5TzzxhAAgYmNjrb7PY489JgCIuLg4uz6HwTvvvCMAiP79+5tM11I7e/XqVdG/f3+LFhCG317z5s3F//73P4fKJS+DeUsTpb/w8HBNx6vcTz/9JAICAgQAMWvWLIfLSUT+jTXEREQOqFWrFvr06WOsETYwJNO6++67K/weERER6NGjB4DyfslK7r//fowYMQJlZWUYN24cPv74Y3z00UcAymsCa9euXeFy2DJ16lTVvpqffvopAOCJJ55AYGCg4jLDhg1DVFQULl++jD///NM43TDWc61atYz9tuX0ej2mT59e0eKrMpT9gQceUE3k1LZtWzRt2hRFRUVYv3694jIjR45UzNSdkJCAzp07AwD27t1rMu+zzz5DWVkZ4uLiMHv2bM1lvvXWW1GzZk3k5+fjiy++sJhvqB3u3Lkzmjdvrnm7QPn3bRiL2pE/X0vQ1rt3b2MCrpdffllxGfl086Ra2dnZAICwsDCr72OYb1jeHidOnMAzzzyDsLAwfPDBB3avHxsbi++//x5Tpkwx9tXOzMxEWVkZgPJ+w5cvX7ZaQ65FQUEBLly4YPUvNzfXrm0eOHAAo0aNQklJCe644w6TVjJERPZgQExE5CBDkGZoNv3PP/9g06ZNiI2NxbBhwzRvZ+XKlRgzZgxSU1MRHh5ukiTGEGxbG+Lpk08+Qe3atXH27FlMnjwZQHlT45EjRzr4yezTpUsXxelnzpxBeno6AOCee+5BUlKS4l+1atWMTTUNywPAzp07AdxI9KWke/fuCAgIcObHAVDeDPaPP/4AAMyaNUu17ElJSThy5IhF2eU6duyo+j7Vq1cHAFy9etVk+tatWwEAffv2tZktWE6v1+O+++4DYNlsOjc3F1999RUAGLMb26NWrVpWm67a+nvyySftfk9Pio+PxyOPPAIA+PnnnzFu3DgcPnwYxcXFOHnyJKZNm4a5c+caH/TodO6/pbrvvvuQm5uLF154AXXq1LF7/b/++guNGjXC22+/jX//+9/Yt28fcnNzcezYMcyZMwdXr17FM888g8GDBxuDZEdMmDABQgirf/YEtBcuXMCgQYOQmZmJm266yaLrChGRPZx/F0FE5CeGDx+O2NhYbNmyBUePHjVm7L399ts1BTGGWt1FixYZpwUEBCA2NhZBQUEAymtrCgoKrNaexMbGYu7cuRg8eDAAIDU1VbE/rqskJiYqTj979qzxtVq2bHOG4Z4A4OLFiwCAGjVqqC4fEhKCuLg4XLhwQdP2tbp69aoxw7DW4XDkZZeLjIxUXccQzBcXF5tMP3/+PAAgOTlZ03vL3XvvvXjxxRexb98+/PHHH+jUqROA8hr37OxsxMTEKPYFJ0v/+c9/cOrUKSxevBgLFy40jkls0KlTJ7Rq1QoffvghYmNjTeYZ9rva78LAMN/a70TJJ598gnXr1qFNmzZ47LHH7FoXKK+RvuWWW3DhwgXMmDHDpCVCamoqHn30UTRr1gx9+/bFmjVrMH/+fNxzzz12v4+z5efnY+jQoUhPT0edOnWwbNkyzUNpEREpYQ0xEZGDgoODcfvttwMovzn9/PPPAUCxea+STz/9FIsWLYJer8eMGTNw9OhRFBYW4urVq8ZaNUMtr1AZ2sdAXht45swZ/PPPP458JIeoNZeWN7M8dOiQzRoiIYTXDJsiL/uaNWs0lX3WrFlOe/+KDDVUvXp1DBkyBADw8ccfG6cbfiPjxo1jMi2NAgICsGjRIqxatQpjxoxBo0aNkJycjG7duuGdd97Bb7/9ZgxoDWMMGxhq/zMyMpCfn6/6HmfOnDFZXovMzEw8+eST0Ol0mDNnDvLz85GTk2PyZzhnlJSUGKfJa3m//PJL44Mkw3jK5m6++Wa0bt0aAIxDSHmSEALjx4/Htm3bEB0djZUrVyIhIcHTxSIiH8eAmIioAgzB75w5c3D69Gk0a9ZMdUxPc4Y+svfeey9mz56NevXqWTS7NNQUWvPee+9h+fLl0Ov1aNKkCQoLCzF27FibNVOulpSUZHyt1pzYGkPNsyFgUFJYWIgrV64ozpM3pS4oKFBcRim7MwDExcUZ13ek7BVl+O4cfe/7778fAPDNN98gKysL+/btw7Zt2wDA2KzeXqdOnbLadNzW3xtvvOHQ+3qDgQMHYvHixTh06BBOnDiB3377DQ8//DACAwONTftvuukmk3XkmaXlGafNGebZGj9aLiMjw9jXt3v37oiMjLT4O3nyJABg4cKFxmnyvuoHDx4EUN6XPSoqSvW96tevDwBIS0vTXD5XefbZZ7FkyRLo9Xp8/fXXaNKkiaeLRESVAANiIqIKaNeuHZo3b46ioiIA9iXTOnXqFAAYa2DM5eTkGIMYNfv27cPUqVMBlA/vs3r1asTExODQoUN4/PHHNZfFFVJSUozNnVesWGH3+oYHCxs3blStIf/tt99QUlKiOE/ehNXwXZtT+34DAwPRoUMHAI6VvaIMwdXPP/+sGsxb06dPH9SrVw+5ublYuHChSTIta0MAWVNaWmozMZK1P0M/8crkr7/+MgaW48ePN5nXtWtXY038jz/+qLh+eno6Dh06BADo16+fC0tqyfDw7fLly1Yfnhlqke1t0u1s8+bNw3//+18AwDvvvGMcYoyIqKIYEBMRVdCrr76KKVOmYMqUKRg3bpzm9QyZi/fs2aM4/8UXX7SaeVY+3nDXrl3x3HPPITk52dhM9uOPP/Z4M0dDgqdPP/0Uf/31l9VlzRNLGfq5njx5Ep999pnF8mVlZXjppZdUt9egQQNjQKL0PZSVleGVV15RXd+QeGr16tVYvXq1XWWvqIkTJ0Kv1+PKlSsOZc+VJMlYE/z+++/jyy+/BOBYMi2DlJQUTU3H3dGk3Bvk5eXhgQceAKCcSTw8PBy33XYbAOCDDz5QbI3w6quvAigPNu1JxKdlXxj6n8sTWrVq1cq4jTZt2gAob4b84YcfKr7P/v37jRnuDRnRPeHXX381tnp45JFH8O9//9tjZSGiyocBMRFRBd1yyy1444038MYbb9jVn23AgAEAyvt2fvzxx8Za5vPnz+Pxxx/Ha6+9hri4ONX1H3/8cRw8eBAxMTFYuHChsS/vqFGjjMlv7rvvPtXaUXeYMmUKmjdvjoKCAvTq1QvvvfeeSRPna9euYc2aNRg/fjy6detmsm7Hjh2NfWEfeOAB/O9//zMmujp58iTGjBmD33//XXVYm8DAQGNA8p///AfffPON8Ts+cuQIhg8fbjHckdy4cePQp08fCCEwfPhwvPTSSyaJwnJzc7F+/Xo8+OCDSE1NdeDbUVevXj1jzf9rr72Ge++9F0ePHjXOz8rKwtdff43hw4erbmPSpEkIDg7G/v37kZGR4bPJtDIzM3H58mXjnyEBWWFhocl0tebvGRkZJssZ+tHm5eWZTFeqwd62bRv+85//4ODBg8bfTlFREX788Ud07doV27ZtQ61atTB37lzF937hhRcQHh6Oc+fOYfDgwcZ9aMgMbQhEn3/+eYukXMCNDOspKSn2fWkajBw5EtWqVQNQ3hT55ZdfNh6bubm5WLx4Mfr164eSkhIEBQXhoYcecnoZtDh69Chuu+02FBcXY+DAgXjrrbc8Ug4iqsScNqIxEVElNnPmTAFAJCcn27Xe+vXrBQABQKSlpZnMy8jIEI0aNTLO1+l0IiYmRkiSJACIyZMniwkTJggAYsKECSbrfvfdd8b1vv32W4v3zc3NNW67W7duoqSkxK5yq72v0uey5cyZM6JTp07G5SVJEjExMSIqKso4DYCoV6+exbqXL18WLVu2NC4TGBgoYmJijNuZO3euSE5OFgDE/PnzLdY/deqUqF69usn6hveNjIwUGzZsMM5bv369xfqZmZni1ltvNSlnVFSUyX4CIAICAizWtVYuA2vfc0lJiXjwwQdN3jsiIkLExsYa3zs6OtrKNy/EuHHjjOs+9NBDVpf1Vj169DD5DtT+evToobi+YT/Y+lPaB0uXLjX53VapUkXo9XrjtGbNmlkc1+ZWrVolwsLCjOtER0ebbGPSpEmirKzM6me397wj/9xqx7AQQmzbtk0kJCSYfA+RkZEmv+2wsDDFc4yzymBgOMean1Pmz59vnB4bGyuqVq2q+vfII484VE4i8m+sISYi8pCYmBhs3boVjz32GFJSUqDX6xEQEICePXti0aJFqs0YT506hXvvvRdA+fi+SuMNh4WFYdGiRQgODsamTZusNi12terVq2Pz5s1YtGgRhgwZgmrVqiEvLw9FRUVISUnB4MGDMWfOHPz2228W68bFxWHr1q2YPXs2GjVqBJ1Oh4CAAAwYMAA///yzzaaTNWvWxLZt23Dvvfca+zNHRERg/Pjx2LVrF3r06GF1/aioKKxYsQKrV6/GmDFjULt2bRQWFiIvLw81atRAv3798MorrxjHInYmvV6P9957D5s3b8add96J2rVro7i4GEIINGnSBPfcc4/NJvGjRo0yvnY0mZY/a9u2LaZOnYqOHTsiMTER2dnZiIuLQ58+ffDxxx/jr7/+sll7O3DgQOzduxf33XcfUlJSUFBQgNjYWPTt2xdLlizBvHnzKpRVvCI6dOiAQ4cO4cUXX0SnTp0QGxuLvLw8hIeHo0WLFnj88cexb98+t41pbktGRobVfupqrQSIiKyRhLAxlgcREZEXS0lJQXp6OubPn+81wzZ5i4cffhjvvfceOnfujK1bt3q6OERERF6HNcRERESVUFZWlnFsbEPyJyIiIjLFgJiIiKiSKSwsxKOPPoqsrCzUqlXLJ5NpERERuUOApwtAREREzjFnzhzMmTMHFy9eRH5+PgDgrbfeQlBQkIdLRkRE5J1YQ0xERFRJXLt2Denp6cYxZ7/++muvSYhERETkjZhUi4iIiIiIiPwSa4iJiIiIiIjIL7EPsZOVlZXh7NmziIyM9Ni4gkRERERERL5KCIHs7GxUr14dOp1r63AZEDvZ2bNnUatWLU8Xg4iIiIiIyKedOnUKNWvWdOl7MCB2ssjISADlOy8qKsrDpSEiIiIiIvIthmEDDbGVKzEgdjJDM+moqCgGxERERERERA5yRxdUJtUiIiIiIiIiv8SAmIiIiIiIiPwSA2IiIiIiIiLySwyIiYiIiIiIyC8xICYiIiIiIiK/xICYiIiIiIiI/BIDYiIiIiIiIvJLDIiJiIiIiIjILzEgJiIiIiIiIr/EgJiIiIiIiIj8EgNiIiIiIiIi8ksMiImIiIiIiMgvMSAmIiIiIiIiv8SAmIiIiIiIiPwSA2IiIiIiIiLySwyIiVTk5uZCkiRIkoTc3FxPF4eIiIiIiJyMATERERERERH5JQbERERERERE5JcYEBMREREREZFfYkBMREREREREfokBMREREREREfmlAE8XgMhb6fV6jBw50viaiIiIiIgqFwbERCpCQkLw7bfferoYRERERETkImwyTURERERERH6JATERERERERH5JQbERCpyc3MhSRIkSUJubq6ni0NERERERE7GgJiIiIiIiIj8EgNiIiIiIiIi8ksMiImIiIiIiMgv+WxAnJ+fjxkzZqBBgwYICQlB9erVcffdd+PMmTN2b+vnn3/GoEGDkJCQgMDAQMTFxaFfv35YunSpC0pORERERERE3sAnA+KCggL07t0bL774InJycjB06FDUqlUL8+fPR+vWrXH8+HHN25ozZw769euHNWvWoEGDBrjtttvQqFEjrFu3DiNGjMBzzz3nwk9CREREREREnuKTAfFLL72EP/74A507d8bff/+Nr7/+Gtu2bcObb76JS5cu4e6779a0nUuXLmHatGkIDAzE+vXrsWXLFixevBhbtmzBhg0bEBwcjFdeecWuAJuIiIiIiIh8g88FxEVFRXjvvfcAAHPnzkVERIRx3hNPPIEWLVpg48aN+PPPP21ua9u2bSgsLETv3r3Ro0cPk3ndu3dH//79IYTAzp07nfshyCfo9XoMHDgQAwcOhF6v93RxiIiIiIjIyXwuIN6yZQsyMzNRt25dtG7d2mL+yJEjAQArVqywua3g4GBN7xkXF2dfIalSCAkJwapVq7Bq1SqEhIR4ujhERERERORkPhcQ79mzBwDQpk0bxfmG6Xv37rW5rQ4dOiAmJga//vorNm7caDLvt99+w9q1a1G/fn1069atgqUmIiIiIiIib+NzAfHJkycBADVr1lScb5ienp5uc1vR0dH49NNPodPp0KtXL3Tt2hVjx45F165d0bNnT7Rv3x5r165FUFCQ8z4AEREREREReYUATxfAXjk5OQCAsLAwxfnh4eEAgOzsbE3bGzFiBNasWYPRo0djy5YtxulRUVHo168fatSoYXX9wsJCFBYWGv+flZWl6X3J++Xm5iIxMREAcPHiReNvi4iIiIiIKgefqyF2tjfffBN9+vRB9+7dsXfvXuTk5GDv3r3o3bs3ZsyYgREjRlhd/5VXXkF0dLTxr1atWm4qOblDXl4e8vLyPF0MIiIiIiJyAZ8LiA1ZpdWClNzcXABAZGSkzW1t2LABTz75JFq1aoVvv/0WzZs3R3h4OJo3b44lS5agVatWWLVqFdasWaO6jWeeeQaZmZnGv1OnTjnwqYiIiIiIiMjdfC4grl27NgDg9OnTivMN05OTk21u64svvgAADB8+HDqd6Veh1+uNtcO//fab6jaCg4MRFRVl8kdERERERETez+cC4pYtWwIAdu3apTjfML1FixY2t2UInqOjoxXnG6ZnZGTYXU4iIiIiIiLybj4XEHfp0gXR0dE4duwYdu/ebTF/yZIlAIDBgwfb3FZSUhIAYOfOnYrzd+zYAQBISUlxrLBERERERETktXwuIA4KCsJDDz0EAHjwwQeNfYYB4K233sLevXvRo0cPtG3b1jj9vffeQ6NGjfDMM8+YbGvYsGEAgIULF2LlypUm83744Qd89dVX0Ol0GD58uIs+DREREREREXmKzw27BADPP/881q1bh61bt6J+/fro1q0b0tPTsW3bNiQkJGDevHkmy1++fBlHjhzBuXPnTKYPGzYMo0aNwrfffovBgwejXbt2qFOnDtLS0oy1xi+//DIaNmzots9G3kOn06FHjx7G10REREREVLn45F1+SEgI1q9fj+nTpyMsLAzLli1Deno6Jk6ciF27diE1NVXTdiRJwtdff41PP/0U3bt3xz///IOlS5fixIkTGDhwINasWYNnn33WxZ+GvFVoaCg2bNiADRs2IDQ01NPFISIiIiIiJ5OEEMLThahMsrKyEB0djczMTGacJiIiIiIispM7YyqfrCEmcpeM3CIs/es0SkrLPF0UIiIiIiJyMp/sQ0zkDrm5uWh21wsIbNAd6Zey8Fi/Jp4uEhERERERORFriImskJIaAQCW770A9i4gIiIiIqpcGBATqTh7rQABUYkAgOOX83DoXLaHS0RERERERM7EgJhIxZ8nr5n8f/mes54pCBERERERuQQDYiIVu05lAgCKr54BAKzYcxZlZWw2TURERERUWTAgJlLx58nygPja5oUIC9LjzLV87DqZ4eFSERERERGRszAgJlKQkVuEY5fzAAAFJ3ajT8N4AGw2TURERERUmTAgJlKwM728JlifewltmjbAoOZJAIBVe89xTGIiIiIiokqCATGRgh0nrgIARvdqix07dqB30+qoEh6EK7lF2HLsiodLR0REREREzsCAmEjB9rTygLh9SiwAIFCvw8DrtcTLd7PZNBERERFRZcCAmMhMflEp9p8pT6jVPqWKcfrQVjUAAGsPnEdBcalHykZERERERM7DgJjIzF+nMlBSJpAUFYxubZogJSUFeXl5aFs7FtWjQ5BTWIL1hy96uphERERERFRBDIiJzOxIK0+o1bpWNNLT05Geng4hBHQ6CYNbVgfAbNNERERERJUBA2IiMzvTy/sPt60dbTFvSKvygPiXwxeRVVDs1nIREREREZFzMSAmkikpLcOu60Muta1lGRA3qRaFugnhKCopw08HLri7eERERERE5EQMiIlkDp3LRm5RKaJCAlAvMdxiviRJGNKyPLkWm00TEREREfk2BsREMtuvjz/cLqUKdJKkuIyh2fSWfy7jck6h28pGRERERETOxYCYSGaHcfzhKqrL1IkPR4ua0SgtE1i975y7ikZERERERE7GgJjoOiGEMaFW+5RYSJKEJk2aoEmTJpDMaouHGLJN72azaSIiIiIiX8WAmOi6tMu5uJxThKAAHZrXjEZYWBgOHDiAAwcOICwszGTZW1tUhyQBO9MzcDojz0MlJiIiIiKiimBATHTdjuv9h1vVikFwgN7qsknRIehYp7xZ9Yo9bDZNREREROSLGBATXbfjRPlwS+1TYjUtP7RVebbpH3afcVmZiIiIiIjIdRgQE11nqCE2JNTKy8tD06ZN0bRpU+TlWTaLvqVZEgL1Eg6fz8bRC9luLSsREREREVUcA2IiABezCpB+JQ+SBLRJLq8hFkLg4MGDOHjwIIQQFuvEhAWhe/0EAByTmIiIiIjIFzEgJsKN5tKNk6IQFRKoeT3DmMQ/7D6rGDQTEREREZH3YkBMhBvNpTvUUR9/WEnfJlURGqjHyat52HM60xVFIyIiIiIiF2FATARge1p5QNxOY0Itg7CgAPRpUhUAxyQmIiIiIvI1DIjJ72UVFOPw+SwAQIcU+2qIAWBoy/Jm0yv2nkVpGZtNExERERH5CgbE5Pd2pWegTADJcWFIjAqxe/3uDRIQHRqIS9mF2Hb8igtKSERERERErsCAmPzezusJtdolm9YOS5KE5ORkJCcnQ5Ik1fWDAnS4pVkSAGabJiIiIiLyJQyIye9tNybUMu0/HBYWhhMnTuDEiRMICwuzug1DtunV+86hsKTUNQUlIiIiIiKnYkBMfq2wpBS7T10DALR3oP+wQcc6cUiMDEZWQQl++/uyk0pHRERERESuxICY/Nr+M5koKilDfEQQ6sSHO7wdvU7CrS0MYxKfcVbxiIiIiIjIhRgQk1/bnnaj/7B5P+H8/Hy0b98e7du3R35+vs1tDb3ebHrdoQvILSxxfmGJiIiIiMipGBCTX9txvf9w+zqWzaXLysqwc+dO7Ny5E2VlZTa31aJmNJLjwlBQXIZ1hy44vaxERERERORcDIjJb5WVCew0BMQpsTaWtk2SJOOYxD/sZrZpIiIiIiJvx4CY/NbfF7ORVVCCsCA9mlSLcso2Ddmmf/v7EjJyi5yyTSIiIiIicg0GxOS3dqSV1w63qR2LAL1zDoV6iZFoXC0KJWUCa/afd8o2iYiIiIjINRgQk9/acaI8oVZFhltSYkiuxWzTRERERETejQEx+SUhhCyhVsX7D8sNvt6PePuJqzifWeDUbRMRERERkfMwICa/dDojH+cyCxCgk9C6lnpAHB8fj/j4eLu2XSMmFO2SYyEEsHIvk2sREREREXkrBsTkl3aml9cON6sRjdAgveIy4eHhuHTpEi5duoTw8HC7tn+j2TQDYiIiIiIib8WAmPzS9rTy/sMdFMYfdoaBzatBr5Ow70wmjl/Kccl7EBERERFRxTAgJr9k6D/cLtm5/YcN4iKC0aVeeVPr5XtYS0xERERE5I0YEJPfuZpbhH8ultfaWsswnZ+fj549e6Jnz57Iz8+3+32GXk+utXzPWQghHCssERERERG5TICnC0Dkbjuv1w7XT4xAbHiQ6nJlZWXYuHGj8bW9+jWtiuClOhy/lIsDZ7PQrEa0YwUmIiIiIiKXYA0x+Z2d6eX9h9s5efxhc5Ehgbi5cSIANpsmIiIiIvJGDIjJ72xPK68h7uDk8YeVDLnebHrFnrMoK2OzaSIiIiIib8KAmPxKXlEJ9p/JBGC9/7Cz9GyYiMjgAJzLLDDWTBMRERERkXdgQEx+ZfepaygpE6gWHYIaMaEuf7+QQD36N0sCAPyw+4zL34+IiIiIiLRjQEx+Zcf18Yfbp1SBJElueU9Ds+nV+86huNT+5FxEREREROQaDIjJrxjGH26foq3/cFhYGMLCwir0njfVjUN8RBAy8oqx+Z/LFdoWERERERE5DwNi8hslpWXYdfJ6DXEd2/2Hw8PDkZubi9zcXISHhzv8vgF6HQY1rwYAWL6b2aaJiIiIiLwFA2LyGwfPZSGvqBRRIQFokBjp1vce0qq82fRPB84jv6jUre9NRERERETKGBCT3zAMt9QupQp0Ovf0HzZoUzsWNWJCkVtUil8OX3DrexMRERERkTIGxOQ3dp64kVBLi4KCAgwaNAiDBg1CQUFBhd5bkiRjLTGbTRMREREReQcGxOQXhBDGhFod6mhLqFVaWorVq1dj9erVKC2teDNnQ7bpDUcuITO/uMLbIyIiIiKiimFATH7h+OVcXMktQlCADs1qRHukDI2SItGgagSKSsuwdv95j5SBiIiIiIhuYEBMfmHn9drhVrViEByg90gZJEky1hIv38Nm00REREREnsaAmPzC9rTy/sMdNPYfdpXB1wPirccu42J2xfolExERERFRxTAgJr9g6D/cLkVb/2FXSY4LR6taMSgTwKq95zxaFiIiIiIif8eAmCq9C1kFOHk1DzoJaJvs2YAYAJtNExERERF5CQbEVOkZaocbV4tCZEigh0sD3NqiGnQS8NfJazh5Jc/TxSEiIiIi8lsMiKnSs3f8YYPw8HAIISCEQHh4uNPKkxgVgs514wAAK/aylpiIiIiIyFMYEFOltz2tvIbY3oDYlYzNpnczICYiIiIi8hQGxFSpZRUU49D5LABAew8n1JIb0LQaAvUSjlzIxuHr5SMiIiIiIvdiQEyV2q70DAgBJMeFITEqxK51CwoKMGrUKIwaNQoFBc4dIik6LBA9GyYCYC0xEREREZGn+GxAnJ+fjxkzZqBBgwYICQlB9erVcffdd+PMmTMObe/EiRO4//77UadOHQQHByM+Ph6dO3fG66+/7uSSkzsZEmo50ly6tLQUS5YswZIlS1BaWursoplkmxZCOH37RERERERknU8GxAUFBejduzdefPFF5OTkYOjQoahVqxbmz5+P1q1b4/jx43Ztb82aNWjatCk+/vhjxMXFYcSIEWjTpg1OnDiBjz76yEWfgtxhR5ohoZb3NJc26NO4KsKC9DidkY9dJ695ujhERERERH4nwNMFcMRLL72EP/74A507d8ZPP/2EiIgIAMBbb72FKVOm4O6778aGDRs0bevw4cMYMWIEIiMj8fPPP+Omm24yzisrK8OuXbtc8RHIDQpLSrH79DUA3pVQyyA0SI9+Tapi2e6zWLHnrFeMkUxERERE5E98roa4qKgI7733HgBg7ty5xmAYAJ544gm0aNECGzduxJ9//qlpe0888QQKCgqwYMECk2AYAHQ6Hdq1a+e8wpNb7TudiaKSMsRHBKFOvPOGTXKmIa3Km02v3HsOJaVlHi4NEREREZF/8bmAeMuWLcjMzETdunXRunVri/kjR44EAKxYscLmtk6dOoW1a9ciNTUVAwcOdHpZybO2X+8/3C65CiRJ8nBplHWrn4DYsEBczinE78eveLo4RERERER+xeeaTO/ZswcA0KZNG8X5hul79+61ua0NGzagrKwMN910E0pKSvD9999jy5YtKC0tRbNmzTBmzBjExrIZq6/aeeJ6/+E63tdc2iBQr8Mtzavhq20nsXz3WXSrn+DpIhERERER+Q2fC4hPnjwJAKhZs6bifMP09PR0m9s6ePAgACAiIgLdunXDH3/8YTL/ueeew5IlS9CrV6+KFJk8oKxMYOf1GuIOXth/WG5Iy+r4attJ/HjgPF4c1gwhgXpPF4mIiIiIyC/4XJPpnJwcAEBYWJji/PDw8r6i2dnZNreVkVFeg/jJJ5/g8OHD+Oqrr3D16lUcOXIE48aNw9WrVzF8+HCrQzkVFhYiKyvL5I8878iFbGQVlCA8SI/G1SId2kZYWBhycnKQk5Oj+ntzhg4pVZAUFYLsghJsOHLJZe9DRERERESmfC4gdqaysvIkRiUlJfjoo49w++23IzY2Fg0aNMAXX3yB9u3bIzMzE++//77qNl555RVER0cb/2rVquWu4pMVhtrhNsmxCNA79jOXJAnh4eEIDw93aR9knU7C4JbVAAAr9px12fsQEREREZEpnwuIDVml8/LyFOfn5uYCACIjbdcKGrYVERGBUaNGWcyfNGkSAGDjxo2q23jmmWeQmZlp/Dt16pTN9yXX227oP+zlzaUNhrSsAQBYd+gCcgpLPFwaIiIiIiL/4HMBce3atQEAp0+fVpxvmJ6cnGxzW4ZlateurVgDmJKSAgC4ePGi6jaCg4MRFRVl8keeJYTAjrTrGaZTHE+KVlhYiIkTJ2LixIkoLCx0VvEUNasRhdT4cBSWlOGnA+dd+l5ERERERFTO5wLili1bAgB27dqlON8wvUWLFja3ZRi2ydCX2NzVq+VBlXysY/J+pzPycT6rAAE6Ca1rOR4Ql5SU4LPPPsNnn32GkhLX1tpKkoTBLcvHJF7OZtNERERERG7hcwFxly5dEB0djWPHjmH37t0W85csWQIAGDx4sM1t3XTTTYiLi8P58+dx5MgRi/mGptJK4x2T99pxvf9wsxrRCA3ynYzNQ1qVB8Sbjl7GlRzX1kgTEREREZEPBsRBQUF46KGHAAAPPvigsc8wALz11lvYu3cvevTogbZt2xqnv/fee2jUqBGeeeYZk20FBATgiSeegBACDz74oEmG6HXr1mHBggWQJAmTJ0928aciZ9pxvf9wBy8ef1hJ3YQINKsRhdIygdX72WyaiIiIiMjVfG4cYgB4/vnnsW7dOmzduhX169dHt27dkJ6ejm3btiEhIQHz5s0zWf7y5cs4cuQIzp07Z7GtqVOnYv369Vi3bh0aNGiATp064fLly/jjjz9QWlqKl19+GR06dHDXRyMnMNQQ+0pCLbkhLatj/5ksrNh9Fnd1st0PnoiIiIiIHOdzNcQAEBISgvXr12P69OkICwvDsmXLkJ6ejokTJ2LXrl1ITU3VvK3AwECsXr0ar776KuLj47F27Vrs27cPPXr0wIoVK/Dss8+68JOQs13NLcI/F8vHqm6X7Hj/YU8Z3LI6JAnYfuIqzlzL93RxiIiIiIgqNUkIITxdiMokKysL0dHRyMzMZMZpD/jpwHn864s/UT8xAj8/0aNC28rNzTUmVMvJyUF4eLgzimjT6I9+x/a0q3jmlkaY3KOuW96TiIiIiMhbuDOm8skaYiI1xubSPtZ/WG4Is00TEREREbkFA2KqVLZfT6jVvgLjDxuEhYXh4sWLuHjxIsLCwiq8Pa0GNq+GAJ2EA2ezjM2/iYiIiIjI+RgQU6WRV1SCA2cyATgnoZYkSUhISEBCQgIkSarw9rSqEh6EbvXjAbCWmIiIiIjIlRgQU6Wx++Q1lJQJVI8OQc1Y99XouoJhTOIVe86C3fyJiIiIiFyDATFVGtuv9x9u56ThlgoLC/Hggw/iwQcfRGFhoVO2qVXfJkkICdQh7XIu9l2v9SYiIiIiIudiQEyVxk5D/2EnJdQqKSnB+++/j/fffx8lJSVO2aZWEcEBuLlxVQDA8t1sNk1ERERE5AoMiKlSKCktw66T5QFxByfVEHuaIdv0ir1nUVrGZtNERERERM7GgJgqhQNns5BXVIro0EDUT4zwdHGcomfDBESGBOBCViG2p131dHGIiIiIiCodBsRUKRjGH26XHAudzn0ZoV0pOECPW5olAWC2aSIiIiIiV2BATJWCISB2Vv9hbzGkZQ0AwOp951BUUubh0hARERERVS4MiMnnCSFuJNRKifVwaZyrc904xEcEIzO/GJuOXvJ0cYiIiIiIKhUGxOTzjl/OxZXcIgQH6NCsRrSni+NUep2EW1tUA8Bm00REREREzsaAmHzejusJp1rWikFwgN5p2w0NDUVaWhrS0tIQGhrqtO3aa2ir8mzTPx24gLwi9w7/RERERERUmTEgJp+344RrhlvS6XRISUlBSkoKdDrPHSqtasWgdpUw5BeXYt2hix4rBxERERFRZcOAmHxeZU2oZSBJEga3vN5sejebTRMREREROQsDYvJpF7IKcPJqHnQS0KZ2jFO3XVRUhKlTp2Lq1KkoKipy6rbtNbRVebbpjX9fxLU8z5aFiIiIiKiyYEBMPs1QO9y4WhQiQwKduu3i4mK88cYbeOONN1BcXOzUbdurQdVINEqKRHGpwI/7z3u0LERERERElQUDYvJphoRa7Z3cf9gbDW5ZnlzrBzabJiIiIiJyCgbE5NO2G8cfrvwB8ZDrAfEfaVdwIavAw6UhIiIiIvJ9DIjJZ2UVFOPw+SwAQPs6sR4ujevVqhKGNrVjIASwcu85TxeHiIiIiMjnMSAmn/VnegaEAFLiwpAYGeLp4riFoZZ4+e4zHi4JEREREZHvY0BMPsvQf7idHzSXNhjUojp0ErDndCZOXM71dHGIiIiIiHwaA2LyWTuv9x/u4EcBcUJkMLrUiwcArNjD5FpERERERBXBgJh8UmFJKXafvgYAaF/HNQFxaGgo9u/fj/379yM0NNQl7+EIQ7PpH/achRDCw6UhIiIiIvJdDIjJJ+09nYmikjLERwQhJS7MJe+h0+nQtGlTNG3aFDqd9xwq/ZslIShAh38u5uDQuWxPF4eIiIiIyGd5z10+kR12nLgx/rAkSR4ujXtFhQSiV8MEAMByNpsmIiIiInIYA2LySe5IqFVUVIRZs2Zh1qxZKCoqctn7OGJoqxoAyvsRl5Wx2TQRERERkSMYEJPPKSsT2Jnu+oRaxcXFmD17NmbPno3i4mKXvY8jejdKRERwAM5cy8eukxmeLg4RERERkU9iQEw+58iFbGQXlCA8SI/G1SI9XRyPCAnUo1+TqgCAH3az2TQRERERkSMYEJPPMfQfbpMciwC9//6Eh7Qqzza9et85lJSWebg0RERERES+x3+jCfJZO66PP9zej8YfVtKlXjyqhAfhSm4Rthy74uniEBERERH5HAbE5FOEEMaEWv4eEAfqdRjYPAkA8MPuMx4uDRERERGR72FATD7ldEY+zmcVIFAvoVWtGE8Xx+MM2aZ/OnABBcWlHi4NEREREZFvYUBMPsXQf7hZjWiEBuk9XBrPa1s7FtWjQ5BTWIL1hy96ujhERERERD6FATH5FENA7MrhlgxCQkKwfft2bN++HSEhIS5/P0fodBIGX0+uxWzTRERERET2YUBMPmX79f7D7dwQEOv1erRv3x7t27eHXu+9tdFDWpYHxL8euYisAu8aL5mIiIiIyJsxICafcSWnEMcu5QIA2iXHerg03qNJtSjUTQhHUUkZfjpwwdPFISIiIiLyGQyIyWfsTC8fbqlB1QjEhge5/P2Kiorw+uuv4/XXX0dRUZHL389RkiQZk2sx2zQRERERkXYMiMln7HBjc2kAKC4uxlNPPYWnnnoKxcXe3RTZ0Gx667EruJRd6OHSEBERERH5BgbE5DN2XK8hdkdCLV+TEh+OFjWjUVomsHrfOU8Xh4iIiIjIJzAgJp+QV1SCA2cyAQDtUth/WImhlnj5HmabJiIiIiLSggEx+YTdJ6+hpEygenQIasaGebo4Xmlwy+qQJODP9Aycuprn6eIQEREREXk9BsTkE7ZfH3+4fR02l1ZTNSoEHa9/Pyv2spaYiIiIiMgWBsTkE3accG9CLV9lyDa9fDcDYiIiIiIiWxgQk9crLi3DXyevAWBCLVtuaZaEQL2Ew+ez8feFbE8Xh4iIiIjIqzEgJq938GwW8opKER0aiPqJEW5735CQEKxfvx7r169HSEiI2963ImLCgtC9fgIA1hITEREREdnCgJi8nrG5dHIsdDrJbe+r1+vRs2dP9OzZE3q93m3vW1FDWt3INi2E8HBpiIiIiIi8FwNi8no7mFDLLn2bVEVooB4nr+Zhz+lMTxeHiIiIiMhrOS0gfuGFF7B8+XJnbY4IACCEwM4TGQCA9m7uP1xcXIy5c+di7ty5KC4udut7V0RYUAD6NqkKAPhh9xkPl4aIiIiIyHs5LSCeNWsWli1b5qzNEQEAjl3KxZXcIgQH6NC8RrRb37uoqAgPPfQQHnroIRQVFbn1vStqSMvyZtMr955DaRmbTRMRERERKWGTafJqO683l25VKwZBAfy5atW9QQKiQwNxKbsQfxy/4uniEBERERF5JUYY5NW2Xw+IO7D/sF2CAnQY2DwJALNNExERERGpYUBMXs2YYZrjD9tt8PVm02v2n0NhSamHS0NERERE5H0CnLmxnJwcnDx50qF1a9eu7cyiUCVwPrMAp67m/3979x3edLX/AfydJm26m5buXcqm0DJaBGSKUECobMVRwHERB1zX1ftTwXW9irguqNfBcAEKioAIohcQKKOl0NKyoXvv3bRJzu+P0kBtgRbafjPer+fJ88B3fkIOST4553wOLGTAQH+V1OEYnSFBXeDuoER+hRr7zhZgfF9PqUMiIiIiIjIo7ZoQb968GZs3b27zeTKZDBqNpj1DIRPQ2Dvc28sRDtaWEkdjfOQWMkwJ9caXB1KwNSGbCTERERER0V+065BpIcRNPXQ6XXuGQSZCv/4wh0vftMZq07+fzkOVmj86ERERERFdrV0T4nnz5kGn093Ug+ivYi+vPyxVQS2lUont27dj+/btUCqVksRwq/r7OiGgiy1q63XYfSpP6nCIiIiIiAwKi2qRQSqrqceZ3HIAwOBAZ0liUCgUmDx5MiZPngyFol1nF3QamUyGqMu9xFsTWG2aiIiIiOhqTIjJIMWnl0AIILCLLdwdrKUOx6hNDWtIiP88V4CSqjqJoyEiIiIiMhxMiMkgxaZIP3+4vr4ea9euxdq1a1FfXy9ZHLeqm7sD+ng5QqMT2JGUI3U4REREREQGgwkxGSRDKKhVV1eH+fPnY/78+airM+6e1cZe4q0nOGyaiIiIiKhRuyXEa9aswcMPP9xelyMzVluvRUJGGQAgXKKCWqZmyuV5xEdTi5FTViNxNEREREREhqHdEuJ169bhwIEDLe5LT09HcXFxe92KTNzJrDLUaXVwtVcisIut1OGYBB+VDcIDnSEEsD2Bw6aJiIiIiIB2TIj37t2LM2fOtLgvKCgIzz33XHvdikzcUf38YWfIZDKJozEdU1ltmoiIiIioiU6ZQyyEgBCiM25FJiDOAOYPm6JJ/bwgt5DhZFYZLhVUSh0OEREREZHkWFSLDIpWJxCXVgIAiOD84XbVxV6J27u5AmAvMRERERERwISYDMzZ3ApU1GpgZyVHL08HqcMxOVcPm+aoDSIiIiIyd0abENfU1OCVV15Bjx49YG1tDW9vbyxYsABZWVm3dN3z58/DxsYGMpkM48aNa6doqbXi0hqGSw8McIZCLm3zVCqV+P777/H9999DqVRKGkt7Gd/XA0qFBS4VVCE5u1zqcIiIiIiIJGWUCXFtbS3Gjh2L119/HZWVlYiKioKfnx/WrFmDAQMG4NKlSzd97UcffRRqtbodo6W2uFJQS/rh0gqFArNmzcKsWbOgUCikDqddOFhb4o7e7gA4bJqIiIiIqF0T4nXr1kEulzd7yGSya+6Ty+VtTjbeeOMNHD58GEOHDsW5c+ewceNGHDlyBCtWrEBBQQEWLFhwU/F/+eWX2Lt3Lx555JGbOp9ujRACsSyo1eEah01vS8iGTsdh00RERERkvto1IW6sJt3Wh06na/U96urqsHLlSgDAqlWrYG9vr9/39NNPo3///ti3bx+OHTvWptjz8vLw3HPP4c4778S9997bpnOpfWSW1CCvXA1LuQxhfiqpw4FGo8EPP/yAH374ARqNRupw2s3onu5wUCqQU1ar/wGCiIiIiMgctVtCrNPpbunRWgcPHkRZWRmCg4MxYMCAZvtnzpwJANi2bVub4l+8eDFqamrw8ccft+k8aj+Nw6VDfJxgYyWXOBpArVZj9uzZmD17tkkNo7e2lGNCiCcADpsmIiIiIvNmdHOIExISAAADBw5scX/j9sTExFZfc8eOHdi4cSP++c9/olu3brceJN2Uxt7KCA6X7nCNw6Z3nMxBvbb1P0gREREREZkSo0uI09PTAQC+vr4t7m/cnpaW1qrrVVVVYdGiRejZsyf+8Y9/tE+QdFM4f7jzDAvuAld7K5RU1+PA+UKpwyEiIiIikkS7JcRjx47FO++80+K+9PR0FBe3z1zFyspKAICtrW2L++3s7AAAFRUVrbreSy+9hLS0NHz66aewsrJqczxqtRrl5eVNHtR2RZVqXCyoAgAMCnCWOBrTp5BbYHI/LwAcNk1ERERE5qvdEuK9e/fizJkzLe4LCgrCc8891163ajdxcXH46KOP8OCDD2L06NE3dY233noLTk5O+oefn1/7Bmkm4tJKAAA9POzhbNf2Hyao7aaGNQyb3pWci5o6rcTREBERERF1vk4ZMt1YTbo9NFaVrq6ubnF/VVVDL6ODg8N1r6PRaPDII49ApVLh3Xffvel4XnzxRZSVlekfGRkZN30tcxZrQOsPm4uB/s7wdbZBdZ0Wf5zJkzocIiIiIqJO17YFgA2Av78/ACAzM7PF/Y3bAwICrnudzMxMnDhxAp6enpg1a1aTfaWlpQCAY8eO6XuO9+7d2+J1lEollEplK6Ona+H84c4nk8kwJdQbn+y9iK0nsnFXf2+pQyIiIiIi6lRGlxCHhoYCAOLj41vc37i9f//+rbpebm4ucnNzW9xXWlqKffv23USU1BbVdRokZTfMvQ4PMpyE2MrKCmvWrNH/2RRNvZwQ7z1bgLKaejjZWEodEhERERFRpzG6KtPDhw+Hk5MTLl68iBMnTjTbv2nTJgDAlClTrnudwMBA/VDuvz727NkDALjjjjvadbg3tex4eim0OgEflQ18VDZSh6NnaWmJefPmYd68ebC0NM1EsZenA3p42KNOq8OupJZ/GCIiIiIiMlVGlxBbWVnhiSeeAAA8/vjj+jnDAPDee+8hMTERo0aNwqBBg/TbV65ciV69euHFF1/s9Hjpxo5enj88OJDVpTubTCbTr0nMatNEREREZG7aNSFet24d5HJ5s4dMJrvmPrlcDoWibSO3X3rpJQwZMgQxMTHo3r075syZg9tuuw3PPPMM3NzcsHr16ibHFxYW4uzZs8jJyWnPp0vtJC7NMOcPazQa/PLLL/jll1+g0WikDqfDTLmcEMdcLER+Ra3E0RARERERdZ52TYivNQT5Rg+dTtem+1hbW2PPnj14+eWXYWtriy1btiAtLQ3z5s1DfHw8unbt2p5PizpQvVaH+LRSAECEAc0fBhrWmL7rrrtw1113Qa1WSx1OhwnoYocwPxV0AvglkT8aEREREZH5kAlOkG1X5eXlcHJyQllZGRwdHaUOx+CdyCjF3asOwsnGEsdfvhMWFjKpQ9KrqqrSL/NVWVkJOzs7iSPqOKsPpOC17acQ5qfClseHSx0OEREREZmxzsypjG4OMZmWOP1yS84GlQybm7v6e8FC1vADRXpRy2t8ExERERGZGibEJKkrBbUMa7i0uXF3tMbQ4C4AgG2JLK5FREREROaBCTFJRgiBuLQSAIZXUMscNVab/vlElsSREBERERF1DibEJJmLBVUorqqDUmGBfj5OUodj9iL7esFKboFzeZU4k1sudThERERERB2OCTFJJvby/OEwPxWsFGyKUnOytcSonm4AgK0nOGyaiIiIiEwfsxCSTGNCbGjLLTWysrLCypUrsXLlSlhZWUkdTqdoHDa9NSEbLEBPRERERKZOIXUAZL5i9RWmDTMhtrS0xOOPPy51GJ1qXG8P2FrJkVlSg/j0UgwKcJY6JCIiIiKiDsMeYpJEblktMoprYCEDBvirpA6HLrOxkmN8Hw8AwLYEDpsmIiIiItPGhJgk0dg73MfbEQ7WlhJH0zKtVou9e/di79690Gq1UofTaaaGNQyb3p6YDY1WJ3E0REREREQdh0OmSRKGPlwaAGprazFmzBgAQGVlJezs7CSOqHOM6O4GZ1tLFFbW4dClIozo7iZ1SEREREREHYI9xCSJoymGnxCbK0u5BSb28wIA/Mxq00RERERkwpgQU6crq6nH2bwKAEyIDVXU5WrTu5JyUVtvPsPFiYiIiMi8MCGmThefVgIhgCBXO7g5KKUOh1oQHugCLydrVKg12Hu2QOpwiIiIiIg6BBNi6nRHL88fHswlfQyWhYUMd/VvGDa9NSFL4miIiIiIiDoGE2LqdHGNBbWCOFzakEWF+QAA/jidj4raeomjISIiIiJqf0yIqVPV1muRkFEGgPOHDV1fb0d0dbWDWqPD7lN5UodDRERERNTumBBTp0rMLEOdVgdXeyUCu9hKHc51WVpa4p133sE777wDS0vDXCu5I8lkMky5XFyL1aaJiIiIyBRxHWLqVI3rD0cEOUMmk0kczfVZWVnhueeekzoMSU0N88aHf5zHgQuFKKpUo4s9i6ARERERkelgDzF1qlh9QS0OlzYGwW72CPFxhFYnsCMpV+pwiIiIiIjaFRNi6jRancCx1BIAQIQRFNTSarWIjY1FbGwstFrzXYt36uVh01tPsNo0EREREZkWJsTUac7mVqBCrYG9UoFeng5Sh3NDtbW1iIiIQEREBGpra6UORzJTQr0hkwGxqSXIKq2ROhwiIiIionbDhJg6TeNw6QH+KijkbHrGwsvJRl8RfFsCi2sRERERkelgVkKdRl9Qi8stGZ0rw6aZEBMRERGR6WBCTJ1CCKFPiMONYP4wNTWpnxcUFjKcyinHhfwKqcMhIiIiImoXTIipU2QU1yCvXA1LuQxhfiqpw6E2crGzwojurgDYS0xEREREpoMJMXWKxt7hfj5OsLaUSxwN3YyoMB8AwNaEbAghJI6GiIiIiOjWMSGmTsHh0sbvzj4esLa0QGpRNU5mlUkdDhERERHRLVNIHQCZh6ONCXGA8STElpaWWLp0qf7P5s5OqcAdvT3wS2IOfj6Rjf6+KqlDIiIiIiK6JUyIqcMVVapxqaAKADA40FniaFrPysoKy5YtkzoMgxIV6o1fEnOwPTEb/5zUG3ILmdQhERERERHdNA6Zpg4Xm1oCAOjhYQ+VrZXE0dCtGNXTDY7WCuSVq3E0pVjqcIiIiIiIbgkTYupw+vnDRrb+sE6nQ3JyMpKTk6HT6aQOxyAoFXJEhngCALYmZEkcDRERERHRrWFCTB0u7nJCHGFkBbVqamoQEhKCkJAQ1NTUSB2OwWisNr3jZC7qNPyhgIiIiIiMFxNi6lBVag2SsssBAIONrIeYWnZb1y5wc1CirKYef54rkDocIiIiIqKbxoSYOtTx9FJodQI+Khv4qGykDofagdxChsn9vAA0rElMRERERGSsmBBTh7oyf9h4qkvTjUWFeQMAdp/KQ3XpYIhlAABYqklEQVSdRuJoiKgtdDqBjOJqCCGkDoWIiEhyTIipQzUmxBwubVrC/FTwd7FFTb0Wu0/lSR0OEbVSpVqD6DVHMeKdPfjb18dQXFUndUhERESSYkJMHaZeq8Px9FIAxldQi65PJpNhamhDL/E2DpsmMgpFlWrM/fww9p8vBAD8dioPkR/8if3nWQuAiIjMFxNi6jDJ2eWoqddCZWuJbm72UodD7Wzq5WHT+84VoLSavUxEhiyjuBqzPj2ExMwyONtaYsWsUAS72SG/Qo0HvjyKN7afglqjlTpMIiKiTseEmDpMbMrl4dIBzrCwkEkcTdtZWlri2WefxbPPPgtLS0upwzE4PTwc0MvTAfVagV+TcqUOh4iu4UxuOWZ8EoNLhVXwUdlg02PDMGOQL7Y/OQL33+YPAPjiQAqiVh7EubwKiaMlIiLqXEyIqcNcKahlnMOlrayssHz5cixfvhxWVlZSh2OQGnuJt57gsGkiQ3Q0pRizPj2E/Ao1eno4YPNjwxB8ecSOjZUcb9zdD188OBhd7KxwJrcCU/5zAOtiUllwi4iIzAYTYuoQQgjEpZUAAMI5f9hkTenfkBAfTilCXnmtxNEQ0dV+S87FA18eQUWtBoMDnPH934bC08m62XHj+njg1yUjMKqHG9QaHZZuTcaCtbEoqFBLEDUREVHnYkJMHeJiQSWKq+pgbWmBEG8nqcO5KTqdDqmpqUhNTYVOp5M6HIPk52KLgf4qCMHiWkSGZGNsOhZ+cwxqjQ7jervjm4eHwMn22lM/3B2ssXZ+OJZN6QMrhQX2nC1A5Ad/4n9nWEWeiIhMGxNi6hCxqQ29w2F+KlgpjLOZ1dTUICgoCEFBQaipqZE6HIMVFeYDgAkxkSEQQmDVngv4x+aT0Alg9mBffHr/IFhbym94rkwmw7zhQdj2xO3o5emAoqo6LFgbh1d+TkJtPQtuERGRaTLOTIUMXmNBLWOdP0ytN6mfFyxkQEJmGVIKq6QOh8hs6XQCr247heW7zgIAFo0Oxtsz+kMhb9tHfU9PB2x5fDgeuj0IAPDVoTTc9Z8DSM4ua/eYiYiIpMaEmDrEUSMvqEWt5+agxPBurgDYS0wklTqNDos3nsDamFQAwCt39cHzkb0gk91chX9rSzlevqsPvloQATcHJS7kV+LuVQfx+Z+XoNOx4BYREZkOJsTU7nLKapBZUgMLGTAwwFnqcKgTTA1tKK7184ksVqcl6mSVag0eWheLbQnZUFjI8OE9YVhwuXf3Vo3s4YZdS0bizj4eqNcKvLnjNB5YfQS5ZSyiR0REpoEJMbW7xvnDfbwdYa9USBwNdYYJIZ6wUljgYkEVTuWUSx0OkdkoqlRj7ueHsf98IWyt5Fg9L1w/r7+9uNhZ4bMHBuGt6f1gYynHwQtFiPzwT+xMymnX+xAREUmBCTG1O84fNj+O1pYY29MdALCVw6aJOkVGcTVmfXoIiZllcLa1xHeP3IaRPdw65F4ymQz3Rvhj+1O3o5+PE0qr67Hwm3j8Y1MiqtSaDrknERFRZ2BCTO0u9vL84QgmxGZlaljDsOntCTmcY0jUwU7nlGPGJzG4VFgFH5UNNj02DGF+qg6/b7CbPTY/NgyPjQ6GTAZsjMvA5I/2IyGjtMPvTURE1BGYEFO7Kqupx9m8CgDAYCNPiBUKBRYtWoRFixZBoeDQ7xsZ28sd9koFskprcCy9ROpwiEzW0ZRizP7vIeRXqNHTwwGbHxuGYDf7Tru/lcIC/4jshe8evg3eTtZILarGjE9isPJ/56Hlj2FERGRkmBBTuzqWVgwhgCBXO7g5KKUO55YolUqsWrUKq1atglJp3M+lM1hbyjG+rwcAYOsJDpsm6gi/JefigS+PoKJWg8EBzvj+b0Ph6WQtSSxDg7vg18UjMbm/FzQ6gXd/O4d7PzuMzJJqSeIhIiK6GUyIqV01FtQKD2R1aXPUWG16x8kc1Gt1EkdDZFo2HE3Hwm+OQa3RYVxvD3zz8BA42VpKGpOTrSVW3jsAK2aFws5KjqOpxZj44X78fCJL0riIiIhaiwkxtavGglrGPlwaAIQQKCgoQEFBAZcSaqXh3VzhYmeFoqo6HLxQKHU4RCZBCIFVey7ghR9PQieA2YN98en9A2FtKZc6NAANBbdmDPLFr4tHYqC/ChW1GizecAJ/33gC5bX1UodHRER0XUyIqd3U1muRmFkGwDQKalVXV8Pd3R3u7u6oruYQwNawlFtgcj8vAKw2TdQedDqBV7edwvJdZwEAi0YH4+0Z/aGQG97Ht38XW3z/t6FYMq47LGTAT8ezMPGD/fpCi0RERIbI8D5RyWglZpahTquDm4MSAV1spQ6HJNJYbfrnE9n47ki6xNEQGa86jQ6LN57A2phUAMArd/XB85G9IJPJpA3sOhRyCywZ1wM/LBwKPxcbZJXWYM5/D+G9385yGgURERkkJsTUbhp7AcIDnQ36Cxt1rMEBzpg92BdancA/fzqJt3ee4TJMRG1UqdbgoXWx2JaQDYWFDB/eE4YFtwdJHVarDQpwwY6nRmD6QB/oBPDR/y5g1qeHkFpYJXVoRERETTAhpnZzJSE2/uHSdPNkMhnentEfS8Z1BwB8svcintpwHLX1WokjIzIORZVqzP38MPafL4StlRyr54UjKsxH6rDazMHaEu/NDsN/7h0AB2sFTmSUYtJH+/F9XAbrMhARkcFgQkztQqsTOKavMM2E2NzJZDIsGdcD784KhcJChu2JObj/iyMoqaqTOjQig5ZRXI2Znx5CYmYZnG0t8d0jt2FkDzepw7olU0K9sXPJSEQEuaC6TovnNyXi8e/iUVrN9wMiIpIeE2JqF2dyy1Gh1sBeqUBvL0epwyEDMXOQL75aEAEHawXi0kow/ZMYpBVxyCRRS07nlGPGJzFIKayCj8oGmx4bhjA/ldRhtQsflQ3WP3Ibno/sCYWFDDtO5iLyg/2Iuchq9EREJC0mxNQu4i73Dg8McIbcgvOH6Yph3Vyx+bFh8FHZIKWwCtM+jsGxtBKpwyIyKEdTijH7v4eQX6FGTw8HbH5sGILd7KUOq13JLWRYNLobflw0DEGudsgtr8V9XxzBWztOo07DgltERCQNJsTULo42zh8OcJY4kvajUCgQHR2N6OhoKBQKqcMxaj08HPDTomEI8XFEcVUd5n5+GL+ezJE6LCKD8FtyLh748ggqajUID3TG938bCk8na6nD6jD9fVX45anbcW+EH4QA/vvnJUz7+CAu5FdKHRoREZkhmWBli3ZVXl4OJycnlJWVwdHRPIYOCyEw5F9/IL9CjQ2P3obbunaROiQyUFVqDZ5afxx/nMmHTAb8c2JvPDwiiFXJyWxtOJqOf/50EjoBjOvtgZVzB8DaUi51WJ1mV3IuXticiJLqelhbWuClyX1w3xB/vicQEZm5zsyp2ENMtyyjuAb5FWpYymUmM9+NOoadUoHPHhyMB4cGQAjgzR2n8crPydBwfVIyM0IIrNpzAS/82JAMzx7si0/vH2hWyTAATOjriZ1LRmJEd1fU1uvw0pYkPPJVHIoq1VKHRkREZoIJMd2yxuHS/XycTOrLnBACVVVVqKqq4hIh7UhuIcOrU/vipcm9IZMBXx9Ow9++PoYqtUbq0Ig6hU4n8Oq2U1i+6ywAYNHoYLw9oz8UcvP8SPZwtMa6+RF4aXJvWMkt8PvpfEz4YD/2ns2XOjQiIjID5vnpS+0qNuXy/OEg01puqbq6Gvb29rC3t0d1dbXU4ZgUmUyGh0d0xcdzB0KpsMAfZ/Ix57NDyC+vlTo0og5Vp9Fh8cYTWBuTCgB45a4+eD6yl9kPEbawaHhP2PL4cPTwsEdhpRrz1sRi2dZkrmFOREQdigkx3bLYtIaEOILrD1MbTeznhfWP3gYXOyskZZVj2scxOJdXIXVYRB2iUq3BQ+tisS0hGwoLGT68JwwLbg+SOiyD0sfbEVufuB3zhgUCANbGpCJq5UGcyS2XNjAiIjJZTIjplhRWqnGpoGFd2UEmVGGaOs9Af2f8tGgYurraIau0BjM+jsHBC1yblExLUaUacz8/jP3nC2FrJcfqeeGICvOROiyDZG0px7KpfbFmXjhc7a1wNq8CU1cexOoDKdDpOH2FiIjal9EmxDU1NXjllVfQo0cPWFtbw9vbGwsWLEBWVlarr1FaWorvvvsO9957L4KCgmBlZQUHBwcMGTIEH374Ierr6zvwGZiGuMvzh3t6OEBlayVxNGSsArrYYfNjwxAe6IwKtQbRq49i07FMqcMiahcZxdWY+ekhJGaWwcXOCt89chtG9nCTOiyDN6aXO3YuGYk7ermjTqPDa9tPIXrNUU6tICKidmWUCXFtbS3Gjh2L119/HZWVlYiKioKfnx/WrFmDAQMG4NKlS626zrvvvov77rsPGzduhLOzM6ZPn46IiAgkJCRgyZIlGDt2LOeO3kBsagkAIDyIvcN0a5ztrPD1Q0MwJdQbGp3Asz8k4P3d51jQjIza6ZxyzPgkBimFVfBR2eCHhUNZjb8NXO2V+CJ6MF6/OwRKhQX2ny/EhA/+xG/JuVKHRkREJsIoE+I33ngDhw8fxtChQ3Hu3Dls3LgRR44cwYoVK1BQUIAFCxa06jp2dnZ4/vnnkZqaivj4eGzYsAF//PEHTp48CX9/fxw4cABvvPFGBz8b4xZ7uYc4nPOHqR1YW8rx4ZwwLBodDAD48I/zeOb7BNRpuCwTGZ+jKcWY/d9DyK9Qo6eHAzY/NgzBbvZSh2V0ZDIZHrgtAL88dTv6eDmipLoej359DP/86SSq61idnoiIbo1MGFn3S11dHdzd3VFWVob4+HgMGDCgyf7Q0FAkJiYiLi4OgwYNuun7rF+/HnPnzkVgYCBSUlJafV5nLiIttSq1Bv1f/Q1anUDMC2PhrbKROqR2VVVVBXv7hi+vlZWVsLOzkzgi87L+aDpe2pIErU5gaNcu+PSBQXCysZQ6LKJW+S05F0+sP446jQ7hgc744sFwONmy/d4qtUaLFb+dw2d/NowE6+pqhw/vGYB+vk4SR0ZERO2pM3Mqo+shPnjwIMrKyhAcHNwsGQaAmTNnAgC2bdt2S/cJDQ0FAGRnZ9/SdUzZ8fRSaHUCPiobk0uGAUAul2PmzJmYOXMm5HLTWV/ZWNwb4Y/V88JhZyXHoUtFmPlJDDKKOYWBDN+Go+lY+M0x1Gl0GNfbA18/NITJcDtRKuT456Te+PbhIfBwVOJSYRWmfXwQn+y9CC0LbhER0U0wuoQ4ISEBADBw4MAW9zduT0xMvKX7NM5D9vT0vKXrmLKj+uHSpjl/2NraGj/88AN++OEHWFtbSx2OWRrVww0/LBwGT0drnM+vxLSPY5CYWSp1WEQtEkJg1Z4LeOHHk9AJYPZgX3x6/0BYW/IHtfY2vJsrdi4eiYkhntDoBN7eeQb3fXEY2aU1UodGl1WpNYi5WIjv4zL4YyYRGTSF1AG0VXp6OgDA19e3xf2N29PS0m7pPh9++CEAICoq6pauY8oaK0yHB3H+MHWcPt6O+OnxYZi/JhZncisw57+H8Z97B2BcHw+pQyPS0+kEXtt+CmtjUgEAj48JxrPje0Imk0kbmAlztrPCx/cNxA9xmVi2LRmHLxUj8oM/8a/p/XBXf2+pwzMrQgikFVUjPr2k4ZFWijO55bi6036AvwpRod6Y3N8bbg5K6YIlIvoLo0uIKysrAQC2trYt7m+c51lRUXHT9/j000/x+++/Q6VS4YUXXrjusWq1Gmq1Wv/38vLym76vManX6nA8vRQAC2pRx/NyaqjO+/h3x/HnuQI8+nUclk7pi+hhgVKHRoQ6jQ7P/JCAbQkNU2yWTumD+cODJI7KPMhkMswO90N4kAuWbDyBhIxSPPHdcew5U4BXo/rCXml0X3OMQnWdBgkZZYhPL8Hx9BIcTy9FUVVds+O8nazh4WSNExmlOJ7e8Hht+ykMC3bF1FBvTAjxZG0IahcFFWpcyK/EAH8VR+VQm/GT4i/279+PxYsXQyaTYfXq1fD2vv6vzG+99RZeffXVTorOcCRllaGmXguVrSW6mWjVVBbVMiwO1pb4MnowXt6ShA2xGVi6NRkZxdX456TesLBgLxxJo1KtwWPfHMP+84WwlMvw7qxQRIX5SB2W2QlytcOmhUPx0R/nsWrPBWyOz0RsajHenxOGQQGmOa2nswghkF5cre/5jU8vwZncimZztq3kFgjxccRAf2cMDHDGQH9neDo1TDfKK6/F9sQcbE3IRkJGKQ5cKMSBC4V4aUsSRvV0w9RQb4zr7QEbKyYy1HqZJdXYlZyHXUm5iE0rhhCAytYSc8L98MBtAfB1brnzjOivjC4hbkxQrrU+cFVVFQDAwcGhzddOSkpCVFQU6urq8NFHH2HatGk3POfFF1/E008/rf97eXk5/Pz82nxvYxN3ef3hwQEuTEao01jKLfDW9H7wc7HF8l1n8cWBFGSW1OD9OWH8IkWdrqhSjflrY5GYWQZbKzk+vX8QRvZwkzoss2Upt8Az43tiRHc3/H3jCaQXV2P2fw/hqbHd8fiYYCjkRlc2RRLVdRokZpbpE+Dj6SUt9v56OVljoL8zBvirMDDAGX29HaFUtPw+7OFojYduD8JDtwchragK2xKysTUhG+fyKrH7VB52n8qDrZUcd/bxQFSYN27v5gYrBV8vau5iQSV2JuViZ1IuTmaVNdmnsrVEaXU9/rvvEj7/8xLG9fbAvGGBGBrchdNX6LqMLiH29/cHAGRmZra4v3F7QEBAm66bkpKC8ePHo6SkBMuWLcOTTz7ZqvOUSiWUSvObC2PqBbXIcMlkMjw+pht8nW3w3A+J2Jmci9zPD+OL6MFwtTe//4skjYziajy4+ihSCqvgYmeF1fPCEeankjosAhAR5IJfl4zAy1uS8POJbLz/+zn8eb4A788Og38X9hhdTQiBjOKaK3N/00twOqfl3t++jb2//s4YGKCCl9PNrS4R0MUOT4ztjifGdseZ3HJsPdGQHGeW1ODnE9n4+UQ2VLaWmBjihamh3hgSxB/ezZkQAsnZ5diV3JAEn8+v1O+TyRqm7UX29cSEEE94Olrjj9N5+OpQGg5cKMRvp/Lw26k8dHe3x4PDAjF9gA/sOI2CWmB06xDv2bMHY8eORXBwMC5cuNBs/+uvv45XXnkFS5cuxbJly1p1zZycHIwYMQIXL17E4sWL8cEHH9x0fOawDrFOJzDojd0oqa7Hj4uGYaC/aSbFHDJt+I6mFOORr+JQVlMPPxcbrJ0fgWATHcJPhuN0TjmiVx9FfoUaPiobfPUQ252h2nI8Cy9vSUKFWgN7pQKvRfXFtAE+ZttbVFOnRWJmKeLTS/Xzfwsrm/f+ejpaY2CA6nIPcEPvb0fOyxRC4HhGKbaeyMb2xBwUVl6pzeLhqMRd/b0xNdQb/X2dzPa1Myc6nUB8eklDT3ByLjJLrlSPt5TLMCzYFZEhnhjX2+OaBdou5FdgXUwaNsdnorpOCwBwsFZg1iA/PDg0AIGu/E5n6DozpzK6hLiurg7u7u4oKyvD8ePHERYW1mR/aGgoEhMTERcXh0GDBt3weiUlJRg1ahROnjyJ+fPn48svv7ylN1tzSIgv5Fdg3Ht/wtrSAolLJ5jssCYmxMbhYkEl5q+JRXpxNZxsLPH5g4MRwcrn1EGOphTjoXWxqKjVoKeHA756KAIejlyWzZBlFFfj7xtPIC6tYarPlFBvvHF3iMkXcxJCILPkcu9vWgni00txOqccmr/0/lrKZejr7aTv+R3o7wxv1c31/rYHjVaHw5eKsTUhC78m5aKiVqPfF9jFFlNDvTE1zBvd3Ns+NY4MV71WhyOXirEzOQe7kvNQUHHlRxFrSwuM6uGGyBBPjO3l0ab/u+W19dgUl4mvDqUitahhuqVMBozu4YboYYEY2d2NIxAMFBPiG3jppZfw5ptvYtiwYfjtt9/0icp7772HZ555BqNGjcLevXv1x69cuRIrV67EtGnT8NZbb+m3V1dXY9y4cTh06BBmz56N7777DnL5rf0Cag4J8XdH0vHPn07itq4u2PDoUKnD6TBMiI1HYaUaD6+Lw4mMUljJLbB8Vn8WNqJ2tys5F0+uP446jQ7hgc744sFwONmadlJlKjRaHT7ZexEf/HEeWp2At5M13psThtu6dpE6tHZTW6+9au5vQwJ8dU9rIw9HZZOhz329nQy2Kq9ao8W+swXYmpCN30/nobZep9/X28sRU0O9MSXUi8WTjFRtvRb7zxdiZ1Iufj+dh7Kaev0+B6UCd/R2R2SIJ0b2cIOt1a0NddbpBPadL8C6mFTsPVug3x7kaocHbgvAzMG+cLTm+7khYUJ8A7W1tRg9ejSOHDkCLy8vjBgxAmlpaThy5Ajc3Nxw+PBhdO3aVX/8smXL8OqrryI6Ohpr167Vb//73/+ODz74AHK5HHPmzIGlZcv/Ea4+50bMISH++8YT+Ol4Fp4a2w1Pj+8pdTgdhgmxcamt12LJhhPYmZwLAHhuQk8sGh3M4XXULjYcbfghUCeAcb09sHLuAINNIujajqeXYMnGE0grqoZMBjw2KhhLxvUwupFOV/f+Hr88/PlUdsu9v328nTDQX6Wv/uztZG2U74tVag1+P52HrSeyse9cQZPnOijAGVNDvTGpnxfXODZwlWoN9pzJx86kXOw5m68fzgwALnZWGN/HAxNCPDEsuMs1i7TdqtTCKnx1KA0/xGWgQt0wAsHOSo7pA30RPSyAow8MBBPiVqipqcFbb72F7777DhkZGXBxcUFkZCRef/11+Pr6Njn2WgnxvHnzsG7duhveqy3/ROaQEN/+9v+QWVKDrx+KwIjupltRtba2FjNmzAAAbN68GdbWHBZp6LQ6gbd2nMYXB1IAAPeE++H1u0NgyeqydJOEEPh470Us33UWADB7sC/+Na0fKxYbsUq1Bq9tS8b3cQ1FOPv7OuGDOWHoasDzwGvrtTiZVXa557eh9/fqIaWN3B2UTYY+h/gYbu/vrSipqsOvSbnYmpCFIykNy+0AgIUMGN7NFVNCvREZ4skePwNRUlWH3acblkfaf74QddorPf1eTtaY0NcTkSGeGBzg3KnvrVVqDX48noWvYlKbFOu6vZsroocFYmwvd8g5nFoyTIiNmKknxDllNRj61v9gIQMSl02APav1kQFaF5OKV7clQyeAkT3csGruADjwixG1kU4n8Nr2U1gbkwoAeHxMMJ4d39Moe9eouR0nc/DijydRVlMPG0s5XpnSB/eE+0n++gohkFVa01D4Kq2h8FVyC72/CgsZ+nhfve6vCj4qG8nj72y5ZbXYnpiNbQnZSMi8sgyPldwCY3q5YWqoD+7o7W6SPwwYsrzyWvyWnItfk3JxJKW4SeXywC62iAzxwsQQT4MolCaEQMzFIqyNScUfp/PQGKqvsw0euC0Ac8L9oLK1kjRGc8SE2IiZekL884ksLN5wAv18nLDtydulDofomn4/lYcn1x9HTb0WvTwdsGZ++E0vE0Lmp06jwzM/JGBbQjYAYOmUPpg/PEjiqKi95ZTV4JnvExBzsQgAML6PB/49oz9c7Drvy29tvRZJWVfW/Y1PL0F+C72/rvbKhqHPAQ3zf/v7mmbv761ILWxY4/jnhGxcuKrHz85KjvF9PTE11Bu3d3flqKEOkl5UjZ3JOdiZlIv49NIm+3p7OSLyck9wDw97yZPga8korsY3R9KwMTYDpdUNc5qtLS1wd5gPoocForeX6X23N1RMiI2YqSfEL29JwteH07BgeBBemdJH6nCIrisxsxQL1sahsFIND0clVs8LR19vJ6nDIgNXqdZg4dfHcOBCISzlMrw7K5RF2kyYTifwxYFLWL7rLOq1Au4OSqyYHdohU4KEEMguq20y9PlUdhnqtc17f3t7OTZJgH2dza/392YJIXA6pwJbExp6jrNKryzb42xriYn9GtY4jgjkGse3QgiBc3mV+uWRTueUN9k/0F+FyBBPTOjriYAuxlWHpaZOi60JWVgbk9bkeUUEuWDesECM7+PBqTMdjAmxETP1hDjygz9xJrcCn9w3EBP7eUkdToeqqqqCu7s7ACA/P59FtYxURnE15q+NxYX8SthZybHqvoEY3dNd6rDIQBVVqjF/bSwSM8tgayXHp/cPwsgeplsrga5IyirD4g3HcbGgCgDw8O1BeC6y5y0V9qmt1yI5u0zf8xufXoK88pZ6f60woLHys78K/X1VsLFi7297EKJhTdutJ7Lxy8mcJusuezlZ467+Xpga6oMQH0f+4NAKQggkZpZhZ3IudiXl4lJhlX6f3EKGIUEuiAzxxPg+nvB0Mv7aK0IIxKaWYF1MKnYm5+qHfns5WeO+If64N8IfXexZyK0jMCE2YqacEJdV1yPs9d8gBBD7f+NMvpIjq0ybjrKaeiz8+hgOXSqC3EKGN+4Owb0R/lKHRQYmo7gaD64+ipTCKrjYWWHNvHCE+qmkDos6UU2dFm/uOIVvDqcDAHp5OuCjewegh0frqs5ml9Y0Gfqc3ELvr9xCht5eDleWPvJ3hp8Le387g0arw6FLRdh6Ihs7k5uucRzkaocpod6YGuqNbu6GW2BNClqdQGxqMXYm5eK35Fxkl9Xq91nJLTCiuysmhHhiXG+PTp1u0Nlyy2rx7ZE0fHckHUVVDT+sWMktcFeoF+YNC0R/X5W0AZoYJsRGzJQT4v+dycOCtXEIcrXDnmdHSx1Oh2NCbFrqNDq88GMifozPAgAsGt1QIInD5QgATueUI3r1UeRXqOGjssFXD0Ug2ICrDlPH+v1UHp7fnIjiqjooFRb456TeeHBoQJOkVa3RIimrHMcv9/zGp5Uit7y22bW62F3u/b1c+bm/r9Mtr6lKt662Xot95wqw9UTDGsdqzZXKx328HDE1zBtTQr3hozLP2hN1Gh1iLhZiV3IufkvO0yeAAGBrJceYng1rBI/u6WZ2RSvVGi1+SczBupjUJoXcBvirMG9YICaGeBndUm6GiAmxETPlhPjfv57Bp/suYvZgX7wzM1TqcDocE2LTI4TAh3+cxwe/nwcATAn1xvKZ/VmYxswdTSnGQ+tiUVGrQS9PB6xbEAEPR+Mf6ke3Jr+iFs/9kIh95woAAGN6umH6QF+cyLjc+5tV3mT5GKCh97eXp0OTpY/8XWzZ+2vgKtUa7D6Vi60nsrH/fGGTit7hgVfWODb1obHVdRr8ea4AO5Ny8ceZ/CY96E42lhjX2wORIZ4Y0d2Vn5uXHU9vGE79y8kc/WgQNwcl5kb4474h/nDnZ8lNY0JsxEw5IZ75SQzi0kqwfGZ/zBrsJ3U4HY4JsenadCwTL2xOhEYnEB7ojM8eGAxnEx7mRde2KzkXT64/jjqNDuGBzvgiOhxONubV20HXJoTAuphU/OvXM6jT6Jrtd7GzwkB/lX7+b6gfe3+NXXFVHX5NysHWE9k4mnpljWO5hQzDu7liaqg3JvT1MJle0bKaeuw5k4+dSbnYey4ftfVX2rmbgxIT+nogsq8XhnR1YXXu68ivqMX6Ixn49kiavkq8wkKGSf28ED0sEAP9VfxhrI2YEBsxU02Ia+u16L/sN9Rpddj77GgEupp+csiE2LQdvFCIhd8cQ0WtBl1d7bBmfrjRVcGkW7PhaDr++dNJ6AQwrrcHVs4dwF4PatHZ3Aq8vv0USqrrMMBfpZ/7G9CFvb+mLKesBr8k5mBrQjYSr17jWGGBsT3dERXmjTG9jG+N48JKNXafysPOpFzEXCxsMs/d19lGvzzSQH9nTitqozqNDjuTc/FVTCri0kr020N8HBE9NBBTQr2Nrr1IhQmxETPVhPjIpSLM+eww3ByUOPrPO8ziCwATYtN3Lq8C89fEIqu0Bi52VvgiejAG+jtLHRZ1MCEEPt57Ect3nQUAzB7si39N68clNIjomlIKq7D1RDa2JmTpK5EDgL1SgfF9PTA11BvDuxnuGsfZpTX65ZHiUotx1ahwdHe31y+P1Neb1bbbS1JWGdbFpOLnhGz9CBMXOyvcE+6H+28LgLeZzk9vLSbERsxUE+JVey5g+a6zmNzPC6vuGyh1OJ2ipqYGEydOBAD8+uuvsLHhG5cpyi+vxYJ1sUjKKodSYYEP5oSZ/JJi5kynE3ht+ymsjUkFADw+pqG4Gr8AElFrCCFwKqccWxOysT0hp8kaxy52VpjUzxNTQ30wOED63tVLBZX65ZGuLv4EAP18nPRJMKtqd6ziqjpsiE3HN4fS9BW65RYyjO/jgehhgRgS5MLPoBYwITZippoQR68+in3nCrB0Sh/MHx4kdThE7apKrcFT64/jjzP5kMmA/5vUGw/dHsQPKBNTp9HhmR8SsC0hGwD4fkZEt0Snu7zGcUI2fknMaVKJ2dvJGnddXsaps3pdG5P1XZd7gs/lVer3yWRAeIALJoR4YkJfD/g623Z4PNSURqvD76fzsC4mDYcuFem39/J0wINDA3H3AG/WILgKE2IjZooJsVYnEPbqb6hQa7D9ydsR4uMkdUhE7U6j1eHVbafw9eE0AMCDQwOwdEpfyDl/yiRUqjVY+PUxHLhQCEu5DO/OCkVUmI/UYRGRidBodTh4sWGN49+Sc1GhvlKhuaubHaZeTo67tvNybjqdwPGMUuxKzsXOpFykF1fr9yksZBjWzRWRfT1xZx8PuDmYdpVsY3I2twLrDqXip/gs1NRrAQCO1grMCffDA7cFwr8Lf7BgQmzETDEhTs4uw+SPDsBeqUDC0vFMEMhkCSHw5YEUvLnjNIQAxvV2x0f3DuAvtkauqFKN+WtjkZhZBlsrOT69fxBG9nCTOiwiMlG19VrsPZuPrQnZ+ON0fpM1jkN8HDE11Bt39fe+6TmkGq0OR1KKsTMpF7uSc/VVjQFAqbDAqB5uiAzxxB29POBkaxrVsE1VWXU9fjiWga8Opel/zJDJgDt6uSN6WCBu7+ZqtqPVmBAbMVNMiNceTMGybacwsocbvloQIXU4naaqqgqBgYEAgNTUVBbVMiO/nszBko0noNbo0M/HCV9GD+ZagkYqo7gaD64+ipTCKrjYWWHNvHCE+qmkDouIzERFbT12n8rD1oSGNY61V1Wzigh0wZQwb0zu5wWXGyz9V1uvxcELhdiZlIvdp/NQWl2v3+egVGBsb3dE9vXEqJ5u/BHXCGl1AnvP5mPdoTT8eXntc6BhdEH00EDMGOQLe6V5va5MiI2YKSbEj38bj19O5uDZ8T3wxNjuUofTaVhl2rzFp5fg4XVxKK6qg4/KBmvmh6OHh4PUYVEbnM4pR/Tqo8ivUMNHZYOvH4po9+GKREStVVxVhx0nr6xx3EhuIcOI7g1rHI/v66lPfCrVGuw927BG8J4z+aiq0+rPcbGzwvg+HpgQ4olhwV2gVHApH1NxsaASXx9Kw6Zjmai8PPTeXqnAzEG+eHBogNl8jjEhNmKmlhALITDkX38gv0KNjY/ehiFdu0gdUqdhQkxpRVWYtyYWKYVVcLBW4L/3D8Kwbq5Sh0WtcORSER7+Kg4VtRr08nTAugUR8GAvPxEZiOzSGmxPzMbWhGwkZZXrtysVFrijtzvqNDr8eb5Qv1wPAHg6WusrQ4cHOnOpOBNXUVuPH+OzsO5QKi5dtdTXyB5umDcsAKN7uEteybwjMSE2YqaWEKcVVWHU8r2wlMtwctkEs1pMnAkxAUBJVR0e/ToOsaklUFjI8O8Z/TFzkK/UYdF17ErOxZPrj6NOo0N4oDO+iA6Hkw3n0RGRYbpUUImtCQ3J8dWJDwAEdrFFZIgXIkM80d/HyaQTIGqZTidw4EIh1sWk4n9n89GYufm72OLBoQGYNdjPJD/jmBAbMVNLiDcdy8SzPyRgUIAzNj82TOpwOhUTYmpUW6/Fc5sS9cv1LBnXHYvv6G62hS4M2Yaj6fjnTyehE8C43h5YOXeAWf2QR0TGSwiB5Oxy7ErOhaXcAuP7eqCnhwM/a0gvvagaXx9OxcbYDJTXNgyntrGUY9pAH0QPDURPT9OZ2sWE2IiZWkL8j02J2BiXgb+N6ooXJ/aWOpxOxYSYrqbTCbz721l8vPciAGD6QB/8e3p/WCk4ZM0QCCGwas8FvPvbOQDAnMF+eHNaCIcUEhGRyamu02DL8Wysi0nF2bwK/fahXbsgelggxvV2N/rPv87MqcyrXBm1Wezlog8RgS4SR0IkLQsLGZ6P7AVfZ1u8/HMSfozPQk5pLT59YJBJDlUyJjqdwGvbT2FtTCoA4PExwXh2fE/2qhARkUmytVJg7hB/3Bvhh8OXirEuJhW/ncrFoUtFOHSpCD4qG9x3mz/uCfe/YQVzYg9xuzOlHuLCSjUGv/E7ACDhlfFmt5ZdTU0NRo4cCQD4888/YWNzc+sFkunZezYfj38bj6o6Lbq722PN/HD4OttKHZZZqtPo8MwPCfrh7Eun9MH84UESR0VERNS5skpr8O3hNKw/mo6Sy8tyKRUWmBrqjehhgQjxcZI4wrbhkGkjZkoJ8c6kHCz8Jh49PRyw6+8jpQ6HyKCcyi7HgrWxyC2vhZuDEl9GD0Z/X5XUYZmF/PJanMwqQ2JmGfaczUdiZhks5TKsmB2GqaHeUodHREQkmdp6LbYlZGPdodQmFcwHBzgjelggIkM8YWkEw6mZEBsxU0qIX9t2CqsPpuD+2/zxxt39pA6HyODklNVg/ppYnMmtgI2lHP+5dwDG9fGQOiyTUlChRtLl5PdkVhlOZpUir1zd5BhbKzn++8AgjOjuJlGUREREhkUIgfj0EqyNScOvJ3Og0TWkfO4OStw3JABzh/jDzUEpcZTXxoTYiJlSQjx15QEkZpbhw3vCEBXmI3U4RAaporYej393HH+eK4CFDFg2tS8eHBoodVhGqahSjZNZZU0S4Jyy2mbHWciAbu726OejQj8fR9zR2wN+LhyyTkRE1JL88lp8eyQd3x5JR2Flw4/KlnIZJvfzQvSwQAzwd5Y4wuaYEBsxU0mIq9Qa9H/1N2h1AjEvjIW3yvzmz1ZXV6NPnz4AgFOnTsHWll+4qWX1Wh1e3pKEDbEZAICHbw/CPyf15nqR11FaXacf9nzycvKbVVrT7DiZDAh2s0d/HyeE+Dihv68T+ng7wtaKNSGJiIjaok6jw69JOVgbk4rj6aX67fufH2NwPyyzyjRJLj69BFqdgI/KxiyTYaBhqElaWpr+z0TXYim3wFvT+8HPxRbLd53FFwdSkFVag/fnhHENXABl1fVIym7s9S3FyawyZBQ3T34BoKubHfr5OKGfjxP6+6rQx9sR9kp+VBEREd0qK4UFosJ8EBXmg8TMUqyNSUV5jcbgkuHOxm8Z1KLY1BIAQEQQl1siag2ZTIbHx3SDr7MNnvshEb8m5SK3/DA+f3AwXO0Nd45OeyuvrUdS1pVe35NZZUgrqm7x2MAutujnq9L3/vb1cYSjtXlVsyciIpJCf18V3psdxk4fMCGma4hNaVh/eHCg4c0pIDJkUWE+8HKywSNfxeF4eimmfxyDNfPDEexmL3Vo7a6ith7J2eVN5vymFFa1eKy/iy36+Tqh/+Xe374+Tly/mYiISGIyGad3MSGmZuo0OhzPuNxDHMgeYqK2ighywY+LhmHemqNIL67GjE9i8NkDg416xEWVWoPk7PKGXt/MUiReTn5b+mHZ19kG/X0vz/n1USHExxEqW6vOD5qIiIjoBpgQUzPJ2WWordfB2dYS3dxNr1eLqDMEu9njp0XD8fC6OJzIKMX9XxzBu7NDjWKd3Jo6LU7lNC14daGgssXk10dl0zDn19dJP/fX2Y7JLxERERkHJsTUTGxqw3DpQQEuHEZBdAtc7ZVY/8htWLLxOHYl5+Gp9ceRWVKNx0YFG8z/rdp6LU7llF+Z85tZhvP5FdC1kPx6OVlf7vW9kgB3MaP50URERGR6mBBTM0dTGgtqmff8YZlMpl92yVCSFzI+NlZyfHzfILy14zS+OJCCd3aeRUZxNV6LCoGl3KJTY6mt1+JMbgVOZpbqlzw6n18JbQvZr4ej8nKPrwr9fB0R4uMEdwfrTo2XiIiIqKMxIaYmdDqBY2mNBbWMd75je7C1tUVycrLUYZAJkFvI8NJdfeDnYotXtyVj/dEMZJXW4uP7BnbYkkJqjRZncyv0vb6JmWU4l1cBTQvJr6u9Ev2vGvLcz9cJHo5MfomIiMj0MSGmJi4WVKKkuh7WlhYI8XaSOhwikxI9LBA+Khs8uf44/jxXgFmfHsLqeYPh5XRra33XaXQ4l1eh7/VNyirDmdxy1GubJ79d7KyazPft76uCh6OSoyCIiIjILDEhpiYa1x8e4OcMK0XnDuckMgfj+nhg499uw4K1cTidU45pq2Kwel44+ng7tur8eq0O5/MqcTKrVJ/8ns6pQJ1W1+xYZ1tL9PNVoZ+P4+Whz07wdrJm8ktERER0GRNiaqKxoFY41x9GdXU1wsPDAQCxsbGwtbWVOCIyFf19Vfhp0TDMXxuLC/mVmPVpDD6+fxBG9XBrcpxGq8OFgkp94puYWYZTOeWo0zRPfp1sLPXDnfv7NCx55Otsw+SXiIiI6DqYEFMTR1MuJ8RGvF5qexFC4NSpU/o/E7UnPxdbbH5sGBZ+fQyHLhVhwdpY/HNSb6hsLC8PfS7FqZxy1NY3T34drBVNljrq76OCnwuTXyIiIqK2YkJMetmlNcgqrYHcQoYB/uwhJupoTjaWWLcgAi9sTsSPx7Pw+vZTzY6xVyoQ4uOI/r4q/ZJH/i62sLBg8ktERER0q5gQk17jcOk+Xo4dVvmWiJqyUlhgxexQdHWzw4bYDHirbC4Xu2ro/Q3sYsfkl4iIiKiDMOshvSvzhzlcmqgzyWQyPDG2O54Y213qUIiIiIjMCssIk17c5QrTEUEcLk1ERERERKaPCTEBAMqq63E2rwIAMCiAPcRERERERGT6OGSaAABxacUQAujqagc3B6XU4RgEmUyGgIAA/Z+JiIiIiMi0MCEmAEDs5eHSnD98ha2tLVJTU6UOg4iIiIiIOgiHTBOAKwW1Bgdy/jAREREREZkHJsSE2notEjNLAQARQewhJiIiIiIi88CEmJCQUYp6rYCbgxL+LrZSh2MwampqEB4ejvDwcNTU1EgdDhERERERtTPOISb9cOmIQBcWj7qKTqdDXFyc/s9ERERERGRa2ENMOKovqMX5w0REREREZD6YEJs5rU4gPq0hIR7MCtNERERERGRGmBCbudM55ahUa+CgVKC3l6PU4RAREREREXUaJsRmLu7y/OGBAc6QW3D+MBERERERmQ8mxGYulvOHiYiIiIjITLHKtBkTQuDo5R7icM4fbpGrq6vUIRARERERUQdhQmzG0ourUVChhpXcAqF+KqnDMTh2dnYoKCiQOgwiIiIiIuogHDJtxo6mNPQO9/N1grWlXOJoiIiIiIiIOhcTYjMWy+HSRERERERkxpgQm7G4ywW1IoJYUKslNTU1GD16NEaPHo2amhqpwyEiIiIionbGOcRmqqBCjUuFVZDJgEH+7CFuiU6nw759+/R/JiIiIiIi08IeYjPVuP5wTw8HONlaShwNERERERFR52NCbKaurD/M3mEiIiIiIjJPTIjNVGNBrcGBnD9MRERERETmiQmxGapUa5CcXQYAiAhiDzEREREREZknJsRm6Hh6CXQC8FHZwMvJRupwiIiIiIiIJMEq02YoNqVhuDR7h2/M1tZW6hCIiIiIiKiDMCE2Qyyo1Tp2dnaoqqqSOgwiIiIiIuogHDJtZuo0OhzPaEyIWVCLiIiIiIjMFxNiM5OUXYbaeh2cbS3Rzd1e6nCIiIiIiIgkY7QJcU1NDV555RX06NED1tbW8Pb2xoIFC5CVldXma5WUlGDx4sUICAiAUqlEQEAAlixZgtLS0vYPXGJx+uWWXCCTySSOxrDV1tZi8uTJmDx5Mmpra6UOh4iIiIiI2plRJsS1tbUYO3YsXn/9dVRWViIqKgp+fn5Ys2YNBgwYgEuXLrX6WoWFhYiIiMBHH30EhUKBu+++Gw4ODvjwww8xZMgQFBcXd+Az6XxHUzhcurW0Wi127NiBHTt2QKvVSh0OERERERG1M6NMiN944w0cPnwYQ4cOxblz57Bx40YcOXIEK1asQEFBARYsWNDqay1ZsgQXLlzA9OnTcfbsWWzcuBFJSUl48sknce7cOTz99NMd+Ew6l04nEJfWkOCzoBYREREREZk7mRBCSB1EW9TV1cHd3R1lZWWIj4/HgAEDmuwPDQ1FYmIi4uLiMGjQoOteKycnB76+vlAoFEhPT4eHh4d+n1qthp+fH4qLi5GdnQ13d/dWxVdeXg4nJyeUlZXB0dGx7U+wA53Pq8Cd7/8Ja0sLnFw2AZZyo/w9pNNUVVXB3r5hnnVlZSXs7OwkjoiIiIiIyPR1Zk5ldBnRwYMHUVZWhuDg4GbJMADMnDkTALBt27YbXmvnzp3Q6XQYMWJEk2QYAJRKJaZMmaIfNmsKjl6ePzzAz5nJMBERERERmT2jy4oSEhIAAAMHDmxxf+P2xMTETr2WMYhNuTxcOojDpYmIiIiIiIwuIU5PTwcA+Pr6tri/cXtaWlqnXssYxKY2FNSK4PxhIiIiIiIiKKQOoK0qKysBALa2ti3ub5znWVFR0SnXUqvVUKvV+r+Xl5ff8L5SyC6tQVZpDeQWMgzwV0kdDhERERERkeSMrofY0Lz11ltwcnLSP/z8/KQOqUXVdRqM7eWOoV27wE5pdL+DSMLOzg5CCAghWFCLiIiIiMgEGV1C3Fj1t7q6usX9VVVVAAAHB4dOudaLL76IsrIy/SMjI+OG95VCN3cHrJ4Xjm8eHiJ1KERERERERAbB6LoK/f39AQCZmZkt7m/cHhAQ0CnXUiqVUCqVN7wXERERERERGRaj6yEODQ0FAMTHx7e4v3F7//79O/VaREREREREZFyMLiEePnw4nJyccPHiRZw4caLZ/k2bNgEApkyZcsNrRUZGwsLCAvv370d+fn6TfWq1Gtu2bYNcLsekSZPaJXYiIiIiIiIyHEaXEFtZWeGJJ54AADz++OP6eb4A8N577yExMRGjRo3CoEGD9NtXrlyJXr164cUXX2xyLS8vL9x7772oq6vDokWLoNFo9Puef/55FBQU4P7774e7u3sHPysiIiIiIiLqbEY3hxgAXnrpJfz++++IiYlB9+7dMWLECKSlpeHIkSNwc3PD6tWrmxxfWFiIs2fPIicnp9m1PvjgAxw+fBibN29Gr169MHjwYCQnJyMpKQndu3fHe++911lPi4iIiIiIiDqR0fUQA4C1tTX27NmDl19+Gba2ttiyZQvS0tIwb948xMfHo2vXrq2+lqurK44ePYonn3wSdXV1+Omnn1BWVoannnoKR48ehYuLSwc+EyIiIiIiIpKKTAghpA7ClJSXl8PJyQllZWVwdHSUOhwiIiIiIiKj0pk5lVH2EBMRERERERHdKibEREREREREZJaYEBMREREREZFZYkJMREREREREZokJMREREREREZklJsRERERERERklpgQExERERERkVliQkxERERERERmSSF1AKZGCAGgYTFpIiIiIiIiapvGXKoxt+pITIjbWUVFBQDAz89P4kiIiIiIiIiMV0VFBZycnDr0HjLRGWm3GdHpdMjOzoaDgwNkMpnU4TRRXl4OPz8/ZGRkwNHRUepwSCJsBwSwHdAVbAsEsB1QA7YDAgyjHQghUFFRAW9vb1hYdOwsX/YQtzMLCwv4+vpKHcZ1OTo68k2O2A4IANsBXcG2QADbATVgOyBA+nbQ0T3DjVhUi4iIiIiIiMwSE2IiIiIiIiIyS0yIzYhSqcTSpUuhVCqlDoUkxHZAANsBXcG2QADbATVgOyDA/NoBi2oRERERERGRWWIPMREREREREZklJsRERERERERklpgQm4Gamhq88sor6NGjB6ytreHt7Y0FCxYgKytL6tCoBdXV1diyZQseeugh9OzZE9bW1rCzs0NoaChee+01VFZWXvPctWvXIiIiAvb29nBxccGkSZMQExNz3fsdPHgQkyZNgouLC+zt7REREYGvvvrquudkZmZi/vz58Pb2hrW1NXr06IGlS5eitrb2pp4ztU5RURHc3d0hk8nQrVu36x7LtmB6CgoK8Oyzz6Jnz56wsbGBi4sLBg4ciOeee67F47dt24ZRo0bpl80YPXo0fvnll+veIzk5GbNmzYKbmxtsbGzQr18/fPDBB9DpdNc8p6SkBIsXL0ZAQACUSiUCAgKwZMkSlJaW3srTpWuIjY3F7Nmz4e3tDUtLS6hUKowYMQJr1qxBS7PgtFot3n//ffTr1w82NjZwc3PD7Nmzcfr06evep7PaD7Xs2LFj+Pe//43p06fD19cXMpkMMpnshucZ8ns/v4+2XVvagU6nw/79+/H8889j0KBBcHBwgFKpRHBwMBYuXIiUlJTr3sus24Egk1ZTUyNuu+02AUB4eXmJ2bNni4iICAFAuLm5iYsXL0odIv3F559/LgAIAKJ3795i1qxZYsKECcLBwUEAEL169RJ5eXnNzlu8eLEAIGxsbERUVJSYMGGCUCgUQi6Xi59++qnFe23atEnI5XIhk8nEqFGjxIwZM4RKpRIAxDPPPNPiOefPnxeurq4CgAgJCRGzZ88WXbt2FQDE8OHDRW1tbXv+c9BVoqOjhUwmEwBEcHDwNY9jWzA9cXFxokuXLgKA6Nu3r5gzZ46YOHGiCAgIEHK5vNnx77//vgAgFAqFiIyMFFFRUcLGxkYAEP/5z39avEdMTIz+mIiICDF79mzh6ekpAIhZs2YJnU7X7JyCggLRrVs3AUB07dpVzJ49W/Tt21cAED169BBFRUXt/m9hzhr/nwIQAwcOFLNnzxZjxowRCoVCABBz585tcrxWqxXTpk0TAIRKpRIzZswQo0aNEjKZTNja2oojR460eJ/Oaj90bVFRUfrvAlc/rseQ3/v5ffTmtKUdnD9/Xr/f09NTTJ06VUybNk34+PgIAMLBwUHs37+/xXPNvR0wITZx//d//ycAiKFDh4qKigr99hUrVggAYtSoUdIFRy1au3atePTRR8WpU6eabM/OzhYDBgwQAMS9997bZN/u3bsFANGlSxdx7tw5/faYmBhhZWUlVCqVKCkpaXJOUVGRcHR0FADE5s2b9dtzc3P1X3D37NnTLL7hw4cLAOKpp57Sb6uvr9d/6Vq6dOnNP3m6pt9//10AEI8++uh1E2K2BdOTn58vXF1dha2trfj555+b7f9rUnPmzBkhl8uFUqkUMTEx+u1nz54VXbp0EQqFQpw/f77JOXV1dSIoKEgAEO+9955+e0VFhRg6dKgAINasWdPs3vfdd58AIKZPny7q6+v125988kkBQERHR9/ks6a/qq+vF+7u7gKA+Pbbb5vsO3XqlHBxcREAxP/+9z/99sYfWLt37y5yc3P12zdt2iQAiG7dujV53YTo3PZD1/bvf/9bvPzyy2Lr1q0iJydHKJXK6ybEhv7ez++jN6ct7eDChQvizjvvFH/88UeTH6Bqa2vFvHnzBADh7+8v6urqmpzHdsCE2KSp1Wrh5OQkAIj4+Phm+/v37y8AiLi4OAmio5sRExMjAAilUinUarV++8SJEwUA8f777zc756mnnhIAxLvvvttk+9tvvy0AiKioqGbn/PjjjwKAuOuuu5psP3LkiAAg3N3dm/3yl5ubKywtLYWzs3OzL1h0a6qrq0VwcLDo06ePOHfu3HUTYrYF0/PYY48JAGLVqlVtOn7x4sXN9r333nsCgHjiiSeabN+4caMAIEJDQ5udc+zYMX0PwNWys7OFhYWFsLKyapJsCdHwBczNzU3I5fIWR7RQ2508eVIAED179mxxf+P/77ffflu/rXfv3gJAi72DU6dOFQDEpk2bmmzvrPZDbXOjhNiQ3/v5fbT93KgdXEt1dbX+Ndi7d2+TfWwHQnAOsQk7ePAgysrKEBwcjAEDBjTbP3PmTAAN84TIOISGhgIA1Go1ioqKADTMxfjf//4H4MprerVrvc6Nc8FaOmfy5MmwtrbG77//3mQeSOM5U6ZMabY2nYeHB0aMGIGSkhIcOHDgpp4ftezVV1/FpUuX8Omnn8LS0vKax7EtmJ6amhp88803sLOzw/z581t1zvVez5tpAwMHDkTXrl2RlJSE1NRU/fadO3dCp9NhxIgR8PDwaHKOUqnElClToNVqsWPHjlbFTdfX2vVAu3TpAgBISUnB6dOnYWNjg8mTJzc77mbaQnu2H2o/hv7ez++j0rOxsUGPHj0AANnZ2U32sR2wqJZJS0hIANDwYdSSxu2JiYmdFhPdmkuXLgEALC0t4eLiAgA4e/Ys1Go13Nzc4Ovr2+yca73O12sfVlZWCAkJQW1tLc6dO9eqc653L7p5iYmJWLFiBebPn48RI0Zc91i2BdMTFxeHiooKDBgwADY2Nvj111/x9NNPY9GiRfjggw+afbEpLS1Feno6ALT4hcPPzw+urq5IS0tDeXm5fvvNvJ5sA52ra9euCA4OxtmzZ/Hdd9812Xf69Gl88803cHZ2xrRp0wBceX1CQkJa/CGtpdenM9sPtR9Df+9n+5CeTqdDWloaAMDT07PJPrYDJsQmrfFDraU3x6u3N/4HIcP34YcfAgAiIyP1v8jd6HW2s7ODSqVCSUkJKioqAADl5eUoKyu77nkttQ+2qc6l0+nw8MMPQ6VS4Z133rnh8WwLpufUqVMAAHd3d9x9992YNGkS3n//fXzyySf4+9//jm7dumH9+vX64xtfF2dnZ9jZ2bV4zfZ6PdkGOpdcLse6deugUqlw3333YdCgQbjnnnswduxY9O/fH76+vvjjjz/0P5beymvaGe2H2o+hv/ezfUhv/fr1yM/Ph5ubG4YNG6bfznbQgAmxCWtcnsfW1rbF/Y0fdo1vjmTYduzYgS+//BKWlpZ4/fXX9dtv9DoDzV/rq5duakv7YJvqXP/5z38QGxuL5cuX64dBXg/bgukpKSkBAGzduhU7d+7EqlWrkJ+fj9TUVDz77LOoqalBdHQ0Tpw4AeDm2kBrzmMbMAzDhw/Hvn370LVrV8THx2Pjxo3Ys2cPLCwscOedd6Jr1676YzviNb3Z89gWOpahv/ezfUgrIyMDS5YsAQC89tprTYY4sx00YEJMZATOnDmD+++/H0IILF++XD+XmExXeno6XnrpJYwaNQrz5s2TOhySSOP6rRqNBq+99hoWLVoENzc3BAQEYPny5Zg1axbq6+uxfPlyiSOlzrB+/XpERETAz88PR44cQWVlJc6dO4d58+ZhxYoVGDt2LNRqtdRhEpGBqKqqwvTp01FYWIi7774bCxculDokg8SE2ITZ29sDAKqrq1vcX1VVBQBwcHDotJio7bKyshAZGYmSkhI8/fTTWLx4cZP9N3qdgeavdeM51zuvpfbBNtV5Hn/8cdTV1eHTTz9t9TlsC6bn6tenpaJajdv27dvX5Pi2tIHWnMc2IL3z588jOjoarq6u2L59OyIiImBnZ4fu3bvjv//9L+666y7Ex8dj9erVADrmNb3Z89gWOpahv/ezfUijvr4es2bNQlxcHG6//fZmtQcAtoNGTIhNmL+/PwAgMzOzxf2N2wMCAjotJmqb4uJijB8/HmlpaZg/fz7efffdZsfc6HWuqqpCaWkpnJ2d9W8yjo6OcHJyuu55LbUPtqnOs337dtja2mLhwoUYPXq0/nHPPfcAaPihpHFbbm4uALYFU9T472draws3N7dm+wMDAwEA+fn5AK68LiUlJfovF3/VXq8n20Dn2rBhA+rr6xEZGdnkS2yj2bNnAwD+/PNPALf2mnZG+6H2Y+jv/WwfnU+n0yE6Ohq//vorwsLCsG3bNtjY2DQ7ju2gARNiE9Y4rDY+Pr7F/Y3b+/fv32kxUetVVlZi4sSJOHXqFKZPn47PP/8cMpms2XE9e/aEUqlEQUEBsrKymu2/1ut8vfZRX1+PpKQkWFtb68v03+ic692Lbk5paSn27dvX5HHkyBEAQG1trX5b41IIbAump7HSb01NTYtDYYuLiwFc+eVdpVLpv3QcP3682fEZGRkoLCxEQEAAHB0d9dtv5vVkG+hcjV8WG7+8/lXj9sZ5542vT1JSEurr65sd39Lr05nth9qPob/3s310vieffBLr169Hjx49sGvXLqhUqmsey3bAhNikDR8+HE5OTrh48aK+4MrVNm3aBKBhDTEyLGq1GlFRUTh69CgmTJiA9evXQy6Xt3isjY0Nxo4dCwD44Ycfmu2/1uvcuC5l4/6rbd++HbW1tRg3bhysra2bnbNt27ZmX87z8vKwf/9+ODs7Y/jw4a19qnQNQogWHykpKQCA4OBg/bbGXkK2BdPj7++P0NBQCCH0w6Kv1rjt6iVyrvd63kwbOH78OC5duoSQkBB9WwMaqt1bWFhg//79+h7qRmq1Gtu2bYNcLsekSZNa81TpBhqXSomLi2txf2xsLIArowaCgoLQu3dv1NTU6NcMvdrNtIX2bD/Ufgz9vZ/fRzvXSy+9hI8//hj+/v7YvXs33N3dr3s82wEAQSbt//7v/wQAMWzYMFFZWanfvmLFCgFAjBo1SrrgqEUajUZMmzZNABAjRowQVVVVNzxn9+7dAoDo0qWLOHfunH57TEyMUCqVQqVSiZKSkibnFBUVCUdHRwFAbN68Wb89Ly9PdOvWTQAQe/bsaXav4cOHCwBi8eLF+m319fVi+vTpAoBYunRpW58ytUFKSooAIIKDg1vcz7Zger799lsBQPTr109kZ2frtx8/fly4uLgIAOL777/Xbz9z5oyQy+VCqVSKQ4cO6befO3dOdOnSRSgUCnH+/Pkm96irqxNBQUECgHjvvff02ysrK8XQoUMFALFmzZpmsd13330CgJgxY4aor6/Xb3/qqacEABEdHd0O/wIkhBDHjh0TAAQA8fHHHzfZd+jQIWFnZycAiN27d+u3f/755wKA6N69u8jLy9Nv37x5swAgunXr1uR1E6Jz2w+1nlKpFNf72m7o7/38Pto+btQO3nvvPQFAeHp6NmkH18N20NC7QCaspqZGDBkyRAAQXl5eYvbs2fq/u7m5iYsXL0odIv3FBx98oP/SM23aNBEdHd3io6CgoMl5ixcvFgCEra2tiIqKEhMnThQKhULI5XLx008/tXivTZs2CQsLCyGTycSYMWPEzJkzhUqlEgDE008/3eI5jV+KGr+gz5kzR3Tt2lX/BldbW9ve/yR0lRslxEKwLZii6OhoAUCoVCoxadIkMWbMGP0Xo0ceeaTZ8Y1fihQKhZg4caKIiooSNjY2AoD46KOPWrzHwYMH9ccMGTJEzJ49W3h5eQkAYubMmUKn0zU7p6CgQAQHB+vb5Jw5c0RISIg+CSsqKmr3fwtz9uyzz+o/H/r27StmzZolhg8fLiwsLAQA8eijjzY5XqvV6n9gdXZ2FjNnzhSjR48WMplM2NjYiMOHD7d4n85qP3Rt27dvF0OGDNE/ZDKZ/t+28bF9+/Ym5xjyez+/j96ctrSD48eP6/cPHTr0mt8f9+/f3+w+5t4OmBCbgerqavHyyy+L4OBgYWVlJTw9PcW8efNERkaG1KFRC5YuXar/wnO9R0pKSrNz16xZIwYNGiRsbW2FSqUSkZGR4uDBg9e934EDB0RkZKRQqVTC1tZWDB48WKxdu/a656Snp4t58+YJT09PYWVlJbp16yZefvllUVNTcytPnVqhNQmxEGwLpkan04nPPvtM/5ra2dmJoUOHXvf12bp1qxgxYoSwt7cX9vb2YsSIEWLbtm3XvU9SUpKYMWOG6NKli7C2thZ9+/YV7733ntBqtdc8p6ioSDz55JPCz89PWFlZCT8/P/HUU081642i9vHjjz+K8ePH63trnZ2dxZgxY8R3333X4vEajUasWLFC9O3bV1hbW4suXbqImTNniuTk5Ovep7PaD7VszZo1N/we0FKvuyG/9/P7aNu1pR3s2bOnVd8frzVaw5zbgUwIIUBERERERERkZlhUi4iIiIiIiMwSE2IiIiIiIiIyS0yIiYiIiIiIyCwxISYiIiIiIiKzxISYiIiIiIiIzBITYiIiIiIiIjJLTIiJiIiIiIjILDEhJiIiIiIiIrPEhJiIiIiIiIjMEhNiIiIyejKZ7LqP0aNHSx0itcGFCxdgZWWF5557rsn2ZcuWQSaTYdmyZdc9PzAwEDKZDKmpqTd1/5ycHNjY2GDRokU3dT4RERkPhdQBEBERtZfo6OgWt/fq1auTI6Fb8eKLL8LKygrPP/+8JPf38vLCo48+io8//hhLlixBjx49JImDiIg6HhNiIiIyGWvXrpU6BLpF8fHx2LRpE5566im4ublJFsfzzz+PlStX4uWXX8bGjRsli4OIiDoWh0wTERGRwfjkk08AAA8++KCkcfj4+GDMmDH46aefkJeXJ2ksRETUcZgQExGR2Zg3bx5kMhn27t2LXbt2YcyYMVCpVJDJZCgtLdUft3PnTkyePBlubm5QKpXo2rUrnn76aRQVFbV43eLiYjzxxBPw9vaGtbU1+vTpgw8//BBCCMhkMgQGBjY5vnEu7LV6tBvnwLbk9OnTmDdvHvz8/KBUKuHh4YF77rkHycnJzY5du3atfs5teno65s6dCzc3N9jY2GDw4MHYtm3bNf+tTp8+jYceegiBgYFQKpVwd3fH8OHD8e6770Kj0QAAQkJCIJPJcPbs2RavkZGRAblcjqCgIAghrnmvRpWVldiwYQO6d++OQYMG3fD4thg9evQN55r/dc7x3LlzUV9fz5EHREQmjAkxERGZne+++w4TJ05EVVUVJk6ciPDwcH0C+sILL2DixIn4/fff0bNnT0ydOhUKhQLvv/8+hgwZ0qy3sKSkBLfffjtWrVoFIQSioqLg4+ODZ599Fk899VS7xr1lyxYMGDAA69atg6urK6ZOnYqgoCB8//33iIiIwJ9//tnieampqQgPD8fRo0dxxx13YMCAATh27Bjuvvtu/Pbbb82O/+GHHzBgwACsXr0atra2mDZtGgYNGoSMjAw899xzqKysBAD87W9/AwB88cUXLd539erV0Ol0ePjhh6+Z4F9t3759qKys7JAiaJGRkYiOjm72mDFjhv4YuVze5JzGOH755Zd2j4eIiAyEICIiMnIARGs+0qKjo/XHbtiwodn+77//XgAQISEh4vz58/rtOp1OvPLKKwKAmDNnTpNzFi5cKACIyMhIUVVVpd9+5MgRYW9vLwCIgICAJucsXbpUABBr1qxpMc6AgIBmzyclJUXY2dkJe3t7sXv37ib7fv31V2FpaSn8/PyEWq3Wb1+zZo3++T7zzDNCq9Xq973//vsCgBgxYkSTa507d05YW1sLhUIhvv322yb7dDqd2LVrl6itrRVCCFFaWipsbW2Fm5tbk/sKIYRWqxX+/v5CLpeLrKysFp/nX/3jH/8QAMRnn33W4v7Gf7elS5de9zqN/34pKSnXPU6n04m7775bABCLFi1q8RhXV1ehVCpFTU1Na54CEREZGfYQExGRyWjtUNjJkydjzpw5zc5/8803AQDr169Ht27dmlx32bJlCAsLw6ZNm1BYWAgAqKqqwrp162BhYYGVK1fC1tZWf05ERAQef/zxdntuH3zwAaqqqvDWW29h3LhxTfZFRkbiscceQ0ZGRou9mUFBQfjXv/4FC4srH/tPPPEEnJ2dcfjwYdTV1em3v//++6itrcXDDz+MuXPnNrmOTCbD+PHjoVQqAQBOTk645557UFBQgJ9//rnJsb/99hvS09MxefJkeHt7t+o5JiYmAgB69ux53eNeffXV6w59TktLa9X9Xn75ZWzZsgVjxozBhx9+2OIxPXv2hFqtxunTp1t1TSIiMi6sMk1ERCbjWssu2dvbN/n71KlTmx2Tn5+PhIQEdO/eHSEhIc32y2QyDB8+HCdOnMCxY8cwYcIEHDt2DDU1NYiIiEBwcHCzc+699168/fbbN/lsmmoc2jx9+vQW948YMQIfffQRjh49imnTpjXZN3r0aFhZWTXZplAoEBQUhPj4eBQVFcHLywsA8PvvvwO4Mhz6RhYuXIjVq1fj888/x6xZs/TbP//8cwDAo48+2qrrAA2vAQA4Oztf97jQ0FCEhYVdc/+mTZtQVVV13Wts2LABb775JoKDg7Fp0yYoFC1/JXJxcQEAFBQUXPd6RERknJgQExGRyWht8SN/f/9m2xp7kc+fP3/D+a6NPcTZ2dkAgICAgBaP+2sxrVvRGJ+Pj0+rYruar69vi8c6ODgAANRqtX5bRkYGALSY4LckPDwcAwcOxO+//46UlBQEBQUhLy8P27Ztg6+vLyIjI1t1HQAoKytrEte13H333Vi2bNk19+/du/e6CfGxY8ewYMECODg4YOvWrfqktyWOjo4A0KToGhERmQ4mxEREZHasra2bbdPpdAAAT09PTJgw4brnXysBbi+NsbS07Vq94I2GDBnSbNvVQ6U7wsKFC/Hoo4/iyy+/xBtvvIF169ahvr4eCxYsaFao6nqcnJwAABUVFR0VKnJzcxEVFQW1Wo3vv/8effr0ue7xjUm6SqXqsJiIiEg6TIiJiIhwpRfV1dW11T3NjcOMrzVn9VrbG4cvN1ZrvppWq0Vubm6L8V28eBErVqxAly5dWhXfzfDz88P58+dx8eLF6w5LvtrcuXPx7LPPYs2aNVi2bBm++OILWFhY4KGHHmrTvd3d3QE0LGPVEdRqNe6++25kZWXh7bffxl133XXDc0pKSgAAbm5uHRITERFJi0W1iIiI0JBw9urVC6dOncK5c+dadc6gQYNgY2ODY8eO4dKlS832b9iwocXzGhPplu6zZ88e1NfXN9t+5513AgB++umnVsV2sxoLdn322WetPsfOzg73338/srOz8fzzz+P8+fOYMGFCi0PTryc0NBQArrmu8a165JFHcOTIEdx///14/vnnW3XOmTNnoFQq0bt37w6JiYiIpMWEmIiI6LKXX34ZOp0OM2bMwIkTJ5rtLyoq0heLAhqKdT3wwAPQarV48sknUVNTo98XFxeHlStXtnifkSNHAgC++eabJhWwU1JSrrl28TPPPAMbGxs8++yz+PHHH5vtV6vV2LRpEzIzM1vzVK9pyZIlsLa2xueff46NGzc22SeEwO7du5vMOW60cOFCAA1VqoGG5LOtRowYAQCIjY1t87k38s477+Drr7/GkCFDmryG13Px4kUUFRUhIiKixWH2RERk/DhkmoiI6LK5c+ciOTkZ//rXvzBo0CCEhYUhODgYQghcvHgRiYmJsLe3b5LsvfXWW9i3bx927NiB4OBgjBw5EiUlJfjf//6Hv/3tb1i1alWz+wQHB+PBBx/EV199hbCwMIwcORLV1dU4fPgwJk2ahOrq6mbDrbt164b169dj7ty5mDFjBrp164bevXvDzs4OWVlZiI+PR1VVFY4fP37NIlqt0aNHD6xZswYPPvgg7rnnHrz22mvo378/ysrKkJSUhIyMDJSUlOiXXmrUr18/DBs2DDExMfD09MSUKVPafO+RI0fC3t4ee/fuven4r+XFF18E0FDBujF5/6t3330Xrq6u+r83xjF58uR2j4eIiAwDe4iJiIiu8uabb2Lfvn2YMWMGcnNzsWXLFuzZswdarRaPPfYYtm7d2uR4FxcXHDx4EI899hiEENiyZQvS09Px73//G//5z3+ueZ/PP/8cL7zwAhwdHbFr1y6kpqbixRdfxPr16695TlRUFBITE7Fo0SLIZDLs3r0bv/zyC/Lz8zFlypRWFYlqjXvuuQdxcXG4//77UVZWhs2bN+PYsWPw9/fHihUrmi1j1Wjs2LEAgPnz519zGaPrsbe3x7333osLFy60ey9xY1GynTt3Yt26dS0+/jqn+7vvvoOlpSXmzZvXrrEQEZHhkAkhhNRBEBERmSqZTIaAgIAmQ6NNkRACvXv3xrlz53DhwgV07dr1pq5z4sQJDBgwAE888cR1f1DoaJmZmQgICMDMmTObDR0nIiLTwR5iIiIiumWbNm3C2bNnMWnSpJtOhgEgLCwMs2bNwurVq5Gfn9+OEbbN8uXLYWFhgddee02yGIiIqOMxISYiIqKb9vDDD2PmzJl44IEHIJfL8eqrr97yNd966y3U19fjnXfeaYcI2y4nJwefffYZHnnkEfTs2VOSGIiIqHNwyDQREVEHMvUh0zKZDAqFAt27d8drr72GmTNnSh0SERFRqzEhJiIiIiIiIrPEIdNERERERERklpgQExERERERkVliQkxERERERERmiQkxERERERERmSUmxERERERERGSWmBATERERERGRWWJCTERERERERGaJCTERERERERGZJSbEREREREREZJb+Hxv+OVQhrqthAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240330032940_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            # guess = [2, 30, (-1)**(i//2+1)*0.1, (-1)**(i//2+1) + 0.5, 0.9-i*np.pi]\n",
    "            # # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            # est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            # #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            if(i==3): \n",
    "                q1, q2, q3 = np.polyfit(x, pops[i], 2)\n",
    "                optimum_duration = fine[np.argmax(q1*fine**2 + q2*fine + q3)]\n",
    "                optimum_duration = abs(q2/q1/2)\n",
    "                print(q1, q2, q3)\n",
    "                ax[1].plot(fine, q1*fine**2 + q2*fine + q3, color = colors[i])\n",
    "            # ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "\n",
    "try: \n",
    "    plt_label = \"$\\Delta T_{\\pi} = %.3f$ ms \"%(optimum_duration)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin_rabi.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8aa7f9c-4a1e-45ee-bf4e-55860ddf573b",
   "metadata": {},
   "source": [
    "### Rabi on b CNOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cd1874f-04c1-4b3c-8295-8e342fc4f730",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-10e3)\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.73e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.44e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(1.2e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,10,21))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_cnot, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot, detuned_sideband_amplitude_cnot, duration_set, ramp_time_cnot)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4721845a-9dad-4a1b-96c1-534f32ee8049",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*0.75)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "state_to_fit = 2\n",
    "threshold = 80\n",
    "\n",
    "\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,3]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "#p_data = (data[:,:,0]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,0.5,1,0.5*(1-(-1)**(i))*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            if plot_guess: ax[1].plot(fine+0.04*i,rabi_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "try:\n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi          = fine[np.argmin(data_fit)]\n",
    "    T_pio2        = T_pi-T_half_period/2\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except:pass\n",
    "#ax[1].set_ylim(0.95,0.97)\n",
    "#ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "# bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            # 'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313f14c6-dbb8-4b88-86e3-db51f4779635",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-29T06:31:45.363684Z",
     "iopub.status.busy": "2024-02-29T06:31:45.363684Z",
     "iopub.status.idle": "2024-02-29T06:31:45.556661Z",
     "shell.execute_reply": "2024-02-29T06:31:45.556661Z",
     "shell.execute_reply.started": "2024-02-29T06:31:45.363684Z"
    }
   },
   "source": [
    "### Rabi on b (pi/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4f6c8d7-91c7-46cc-9fb3-a7d65f1d1010",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "815c854f-ace0-4ffb-abea-5b32b13a3cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2+32.5e3)\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(318e6//4) # in ns \n",
    "raman_pi_duration_b = int(4.94e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(1.2e6/4)\n",
    "# raman_pulse_durations = (1.1e6+ sinhspace(-0.1e6,0.1e6,6,nonlinearity=2))//4\n",
    "# raman_pulse_durations = (1e6*np.linspace(1,1.5,6))//4\n",
    "raman_pulse_durations = (1.874e6+np.linspace(-150,150,6)*1e3)//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=150\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq,pump_steps = 50, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = 50, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f7eac9f-a482-42b6-9ee6-68c6270bb7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "#ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            vals = np.polyfit(x, pops[i], 1)\n",
    "            ax[1].plot(x,np.polyval(vals, x),\"--\", color = colors[i], alpha=0.5)\n",
    "            #ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        # ax[1].plot(x,pops[i],\"o-\", label = labels[i], color = colors[i])\n",
    "        ax[1].errorbar(x, pops[i], np.sqrt(0.25/len(data)), label = labels[i], color = colors[i],fmt = \"o-\") \n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0.4,0.6)\n",
    "ax[1].set_xlim(0.99*x[0],1.01*x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "#ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = pops[-1]#(data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean(0)))\n",
    "\n",
    "# fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9bdf541-bbe9-474d-b69f-7f4f18462953",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "piece_size = 600\n",
    "pieces = len(data)//piece_size\n",
    "data_vals = []\n",
    "for i in range(pieces):\n",
    "        data_vals.append(data[i*piece_size:(i+1)*piece_size])\n",
    "pieced_data= []\n",
    "for k in range(len(data_vals)):\n",
    "    data_k = data_vals[k]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_k, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    pieced_data.append(pops)\n",
    "    \n",
    "for pops in pieced_data[:]:\n",
    "    for i in range(4):\n",
    "        plt.plot(x,pops[i]+i*0,\"o--\", label = labels[i], color = colors[i])\n",
    "plt.ylim(0.4,0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62b17d3-10be-4749-a12b-8c97da4d72a6",
   "metadata": {},
   "source": [
    "### 16 pi/2 B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2002,
   "id": "54b3a827-e716-417f-83e4-5b68e5619785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T04:30:53.983418Z",
     "iopub.status.busy": "2024-03-30T04:30:53.983418Z",
     "iopub.status.idle": "2024-03-30T04:30:57.437164Z",
     "shell.execute_reply": "2024-03-30T04:30:57.435157Z",
     "shell.execute_reply.started": "2024-03-30T04:30:53.983418Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-62500, -56250, -50000, -43750, -37500, -31250, -25000, -18750, -12500, -6250, 0, 6250, 12500, 18750, 25000, 31250, 37500, 43750, 50000, 56250, 62500]\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "#raman_pulse_durations = (1e3+1e6*np.sinhspace(1, 5.4, 15, 5))//4 \n",
    "raman_pulse_durations = np.linspace(-0.25e6,0.25e6,21)//4 \n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=250\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    kk = declare(int)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    delta_duration_shift  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(delta_duration_shift, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_a_prep, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "            with for_(kk, 0, kk < 16, kk + 1):\n",
    "                Pauli_swept( 'bX90', delta_freq, pulse_duration_adj=delta_duration_shift)\n",
    "                align()\n",
    "                wait(int(1000e3/4))\n",
    "                align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #play('ON',fsv_trigger)\n",
    "            align()\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2003,
   "id": "5e199d29-e43a-434b-8df9-d5b3ca8adb76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T04:30:57.439161Z",
     "iopub.status.busy": "2024-03-30T04:30:57.438166Z",
     "iopub.status.idle": "2024-03-30T04:36:02.226669Z",
     "shell.execute_reply": "2024-03-30T04:36:02.224662Z",
     "shell.execute_reply.started": "2024-03-30T04:30:57.439161Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 300 s                                                        \n",
      "\n",
      "(7, 21, 4)\n",
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n",
      "-5.566711810582362 0.6753246753246752 0.6717881660673422\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240330053054_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(300)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            # guess = [2, 30, (-1)**(i//2+1)*0.1, (-1)**(i//2+1) + 0.5, 0.9-i*np.pi]\n",
    "            # # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            # est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            # #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            if(i==3): \n",
    "                q1, q2, q3 = np.polyfit(x, pops[i], 2)\n",
    "                optimum_duration = fine[np.argmax(q1*fine**2 + q2*fine + q3)]\n",
    "                optimum_duration = abs(q2/q1/2)\n",
    "                print(q1, q2, q3)\n",
    "                ax[1].plot(fine, q1*fine**2 + q2*fine + q3, color = colors[i])\n",
    "            # ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "\n",
    "try: \n",
    "    plt_label = \"$\\Delta T_{\\pi} = %.3f$ ms \"%(optimum_duration)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin_rabi.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2004,
   "id": "544d8e21-687d-4415-809e-de876f6a71c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-30T04:36:02.228665Z",
     "iopub.status.busy": "2024-03-30T04:36:02.227669Z",
     "iopub.status.idle": "2024-03-30T06:37:13.406587Z",
     "shell.execute_reply": "2024-03-30T06:37:13.405589Z",
     "shell.execute_reply.started": "2024-03-30T04:36:02.228665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 7200 s                                                       \n",
      "\n",
      "(194, 21, 4)\n",
      "[0.05, 100, 0.5, 1, 3.141592653589793]\n",
      "-1.4168096170502493 -0.07524434328558013 0.5047316857810146\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:\\SMPD3-8\\SpinRun3-1\\raman_rabi_a\\20240330053054_raman_rabi_a.hdf5\n"
     ]
    }
   ],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 1\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])[0:]\n",
    "\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "guess = [0.05,100,0.5,1,1*np.pi]\n",
    "#guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([x[0],x[-1]])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            #guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,(-1)**(i//2+1),0.9-i*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            \n",
    "            # guess = [2, 30, (-1)**(i//2+1)*0.1, (-1)**(i//2+1) + 0.5, 0.9-i*np.pi]\n",
    "            # # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            # est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            # #ax[1].plot(fine,rabi_fit(fine,*guess)+0.05*i, color = colors[i],alpha = 0.5)\n",
    "\n",
    "            if(i==3): \n",
    "                q1, q2, q3 = np.polyfit(x, pops[i], 2)\n",
    "                optimum_duration = fine[np.argmax(q1*fine**2 + q2*fine + q3)]\n",
    "                optimum_duration = abs(q2/q1/2)\n",
    "                print(q1, q2, q3)\n",
    "                ax[1].plot(fine, q1*fine**2 + q2*fine + q3, color = colors[i])\n",
    "            # ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "    \n",
    "# if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "\n",
    "try: \n",
    "    plt_label = \"$\\Delta T_{\\pi} = %.3f$ ms \"%(optimum_duration)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "#ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(2.5,3.6)#x[0],x[-1])\n",
    "ax[1].set_xlim(x[0],x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "# ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin_rabi.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6081d5-864b-4aac-b6cc-bff47a5843c3",
   "metadata": {},
   "source": [
    "### Rabi on b (a up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99714c65-e91b-45d6-a98e-82617d6304db",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2 + 32.5e3)\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.469e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.45e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.13e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,7,8))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[True, False,True,False])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da88464-aee3-48fa-aa40-01fdc0f3392c",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2*3600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "state_to_fit = 0\n",
    "threshold = 55\n",
    "\n",
    "\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "#p_data = (data[:,:,0]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = pops[1]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    # est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data)\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try:\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except:pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "# bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "# ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "# fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            # 'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa093e71-81e1-4e00-9304-e332b9f36b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "\n",
    "## USE prep params ##\n",
    "nuclear_spin_freq_b = int(810.443e3)#int((810.443e3 + nuclear_spin_freq_b_prep)/2)\n",
    "nuclear_spin_freq_a = int(808.762e3)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,12,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ddd3a15-1735-4397-8aef-66055ccc6414",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "state_to_fit = 0\n",
    "\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "p_data = (data[:,:,state_to_fit]>threshold_prep)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,-0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [0.1, 30, 0.5, 1, np.pi/4]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "mean_val = np.mean((pops[2]+pops[3])/2)\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax[1].axhline(mean_val, linestyle = \"--\",color = \"black\")\n",
    "ax[1].grid()\n",
    "print(mean_val)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2743c67c-f236-43e9-81bc-2bb226519650",
   "metadata": {},
   "source": [
    "### CORPSE Raman rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ca06181-9198-413e-a238-838661dd30e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def psi(theta):\n",
    "    return np.arcsin(np.sin(theta/2)/2) \n",
    "thetas = np.linspace(0,2*np.pi,21)\n",
    "\n",
    "pulse1_durs = thetas/2 - psi(thetas)\n",
    "pulse2_durs = 2*np.pi- 2*psi(thetas)\n",
    "pulse3_durs = 2*np.pi + thetas/2 - psi(thetas)\n",
    "pulse_durs = np.vstack((pulse1_durs,pulse2_durs,pulse3_durs)).T\n",
    "# pulse_durs = list(zip(pulse1_durs,pulse2_durs,pulse3_durs))\n",
    "\n",
    "pulse1_durs = [int((pulse1_dur/np.pi*5.44e6*0.87)//4) for pulse1_dur in pulse1_durs]\n",
    "pulse2_durs = [int((pulse2_dur/np.pi*5.44e6*0.87)//4) for pulse2_dur in pulse2_durs]\n",
    "pulse3_durs = [int((pulse3_dur/np.pi*5.44e6*0.87)//4) for pulse3_dur in pulse3_durs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10b66065-c032-4fde-976e-f80044ac9fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "\n",
    "## USE prep params ##\n",
    "nuclear_spin_freq_b = int((810.443e3 + nuclear_spin_freq_b_prep)/2)\n",
    "nuclear_spin_freq_a = int(808.762e3)\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,12,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    pulse1_dur = declare(int)\n",
    "    pulse2_dur = declare(int)\n",
    "    pulse3_dur = declare(int)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_((pulse1_dur,pulse2_dur,pulse3_dur), (pulse1_durs,pulse2_durs,pulse3_durs)):\n",
    "        #with for_(k, 0, k < len(pulse1_durs), k + 1):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "\n",
    "            # Reset frames for everything\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            # Now play the CORPSE sequence\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            # (theta/2-psi)_0\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, pulse1_dur, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            \n",
    "            # (2pi-2psi)_pi\n",
    "            align()\n",
    "            frame_rotation_2pi(0.5, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, pulse2_dur, ramp_time_prep)\n",
    "            align()\n",
    "            wait(100)\n",
    "            \n",
    "            # (2pi+theta/2-psi)_0\n",
    "            align()\n",
    "            frame_rotation_2pi(1.5, spin_sticky_element)\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, pulse3_dur, ramp_time_prep)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25070dec-bc4b-47ca-9663-c21c0adac59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = np.array(thetas)/np.pi\n",
    "state_to_fit = -1\n",
    "\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "p_data = (data[:,:,state_to_fit]>threshold_prep)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,-0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [0.1, 30, 0.5, 1, np.pi/4]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==state_to_fit: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[state_to_fit])\n",
    "mean_val = np.mean((pops[2]+pops[3])/2)\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax[1].axhline(mean_val, linestyle = \"--\",color = \"black\")\n",
    "ax[1].grid()\n",
    "print(mean_val)\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc94c9c9-3d6d-4c97-8a87-954a83c49153",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "amplitude_rescaling_factors = np.linspace(0.55,0.75,11)\n",
    "data_all = []\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "freq_sideband = int(Photon_IF + centre_freq*1e3 - 0.745e6)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 90\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "rabi_amplitude = 0.3\n",
    "rabi_duration = int(40_000/4)\n",
    "\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "total_measurement_time = 3600*2\n",
    "one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "for i, amplitude_rescaling_factor in enumerate(amplitude_rescaling_factors):\n",
    "    \n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "\n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a = int(808.844e3)\n",
    "    nuclear_spin_freq_b = int(nuc_freq_b_func(amplitude_rescaling_factor))\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1*2\n",
    "    detuned_electron_amplitude_b = 0.2/amplitude_rescaling_factor\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.8/2\n",
    "    detuned_sideband_amplitude_b = 0.8*amplitude_rescaling_factor\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    \n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,40,21))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    # print(raman_pulse_durations)\n",
    "    print(\"detuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\\n\"%(detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "    ####################### Run program #######################\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        duration_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "\n",
    "        \n",
    "        click_acc=declare(int)\n",
    "\n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "        \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "\n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                #Raman_pulse_chirped(freq_set, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration,        ramp_time)\n",
    "                \n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                '''\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(\n",
    "                            rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                            freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                    )\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                '''\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    better_sleep(3600*2)\n",
    "\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    \n",
    "    data_all.append(data)\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    \n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>100)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    guess = [0.01,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "    \n",
    "    \n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi        = fine[np.argmin(data_fit)]\n",
    "    T_pio2      = T_pi-T_half_period/2\n",
    "    \n",
    "    std[3]/(2*np.pi*est[0])\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)):\n",
    "            guess = [0.04,100,max(pops[i])/2,1,1*np.pi]\n",
    "            try:\n",
    "                est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "                print(est[2]*2)\n",
    "                ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            except: print(\"fit failed\")\n",
    "            ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "            \n",
    "    else:\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    print(fullpath)\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf21dbf7-1ac1-49ec-b34f-b437c5706cfc",
   "metadata": {},
   "source": [
    "### Rabi on b (a down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d8dd1c-3e13-45ee-a027-6e32ab5b6ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2+32.5e3)\n",
    "nuclear_spin_freq_a  = int(808.37e3)\n",
    "nuclear_spin_freq_b = int(810.47e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,10,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, False, True, True])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07a86f0-cc8c-42aa-9b37-49eba1a6d5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(600)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "c_index = 1\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,-0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==c_index: \n",
    "                target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[c_index])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a61bc12-2aa4-4471-8533-08ece89d8b14",
   "metadata": {},
   "source": [
    "#### Rabi chevrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92aa66ac-986f-4b85-9943-4f144f6197c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.795e3)\n",
    "freqs = nuclear_spin_freq_a + np.linspace(-0.04, 0.04, 11)*1e3\n",
    "\n",
    "for nuclear_spin_freq_a_swept in freqs:\n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a  = int(808.795e3)\n",
    "    nuclear_spin_freq_b = int(810.702e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1\n",
    "    detuned_electron_amplitude_b = 0.2\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.80\n",
    "    detuned_sideband_amplitude_b = 0.80\n",
    "    \n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,13,11))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    print(raman_pulse_durations)\n",
    "    \n",
    "    \n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_rabi_b'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Readout parameters #######################\n",
    "    \n",
    "    N_ROcycle=200\n",
    "    \n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    \n",
    "    threshold = 100\n",
    "    prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_freq = readout_freqs[-1]\n",
    "    \n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "    \n",
    "    ####################### Measurement time estimate #######################\n",
    "    total_measurement_time = 3600*2\n",
    "    one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "    N_repetition = int(1e6)\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        duration_set = declare(int)\n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        \n",
    "        preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        preparation_flag_stream = declare_stream()\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                raman_preparation_b(prepare_stream, click_acc, freq_electron+delta_freq, threshold, N_ROcycle, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "                save(preparation_flag, preparation_flag_stream)\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "    \n",
    "                ################# Now play the Raman sideband sequence #################\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                align()\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_swept, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "            \n",
    "            preparation_flag_stream.save_all('preparation_flag')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    #better_sleep(total_measurement_time)\n",
    "    better_sleep(1800)\n",
    "\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    \n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>100)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    guess = [0.05,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "    T_pi = (1-est[-1]/2/np.pi)/est[0]\n",
    "    T_pio2 = (0.75-est[-1]/2/np.pi)/est[0]\n",
    "    \n",
    "    std[3]/(2*np.pi*est[0])\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "    else:\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax[1].plot(fine,data_fit)\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    ax[1].set_title(\"Nuclear drive frequency  = %.3f kHz\"%(nuclear_spin_freq_b_swept*1e-3), fontsize = \"small\")\n",
    "    \n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "\n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'nuclear_spin_freq_b_swept': nuclear_spin_freq_b_swept,\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    print(fullpath)\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d834b9-9dc1-49b5-81d8-c02f1bf53703",
   "metadata": {},
   "source": [
    "#### Chevrons on a (b up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2350dc22-ebb8-4e3e-a928-b378daf9bc89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "flip_b = False\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.90e3)\n",
    "freqs = nuclear_spin_freq_a + np.linspace(-0.15, 0.15, 21)*1e3\n",
    "\n",
    "for nuclear_spin_freq_a_swept in freqs:\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    \n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a  = int(808.90e3)\n",
    "    nuclear_spin_freq_b = int(810.49e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "    raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1\n",
    "    detuned_electron_amplitude_b = 0.05\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.8\n",
    "    detuned_sideband_amplitude_b = 0.15\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,20,21))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    print(raman_pulse_durations)\n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_rabi_a'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Readout parameters #######################\n",
    "    \n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    threshold = 90\n",
    "    prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "    ####################### Measurement time estimate #######################\n",
    "    total_measurement_time = 3600*2\n",
    "    one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "    N_repetition = int(total_measurement_time//one_measurement_time)\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        duration_set = declare(int)\n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        \n",
    "        preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        preparation_flag_stream = declare_stream()\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "    \n",
    "                save(preparation_flag, preparation_flag_stream)\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "    \n",
    "                ################# Now play the Raman sideband sequence #################\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                if flip_b:\n",
    "                    align()\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                align()\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_swept, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set,        ramp_time)\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                \n",
    "                with for_each_(freq_set, readout_freqs): \n",
    "                    align()\n",
    "                    # play('ON',fsv_trigger)\n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "            \n",
    "            preparation_flag_stream.save_all('preparation_flag')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "    better_sleep(1800)\n",
    "\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>100)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        T_half_period = 0.5/est[0]\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "    else:\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax[1].plot(fine,data_fit)\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    # ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'nuclear_spin_freq_a_swept': nuclear_spin_freq_a_swept,\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    print(fullpath)\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0e0db98-49a4-4fd3-b3cc-315a391430b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>100)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    T_half_period = 0.5/est[0]\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "# ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'nuclear_spin_freq_a_swept': nuclear_spin_freq_a_swept,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cccad7c-a3c5-4814-984b-ab1bfa9438af",
   "metadata": {},
   "source": [
    "#### Chevrons on a (b down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6f20cc1-7323-4281-b24d-45ab7f3dafd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "(808.90e3-105)*1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1128011d-02df-4269-9e34-2a94e2a4a9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "flip_b = True\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.795e3)\n",
    "freqs = nuclear_spin_freq_a + np.linspace(-0.15, 0.15, 21)*1e3\n",
    "\n",
    "for nuclear_spin_freq_a_swept in freqs:\n",
    "    ####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    \n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a  = int(808.795e3)\n",
    "    nuclear_spin_freq_b = int(810.49e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "    raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1\n",
    "    detuned_electron_amplitude_b = 0.05\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.8\n",
    "    detuned_sideband_amplitude_b = 0.15\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,20,21))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    print(raman_pulse_durations)\n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_rabi_a'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Readout parameters #######################\n",
    "    \n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    threshold = 90\n",
    "    prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "    ####################### Measurement time estimate #######################\n",
    "    total_measurement_time = 3600*2\n",
    "    one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "    N_repetition = int(total_measurement_time//one_measurement_time)\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        duration_set = declare(int)\n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        \n",
    "        preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        preparation_flag_stream = declare_stream()\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            # with for_each_(detuning, freqs):\n",
    "            # with for_each_(amp_set, amps):\n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "    \n",
    "                save(preparation_flag, preparation_flag_stream)\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "    \n",
    "                ################# Now play the Raman sideband sequence #################\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                if flip_b:\n",
    "                    align()\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                align()\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_swept, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set,        ramp_time)\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                \n",
    "                with for_each_(freq_set, readout_freqs): \n",
    "                    align()\n",
    "                    # play('ON',fsv_trigger)\n",
    "                    click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "            \n",
    "            preparation_flag_stream.save_all('preparation_flag')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "\n",
    "    better_sleep(1800)\n",
    "\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>100)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]\n",
    "    \n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        T_half_period = 0.5/est[0]\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "    else:\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax[1].plot(fine,data_fit)\n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    # ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'nuclear_spin_freq_a_swept': nuclear_spin_freq_a_swept,\n",
    "                'click_array': data,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    print(fullpath)\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f606627a-9a33-4ce7-ab53-6b408e4f9f03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T20:13:13.239879Z",
     "iopub.status.busy": "2023-12-06T20:13:13.238878Z",
     "iopub.status.idle": "2023-12-06T20:13:13.423897Z",
     "shell.execute_reply": "2023-12-06T20:13:13.422896Z",
     "shell.execute_reply.started": "2023-12-06T20:13:13.239879Z"
    }
   },
   "source": [
    "### Rabi ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60c162f4-ece6-493b-b26b-82204f539247",
   "metadata": {},
   "outputs": [],
   "source": [
    "sinhspace_asymm(1,201,51, nonlinearity = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7186c015-b7a4-40d3-90bf-3219c3c453b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = -int(809.791e3/2)\n",
    "nuclear_spin_freq_a  = int(808.387e3)\n",
    "nuclear_spin_freq_b = int(810.454e3)\n",
    "\n",
    "raman_pi_duration_a =  int(0.47e6//4) # in ns \n",
    "raman_pi_duration_b = int(3e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.05\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.05\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pulse_durations = sinhspace_asymm(1,201,101, nonlinearity = 0) # In ms, be careful!\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_ms'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron + delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning + nuclear_spin_freq_b)                   # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning)                                        # Detuned Electron frequency\n",
    "    \n",
    "            Raman_pulse_ms(k, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9974d4c-0851-439f-b544-4236d0fd6b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "# timing_downdown = timing\n",
    "# data_downdown = data\n",
    "# data_prep_downdown = data_prep\n",
    "\n",
    "p_data = (data[:,:,-1]>75)\n",
    "x = np.array(raman_pulse_durations)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "sigmoid_length = 5\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "# def rabi_fit(t,f,a,b,phi):\n",
    "#     return a*(b-np.cos(2*np.pi*f*(t)+phi))\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,20))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        guess = [1e-3*rabi_freq,100, -0.5, 1, np.pi]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            print(est[2]*2)\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except Exception as e: \n",
    "            print(\"fit failed, \", e)\n",
    "        ax[1].plot(x,pops[i],\"o-\", label = labels[i], color = colors[i])\n",
    "        if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.25)\n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(0,200)\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "#ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[0].grid()\n",
    "\n",
    "ax[2].plot(fft_x,fft_y,\"o-\")\n",
    "ax[2].set_ylim(0,None)\n",
    "ax[2].set_xlim(0,None)\n",
    "ax[2].axvline(rabi_freq,linestyle = \"--\", color = \"black\", alpha = 0.5)\n",
    "ax[2].set_xlabel(\"Rabi frequency (Hz)\")\n",
    "ax[2].set_ylabel(\"Population\")\n",
    "ax[2].set_title(\"Max FFT freq = %i Hz\"%rabi_freq, fontsize = \"small\")\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[3].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[3].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[3].legend()\n",
    "ax[3].set_ylabel(\"instances\")\n",
    "ax[3].set_xlabel('counts')\n",
    "\n",
    "ax[4].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[4].set_xlabel(\"Readout iteration\")\n",
    "ax[4].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "print(directory+filename)\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'click_array_prep': data_prep,\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            \n",
    "            'raman_detuning': raman_detuning,\n",
    "\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31702f04-184d-4e42-b89d-27c7dc2f9979",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,(ax, ax_res)=plt.subplots(2,1,figsize=(14,9), tight_layout=True)\n",
    "ax.set_ylabel(r\"P$^{a}_{|\\downarrow\\rangle}$\")\n",
    "ax.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax.set_ylim(0,1)\n",
    "ax_res.set_ylabel(r\"P$^{a}_{|\\downarrow\\rangle}$\")\n",
    "ax_res.set_xlabel(\"Rabi duration (ms)\")\n",
    "ax_res.set_ylim(0,1)\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "def decay_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.exp(-t/T))\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        guess = [1e-3*rabi_freq, 100, ((-1)**i)*max(pops[i]), 1, -1*np.pi/2]\n",
    "        \n",
    "        if plot_guess: ax.plot(fine,rabi_fit(fine, *guess),\"--\", color = colors[i], alpha = 0.5)\n",
    "        \n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            ax.plot(fine,data_fit, color = colors[i], label = f'{labels[i]} | $T_1$ = {est[1]:.2f} ms')\n",
    "            residuals = pops[i]-rabi_fit(x, *est)\n",
    "            \n",
    "            if i==3:\n",
    "                Tpi = (1-est[-1]/2/np.pi)/est[0]\n",
    "                T_pio2 = (0.75-est[-1]/2/np.pi)/est[0]\n",
    "                print(f'pi pulse: {Tpi:.2f} | pi/2 pulse {T_pio2:.2f}')\n",
    "                \n",
    "                print(est, est[3]-1/est[2])\n",
    "                x_min = -est[1]*np.log(-est[3]+1/est[2]) \n",
    "                ax_res.plot(x,pops[i],\"o\", color = colors[i])\n",
    "                ax_res.plot(fine, data_fit, color=colors[i])\n",
    "                fine2=np.linspace(x_min,fine[-1],100)\n",
    "                ax_res.plot(fine2, decay_fit(fine2, *est), \"--\", color = 'black')\n",
    "                ax_res.set_title(f'0 decay at {x_min:.2f} ms')\n",
    "                ax_res.set_xlim(x_min,fine[-1])\n",
    "\n",
    "\n",
    "\n",
    "        except Exception as e: \n",
    "            print(\"fit failed\", e)\n",
    "            \n",
    "        ax.plot(x,pops[i],\"o\", color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax.errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "#ax.errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data)), label = f'{l[i]} | f = {est[0]*1e3:.2f} Hz', color=colors[i], fmt = \"o-\") \n",
    "ax.plot(fine,data_fit, color=colors[i])\n",
    "# if plot_guess: ax.plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "\n",
    "ax.legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax.set_title(r\"$\\tau_\\pi = %.2f$ ms\"%(abs(Tpi)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7abf09dc-1142-409c-8b90-e711607e9584",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (14,8))\n",
    "\n",
    "x = fine\n",
    "y = rabi_fit(x,*guess1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "y = rabi_fit(x,*guess2)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685bda44-7ddb-43bb-9319-12cf43f10d15",
   "metadata": {},
   "source": [
    "#### Sweeping amplitude rescaling factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae9b7c6-17da-46ec-bcc7-2828678e3133",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "factors = np.linspace(1,0.5, 6)\n",
    "\n",
    "nuc_freq_b_func = interp1d(factors,centres)\n",
    "plt.figure(figsize = (6,5))\n",
    "plt.plot(factors, centres,\"o\", label = \"data\")\n",
    "plt.plot(factors, nuc_freq_b_func(factors), label = \"interpolation\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Amplitude correction factor\")\n",
    "plt.ylabel(\"Nuclear spin b frequency (kHz)\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig(directory+filename+'_'+'nuc_b_freq_vs_sideband_rescaling.pdf')\n",
    "#np.savetxt(directory+filename+'_'+'nuc_b_freq_vs_sideband_rescaling.txt',np.transpose([factors, centres]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6ec360b-d115-4ba9-af31-288ef81e715a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_ms'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Amplitude rescaling factor sweep params #######################\n",
    "data_all = []\n",
    "amplitude_rescaling_factors = np.linspace(0.55,0.75,11)\n",
    "\n",
    "for i, amplitude_rescaling_factor in enumerate(amplitude_rescaling_factors):\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    \n",
    "    raman_detuning = 1400e3\n",
    "    nuclear_spin_freq_a = int(808.844e3)\n",
    "    nuclear_spin_freq_b = int(1e3*nuc_freq_b_func(amplitude_rescaling_factor))\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.1*2\n",
    "    detuned_electron_amplitude_b = 0.2/amplitude_rescaling_factor\n",
    "    \n",
    "    detuned_sideband_amplitude_a = 0.8/2\n",
    "    detuned_sideband_amplitude_b = 0.8*amplitude_rescaling_factor\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    \n",
    "    raman_pulse_durations = sinhspace_asymm(1,201,100, nonlinearity = 0) # In ms, be careful!\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    #print(raman_pulse_durations)\n",
    "    print(\"detuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\\n\"%(detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        duration_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "    \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron + delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "        \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "    \n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning + nuclear_spin_freq_b)                   # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning)                                        # Detuned Electron frequency\n",
    "        \n",
    "                Raman_pulse_ms(k, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    better_sleep(3000)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    data_all.append(data)\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        \n",
    "    p_data = (data[:,:,-2]>75)\n",
    "    x = np.array(raman_pulse_durations)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    sigmoid_length = 5\n",
    "    \n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,20))\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)):\n",
    "            ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "    else:\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    ax[1].grid()\n",
    "    \n",
    "    \n",
    "    ax[2].plot(fft_x,fft_y,\"o-\")\n",
    "    ax[2].set_ylim(0,None)\n",
    "    ax[2].set_xlim(0,None)\n",
    "    ax[2].axvline(rabi_freq,linestyle = \"--\", color = \"black\", alpha = 0.5)\n",
    "    ax[2].set_xlabel(\"Rabi frequency (Hz)\")\n",
    "    ax[2].set_ylabel(\"Population\")\n",
    "    ax[2].set_title(\"Max FFT freq = %i Hz\"%rabi_freq, fontsize = \"small\")\n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[3].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[3].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[3].legend()\n",
    "    ax[3].set_ylabel(\"instances\")\n",
    "    ax[3].set_xlabel('counts')\n",
    "    \n",
    "    ax[4].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[4].set_xlabel(\"Readout iteration\")\n",
    "    ax[4].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    print(directory+filename)\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'click_array_prep': data_prep,\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e1c323-3571-4d80-9a2e-76a6525656e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8fff7316-000c-432d-b2ef-a2c13445ccb1",
   "metadata": {},
   "source": [
    "### Auto sweep amplitude ratio spec + rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46d00095-437a-4540-8ea1-615f8b06f644",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_auto_rabi_a(total_measurement_time, nuclear_spin_freq_a, factor):\n",
    "\n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman sweep params #######################\n",
    "    \n",
    "    raman_detuning = 2000e3\n",
    "    nuclear_spin_freq_b = int(810.762e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.48/factor\n",
    "    detuned_sideband_amplitude_a = 1.15*factor\n",
    "\n",
    "    detuned_electron_amplitude_b = 0.2*1.5/1.5\n",
    "    detuned_sideband_amplitude_b = 0.8/1.5/1.5\n",
    "    \n",
    "    \n",
    "    print(\"Nuclear spin A Raman Rabi\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "    \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_durations = (1e3+1e6*np.linspace(0,20,20))//4\n",
    "    raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "    print(raman_pulse_durations)\n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_rabi_a'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Readout parameters #######################\n",
    "    \n",
    "    N_ROcycle=200\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    threshold = 80\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    \n",
    "    #readout_freqs = [readout_freqs[0]]\n",
    "    \n",
    "    ####################### Measurement time estimate #######################\n",
    "    N_repetition = int(1e9)\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "        \n",
    "        freq_set = declare(int)\n",
    "        duration_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            with for_each_(duration_set, raman_pulse_durations):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "    \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "        \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "                '''\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(\n",
    "                            rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                            freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                    )\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                '''\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "    better_sleep(total_measurement_time)\n",
    "\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(raman_pulse_durations)\n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    \n",
    "    ############################### Fit #################################\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    def rabi_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "    \n",
    "    #guess = [0.05,100,0.5,1,1*np.pi]\n",
    "    guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "    \n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "        print(guess)\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        est = guess\n",
    "        std = np.multiply(guess,0.1)\n",
    "        fine = x\n",
    "        data_fit = rabi_fit(x,*guess)\n",
    "    \n",
    "    \n",
    "    T_half_period = 0.5/est[0]\n",
    "    T_pi        = fine[np.argmin(data_fit)]\n",
    "    T_pio2      = T_pi-T_half_period/2\n",
    "    \n",
    "    std[3]/(2*np.pi*est[0])\n",
    "    ##############################################################    \n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[0].legend()\n",
    "    ax[0].set_xlim([0,None])\n",
    "    \n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)):\n",
    "            try:\n",
    "                guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "                # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "                est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "                print(est[2]*2)\n",
    "                ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            except: print(\"fit failed\")\n",
    "            ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "            \n",
    "    else:\n",
    "        pass\n",
    "        ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "    \n",
    "    if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlim(0,x[-1])\n",
    "    ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "    \n",
    "    bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    p_data = (data[:,:,-1]>threshold)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    ax[4].plot(fft_x, fft_y)\n",
    "    ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "    ax[4].set_ylabel(\"FFT\")\n",
    "    ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'x': x,\n",
    "        \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "        \n",
    "                'readout_freqs': readout_freqs,\n",
    "\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "    print(fullpath)\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()\n",
    "    return(qm,job)\n",
    "    \n",
    "def run_auto_spec_a(total_measurement_time,nuclear_spin_freq_a, factor):\n",
    "    spectroscopy_b = False\n",
    "    \n",
    "    ####################### Define the readout frequencies #######################\n",
    "    \n",
    "\n",
    "    readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "    readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "    freq_electron = readout_freqs[-1]\n",
    "    \n",
    "    ####################### Raman pulse params #######################\n",
    "    raman_detuning = 2000e3\n",
    "    nuclear_spin_freq_b = int(810.762e3)\n",
    "    \n",
    "    raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "    raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "    \n",
    "    \n",
    "    detuned_electron_amplitude_a = 0.4/factor\n",
    "    detuned_sideband_amplitude_a = 0.8*factor\n",
    "    \n",
    "    detuned_electron_amplitude_b = 0.2 \n",
    "    detuned_sideband_amplitude_b = 0.35\n",
    "        \n",
    "    ramp_time       = int(6e6/4)\n",
    "    raman_pulse_duration = int(2e6//4)\n",
    "    \n",
    "    nuclear_spin_frequencies = nuclear_spin_freq_a + sinhspace(-0.1e3,0.1e3,11,nonlinearity=1)\n",
    "    nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "    \n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='raman_spectroscopy'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    \n",
    "    ####################### Define preparation parameters #######################\n",
    "    \n",
    "    N_repetition = 1e6\n",
    "    N_ROcycle=250\n",
    "    switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "    #readout_freqs = [readout_freqs[-1]]\n",
    "    \n",
    "    ####################### Run program #######################\n",
    "    \n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        prepare_stream = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep Raman frequency detunning sideband interpulse delay\n",
    "    \n",
    "            with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "    \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "        \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                #Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "    \n",
    "                ################# Now play the Raman spectroscopy sequence #################\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                \n",
    "                #Raman_pulse_chirped(freq_set, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration,        ramp_time)\n",
    "                \n",
    "                # Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "                Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pulse_duration, ramp_time)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "                '''\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                with for_each_(freq_set, readout_freqs):  \n",
    "                    click_acc = nuclear_spin_RO(\n",
    "                            rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                            freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                    )\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                '''\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            prepare_stream.save_all('clicks_prep')\n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    \n",
    "    better_sleep(total_measurement_time)\n",
    "\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "    ###########################################################\n",
    "    \n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    print(data.shape)\n",
    "    \n",
    "    sigmoid_length = 5\n",
    "    x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "    \n",
    "    ############################### extract populations #################################\n",
    "    \n",
    "    if len(readout_freqs) == 4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data = p3\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    \n",
    "    ############################### Fit ###############################\n",
    "    \n",
    "    guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "    try: \n",
    "        est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "        fit_success = 1\n",
    "    except: \n",
    "        print(\"fit failed\")\n",
    "        fit_success = 0\n",
    "        fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "    \n",
    "    ############################### Plotting ###############################\n",
    "    \n",
    "    fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "    \n",
    "    for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0].set_ylabel(\"Mean counts\")\n",
    "    ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[0].legend()\n",
    "    \n",
    "    \n",
    "    ax[1].set_ylim(0,1)\n",
    "    ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "    ax[1].set_ylabel(\"Population\")\n",
    "    ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "    \n",
    "    \n",
    "    ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "    if fit_success: ax[1].plot(fine,data_fit, label = \"centre freq = %.3f kHz\"%est[0])\n",
    "    if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "    ax[1].set_ylabel(\"P\")\n",
    "    ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "    ax[1].legend()\n",
    "    \n",
    "    bins=np.arange(0,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "    ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "    ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "    ax[2].legend()\n",
    "    ax[2].set_ylabel(\"instances\")\n",
    "    ax[2].set_xlabel('counts')\n",
    "    \n",
    "    delta_freq = res.delta_freq.fetch_all()['value']\n",
    "    ax[3].plot(1e-3*delta_freq)\n",
    "    ax[3].set_xlabel(\"Readout iteration\")\n",
    "    ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    ############################### Save ###############################\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "    #display.display(plt.gcf())\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'raman_detuning': raman_detuning,\n",
    "                'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "                'click_array': data,\n",
    "    \n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "                'readout_freqs': readout_freqs,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "    }\n",
    "    \n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()\n",
    "    \n",
    "    if fit_success ==1:\n",
    "        return(qm,job,int(est[0]*1e3))\n",
    "    else:\n",
    "        return(qm,job,nuclear_spin_freq_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d780ed6d-62d2-4ed9-83c2-d9dddc36f9ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "nuclear_spin_freq_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd91893f-b0e8-4c6b-9685-0f36fef46d91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "\n",
    "total_measurement_time = 600\n",
    "\n",
    "factors = np.linspace(0.8,1.2,5)\n",
    "nuclear_spin_freq_list = np.array([808.870, 808.846, 808.845, 808.835, 808.843])*1e3\n",
    "\n",
    "for factor, nuclear_spin_freq_a in zip(factors, nuclear_spin_freq_list):\n",
    "    print(\"#####################\\nAmplitude adjustment factor = %.2f\\n#####################\\n\"%factor)\n",
    "    qm,job = run_auto_rabi_a(total_measurement_time, nuclear_spin_freq_a, factor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "493a231c-66c6-4ff4-9c88-176d276a5916",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "\n",
    "total_measurement_time = 600\n",
    "\n",
    "for factor in np.linspace(0.8,1.2,5):\n",
    "    print(\"#####################\\nAmplitude adjustment factor = %.2f\\n#####################\\n\"%factor)\n",
    "    qm,job,nuclear_spin_freq_a = run_auto_spec_a(total_measurement_time//2,nuclear_spin_freq_a,factor)\n",
    "    qm,job = run_auto_rabi_a(total_measurement_time, nuclear_spin_freq_a, factor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eda8d371-1103-4757-8594-54f320e7c51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = qm.get_running_job()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10e92d57-e997-4ca9-884d-af9546b44226",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = 2000e3\n",
    "nuclear_spin_freq_b = int(810.762e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.4/factor\n",
    "detuned_sideband_amplitude_a = 0.8*factor\n",
    "\n",
    "detuned_electron_amplitude_b = 0.2*1.5/1.5\n",
    "detuned_sideband_amplitude_b = 0.8/1.5/1.5\n",
    "\n",
    "\n",
    "print(\"Nuclear spin A Raman Rabi\\ndetuned_electron_amplitude_a = %.3f\\ndetuned_sideband_amplitude_a = %.3f\\ndetuned_electron_amplitude_b = %.3f\\ndetuned_sideband_amplitude_b = %.3f\"%(detuned_electron_amplitude_a,detuned_sideband_amplitude_a,detuned_electron_amplitude_b,detuned_sideband_amplitude_b))\n",
    "\n",
    "ramp_time       = int(6e6/4)\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,20,11))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,100,0.5,1,1*np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "            print(est[2]*2)\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms\"%(T_pio2)\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5e52ab-5583-4665-bc6e-857274175d8f",
   "metadata": {},
   "source": [
    "### Pulse train error amplification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a5f522c-695b-47e6-81f1-f2f9260ec1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "N_pulses = 5 # Number of pulses in the train\n",
    "\n",
    "raman_detuning = -int(809.791e3/2-10e3)\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.466e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.70e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.40e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "raman_pulse_durations = (1.73e6+ sinhspace(-0.1e6,0.1e6,6,nonlinearity=2))//4\n",
    "\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "                \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "                align()\n",
    "                wait(int(20e3/4))\n",
    "                align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            #nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[False, True, False, True])\n",
    "\n",
    "            '''\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                )\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "            '''\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04794767-92dc-4f0d-9707-eb23ec257d2a",
   "metadata": {},
   "source": [
    "## Bell State preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0127de69-949d-43ca-b0a8-7b9856debd3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = 110e3\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.464e3)\n",
    "nuclear_spin_freq_b = int(810.123e3)\n",
    "\n",
    "raman_pi_duration_a = int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.1\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,6.19,2))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "threshold = 80\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            # wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a7a7dc-5dbf-498e-9d7d-96051064b566",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Nuclear readout fidelity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870c4db6-2763-41db-81e8-85a496e610dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_4stateprep'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle_array = np.linspace(250,250,1)\n",
    "N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    N_ROcycle_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    \n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    preparation_flag_stream = declare_stream()\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "            \n",
    "            with for_(k, 0, k < 4, k + 1):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "                )\n",
    "\n",
    "                \n",
    "                raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "                save(preparation_flag, preparation_flag_stream)\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "    \n",
    "                ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                # align()\n",
    "                # play('ON',fsv_trigger)\n",
    "                \n",
    "                # If k==0, we flip a and b\n",
    "                with if_(k==0):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()\n",
    "                \n",
    "                # If k==1, we flip a\n",
    "                with if_(k==1):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()     \n",
    "                \n",
    "                # If k==2, we flip b\n",
    "                with if_(k==2):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()\n",
    "                \n",
    "                # If k==3, we don't pulse at all, we just read out\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                # nuclear_spin_RO_4freq_interleaved(rabi_stream,\n",
    "                #                                   spin_gauss_pulse,\n",
    "                #                                   N_ROcycle_set, spin_element,\n",
    "                #                                   amplitude_readout_pulse,\n",
    "                #                                   gauss_duration,\n",
    "                #                                   waiting_time_spin, \n",
    "                #                                   N_readout,\n",
    "                #                                   waiting_time_SMPD,\n",
    "                #                                   readout_freqs,\n",
    "                #                                   delta_freq,\n",
    "                #                                   wait_time_after_spin_readout=waiting_after_spinreadout,\n",
    "                #                                   enable_fsv_trigger = True)\n",
    "                # \n",
    "                # align()\n",
    "                # wait(int(5e6//4))\n",
    "                \n",
    "                ################# Wait a long time before final readout to ensure heating does not affect the result #################\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                \n",
    "                ################################## Final readout ##################################\n",
    "                \n",
    "                \n",
    "                with for_each_(freq_set, readout_freqs): \n",
    "                    align()\n",
    "                    # play('ON',fsv_trigger)\n",
    "                    click_acc = nuclear_spin_RO(\n",
    "                        rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle_set, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                        freq_set+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep, enable_fsv_trigger=True\n",
    "                    )\n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "        preparation_flag_stream.save_all('preparation_flag')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe06c67-2b79-41d0-875d-1ccb080ae226",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "# timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure()\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 40, range = (30,150))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*2.5,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    probs.append(kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i]))\n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "\n",
    "for i in range(4): \n",
    "    for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "    ax[i].set_ylabel('Probability')\n",
    "    ax[i].set_xlabel('Number of readout pulses')\n",
    "    ax[i].legend()\n",
    "    ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "    ax[i].set_ylim([0,1])\n",
    "    ax[i].grid(True)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcyclbetter_sleep(0)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "# timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure()\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 40, range = (30,150))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*2.5,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    probs.append(kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i]))\n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "\n",
    "for i in range(4): \n",
    "    for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "    ax[i].set_ylabel('Probability')\n",
    "    ax[i].set_xlabel('Number of readout pulses')\n",
    "    ax[i].legend()\n",
    "    ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "    ax[i].set_ylim([0,1])\n",
    "    ax[i].grid(True)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)e_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883c0bd1-ef97-494a-ae65-28bb7a84b970",
   "metadata": {},
   "source": [
    "### Sideband pumping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508ab6b6-0024-4719-ba7a-46693cb96dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "#centre_freq += delta_freq[-1]\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_4stateprep'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle_array = np.linspace(250, 250, 1)\n",
    "N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    N_ROcycle_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    \n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "            \n",
    "            with for_(k, 0, k < 4, k + 1):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                Pauli_swept('aX90', delta_freq)\n",
    "                Pauli_swept('bX90', delta_freq)\n",
    "                wait(int(10e6//4))\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "        \n",
    "                ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                # align()\n",
    "                \n",
    "                # If k==0, we flip a and b\n",
    "                with if_(k==0):\n",
    "                    Pauli_swept('aX', delta_freq)\n",
    "                    Pauli_swept('bX', delta_freq)\n",
    "                    \n",
    "                # If k==1, we flip a\n",
    "                with if_(k==1):\n",
    "                    Pauli_swept('aX', delta_freq)\n",
    "                    \n",
    "                # If k==2, we flip b\n",
    "                with if_(k==2):\n",
    "                    Pauli_swept('bX', delta_freq)\n",
    "                    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "                align()\n",
    "                wait(int(10e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_set, readout_freqs, delta_freq, enable_fsv_trigger = True)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2166,
   "id": "f20e2226-1465-4b47-a63e-f7bd154a9de3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T15:30:11.123400Z",
     "iopub.status.busy": "2024-04-01T15:30:11.123400Z",
     "iopub.status.idle": "2024-04-01T15:30:16.683314Z",
     "shell.execute_reply": "2024-04-01T15:30:16.682314Z",
     "shell.execute_reply.started": "2024-04-01T15:30:11.123400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(241, 1, 4, 4)\n",
      "[250]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "# timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[10:]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans_standard)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 60, range = (0,200))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "    probs.append(prob)\n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "\n",
    "########################## kmeans 4d #########################################\n",
    "probs_4d, kmeans_4d_model = kmeans_4d(data)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(8,7), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(N_ROcycle_array)>1:\n",
    "    for i in range(4): \n",
    "        for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "        ax[i].set_ylabel('Probability')\n",
    "        ax[i].set_xlabel('Number of readout pulses')\n",
    "        ax[i].legend()\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].grid(True)\n",
    "else:\n",
    "    for i in range(4):\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "        \n",
    "        bar = ax[i].bar(labels,probs_4d[:,0,i].flatten().round(3), color=None, edgecolor = 'k', alpha = 0.2, linestyle='--')\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].bar_label(bar)\n",
    "        ax[i].set_ylabel(\"$P_N$\")\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\"\"\"\n",
    "plt.figure(figsize=(6,5))\n",
    "plot_2d_sweep(\n",
    "    prep,\n",
    "    x=labels,\n",
    "    y=labels,\n",
    "    xlabel = \"Prepared state\",\n",
    "    ylabel = \"Measured state\",\n",
    "    clabel = \"Probability\",\n",
    "    vmin = 0,\n",
    "    vmax = 1,\n",
    "    cmap = \"Blues\"\n",
    ")\"\"\"\n",
    "\n",
    "plt.figure()\n",
    "delta_freq = 1e-3*res.delta_freq.fetch_all()['value']\n",
    "plt.plot(delta_freq)\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "filename_kmean = directory+filename+'kmean.model'\n",
    "pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c9baf1-0121-453b-9ea6-b4c6c6982b6b",
   "metadata": {},
   "source": [
    "### New detunings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2153,
   "id": "6b010a45-1a72-4e48-ac96-214907727f30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:40:44.369054Z",
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     "iopub.status.idle": "2024-04-01T14:40:48.842711Z",
     "shell.execute_reply": "2024-04-01T14:40:48.840710Z",
     "shell.execute_reply.started": "2024-04-01T14:40:44.369054Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "#centre_freq += delta_freq[-1]\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_4stateprep'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "##################### Raman pulse parameters #####################\n",
    "\n",
    "raman_detuning_a =  raman_detuning_a_prep # -int(809.791e3/2-10e3)   # \n",
    "raman_detuning_b =  raman_detuning_b_prep # -int(809.791e3/2+32.5e3) # \n",
    "\n",
    "nuclear_spin_freq_a  = nuclear_spin_freq_a_prep # int(808.777e3) #  \n",
    "nuclear_spin_freq_b  = nuclear_spin_freq_b_prep # int(810.470e3) #  \n",
    "\n",
    "raman_pi_duration_a = raman_pi_duration_a_prep # int(3.18e6//4) #  in ns \n",
    "raman_pi_duration_b = raman_pi_duration_b_prep # int(4.94e6//4) #  in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = ramp_time_prep # int(1.2e6/4) # \n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle_array = np.linspace(250, 250, 1)\n",
    "N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    N_ROcycle_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    \n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "            \n",
    "            with for_(k, 0, k < 4, k + 1):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "                wait(int(10e6//4))\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "                \n",
    "                align()\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(50e6/4))\n",
    "                align()\n",
    "                save(0, timing_stream)\n",
    "        \n",
    "                ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                # align()\n",
    "                \n",
    "                # If k==0, we flip a and b\n",
    "                with if_(k==0):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                \n",
    "                # If k==1, we flip a\n",
    "                with if_(k==1):\n",
    "                    align()\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                \n",
    "                # If k==2, we flip b\n",
    "                with if_(k==2):\n",
    "                    \n",
    "                    align()\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_set, readout_freqs, delta_freq, enable_fsv_trigger = True)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2165,
   "id": "4de450f5-5fa8-4ca2-b422-046e5b0579c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:52:07.988116Z",
     "iopub.status.busy": "2024-04-01T14:52:07.987117Z",
     "iopub.status.idle": "2024-04-01T15:22:31.220218Z",
     "shell.execute_reply": "2024-04-01T15:22:31.218211Z",
     "shell.execute_reply.started": "2024-04-01T14:52:07.988116Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 1800 s                                                       \n",
      "\n",
      "Timing 0: 1817.665 s\n",
      "(210, 1, 4, 4)\n",
      "[250]\n",
      "Timing 1: 1817.689 s\n",
      "Timing 2: 1817.689 s\n",
      "Timing 3: 1819.393 s\n",
      "Timing 4: 1820.452 s\n",
      "Timing 5: 1820.639 s\n",
      "Timing 6: 1821.107 s\n",
      "Timing 7: 1821.110 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LJTRkU9YetREKNdIAZulj5f+HLZgKAHh89fY2HREhhBBSgUKNEBJJXtw0jH+t3wnA6qhllYj+kRyFGglOSbeWPr5qfkWoPfYyhRohhJD2kmr3ARBCiIqu63jDFfcDAJ780kmWHrVs2uqosfSRNIIofRSO2qHzpwAAVmwexVi+iL4ML5OEEELaAx01QkjkKEqJe+t2jEuOWgJnHjXfctvhCYaJkOCIUy1ZVWpTpLCaHBNFCSGEtBEKNUJI5JCj0QulssVRO2r36bj3MyfgjfvNAsDSR9IYum4deC2cNcAsiySEEELaAYUaISRyyI5avlRGqRqVLqLTF83ox4yBivPBMBHSCGY8P6r/1wx3jbPUCCGEtBMKNUJI5BADrgEgX7Q6aoKBbKV3iI4aaQS19FH+mkKNEEJIO6FQI4REDhHHD1SEmpz6KBjIpgEAQ3TUSAOopY+AOVONQo0QQkg7oVAjhEQOeYE8XijZOmqDPXTUSOOopY+AeZ4VKdQIIYS0EQo1QkjkkBfI4/mSJfVRMCCEGlMfSQOIeP6kpNQSLH0khBASATgghhASOeQF8trt4/j23c8DUBy1ao8a56iRRhDBjgnp3EpRqBFCCIkAdNQIIZFDdtSESAOUHjWWPpIQUAdeA3TUCCGERAMKNUJI5CiV7QcN26U+0lEjjSBmpWkaHTVCCCHRgkKNEBI5nEIcxBw1ABjsqaQ+0lEjjSD2BCw9aiL1kQOvCSGEtBEKNUJI5CiW7BfITqmPOhfUBJWo/U3DE77vA1jj+VNJ4ajZO7uEEEJIK6BQI4REDqeSM9n1EKWPpbKO8UKpJcdFos0Vf34eR371Hvz80dWe72OWPpo/M+eohXp4hBBCiC8o1AghkcOp9HE0bwqyvkzSCIAYYZ8aAfC9v7wIAPjyb/7l+T7iVJODapLGHDUqNUIIIe2DQo0QEjmcHLXtY3nja03TzEAR9qkRiUzS+6XNrvRRCDXqNEIIIe2EQo0QEjmcnIzto3nL9yJQhMmPRCad8n5pE5sCcjw/HTVCCCFRgEKNEBI5nBy1bWMFy/fCURsaL9jdnMSUtJQOWo+yzcBrw1FjSA0hhJA2QqFGCIkcTj1qx+4x3fL9rlN6AACrto42/ZhI55D2UfpYdil9dEofJYQQQloBhZrE8uXL8bWvfQ2ve93rMGPGDKTTacyePRvvfOc78cADD7T78AiJDSVlgXz/f70Ol7/zQFzw+r0sP99/18kAgGfWD7Xs2Ej08dqjtmlowiibTdikPtJRI4QQ0k5S7T6AKPGGN7wB69atw8DAAI4++mhMmzYNzzzzDG677TbcfvvtuOKKK/CpT32q3YdJSNejOmrzp/dh/vT5Nbfbb9dJAIB/UagRCa+O2nFfu9f42tZRc3B2CSGEkFZAR01in332wQ033IDNmzfjz3/+M2655RY8/fTTuPLKK6HrOj7zmc/gmWeeafdhEtL1yD1qN51zlOPt9q8KtedeGUaBQ69IlXTKW49aXjpn7HrUnHolCSGEkFZAoSZx9913433vex96enosP//IRz6Ck046CaVSCbfeemubjo6Q+CDS9o7ZYzqO2XOG4+3mTe1DJpVAvljGxp0TrTo8EnFSCf+XNrvURwo1Qggh7YRCzSMHH3wwAGD9+vVtPhJCuh+xQJaHENuRSGjoqUax5+moxRpZVHnpUVNFmF3pI4UaIYSQdkKh5pGXXnoJADB79uw2Hwkh3Y/oDUrVEWoAkEklAYCljzFnLG/O0vNS+jheKFm+l4VaikKNEEJIBKBQ88CKFSvwu9/9DgBwyimntPloCOl+TEet/kdUpjozK1+kUIsz43lTeMmiywlZ2FXuI39dFWpMfSSEENJGmPpYh2KxiLPOOgu5XA5nnHEGDjvsMNfb53I55HI54/uhIabREeIXf45aRczRUYs3Y5JQ8zL/bCzn4qgl6agRQghpP10l1E477TQ8++yzvu5zww034Mgjj3T8/QUXXIAHH3wQu+++O374wx/WfbzLL78cl1xyia9jIIRYKVVFVzJZX6iJKPYcHbVYYxFq5frngnx7wJr6aDhqFGqEEELaSFcJtZUrV+K5557zdZ+xsTHH3331q1/Fj370I8yaNQt/+tOfMG3atLqPd9FFF2HJkiXG90NDQ5g3b56vYyIk7gRx1Fj6GG/GC2YpY8GLo+ZS+sgeNUIIIVGgq4TasmXLQnusK6+8El/84hcxefJk/PGPf8See+7p6X7ZbBbZbDa04yAkjnhNfQTk0kcuquNMo46afPaI3kgKNUIIIe2EYSI2/PznP8f555+Pvr4+/P73v8chhxzS7kMiJFb4cdRE6SMdtXjju0dNuv3B86ZgMGvuW4p0/yKFGiGEkDZCoaZw55134v3vfz9SqRRuu+02HHvsse0+JEJih5/UxyzDRAisqY9ezgVR+njcnjNw20ePgWaZo1Y5p8oUaoQQQtpIV5U+NspDDz2E008/Hbqu4xe/+AVOOumkdh8SIbGEjhrxi7X00buj1p9NWoJEADpqhBBCogGFmsTb3/52jI+PY9GiRbj99ttx++2319zmuOOOwznnnNP6gyMkRpSqPUaeetSEUKOjFmsmCn5LHyuOWl+m9jKYEo4a56gRQghpIxRqEjt27ABQSY9cuXKl4+0o1AhpLr4cNaY+ElhFlbfSx4qw68ska34n4vnpqBFCCGknFGoSOndPCYkEpaoj4mWOGh01AlhFlZ/SRzuhJgZes0eNEEJIO2GYCCEkcvibo1a5TYGOWqyRo/T9hInYlT7SUSOEEBIFKNQIIZHDT+ojHbXOJUzHSn4sP/H8to4aB14TQgiJABRqhJDI4c9Ro1DrRO57fjMOvvQu3Pn0hlAez1r66MFRy7n0qFGoEUIIiQAUaoSQyOEn9ZHx/J2F6AX+wE8exfBEER/72eOhPK41TESv23M8VhBCzS71kaWPhBBC2g+FGiEkcgRx1DjwOvp8+Y5/4riv3YsdY/nQH1t1v+q5YWM50aNW66iJDYKohIn8fdU2/H3VtnYfBiGEkBbD1EdCSOQwe9S8O2ovbx1Dvlg2hBuJHtf/9eXK/x9+OfTHVoVZsawjVavBDIwetWztZTAZIUdtolDCu6/8KwDg2UvfjF4bYUkIIaQ74YqGEBI5xAJZiDA3slVh9sALW3DmNX9r+G/ruo6P/vQxfPSnj3FkR5PYNDwR+mOqQq2ew2qmPto4atXUxygMvBaCEgBGqi4gIYSQeEBHjRASOYw5aj4cNQB4dGXj5WHbRvP4wz83AgB2jBUwtT/T8GMSK5uHc6E/ZkkRVfWSH91SH6PkqMnoiNbxEEIIaS501AghkSNIj1pYyEvhKDgq3chdz7wS+mPWOGp1kh9NoeZc+hiFHjU5wZIplIQQEi8o1AghbWeiUMIZ//dXfP2PywEES30MC1mbqS4NiS41PWoujpqu6+6lj4aj1v6AGssg7yLPR0IIiRMUaoSQtnPXM6/gbyu34YdLVwCQHLWkf0ftL8tfwYW/fBLjUm+PH+TFuZfBySQaqO6n22uXK5Yh9I+bUItCkKj876jnEhJCCOku2KNGCGk7IxNmSMJEoSSlPtbfS8oojtrZ1/0DALDblD588g17+T4WeWFModY5qK+Vm6iRAzrcSh9LERBGcp8cR1AQQki8oKNGCGk7ogwNqARN+OtRs7/NxqFgyYLywjjPhXFoNLvfy0+YiDjfsqmEbXmtSH2Mgk4vSucgSx8JISReUKgRQtrOJikFcNNwztcctUzSfq6Uh7vaIi+Mo9Cj1C00u2xPFYJu7pNb4iNgltxGzVHjxgEhhMQLCjUSO9ZsG8PZ1/0dD6/Y0u5DIVU27jTdr83DE74ctbRDH1tCC6bUigxvaArNLiNVo/TdovXdEh8B89yJQsqipUeNQo0QQmIFhRqJHZf89l/4y/JN+I8fNz4cmYTDK1KZYsVR85766BTPH1CnWRbGdDDCo9lCrTZMxMVRyzknPgLmBkEkhJrk6lGoEUJIvKBQI7Fj22i+3YdAFGShtnk4ZyzqUx7CRJzi+YM6agVL6iMXxmEhP69eBLhfasJEXHvUqo5a1sFRi5RQo6NGCCFxhUKNxI5p/Zl2HwKR0HXdEvyxachfj5oTQR01y9yqKKRJdAlCSCUTGrIhDykHbBw1t9THQlWopTvAUSvxfCSEkLhCoUZix9Q+U6g1O4mO1CdXLGOiYC6qNw1PYKJYWUg7lTXKOPUiaQjoqMkpexEIk+gWxPOaSmg1r+tIrmh3F1/4GXhdr/TRcNQiMPCcpY+EEBJfKNRI7JjSlza+3jKac7klaQWq0No5XsDQeGUhPbm3/qjHA3ebjFfvPr3m58FTH+UwES6Mw0K8zulkomb23QFf/hPWbBsL5fEFnlIfHUofhaMWhTl6LH0khJD4QqFGYoe8nts0RKHWbkrKYnhooojhiQIAYFJP2u4uFpIJDTd9+KianycCKjXZmXFLDiT+EP1+qWStowYAtz62tqHHV0sf3YJgxBw1p9JHUXKrPmY7sG4ctP94CCGEtA4KNRI75F3pVwIORSbhoZYXvjI0YYjpSb31hRoAaJpWE9MftLvNUvpIByM0ClJAjJ1Qa3RmmVr6uGbbOHaOF2xvazpqDkKt2uAYBaEuPy9MISWEkHhBoUZih7z43kih1nbUBfbwRMXtyCQTvkIn1IRILYw5ahEofesWRK9VOqnVlD4CjZcZivNIPPbX/rgcr778HttAkPFqmEivU5hIVfRHoYe1wDlqhBASWyjUSOzIS+VDr7D0se04LT4n9aZ8iS3VUQvco8aeoKZgOGpJ+9THRt0rIchkF3YsX8ILm4Zrbpur9h46hdUkIuWoSaW43DgghJBYQaFGYoe8+N5ER63tlIyQCauyGvTQnyajzlMLPvCapY/NQDyv6UQCKRtHrdEofKFh1ACaJ1bvqLltvo5QE+5sNBw1lj4SQkhcoVAjsYOlj9FCOC19mZSlJG5ST/3ERxlVqAUdeM3Sx+Ygpz7avTKNimLRyzWlzzon8YnV22tuK4RaNuUeJpKPwOtfosNLCCGxhUKNxA558c3Sx/YjFqKphIZBSZx5DRIRpGpKH63f3/zoalzy23/VdUmKSk/QE6u348t3/BM7x+yDKYg3ClLqo52GHm1wlprQMJOV8+bpdUM1t63nqIn5auP5xue7NUqBQo0QQmKLvy1rQroAlj5GC3kB35NOYutoHoC3aH4ZNaBCFgO6ruOiXz8NADhxn13wmr1mOj6OPGC4WCrjtB8+DADIppP4/Fv39XVMxKRo9KglbHsPRYhMUIQAn6IItTEbsSVKCLM2JZgA0F+drzaaL6Fc1gOPegiDkqUUt/0OHyGEkNZBR43EDlmobR3NG7vrpD2YjlpCcdT87SOpjpo8AkuIPwBYu33c9XFkRy1vcV8p6hvBSH1MaLalj40KNfH4qhNrF8CRK1ZSH50ctQFpEPZYNSGyXciluPysIoSQeEGhRmKHWj60aZgL8HYiFtippIbBrLnI9uuoqT1q8rDi1dvGjK9XbRn1dDwAkJMW6XMm9/o6HmJFTn20K30cmmistLRshIlYzxu7AA6zR83+EtiTThipoY2WZDYKU0gJISS+UKiR2KEGBLBPrb0IxyOZ0DDQUI+a9eNMDmFYIwm1FzaNuD6OXF4mC7wZAxm7mxOPmHPUErDz1Bp11MTrPaVPddSchZqTo6ZpmlH+ONJuoVaSS3FZ+kgIIXGCQo3EjoJSPrSZjlpbMdIAa0of/faoWRf/Jd1eqD3/Su1cLcv9JIG3YrMp6mSHjvjHcNQSzXHUxOumOmp2fV315qgBZvkjHTVCCCHtgkKNxA6xsy/KnoYa3MknjSEWosmEZhFUJyx2DvywQ8y+EsjpjrIztnb7OHQX0SU7GM+/Ygq1KAw/7mSsYSK1vx/JFRuaW+Yk1NxKH9UAGpnoOGpyzySFGiGExAkKNRI7xA779P5KKVujJVekMYxByEkNr993FgBgvzmTMG9an6/HSafU0kfz63U7xpXfOQuCgsPvWHbWGGbpo2Zb+qjrwGgDcfhFH6WPXhw1I/kxF50wETpqhBASLxjPT2KH2E2fNpDB+p0TGG6w5Io0hliIppIJvO/oBZg9qQcn7T/L9+OklQh1uVRxRFlsl3Td8cPPScTRUWsMs/TR3lEDgPF8CYM+Q2QE4vUeyKaVn1de06R0fhjx/A4DryuPU/ld20sfGc9PCCGxhUKNxA6xKz21j45aFJDDRPqzKbzj0N0CPY6a+igLrpwSse7qqDm4FqUy3YxGKFoGXtsrtUZK+0pSCa1KoVRGMmGKsnphIgDQn4lI6SMdNUIIiS0sfSSxQyx2ROnjCIVaW5FL4hpBnaMmh4moAsBNqDmVONJRawzxvksnErZz1IDG5oSZ8/g0XPaOA3D6YXNr/rb6d5zi+YEohYnIjhqFGiGExAkKNRI7RPnQVNGjlmPpYzsxHbXGPo7UYIiyxVHzIdQcfldi2VlD1JujBoTnqL3v6AX42rsOMn6niu96A68BuUetvUKtZHHUeA4SQkicoFAjsUPsSk9j6WMkKBnx/OE6amVdx8otoxjLF306atbbTqsKejpqjWGdo2ZPQ45a1UFNVM+jZEIzhlYXLLPIysZwbDdHzUx9bG+YiCzO6KgRQki8YI8aiR2GUBugUIsCheoC3q63yA9qj9qyNTvwum8uxdypvbU9am7x/IogmzGQwbbRvKUEjfinKM1Re+3imbj3uc01t2lEiMilj4JUMoF8sWwR6vLX7nPUohEmIm8qNCJkCSGEdB4UaiRWlMq6sZtuxvOz9LGdiAW8KrT8ot5fzEBbu30cPWnnoJGa41F+N70/C2DE9T6kPgVpjtr7jl6AKX1pzBzowZNrd+Cmv63Guh3jRmx+EMTrk5DqKjNVoWaZRSb9DU9z1BoYGRAGhRJ71AghJK5QqJFYIS90pvVnAdBRazdFl7Q+P7iFkagCwE/p44zBbPXnFGqNIIfGpJIJnHZoJezjuL1m4PdPbcC6HeOBHSO5H1E+j8Q5Ib/vxd9IaBXR6AR71AghhLQb9qiRWJG3CLXKvKV2x2/HHTm2vRHcFt1qpaN7PL/qqLFHLQzkOWoqogQxqFArOgg1cU7Ir2nOSHx0nqEGAH2Zyu/H89HpUds6kqOzSwghMYJCjcSKgrQQFHPUxvKlGheFtI6iTW9RELyUTopSN7fFrjovbaZw1LhAbgg3QS6E2s7xAu5Ytg47x/yVI8vDzWWhljGEmvma5jzMUAPM86nd5Yby+TiaL2GPz9+Jpc9tauMREUIIaRUUaiRWyKJgsCdt/JyuWvsoSr1LjeAlNbK36pL46VEb7ElV70Mx3wjiebUrURXpixf9+ml88ufL8OlfLPP12PLraQ0T0ap/u7b0sZ5Qy9i4ce3AboPg23e/0IYjIYQQ0moo1EisEIu0dDKBTCphLBDZp9Y+hABq2FGr65BohkhwTX1UFuaiVI89ao0hnCm70kfhXglR8pfl/hwjWczIYSLicfPF2uHnbkEiQOPlmGEhzrvJvebG0typve06HEIIIS2EQo3ECrFYFAt24apRqLWPQtm5d8kP/Rn3nqNsKmmUxTmJrvU7xvHXl7YCqJTQXXrq/oaAZG9QY5jpnjaljw26qU5hIuK1kx01MarBbYZa5TijUfoojv284/cwfpZt8PkihBDSGfDTnsQKUcYkdssnVcvaGNHfPoz5Vw2GiUyp9hw6kUklkKy6LWUHR+3fr3rE+PrKMw/D+1+90Fj4FyjUGqJozMtzDhMRDGb9BRLLDqlszIrHLdjMUavfo6ZZbt8uhFu4x8x+XHLK/gBqU0wJIYR0JxRqJFao5VcDVaHGHrX2Yb4mjQm1qXWEWjaVQNLoWbIXXau3jRlfC+Eo/s8etcYoughyVTRNH3B/LVVK0ogHrV7po5H62Fmlj+mkWaqdK7Y3iZIQQkhr4Bw1EivE7ng6JUofhaNGodYuSiGlPk7pS7v+PpNKQPyFsgd3TBwPe9TCwe11VhM7pw9kAz12UrM+tl3po/8wkWg4asmEhmxaCDVuGhBCSBygo0Zihbw7DQCDWdGjxtLHdlEIKfVxar8HR81Hv5kQaH7uQ5yRXS8V1d3qq9Nv6PWxGyt9jEjqozTWQMx+yxUo1AghJA5QqJFYMVENEkgrpY/DLH1sGyWjd6nR0kd3R00OE/EiupKGo+ZeLkm84SbUVNHk18Vyemzx2lkGXhc6K/WxJIXtsPSREELiBYUaiRX/eHk7AGDRjH4ALH2MAm5pgH7oTbu7MJlUwohud4rnlxfvW0ZyACD1tdHFaAS30kdVNPktMxWvp/rQdsmN5jw3b45avlSG7jLOodkUyjaOGksfCSEkFrBHjcSKu595BQDwhv1mAZDj+Vn62C7MHpzG9o00zV3oZVMJ5Iq17liuWMKFv3wK9z2/2ZLwd9SiaQBM95U9ao0hnvOEzeukiqagjppaPmvMZ5Neu5IkfNyQXb5iWW94IyEoxkZGIoFsuvI1hRohhMQDCjUSG7aN5vHMhiFoGvC6vWcCMGPAR+iotQ3hVDV7IWyJ55eE2pNrduKOZestt3308683Ai3YoxYOYiSCl9THvF9HzUEEinPKzlGzE4yWY5JEX75YruvANQtRrt2TNv++mAVHCCGku6FQI7Fhx1geADCQSRmLcJY+th/hGDTao1YPOUxEdtS2jeYst0towMxBM3XQjOenUGsE83WuP0etGLhHzfpzuXxRvW29lFF546CdyY8T1Z66nnQS4gyko0YIIfGAQo3EhrF8ZRe6V0qUM0sfKdTahdEz1GDpYz0yUpiI7KhtH7OWvfZnU5YySnPgNRfHjeAUoQ+EFyaSUs6hlG3po7dS21QygYQGlPX2Db3Wdd0IDsmmEoYrSaFGCCHxgGEiJDaMV8uF5Ohvpj62n6JLGqBfzj52kePvnBy1HapQy1j3r4TzUmKPWkOIwA/b1EelHNJvJL4RJqJc0TIupY9e5vaZA7PbI4wKJR3iVM2mk1KYCEsfCSEkDlCokdgwbjhq5kLcLH1kmEi7kOdENcrFb98X//vvh9j+ThZqZV0WannL7dT1uzHwmqWPDWEGfoTvqJUd3LqUzSw0o5/Ng1DLtHmW2oQkyHrSZjx/oaSzFJcQQmIAhRqJDaL0UXbUJrFHre0UHcrWgqBpGnab0mv7u0wqYZS7lSylj1ahppa5sUctHJwCPwAgk7SOVvAr1JxcWbd4fi+Omt3A7FYiZr5pWkU0ZtPWgBNCCCHdDYUaiQ3jhYoYs5Q+Zis9aiO5YltnJcWVsXwRj67cBiAcRw1wdkqyqSTEnyi69Kip/T925ZLEP65z1GocNX/PddlRqFVfO0loOd3WjnaXPorEx2wqAU3TLEmUE0x+JISQrodCjcQGI0xEGowsvi6VdS7E28Bnbn3S+NqLw+EFu7AKwOqolcvOpY/qolwcl98kQmJFjGGwE0jqaAa/DlbBISDETH00X29fPWoprXr/NjlqRRHNX/mcSiUTxnEzUIQQQrofCjUSG8ZtSh/lUiIufFrPnU9vNL5WhxUHxU4I9KQTOGrRNCO+nY5a6xFaxzZMpMEetfF8xS3vz1hLKO1KH8XA66QHB9foUWubo1b5u1np+RFfM1CEEEK6Hwo1Ehvs4vnlUiIOkW0vYTlqag/Upafuj2VfOgnH7jnDIUzEPUhGLPbZo9YYQiDZvs7KU1vW/T3fo7nqJkzWmthpV/pYdAgescNuDlsrUR01oJL+WPkdN5YIIaTboVAjscEsfTQXc4mE2ffBhU/r2XVyj/F1OaQeQdWxGexJGQtdURon5mrpul5T+uj0eMWyzj7GBii6pC3avff8uGpjdR212tTHTggTMYZdpyShJhy1Aj+vCCGk26FQI7FBlEf1KYs5s5SIC59WM0sSamHhFK8PwAgTEaJwNF8yBMR333MopvdncN0Hj1Dubz4gXbXglH2EiQD+xNGoUdaszMCzmaPmdeA1YDru+WKb4vlFmEiapY+EEBJHUvVvQkh3IAZe96pCLZ3AcI4Ln3YgnK3FswZwzB4zQnlM1bGRgyqSykw00bcIACcfNAcnHzQHmlISJzt0xbKOlPX0IR5xG2z+6t2n4/TD5mKf2YP4798/W7m9j+THserA+v6slx4153luKu0vfbRz1Fj6SAghcYGOGokNdnPUAGnhw1KiliMW0Be/fT9PceleqBl6LDtq1S/FYl2IcxF/roo0wFysy/cj/im5CLVEQsM3330wznnN7oYjGoajJhwxOQjGKMH00qOWaneYiI2jlqajRgghcYFCjcQGu9RHgKWP7STMYdcCVQgkbRw1U6hVXnO70ju7x2PyY3BKurf5ZUFcLKdNGOGaPfDCFrztuw9g484Jfz1qNo5cKzFTH9mjRgghcYRCjcQGM/VR2XVnz0fbEAtgdY5WI9SUPro4amJmWtalnlF26OioBaNc1iFyWOqJcsMF81P66NB/Kruh/1o/hIdXbHEtwaw5lsjMUZN71Fj6SAghcYFCjcSGsWoZUV9a7VFj6WO7EIvxdEgz1ACb0kdJBAqRIMJEcoZQc/77iYRmlONx6HUwSlJaZr1YfLsAkHqIeP5+h3h+wUShbIaa+OlRa/McNTmeX4i2cY4TIYSQrodCjcQGkfpYEybC0se2IRbjXhbNXlENG3mxLvqShKsiZue5CTXAFHgsfQyG7ETWGzQdrPSxvqMGVMRNUQy89lX62ObUR+n8FH14o9UAFUIIId0LhRqJDXYDrwHGXbcTIXya6ajJMexCEApXRYgBtx41+X4sfQyGLHDr9YalA5Q+ijCRfjWeX1HtE4WSGWriI0ykXY6akfooOWoDPUKo8fOKEEK6HQo1Ehucw0TY89EuDEctpMRHoNYpkR+71lGrlj6m3TP35aHXxD+ywK2XtpgOUPoo4vn7lHh+0WMmmCiU/PWotT1MpLZHbaBa3jmSK7TlmAghhLQOCjUSG4xFT6p2jhpglsERd5ZvHMJfV2wN5bHMMJHwPopq56i5xfNXhVqdvy/EXqlMMR+EUgBHzU+54ZhDPL96XsmOmpdyW+G0tkuoGWEi0meWKdT4eUUIId0OB16T2GCU2SllbuxR8854voQ3f+cBAMA/vvgGzBjINvR4rQ4TUeP586XaOVW2j5lob69SpyP6wjStVkirpAK4WKJHrV+N50/U9qiZ89zqn3PC3WvXZ4Pp+JrH2m8INfaoEUJIt0NHjcSGosP8JFH62K4+lE7iT//aaHy9Y6yx0itd183XJMQwEbfSRyHiRAqhsRCu06OWZo9aQwgj0ktfWCZI6qNw1LLq6A3r3xvPl83SRw/Hogr7VjNhxPObAnQwyzARQgiJCxRqJBbouu446JaOmnd+++R64+uy3tjiVe73Soc48FrtgUpJbp0RClJS4/nZo9ZM/CQt+i19LJTKxiZLPUdtoig7avWPxSh5bfBcD8qEzUaCCBMZmaBQI4SQbodCjcQCa+qcUvooetSY+liXLaN542s/qXx2yPcPNZ5feai0TZiIWHiLBX7d1Ech1CSX54dLX8RR/3M3Lrj5CehtWsh3Ck6bJHb4naMm+tMAmx415XWdyJccnXU7hJgrtankVXwmyRsJovRxmI4aIYR0PRRqJBZYwgyS9qWPdNTqU5aex0bLweQ5WU0tfZQdtYQ1nt9cCNcrfax1eX75j7V4ZSiH3zy5Hi9vHWv8wLsYca7U608D5Ofa2/txaLxSgptJJWoEd1r5exPFkvHa15vnBtQK+1Zjt5EwwNJHQgiJDRRqJBbIiz51IW+UPhYo1OohlzsWG0xAlN2pMEsftZo5apKjppQw5oveetTsAi5kobly62gDR9z9+HHUMsoctSfX7MDx37gXf/znRtvbr90+DgDYbUpvze9qBl77dNTMtM/2CrWsjVBjmAghhHQ/FGokFsgLLXXxxoHX3pHXq40uXuV5Vl6clqCkJedE7TnKeSx9FI8hi1P5379qC4WaG+L59pK0KNxVIYQffHELXt46hruffcX29mu2V9zMuVNthJpa+lgoGyMWvISJJNos1OzOT6NHjUKNEEK6Hgo1EgvkkjVVE4hhxyx9rI9c+thosEYzhl3bIfckJpSeI69hInalj/LXFGruCHfMyxQGtfRRnHNFh1JI4ajNndpX8zv13PI78LrdYSJCrGakJ26g2oeXL5aZVEsIIV0OhRqJBcIJSSe1mtI4pj56Ry59bNhRa8IMNTvkxbqTo1a39DFRG3Ahu2vPbBgK52C7FLP00YOjprhY4rUqOJxva7dVHLV50+qXPk4UzB41L32RqrBvNaIcW3bU+rPmpgL71AghpLsJNPA6n8/j4Ycfxn333Ydly5Zh8+bN2LFjB6ZMmYKZM2fikEMOwfHHH49jjjkGmUwm7GMmxDfmjn7t4oylj94JU6gZjlqIQSKChGaWacpllcYcNSVMpH7po7VvSv3676u244o/P48lb1zc+MF3IUJseWlFFP2AwvkSr2PBYSPFzVFT3+/jhZJxPngpw1Tn7rUaw1GTzs9UMoHedBLjhRJGckVM7ec1lhBCuhVfQm358uW48sor8dOf/hTbt293jKS+4447oGkapkyZgve///0499xzse+++4ZywIQEQSz67EIrjNRHhonUJcwetUITHbVrPnAEfnTfCiyeNWD5udpz5NVRSyt9U0BtKuGdT2+gUHOgEUfNKH10ON9Ej9o8mx41lfFCyRge3alhIkAlon+8UMIwZ6kRQkhX40morV27Fl/84hfx05/+FOVyGfPnz8fb3vY2HHnkkdhnn30wbdo0TJo0CTt37sT27dvx7LPP4tFHH8V9992H//3f/8X3vvc9vO9978Nll12GuXPnNvvfREgNRoCAjXtjzlGjUKuHNfWx0TCRajlqE3rUXrfPLnjdPrvU/FxdeBsL4bR7j1rKxlETj/GLj7wa//Z/f8XqrWMolXVPvU9xw83RVkkqZabinLOL688Xy9g4NAHA3lFTmSiUjQ0CdTC6He0OE8k79FD2Z5PYMgKMFyjUCCGkm/Ek1BYvruwSf/jDH8aZZ56JY4891vX2r3/9642vH3zwQdx444248cYbceutt2JkZKSBwyUkGGJxZrejn02y9NEr1tLHYMK2WCrjyvtWGIvQVJN71GScHLVMnWNQUx91XTeE6oLpfUgnNeRLFdFgFxMfd8R54yVp0alHzW7A+vod49B1oCedwIwBbyWA4/mKuPFScmvM3YtQ6SNgitlGh84TQgiJNp6E2kc+8hF89rOfxezZs33/geOOOw7HHXccvvKVr+DrX/+67/sTEgZuc5zUnhjijKzNgj5f3/rz8/jR0hXG983oUXNCFQG5QnXgddpbj5oQl/K/PZtKYN60Pry0eRSrtoxSqNngK2lReT8KjWQ3t0/uT1NDgpwYzZc8H0uijYKoWCob56m6kdDu3jlCCCGtwdNW9re//e1AIk1mzpw5+Pa3v93QYxASFLfgimSby5s6iUbDRMpl3SLSgHCHXdcjoSxwhWNRL55fOLFCPMgL91QygYXT+wEAqzj42hbhvvpxsQxHrSxKH2vPN7cZal7/jhvtFERyP6STo9bgzHlCCCERh/H8JBa4OmosI/KMpUctwPM1mq/tqUmn2umo+Rx4XRKOmrlCTiU0LJhe6Y96eetYuAfcJQjN4aUvzKlHzd5RE0Ei9fvTVPwcS7kNmzjyjDT1/FQ3HAghhHQngYTa2WefjXPPPRcvvvii6+2uueYanH322YEOjJAwMXrUbHqR6Kh5R85zCOao1f7MSxJgWKivtehLrJ/6WC19LNU6aulkAjMGsgCAofFCuAfcJRiOmo/Sx5rURztHbZsofQzgqHlw94xesDYKNU2rfd7aKSAJIYS0jkArpOuuuw7XXHMNjj76aNxzzz2Ot3vwwQdx/fXXBz44QsLCzVFr52Ks09AbTH20c0XSLexRU4WaWfpYZ+C14qgVpH9HQjN7iJgcao8Q+J561JT3o1qmKrNhZ0Wo7RZAqPk5lnaEicijI9T+O35mEUJIPAi8lb377rtjdHQUb3nLW/D9738/zGMiJHQKLj0y7U526yQaTX20c+Ha6qh5Ln2071FLJzVommaEkeQp1GwRAt1PPH/RKH2sPoaNoyae776Me4+hHV7Ou3aGibglkrIKgBBC4kHgFdJrXvMaLF26FNOnT8cnP/lJfOQjH0GxyJkuJJqUjDlOzoueos2OPbEirwuDOWo2Qq0NjpoQDl4dNWPgtUh9VMY90FFzRwiKII6aWfpY+9yK23jpN1PxJBq19m3iiHMtYxN0087jIoQQ0joa2so+6qij8Pe//x2HHHIIrr76arzhDW/A1q1bwzq2SHDZZZdB0yq75j/96U/bfTgkIG7DlcVim7vT9ZF7YoI8X3b3qTfDLEzUMJFCdTGcrnMMZuqjNUxEiEzhyHEWnz1upccqNT1qYuC1zbljPq7/c8iPu9eOzwa3TQTxz+VnFiGEdDcNr5Dmzp2Lhx56CKeffjruv/9+HHnkkfjnP/8ZxrG1neeeew5f/epXPc/nIdHFbY5TMsl+D680Gs9vW/rYyjlqVREgwmWcBgqrmKmPIoFQlD4mLPdn6aM9jThqwkizc9T8PK7T33GjrUKt6HxuJlmuTQghsSCUreyenh7ccsstuOSSS7Bq1Soce+yxuP3228N46Lah6zrOPfdcTJkyBaecckq7D4c0iNlTZLPo0djv4ZXmlD62x1Erl3VDsNVz9czUx2qYiJjLV308MYeNpY/2+Bl4rZYiiwAbuz4xw1ELIPa9HUv177Sz9NHm3BSlnhwpQggh3U2oK6SLL74Yv/zlL1Eul3H66afjrrvuCvPhW8rVV1+N+++/H9/61rcwZcqUdh8OaRBXR03awde5Q+1KqQmOml05arMw0hvLZUtyY7pu6mO19FGJ5xdCjY6aO8L58VKiKNxLY+C1UfoYco+apzlq1te9lRijI9K1z5mx4cDPK0II6WpC38o+7bTT8OCDD2Lu3LnYsGFD2A/fEjZu3IgLL7wQr3/96/He97633YdDQkDszttFwcslUDTV3Gk0nt++9LGVjppZ+mgZKFzXUbOGkBQNJ8caJmIXIU9MoZPw5GJZEzbdUh/99L7JaJrHY4lCmIhL6iPnqBFCSHeTCnKne++9F7Nnz3b8/cEHH4x//OMfuPLKK1EqdV5z/QUXXIDx8XH86Ec/avehkJAwFtZ2qY+SeCuVdQyNF7BtLI89Zg607Pg6BXldGFY8f70gjzCR3ZqCtPD3XPpYtCYQCodOuB4ME7HHdNR89KiVlNTHquMt9wz7if23+xv1iEKYiF2PmnAQ6agRQkh3E0ioHX/88XVvM2PGDHzxi18M8vBt5Xe/+x1uvfVWXHLJJdhrr73afTgkJMTCOlnHUVuzfQwnf+9BFEplPPTZE7HLpJ6WHWMnIC9YO3ngdaFUNhyLVEKr666YARdWRy2txPOz9NGeQD1qZTFHzTzPCiUdmZS8sVL5v98eNa/Crp1CLeclTISOGiGEdDWBhFq3MjIygo997GNYvHgxPvvZzwZ6jFwuh1wuZ3w/NDQU1uGRBjAX1s49agDwP79/FmP5iiuycssohZqE2r9XCtC30+6B1/Lg6oJRDuulb0qUTCphIsJRY4+aIxOFEn60dAUAb31hNT1qls2BMjJSxb5wdb08rozXcy6yYSJKMiYhhJDuxJNQO/vsswP/AU3TcM011wS+vx9OO+00PPvss77uc8MNN+DII48EAHz+85/HmjVrcM899yCbzQY6hssvvxyXXHJJoPuS5mHu6DunPgLAqq2jxtfbx/LNP7CIsXb7GPozKUztz9T8Tl0ThtWj1ptpnVCTHRI3x0IlrcT6q2EiTH105tZ/rMHO8QIAe0dbxalHDYClXFW+jZtDduj8KXhi9Q7lb3h11Koz3doSJuJ8fqrzAAkhhHQnnoTadddd5/g70S/glJbXSqG2cuVKPPfcc77uMzY2BgB49NFH8YMf/ADve9/7cOKJJwY+hosuughLliwxvh8aGsK8efMCPx4JB7GgsSuzkxdtozmzx2jbaKH5BxYhdo4VcNzX7oWmAff+5wmYO7XXEvShLgrDSn3sy7TO2E9LQn286px6cdSMtMiSQ5gIHTVHXhkyKwxyhfrPT02Pmhxgo4S1eBl4ffX7D8fvntqA4YkCvnnX8wB8CLU29oKJc0lsAsi0M+SEEEJI6/C0Qrr22mttf67rOs4++2wcd9xx+NCHPhTqgQVh2bJlge975513olwu4+mnn8YJJ5xg+d3y5csBAF/96ldx9dVX481vfjM+97nP2T5ONpsN7MaR5iFK1ewWaJqmIZnQUCrrGMkVjZ9vG83V3LabWbdjHACg68AJ31yKz71lH5x3/B7G79VFYVhz1PoztQvRZiH3Mo3lK6911oOjllEdtbJ1jpoQasWyjlJZDzSAuVvZdUqv8fVjL2+re/uUMoC+7JI0agy8dnHqpg9k8YFjFuIX/1hj/MyzUEu2z7lyG3idMBy1lh4SIYSQFuNJqH3gAx9w/N3ZZ5+NPffc0/U2nYSb2Fu+fDmWL1+OhQsXtux4SDi4DbwG4CDU4uWoqYvXb/zpOYtQUzfvgwQZ2DkTfdnWOWryv3GsUHHUvJQ+CvEgBL96PsliL18so7eF4jPqyK/5whn9dW9vlqeWq/+Xw0TsHTUvPWo9afM18Zz6qLVRqFUTk+02EuioEUJIPGhdc0jE+cpXvgJd123/EyL0xhtvhK7rrqWgJJrU62WxW7jFrUdNTWR8zV4zLN+rIitQj5pNr09/K0sfk3alj14i4619U2qYSEYRasREFvSXnnJA3dunlCHT8mknz1LTdd1XmmSvJNS8OmqiorKtYSIujlo7BnETQghpHRRqJBaoc69U7BZu20bjJdTU5PwBxelSd++DzFGzE3d92da5T8mEBmG+jFbdUy+OmoiEFwJN7Y1KSY+b68DZkc1EvOanHLwr5k/vq3t78V58acso7li2rib1USCfSl4csp60+TrXm5tnPm7ldrre+ij8iWo/X49bmAgdNUII6Woo1EgsMAdee3fU4ibUVEdNLffSFV0WVupjKx01wHytxws+wkQUl6egnE+aphklal4CM+JE2YfrBVgdzk/+fBnW7hgzvhcDxwHrueQlTVJ21OZNqy8YAWtJZatF0UT1/MymbcJEOEeNEEJiAYUaiQVm+INzj5pK3ISaKqJqghtqHLWQetRa3M8lzgExL8+Lu1Lbo1br0BpDr5nwYEG85n6HTAu2jZjvQ3kzwSLUfPaoLfLQKwdYBWCr+9QmqqWPPTZCLdHGNEpCCCGtg0LNA9dddx10XceZZ57Z7kMhASnVcdTsFpHx61FThJoiOMJIfbQrl+xvYZgIYIqrMT+lj8rAa7twmoyYpUZHzYKfwA+gdjMlId1PnqMmizYvIlAWPHvM9CjUpL/94/tfchxD0wyEoyaXbBrHVf0RHTVCCOluKNRILBALvJSDe2LntI3lS8ZiqduwW3Cqiz5ViNX2qAWI57cNE2m1o1YVar4cNbX00RrPD5jpfHTUrIjzJBHQUZORNw/k889vj9qiGQOejkX+WPjWn5/H757a4Ol+YWAINZs5akaYCIUaIYR0NZ6EWjKZdPxP0zRcf/31jr9PpVq7W06IHX4ctZ50wuiT6cbyx/+581m89hv3YueYdfyA04wqgWqGhdWj1sp4fsAUXWO+etSqpY8iMt5G+Gc59NqWeu89FTWFMyeJM/mck7/24qjJzunCGd561NQNnH+tH/J0vzAQzqxd6WM7xwYQQghpHZ6EmlNsvZf/ygGS4QgJGzVOXUVeRPamk5jalwHQnULtqvtfwppt4/jZoy9bfl7To1aq56j5f2/b9dT02ixEm0lahInkvc9RE7cpOISJyLfJFbvThQ1KyWeYiHo7WfjKc9SEA5zQKmEu9Zjen8U+swdx8NzJ2HVyb93bi8eWaaXDPlF0Ln0U5x3nqBFCSHfjaSubYot0Ov4ctSQm96axaTgXqz612jCROj1qAWY42TkAXhfwYSECIsbylR41P45aqVzZgLILE6GjZo8Q54mAPWoyxVKto+Z2e5lkQsPvL3iNZ2EHVG6X0MxRAC0VakaPmnPpIx01QgjpbtijRmJBvR41VahN6+9eR02ghjv4LX0Mq0et1aTV1EcPjpp83hRK5qBla5gIhZodwvlycrNV3G5nl/roR+hX5uj52xiQhWArhdq4W5iIRkeNEELiQCChtnTpUs+3veSSS4L8CUJCRZTpOS3qkkooxNQ4CDXluVCFV6Fe6WOARWIUHAAj9bEq1LI+Uh+BSvmdUUprW/pIoSZTLPt11Jxvl7d11JrryMqHPd5SR61yHmXdwkQisPFBCCGkeQQSaqeeeioef/zxure7+OKLcemllwb5E4SEiumAeOhRyyQxrdqjtr2LhZq6cK43Jy2M1McozH1KKo6a0zkhI7tuE4WSWUprCROpLKjpqFkxnS9vt/eb+uhl2HUjyKfsRAtHLwj3rtcmFVU8R1F4PxFCCGkegUsf3/rWt+KFF15w/P0XvvAFfPWrX8XixYuD/glCQkPsPCc9DLzuSSUNR21rVws16/fCdRSipFU9aq1GCLPxvPc5asmEZowRGJooGm5jWnbUqkokx3h+C+K8cXrvqXjtUfM7ny0oBel90MrSR7fURyNMJALvJ0IIIc0jkFC74447MDQ0hJNOOgnr1q2r+f3nPvc5XH755dhnn31w7733NnyQhDSKEB1ph916eXHYk05gelWodVuYiDw/TXUuxCI4awg11VGzPlan9qiJf7fpqHn7GJzcmwYADI0XDGdHdnP6qkJODNImFYo+BZVbj5osmop1ypnDQt6fEOdMsymVdWMeX4/NRoJwwyPwdiKEENJEAgm1E044ATfffDPWrl2Lk046CVu3bjV+91//9V/4+te/jv322w/33nsvZs+eHdrBEhKUvM8wkW7tUZPFlTqAWPxOlPDVi+dXHTdPf7/6GG89cDZev88uuOYDh/t+jEYRYSJ+4vkBYFJVqO0cL5iltJLAl39PTMp+Sx9dBJ2do9bsHjWZVr228ogH2zlqdNQIISQWBC59PPXUU/HjH/8Yy5cvx1ve8haMjIxgyZIl+Na3voUDDjgAf/nLXzBr1qwwj5WQwGwemgAAzBjI2P5eFmq9abNHrduEmhwQ4tSjJlLmVCGmOmiBetSqjzlrUg+uOesIvH7f1n9GCMdmVJQ+elQQQogNTZhCTXZ/pvRVfr+DQs2C2aPm7XlWNxBkCjbDr5vdoybTKqEm98K5xfMH2SwhhBDSOXiao+bEWWedhW3btuEzn/kM9t57b2zYsAEHHHAA7rnnHsycOTOsYySkIYqlMjZWhdpuU+wH3VpSH9NJ9GUri6NWpry1gry00K3tUassfIXDpAoxNbdALY30QquS+tww3Ijq4Xt11CZLjlmuel7IDu0U8fsxCjUZv2EibsjnXLlFPWoyQxMFlMu6q5gMA9ELl05qtqWd4t/MdkhCCOluGr50LlmyBJ///OexYcMGHHzwwbj33nsp0kikeGU4h7JeWfTMGMja3iZlKX1MGN9HoacqTGRHQp0nZfaoVUsfm5H6WP0bzV7ouqH2pPntUds5XsCqraMAgLlTTeE/2XDUusuFbRS/A6/dKNo5ai08l3QdGJ5ofg+iMezaJpofMEUv56gRQkh348lRO/HEE+veJp2uLFLe/e53W36uaRruueeeAIdGSDhs2DEOAJg9ucdRIKg9aiJcJIhrFGVkoabr9kLMKH0suQuzIM+NWLS301FT/7bn0seeymfcluE8VmyuCLV9Z08yfj+lt1Iuyx41K2G6qHnbHrUQrDofVPrH0k39G8YMNZuyR8AsI41CiiohhJDm4UmoeR1w/eSTT9b8TN21J6TVrKsKtV0n25c9AtZeo9500ohwL3ZZbVGhKC90rb8TC2ohXGrj+a23D9aj5q9fqRmoqYJ+Sx8fW70dpbKOKX1pzJpkOrSGo8bSRwvlEJ2vdjtqAFBogTiaqIaJiE0TFTpqhBASDzwJtZUrVzb7OAhpGut3uPenAVbh0J9NGb1H3Vb6mLcMDLYPCxG7+LU9auocNf8iNgo9aqoD02czUNiOyb2Vj8sn1+wAAOwze9CyEcUeNXvEeRRGuattj1qLz6VWbN4Yw64dHDVRRtptn0+EEEKseBJqCxYsaPZxENI0NuysOGpzpvQ43kYWDgPZpPF9octS1WSXzCnFUcxtKpR06LpuiBE5Dr1Y1mscNi+USu1ZXMuojprTYlhFpD4K9tpl0PK9cNyGc0UUS2XHURBxI8wYfdvUx1Y7ai0Qam7DrgHz31yio0YIIV0NVxKk6xmpNv9P7nXuK5GDDvoyKWMx3209IJbSR4cUR7kUUP7ni6/TDqWRXhALy7YKNeVv93h21Kznj1z2qP5+qAWBE51CuGEiculu5fxrtTtbaIGLZYSJOJU+apyjRtrLSK6IS3/7DB5fvb3dh0JIV+NJqG3ZsiWUPxbW4xDih6KHviiro5YyyuOEq9QtyKWP6iJPLHyzUtKcLMbE89BfHV2QK5Ytg3m90I4hxSqq0+XVUVOF2nQlQTSVTGAwWylS2DHG5EdBKUTn68ZHXsZ//PgRlMp62xy1VpQbmj1qdNRINPnmn57DTx5aiXf+8OF2HwohXY0nobZo0SJcdNFFgYXWpk2bcOGFF2LRokWB7k9IIwixkXYZjCsPze3LJC237SZXza50TP1e3sW3OBjVReGMgSym9KWh68ALr4z4+vvib4ThrgQlnQhW+ji13zosfVp/7fB0UR65eTgX8Oi6jzCFGgA8vGIrHq8GuoT5uF5pRTm0KH3MOgTdGLMAu+iziXQWz20cbvchEBILPAm1d7/73fjGN76BuXPn4h3veAduueUWbNy40fU+GzZswM0334yTTz4Z8+bNwxVXXIF/+7d/C+WgCfFD0UNflOzw9GdTltt2U0S/LNTUxLiyTemjJbyh+mVC04xY+mc2DPn6+0a5motobjaqs9rrsfRRTQ2dMVAr1PaaNQAA+Mpvn+EiukozBFWxpLdPqBWbL9TM0B37S7QIZummzybSWTBxlJDW4ClM5Cc/+Qk+/elP44tf/CJ+97vf4be//S0AYNddd8Xee++NqVOnYnBwEMPDw9i2bRuee+45bNiwAQCQSCRw8skn47LLLsP+++/fvH8JIQ6IxUzapfQxqQg1eQhyNy2GCiXnMBHx75RLH0sWoWYujPedMwl/fWkrnvUt1Nrfo6Y6q07lZSq9mSSm9WewbbRS1ji9v3Z4+n+/4wC89uv34tkNQ9g0nMPsyc4BNnGhHGB23q8++mq860d/db1NuxJEW/F5IJIlkw4bGkkl4IeQVkOdRkhr8CTUAODAAw/EHXfcgbVr1+Kaa67B7373Oyxbtgzr1q2rfdBUCocffjje9ra34eyzz8bcuXNDPWhC/CDEiZuLk9RkoZa0LP66aZZa3jJHzT71MZPUkNAqDlrRpqctoQH7zqkkHgYVau3tUQtW+gjAItSm2Thqc6f2IZNKYKJQDhS20o0EKXc9bME0fPx1e+L7975o+3tNa99MvlakPtYToUbpI1fLpE2wP5KQ1uBZqAnmzp2LL3/5y/jyl7+M0dFRPPPMM9i0aRN27tyJyZMnY5dddsH++++Pvr6+ZhwvIb7x4uLIi6/+jLX0sRUpb63CrfRRnneVSiSQL5VtSx81TTNm0gnR4oWyFADRzh41eWGfSmieB14D1v49ERyiUilXK9PtqBJ03lm925thIsGOKyitCBOp95llhInwHCNtgpsEhLQG30JNpr+/H0cccURYx0JIUxALq7TLii4n9Z30ppPQNM2YF9ZNiyG3MBHZ7UomNKAEnP6jh/HHT78Wk3rSltLHdFXc5D3269z59Ab8161PYjRfSbNrZ4+aHCbix00DrGWhmoPYFP+2biqZbYSgIxncwn/KZd0QgE59XM2ilY6aU7m26ag1/VAIsYXnHiGtgXPUSNdT8DBvScwtAsxGfbHgbsXCrFUUXOL55TEG4t++fucErnlgpeX2CQ3IJM3xBV742M8eN0Sa+BvtQo7n9zpDTeCUwmd5fBH00EVObCN4CfOxw21guOz2tj71sYWOmoNYFY40y2tJu+imsTWERBkKNdL1GE6Ryw59zsYZErvZ3eSM5C0Dg+1TH5MJq6gVs9fk0kfhTto9b15oa49aA47a+45eAAA4YuFUx9sI4RDlRbSu67jsd8/gxkdebvrfMpxYn+WubudIoaS3beB1K3pWPfeoRfcUI10OSx8JaQ0NlT4S0gkI18etREp21ARGCVs3OWqSsFKbwWVHTXa8hHsmL7hFX1dQt7GdPWqyYPcr1N58wGzc9rFjsNesQefHrz53US6ZfXz1dlzzYMUpXTCtD9+/90Vc/s4DscfMgdD/VtCkT7dS5YLkqCWaLNQuOWV/fPNPz6Enk8Tm4VxLnFIj9dFJqDH1kbSZLrosEhJp6KiRrsfLzrudM5TsQkfNLZ5f7lGT+4OyaatQSyTk0sf6V+tc0UYEt9FRk/vM/JY+apqGQ+dPxYBDkAggO2rRPW+GJorG1+//yaN4dOU2fO5XTzXlbwUVam63zxfLUo9ac8+lDxyzEE9++SQcOm8KgNYMvK6Xjir2UZi8R9oFSx8JaQ0UaqTrETvgbj0vtqWPye7rNXKfo1b5XSKhQV4eCmFjCDXJUfMSJrJ+x0TNz5x6b1rB7Mnm/LPedPgfgWJxHWW3w87RtHudwqCkB3O+3MJEWt2jlkiY5b4tHXjt8JklXFsOVSftIsqfb4R0ExRqpOsxwkRcFn5upY+t2EFvFXL4R208f+X/qYRmCUwQokw8DQnNdNyKUvqeE2u2jdX8rJ2O2m5TzNEhfksfvZDsgDARu6ffTRg1Qlk6r/zgVqpcKJWxfsd4oMcNSrqFaZ71HDWh36Ls2pLuhj1qhLQGCjXS9ZRK9Uuk3nPkfADAq3efbvxMLBSjvOD2i3vpo9kXIztlhkNkOGqwzB7L1yl/FAtqGb/BEmGy65Qe4+tmvLKdMOPKzlFz6wlrBMOp9Rsm4iIcf//UBvziH2sBtC5BNOUz6bQRimX3HjXxXNJRI+2CZx4hrSFQmMj111+PM844Az09PfVvTEibKRi7084Luv84cj4O2G0y9pZCIoyY9a5y1JznqBmlZJpm6SsTgkOXSh/lRX2hVEaPizO1faxQ87NWR6rLDPakja+3+xjY7RVzjlp0zxu7Z79ZO+TilAszTOThFVuNr1s18DrdwnCh+o6adfOEkFbDTQJCWkOgS9wHP/hB7LrrrvjEJz6BJ598MuxjIiRUxKLHrbQrkdBwyLwp6JXCJcQOerGkY6JQwqah5vTwtBJL6aMaz6+bYwzknr2iEs+fSGhGmAhQv09tx1itGGrnwGuZbTbH1ijJDkh9tBvWvbUJohWQzqsQw0Sst2uRo5ZoLOnUDwVj9pz9vy3B1EfSZnjqEdIaAl3hzjnnHBSLRfzgBz/Aq171Khx99NG45pprMDo6GvbxEdIwhTpR106kJWfkjd++D0f+zz22/VadhFymqFZwyYOJ5Y164bSJRWFCq4g1sfCuVwq2w8ZRa2c8v8z20dpjaxTTiY3uSsburbBjrNAUESKEfphhIjKtGkhvhIlEoEdNbHSwT4i0C24SENIaAgm1q666Chs2bMBVV12FI444Ao8++ijOPfdc7LrrrjjvvPPwj3/8I+zjJCQwQoD47cFJSkJkzbZKn9XS5zaFe3AtRk6sUx21klT6aPdzufQRMJ/Puo7aeK1TM2Mga3PL1nHgbpMBACfus0voj53qhDARBwGwrQmumjjN/A+89vZ+Xbu9NZsnrSx9NFMf3eeoRXkzgHQ3jOcnpDUErhnp7+/HOeecg0ceeQRPPfUUzj//fKRSKVx11VU46qijcOihh+LKK6/E0NBQmMdLiG8CD9y1KWGzi/HvJKw9atZ/i+h3UZ8nsRiUSx8BM1CkXpiIcNROPnhXvGavGbjxQ0di3rQ+1/s0m5+cdQS+9Pb9cNmpB4T+2J3Qo+a0xto8nAv9bwV9/3ktj129rTasphkYKbAtEOD1Zj+K96Cuc8FM2gP3CAhpDaEU9x9wwAH47ne/i/Xr1+OnP/0pXvva1+LJJ5/E+eefj1133RUf+tCH8Nhjj4XxpwjxjZd4fjvMhZm54G7FIq2ZyMev6quSwy6+cIbM0kero1av9GzneEWo/dvhc3Hjh47Ca/aaGfDow2PmYBZnH7cIk/vS9W/sk07oUXMqmWuGoxZYqHl01PabM8n3MQXBSIFtgQAv1ulRk93JKJ9npHth2S0hrSHULuxCoYDh4WEMDw8DqOz0FQoFXHvttTjyyCNx+umnY8eOHWH+SUJcKZV1wz1I+wwdkMNEBK3qh2kGY/ki/vqSmZanXmidFodid78sxfMDQNbj0GvhqE3pzQQ88s6iE3rUnBLbxm3mCTaKk1NbDy8bK4M9KXzhbfsGOi6/iNLHQrH9PWpy6SqTH0k7kD9CmABJSPMIRag98sgj+NCHPoQ5c+bgYx/7GJ566im8853vxF133YWhoSH87Gc/w4EHHojbbrsNF1xwQRh/khBPyLvfSb+OWqLWUasnSqLMrx9fZ3FM1J14IcTUXqLHV+/AS5tHDMGbNBw16/OzYyyPkVyx5u+KHrUpTXCvokgnzFFzOrSwS3t1XW+49Njtvp97yz6Y1t+aDQAzTKQFqY91njP55xGusCVdjLzRx80CQppHoDlqALB9+3bceOON+PGPf4xnnnkGuq5j3rx5+OxnP4tzzjkHs2fPNm77nve8B+9+97tx6KGH4s477wzlwAnxguyG+XbUqosh2WXoZEdNDJ6e2pfG9rFCjZAoOiwOH3xxC0781n246C37ADCj3TOSozaeL+GQS/8MAFh5+VuN20wUSpgoVJ6zuAi1TnDUnBZWuZAdNfkp8B0mIm2sZFMJjOVrjy2bcp7fFzZ2DnuzKNUp15adtspmVOueB0IARaiVdbiM0iSENEAgR+3MM8/Ebrvthk9/+tN49tln8Za3vAW/+c1vsHLlSnzxi1+0iDRBKpXCEUccge3btzd80ITYMZYv4lePrbW4RvJiOejAXXmBWC84I8qIxblY3JbKlflwf3h6A4YnCo49aur9xdNopD6Wyli/0wx0kF0ZUfaYTGgYyAbeF+ooDEctwueKU39J2I6avBnQSDy/2BRQyTr8vBnI4zp2jOXxx39ubJrDLsSgU5+e/FkWZeeWdC9yuSPPQUKaR6CV00033YTZs2fj7LPPxrnnnov58+d7ut9pp52GBQsWBPmThLhSLut46/8+gFVbx3Dua3fH599a6VuRo7S9zmUSCMEyLgu1Di59LFUXf2LRW9J1XPq7Z3DT31bjxH12McpEnWacGaWPaupjsWxZMI/kiuipbq8aZY+9adshy91IJzhqTj0lYQs1WRD6HXidSSalr+0Fi5OAawZCNOWLOv79qkewfOMwPvn6vfDpNy4O/W/VKxdNJTQktIpj2elJtKSzeHLNDrywacTilrP0kZDmEUio3XrrrTj11FORSvm7+8knn4yTTz45yJ8kxJX/u/8lrNpamae0bPUO4+dyOZ9foSAWSbKjNm5TftUpiIupEKzlso6b/rYaAPCX5Zuw25ReAJVF4PuOXoAbH3nZcn+xuNdqUh91S9z7aK6IGQNZ/O6p9bj+4VUA0JR0xajSGamP9j8PeyOiEUd7cq95zjg9k+1y1JZvrARm/f7pDU0RasU6YSKapqEnncRYvoSJJgTAEOLEqT94qOZnpQ5PQyYkygQSau9617vCPg5CArN5OIdv3vWc8f2syT3G1059V14QPW3jBTMgYzRfG5bRKQjhkBGlj2rqY9VRSyY0XHrq/iiWddz86Grj9yLgoCb1sVSyiBIRKPLxm54wfjalNz5CrRMcNScRmSuGu+i3lD763CgZ7DEvT0PVEQ8qrexRS9v0qKUdnL5GMRw1lyqA3qpQa0ZSJyF+iPJnHSGdTqCrzOOPP44lS5bg73//u+NtHn30USxZsgTLli0LemyEeOKFTcOWBaFc7ii+TgcQaqL0cTRnLoTkrzsNU6jZOz4lxX1cMN06lFoEqQjRazhqRd0i+kZzpZrSuil98YjmB8zFdZQdNXVIsigtDLOM7v7nN+PH979kfO+39FHuaXM6rpaWPtrMVWzW3xd/w+05E+XFIqyHkHbBmWqENI9AV5nvf//7+OEPf4iFCxc63mbRokX44Q9/iB/84AdBj40QT6zdNm75Xl5IGSVEAXa+7cJE7OLnOwVxMc04CAl1dpO6SBSiN6HE8+dLZYswG80VMaI4j3Fy1MSmQDHCYSKqm9qfrSz6cyEt+p/bOIz3/+RRfP/eF42f+Q0T8UIrSx9Fj5r8+ZJttqPm8pxl05W/zdJH0m7oqBHSPAJdZR544AG86lWvwsyZMx1vM3PmTLzqVa/CfffdF/jgCPHCmu2V3rSetNkzJTDT0/wvEsUiSS59HOvg0seiGiZSJ55ffc7E8yoq2EQJZb5YtlyoR3JF7ByzlqrFylGrLuijvHhRD22gWmYYVumjLNCAYKXHXhDv+VaQSdWWtKZTzfl3ifemW2llb9VRY+kjaTcceE1I8wh0lVu3bp2rmyZYsGAB1q9fH+RPEOKZtdsrjtqiGQMArDveRgmRz8RH+T6yo9bRpY+6dfGnlquou/hJZZEoRhPYDbxWe9S2j+Ut943LDDXAPG+iXPqoLqz6M0KoheOobRnOWb4PKtTqOWZyMmSzMR0187lzSqNsFC+9taL0MezZd4T4xWlT6onV2/Hwi1tafDSEdBeBrjLZbBY7duyoe7uhoSEkW3ghJfFkzbaKo7b7jH4A1mZ/s5wvQOmjCBPpktJHo0ctWev4pBJajVBT+/qM0sfqz7NSPH9JKX3cUeOoxUeoJTsgTEQV6WLGXVhCTZ036HfYtWBSnZLZbAsdNSHArSM/mlv66FYJ0MseNRIRxIB2GV3XcdoPH8Z/XP03bBnJ2dyLEOKFQFeZ/fffHw8++CC2bdvmeJtt27bh/vvvx3777Rf44AjxgumoVYRa3tKj1rijJic9dnLpoxomMia5g5lUotZRq1P6aMbzly09TyO5InYoKX2TY9SjJhbXUXbU1GMzSh9DcmfCivmXkx/taG08f+0GR7PCROQEVidE2SdLH0m7sWvHld8nW0fytTcghHgi0FXmzDPPxMjICE4//XSsXbu25vfr1q3Dv/3bv2FsbAzvfe97Gz5IQtzYXN2tmzetMgesWJZLH4P3qIn7jOetjxd2hHmrMMNEKm/7bVJ5ogbzwip+r4pbtfTRSAos1TpqO2tKH+PUo1Z5fq57eBWeXLMDALBh5zhWbB5p41FZUUPa+kN21NT3SFAxMamnjqPWwnh+8Xkgh3c0TaiV6lcCZA1HrTM/j0j3ULRx1IqcrUZIKASao3bOOefg5ptvxtKlS7F48WK8+c1vxh577AEAWLFiBf70pz9hfHwcxx57LM4777xQD5gQmXJZN0RCX7XPplAMp/RRJEWOKy7aRL7c0gViWKhhIrLrMSqVd4reF/U5KxSV1MeUFM9v6VErYbta+hhDRw2oDIddeflb8erL/wIAePJLJ0Vi+HdN6mOm2u8U0iZEWI5avdLHVsbzC0dteML8PAizR23t9jE8unIbTjl4V089aix9JFHBRqehIP0wYOUzIQQBhVoqlcIf/vAHXHDBBbj++utx++23W36fTCbxwQ9+EP/7v/+LVCrQnyDEE3J5hVi4FMohhYkkROmjdfE6USxhMtq/2PaL4ajVWdyaQk0tfRRCrfJ9Ri59ZI+aQVIRuGuk8RFbRnOREGpqj1rYjpoQaplkoqZfzQ+TpNLHr552AH58/0tYtbXSk6ppzUuTtEMINblPNcy//7pvLkWhpGPHWEFKfWTpI4kO6vxFQT1HjTqNkOAEVlF9fX24+uqrcdlll2Hp0qVYs2YNAGDevHk44YQTMGfOnNAOkhAnZIHQW3UF5NRHw1ELsPPtVC45nu/MhVHRWPw5PxfppFa3R02EiWRcwkRUYTylNz6lj+p586vHzfLwRES2ltXUx8GqUAvLCRPi7IDdJuHx1TsCP855x++B3z21AScfvCvee9QC/PsR87HH5+8EUFu+2Wzs+izDbEMU768HX9zirUctxdRH0lqc+m7tBl7L1+EIt+sSEnkatrvmzJmD97znPWEcCyG+kXfyhBMk7+Q11KPmIGgmItqjNlEoGc+BHWqYiB3y/VVBV1AGXjs5asNKMuaMgUzdUIhuQl1c/+89Lxhf26WjtQN14RR6j1q1HO/tB+3akFA7YLfJePJLJxnnTysdNJWZg1kkpXRUoHmvp5eSbbExxR410irkCpYZA1kjzdGuH81uTA4hxD+tK/AnpAnIiyZz4LVN6mOABZ5T/0kUe0K+9sfl2PdLf8Q/1+10vE1Zr++oyUKt1lGzlj6KsqycjaMm5qhd8W8H44ELTzRcuDjgVmYblch+dWfcFGrhLPpz1XPlpP1n4dtnHIw7zj828GNN7ktbzp8ZA+1xZ5MJDbsMZi0/a9brKTaYvMxRY+kjaRWyc3b/hSdg8azK7FI7p81uTA4hxD8NbXMvXboU999/PzZs2IBczn5OhqZpuOaaaxr5M4Q4Ig+fFQEfonxr51gBH7/pCQDBetScZjRFcQf7R0tXAKgIths/dJTtbcSF0y3SvEf6N9f2qIl4/uocNTFwt2iN5980nDMckF2n9Bo7/3HBbXEdlQWL4xy1EDYhdF033oPZVBKnHTq34ceUmTWpB1vaFPc9e3IPNuycML5vxuspP6bbBlMPw0RIi5HPzWRCM6or1HAiwFrtEpUNKkI6kUBCbefOnTj11FPxwAMPODaXCijUosO9z23C317ahiMXTcWJ+8xq9+GEgjwYVh3kfMs/Vhu3C5L66OSoRXkHW3PpgVLj+e3okdIs1dJP4bYIJ008Tl4pfdw8nMNINRkvTiEigrTLuRZ5oRZC6aO8edKMVMZZk3rwr/VDoT+uF+ZM7sET0vdNF2oewkSiuHFEuhOLUNM0cwi8zftA/hwosvSRkMAEEmqf/exncf/992PPPffEeeedh8WLF2NwcDDsYyMh88iKrfi/+19Cqbyoa4Sa3HSfTlUuGkbSo7RgDrKgcnLUoty871Zh6C1Jzrn0USziRaqheH5yhVLN8yvEbJxCRARujlpUdpade9QaP7fllMdmDKQ+fOFU/GX5ptAf1wuzJ/Vavm++o1Z/UyXKG0eku1AdNXEtUMOJAPtQL0KIfwIJtTvuuAOzZs3CI488gmnTpoV9TKRJZG3mZ3U6pvhIGIuaQkmHrusYkAIsXt426vuxM0n7kr0olxq5pQqaYSLOpYhupY+iLK6eoyYTR0fNzgXZZTCLTcM52wVNO6jtUTPLWHVdd3Vm6yFvZIQ5Z0xwznG7Y/toHifsvUvoj12POZN7LN83Q3jLJWOuc9TE7LsIfx6R7kKUOCa0SvWG+Kir66hF5HOPkE4k0FV0586dOPbYYynSOgxRhhRWslsUkAfDyovCYlm3LBjlWVZecSrbinKpkdvyuqT7c9RUwSHclpThqJkLRTuh1pNOuKZQdit2i2vRs3f6lX/Fd+5+vtWHVINasi4cNV23LrCCkC+ZAT7NCJHJpBL4wtv2w7F7zgj9seux21Sro9YM4V20OGqco0aiQ0m63gLmtcA+TKQ21IsQ4p9AQm2vvfbC6Kh/h4K0FxG20U1CTe5Rk4VFsaQ37Hw5lW1FWqi5OCEiTMStbygr96gpZVfi+Ux5dNTiWPYI2C+u5efyO3e/UPP7VqO2jIg5akDj5Y9mkEj3hQoft5dVHDbDKRDvJU2Dq9A1w0Si+3lEugtxborKDfGxZivUpJ/ZxfcTQrwR6Er6iU98AkuXLsWLL74Y9vGQJpLpwtJHUQefTGiW2Pl8qWzZaT7j8Hm+H9tJ0IxHrNRIfj3dDAwRIOE19bG2R004aiL1MWH8XLh18v3jWPYImD181p9FazyBGibSZxFqjZ3f4nxsRpBIu5nUkzYiyYHm9N6IUsZ6I0UMoRbRuY6k+xDGmBdHLc8eNUJCIdCV9JxzzsEFF1yA448/Htdeey3Wrl0b9nGRJpBNmQvrbkF21NIWR61s7DQfPG8KLn3H/r4fu1MctfG8eTxurUVyP58TvZaB19YHE9dakQYp9zyK3dO9Z5mhQpN74ynUbB21AOMhmokq1DLJhOGQNirUcl0s1ADglx89BvvMrpznYS1A5cd57pVhAO79poAUJpKP1sYR6V7k8C75//XmqBUo1AgJTKAwkWQ1ZEHXdZxzzjmut9U0DcViMcifISFjOiDdc2E3etSSGjRNQyqhoVjWUZBKH4/ZY7qlpM8r6kIzmdBQKuuR28EezZvvL7cSEzNMJNjAa0FaOGpSz6Po1dltai9e2DSCsXwJU/viWfqoPm8LpvfVXXS3GlWopZOVjY58CShQqLkyqSeNC16/Fz72s8dDE2oFm/jyep/T4vll/0/4bB7OoVAqY9cpvfVvHCPE54Y3oSY7ajxHCQlKIKE2b968hlLBSHsQKYbdJNQMl6hagpFKCqFWNgRVTwCRBqBG3A32pLBjrBC5lLUxSai5BQuUHOaoCXELqKmP9gtt01Ezw0TMUJcE9pg5gKfX7Yxt6aPsqL15/9m4+OT98Mmbn7DcplzWmxK04RVVF2iahnQqAeRLDS/85WHX3YpYoIYlkuyEWj3E+7hRYU2s6LqOI756NwDgn5e8yZgxGDf+uW4n/vzMK/jI8bujL1N5DsRpmtSsQs029ZE9aoSEQqBPoFWrVoV8GKQVZLsx9bFk3eFLJxOYKJQrQq1aEtjjMA+tHqojMKknjR1jBUupYRQYk47HVahVn6u08u+aNakH63ZUUjG9OGpCiBg9jyXTUUslNOwxsx9Pr9uJyTEVavLz9uk3LsZuU3prnsuJYslY/LQDNfURMIV5vhhO6mMzovmjgliohrX+DNI3bM6N5CI4TGR3aMOOcew1K54zYt/+vQcBADMHszjz6AUAbEofjfdBvdRHnqOEBKV7r6SkBnlAcbegXjjE4rAolSgGjYhXe9Qm9VYW1pErfcxJQs1FRDo5artMyhpf97j0qAlEv5V4fkpl3VicJzQN7zlyPg5bMBUnH7Srn39G1yD3o4meP1Woya9ZO7ArVcokw3GJujlMRJBMipKvsBw1/wvZtJS6aie8STDk16Kdrnc72TQ0YXw9mjMrNtQwEeN9YOMIFyjUCAmFULZ0c7kctm3bhmw2y9lqEUaOU+8WxAVALI7F//PFstGj1htQqKmCZnp/RdBELUxELn10O7aSdJHNphKGszpr0BziK4tTZ0etcht5IS6OIZXQcNTu0/Grjx7j81/RPcipjz2ZRPVn1uey3a6s3bpJlLQGKcOTiYNQE65yWCVdQZ5zORSoUNKRScVTVITNzvGC8XXUektbxdLnNxtfT+03e43Ngdci9dHZWbYMvO6iNQchraahK+lVV12FQw89FP39/Zg7dy4+85nPGL/79a9/jXe+852M8I8Q8oDibqFUMnujAHPxUpBSH7MBSx8TyhDtGQNCqEXr+fNc+lg2Y79lQTbLwVFz6lFLK3PU5GOI6w60jOyyiPJGNQlSDoBpB2qYCGC+ro2W0olU2W6coyYQJV92zyNQcSyvfuAlPLV2h6fHC1KOnrEItWh9JnUq3/zTczj68nvafRht51/rdhpfy0PdjWtIUil9tHGWi4znJyQUAl1JS6USTjvtNHz0ox/Fs88+i3333bem9OLggw/G7bffjltuuSWUAyWNk011r6OWlnrUxM+FaAla+ghYXYEZA5WdRTcx1A4sYSJupY9iWGlCM0Q7AOwyyXTU5B1kp0h5Y4ZOMmF8Lf5uvdlPcUDu8epJ2TtqY5EUalZHbe32Mfzh6Q2WhZoXunngtcAtRAEAfvnYGvz375/FKd9/yNPj2Qmt777nUNf7yJ9NFGrh8P17rRvLcRUYI1Jpttx/5hQmYnf6WQZex/R5JCQMAl1Jv//97+OOO+7AW97yFrz88st4+umna26zxx57YM8998Qf/vCHhg+ShIO4sHdTj1rJSBsUQq3qCkilj40INXmxOb0q1KL2/Mn9Tm5un/FcaVZHbZpU2iILiKRD2Y9cciUeRzhqURvs3A7kRbMoJ6wVatHrUVOF2nFfuxcf/dnj+P3TG3w9thEm0sVCLWX0qNkvQJ+WHAkvqELr6N2n4ZSD3Xs8kwnNGHDfTZtvUSKuQk0uobc6auZmHyALNbseNZY+EhIGga6k1113HWbNmoVbbrkFs2bNcrzdfvvth5dffjnwwZFw6crUR6UUQ5TrFcq6IaiC9qgB1sWm2aMWredPFlf5UtnxoliSZuDIQk0WXrIj51TGKDttGQq1GhZO76/5mVpG2u4wEbv1p9jk+J87l+OvK7YaP3/s5e2+HtvoUevi1MeE5i7U/K5L1dRHr59ZpriOp6BoNnGdUSdfU0o2Qi2lCDXbeH6GiRASCoHCRJ577jmcdNJJ6O+vXZDI9Pf3Y/Pmza63Ia1DzDUSKWGdOgtv60gO373nBbz5gDlSPH+1Ry1lzhaaMEofgy8Y5RKxGYNVoRax1EfVnRkvlDBos0iWE7vkGVdyuuOeuwzU/Xuy6BCCb7xQNB477syf3oefn3s0pktOpSp6xfPVLuzKGYX79+KmEbznx48YP+/P+tvo6PaB14D5HnASan5TGFVHzGsVQCZZCQUKEu9PrAxPFGp+FldHTb6myHsATmEidp8n7FEjJBwCCbV0Oo2JiYm6t1u9ejUGB+M5gySKiIWTrnduSliprOPV/+8vyBfLeGbDEN592DwA5gUjbezwlUPpUZNT3cTCu92JfSq2Qq2ndoaZPMpADlhJJxP43SeOwxNrduBN+8+23Ofc1+6Oq+5/yfIzuQ+Njpo9R+8+3fJ9TZhI2x01u3h+e2HV73PgbxyEWr0eNfn59bIppjpinh21VALIsUctDF4ZytX8LK5O0LhD6WNZaTVIuDlq0s/o+BISnEBX0v333x+PPfYYhoeHHW+zadMmLFu2DIccckjQYyMhI5e75SLmCnllxeYRY/f4X+uHzHh+JUwkX9LNHrVUcKEm73QLwRe1eH51N34iX7to03XdKHdLKmmWqYSGA3abjPcdvaDG+fn8W/fFqYdYe2Xk0kfhzAnx6tTXFneiFs9vN6DWKTym3+dgbjNMJPj7LuokXZwEwFr66KV/THXEsp5LH81xJKQxXhmq3XyOqxNkddRqQ0GMQCkjnr+eo8bzk5CgBBJq73vf+7B161acd955yOfzNb8vlUo4//zzMTY2hg984AMNHyQJB3lx3qkXdvm4Zw5ma+KCU3KYiBh4nQm+sy87ar2ZqlCL2HOnjluwS6WUFxxJzZr6mK7jfKgig2Ei/lEFbLvj+cW6aq9dBnDPfx4PwPq6yjgJOCeEu+P0eN2AH0fNiyhXHTH/PWrR+kzqRDburBVqYc3J6zTkc9auR80YeF0tAbZ7nixhIjEVvISEQaAr6bnnnosTTjgBN998M/bee2+cd955AIAnn3wSn/zkJ7F48WL86le/whvf+Ea8973vDfWASXDkuWCdGigiL0hGc0Vph686hDkphEPRWIw2Uvoo/z0RtZ4vln1HljcTdcfeVqhJC8dkUgkTcZiXZtxeERl2pY+rt41VbkuhZktSETtRSX389yPnY4+Zlb7EtIMg8+sqiNs7PV43YDgJDs+N7LqPBhBqXvtqxfuPpWWN88owHTWBHCZiKX0UgVTqwOt6YSI8PwkJTCChlkwmceedd+KjH/0o1q9fj6uuugoA8MQTT+B73/seVq9ejQ9/+MO4/fbbOzawolvp9ORH+YIwkisaFwBxwRC7/0MT5oWmkdJHeSdQFnxRChTJK8dit4MvV56o8fz1HBP193ZhIsZjU6jZovaoRWWOmnxYTg6YXxEgFmjdfC4kpV5YO0Zy5us7lqv/Wqufx14dtQwdtdDYPlpbHRTf1EdvpY9u74MiHTVCQiFQmAgA9PT04Ac/+AG+8pWvYOnSpVi1ahXK5TLmzp2L173uddh1V/cZMKQ9ZKrN551a+igvGicKZaPXLqn0qImFUkILb2ffItQKZfRlXG7cQtTX0q6HTr6QuqU+2pFQHTXp9urivpsX542gPodRCRORXy91hIDA7wwkNcK7GzF71Ox/PyxtFHlxT1WhVa8c2bid0ZPbmZ/nUWJovFZQx9FRW/rcJsvGgVuYiLh22JY+ltmjRkgYBBZqgpkzZ+Ld7353GMdCWoDpqEXHEfKDunMnnDNxwRBiattIZXe0N50MzdUVIRz5UjlSgSLqbrxd6aPFUbNJfXRDXXDLQm2bsgvNMBF71OfQLgq8lYjzQX5vOKXA+t0NNwJ+urhHLeXDUfPSj1hQ3sNe30cMEwmP4VztezJuTtA/Vm3DWdf+3fIz2VFTB16nXOb4WRw1lj4SEpjuvZISW0SIRKeWPqof+DvHKhdXscM3pbcSS79+5zgA/9Hi9RACJ0pCTV2k2ZU+Whw1tfSxTo+amgQp97StqfamGY/dxS5KI6g9ana7962kpPSaAM6C3e8iKw6OmnhPlHX7mWkjkqPmLUzE+hhOw+ZVGCYSHnbvySj1IrcCu+H28qmlvrfdNizkn8VN8BISJoFWsZdeeqnn22qahosvvjjInyFNQPQ0dOoOrPqBv3O8ItSE2JhanXW2bntzhFpPOonhiaKta9UuRNmTcPvqhYkkfJY+ygtuTbMuIuVeQIBCzQnVIRHnbbvQbXrUHEsffZYtqX0s3Yj8niiV9Zo+TqujVv+zQi1d9CpyzTCRzvw8jxJDNi533ARGX6a2N1JOMFUHXouNArvNnHxR7lHj+UlIUAKtYr/yla9A0zTbnUTALKcRgz4p1KKDcIQ6tvRRWZCIBa9YFE6uOmprDaHW2Cyn6f0ZbB3NY87kHgBmGttEIToXHiG6J/WmsWUkZ+v2ieukWADKw4jrlT7KwkxNiFw8awDPvzJifN/Ni/NGUBfedovCVqKWMAFA2qH00W+YiDEyo4vPBfk8L5Z1yHlF5bLuO0xE3Tjz6qgZYSLFeAmKZjBks3kStx41u3+tfTx/5XtjHI7NRoHFUWPpIyGBCSTUrr32Wtufl8tlrFmzBn/+85/x0EMP4fzzz8fhhx/e0AGScMmmouGovTI0gc//+mm879ULcMLeu3i+X0G5cP71pa0AzEXh1GrCh9ih7vM5rFflZx8+Ct/7y4v49Bv2AmAmSOYi5KiJMtbJvSlsGcm5lj6KBaCf1EfZDVJv+3/vOxyv++ZS87ZdvDhvhKQicO0Wha1EvI3kkBOnMQ1+w0SEsFP/zd1EUnHUgEqS57I1O7DfnEmW2wYJE/Heo8YwkbBQqwOA+DlqwzbPgZ1QE+67+Mywe55kcRY3wUtImARaxdYbYv2lL30JX//613HppZfi3HPPDXRgpDlkIhLPf/Ht/8Q9yzfhnuWbsOr/vc3z/ZwWjUlDqKUtP++3KeXwwz6zJ+EH//Eq43tz6HV0hJoQ3VP6MgBG8a0/P48D5062CGDVUcv6cNRkZ0R1SRbN6McFJ+6J7/7lRQAUak6oT/FovoRiqdy2wA0z9dH8mWOPWsA5anFx1EQ52HfufgFX3f8SLnzz3pbbehnFoG6ceT0t0ix9DAVd120DfuKWVmjn9Jddw0ScHTX5Z+oGKyHEO01bJVx44YWYO3cuPv/5zzfrT5AAZA1HqL0XIBH24RenRaNY8E5RMvND71GrPn9nX/cP/GX5K6E+dlBMR80UqWpyl3DUxE59xhImUieeXxZqNivIrDS2gKmP9ti5S3Y7+K2irPSaAM7OKnvUapH7+UpV5+DlraMAgBelUmAgWI/awun9no4j7bJQJt6ZKJTtkwtjJjDqjSgwB15Xvk+59KjJ52TcBC8Jj3JZxz9WbcOohxLybqWp27kHHnggHnzwwWb+CeIT0dOQa/OF3aG9sS5Ote5CbEypcdSak/oIVMRaFBADr6f0Wv/tm4dzxtfGwrz6PFlK3urMbHJz1ADrcN56ZZRxxe55a2f5o7EzLsfzh5b6WO1R6+JzQX45xWJelI1tH7OOrPCS+ij6Sl+7eCa+etoBOGr36Z6Oo9PDoaKCU89o3Er27FzFsq5j7fYxnPbDh3DHsvUAJEfNNfWR8fykcW56dDVOv/KvOPOav7X7UNpGuKtYhRUrVqBYjK8KjiJGmEibe6yCXv+cdveTSo+aoBmpj1EjJ4WJyCzfOISZgzMBmBHLKTuh5iOe3648Tn5O1MHOpIJdOEQ7A0XsetSchJXfMBGxKKs39qGT0TQNyYSGUlk3NkGEUNs2Zn1dRzzsBI9XKxyOXzwT7z1qgefjMHvUuBBuBKdNk7gJDKceta/85hk8sXqH8bPaeH47R01OfYzX80jC49Z/rAEAy/kXN5pyJd2+fTv+8z//E8uWLcORRx7ZjD9BApJtoEdtxeYR/L8/LG9rtLjTolFcMHozSUv/VaOpjyq9ERNquq4bZVOqm/jshiHjazVMRNZb9ZwPi6Nmc9uetPcyyrhi97y0831UVtLbALceNX+fFaUYlD4C5r9PLEKFINuhOGpeSnaEo+b384Vz1MKBjloF2VFbNKNSflsq1wpZce67xfPL/eQUaiQo7erjjhKB7Ibdd9/d8XcjIyPYunUrdF1Hb28vLr/88sAHR8In00Dq4yduegLPbBjCi5uGcfUHjmjoOJxGO9TDqdZdXhRO7ctg49AEgGY4auaHhlOpWCsplnWjjHRyryrUho2vxdMmesgsTkq9HjXptnaLb4uj1uWL86DYPW/tHHotXCCtCaWPhRiEiQDme0n0qIlF7vZRq1DzIsgNoZbx95lizFFj6WNDiH7Ryb1py+sVN4EhnoebPnwU/rluJ/7nzuUo63rN55e4JriFiciBW+xRI0Hp9g0/LwRaxa5atcrxd+l0GvPmzcPxxx+Pz372s9hvv/2CHhtpAkaYiM8Le7ms45mqQ3P3s5swni8ZCYhBCNqjJhy1BdP78PLWMePnshswpS9tCrUGUx9VZFEi96u1C1lwq0Jtq7RgLOlWl0MWX1qdckV5wW1XJklHrT7yxSaTSiBfLLe19FFoL7fRCwL/jlo1uKaLe9QA81wvKaWPYrE72JPC8ETR0+sshFpPyt/nVYZhIqEgHKMZAxmLUIubwBCbDZN60sY1olSuFWpm6aNzPL88azRuJaQkPNJdfh3xQqCVZrlcdvwvl8th1apVuP766ynSIohZ+uivR+35TcOW7x9fvb2h49BtR2vWR3zgH7vnDFz7QdPVky8kc6f2GV83OkdNRXYdotCvlnMRavIiw1g8C6Hm452frFf6mKKjVg/5OZzRX+mj3DHWPqGmG+Ey5s8cSx8D96h197kghGipXEa+WK7Z/NptSi8Ab87puBBqPjeW2KMWDkJcTx/IWn4eN0dNbDZM6kkbn1klO0fNKH2slv/aOWpSH3zcnkcSHt3c6+wVPgMxI2jp4z9WWYXZRINhJIFTH6uCI53QMCCVNcqLwoXTTaEWdumjvBiLQumjeB1TCa3m32odOArjdoC/0A85Wr5ePH+3L86DIj8vu1YX8K9UXd92YJf66LRz6XcGkjoUt1sRbmSxrNsGhojX2YujJpIh/TpqnKMWDrKjJhOnHrVCqWwMZx/sSRnirGzjqCWN0kdx/tU+T/IIoDg9jyRc6KhRqMWOoKWPG3daF5WN7pAFvbf4u6lkwhK9L19IFswwZxCFHSYyKg2vVWcftQMh1DKpRE0QgSUeWQkT2W/OJM9/wxI8Uieen3PU7JHPz3nTKhsJ63cEmyUYBnapj86OWrAwkW6O5wfM13T9jnHb13LXKT0AKiKgXOfzUpSJ+S0nTzOePxSEkzRDcdTiJDDk0JvBnpRr6aNwk53i+Utl3XJ9lDcSrrp/Bb76+2cC96mTeMEetYA9ajfccENDf/T9739/Q/dvBbfffjuuvPJKPPbYYxgeHsbMmTNx+OGH4z//8z9x3HHHtfvwAuM39XE8X0KxXK6Zr9LoBSzoh7RYNKZURy3ZGkdtLFeSvm7/6Il8qXI8mVSiZpEnCzU1TGSvWYO46ZyjsMuknrp/w+Ko2YaJJKTb8kPVDmtpbsVp2bCzfY5a2SaZ0Sldy3fpY0xSH8V7wWmeonDUynplg2ewJ217OyB46iN71MJBuJ7T+uPrqInNglRCQyqZMB01Xa9xx8V1xCn1UW2tEM9juazjf+5cDgB420G74pB5U8L9R5Cug6mPAYXaWWedVTeAwA5d16FpWqSFWrlcxoc//GH85Cc/QX9/P4477jhMmTIFq1evxp133onDDjuso4WaWfrorXTx2K/9BdtG83jz/rMtP2+XoyZKLFJJzeKWyc7AwummoxZ2nL7sqI0VSiiX9bb2ZYmLa9bGUbP0qOm1i+dj9pzh6W/Ui3CXe/W6fXEelJSNo9ZWoWakPpo/cyoxCRrP3+1lsPXe9zMGslJwjDeh1uMzoIjx/OEgSh/VPt849VbJ1RmAlGpa1pFNKY6aCBNx2CiQg0Qqv68G7kibmy9vHaVQI3VJd/l1xAuBhNqXvvQlvPTSS/jpT3+Kvr4+nHTSSVi4cCEA4OWXX8Zdd92F0dFRnHnmmdhjjz3CPN6mc+mll+InP/kJTj75ZFx33XWYNm2a8bvt27djy5YtbTy6xvHjqJXLOrZVkwP/8fI2y+8aTcNqtEctlUhY3DK5Z27OZNMl6gs59fHCN+2DB154EEDl3zBRLIUeWOIHUV6SSSVqUiitPWrWMBE/1BuKHIVQlagjP4fzqmE3W0ZyyBVLRjlyKzGEu5d4fp+LVbFo63bRXk+IDmZTmNSTxpaRHIbGC0a4iIqu60aYiG9HLcUwkTAYkkI0ZOLkqMnVGYC5EVHSrRs6gCTUHAZeqz3s4rq9UwpQWrllNKQjJ92M7Ki1e2O8XQRaYb73ve/FkUceiTPPPBPf+c53LGIGqAiaT33qU/jtb3+LRx55BIsXLw7lYJvN2rVrcfnll2P+/Pm45ZZb0NtrvbBOnToVU6dObdPRhYOfMBG5xnzLiHU2UKNxu8HnqFXul05qlsHW8g5eKpnAD9/7KmwamsACyV0LgwPnTsYzl74J+3/5T9B1YDTXZqEmdkGT7j1q4qUM8iGX8lH66DSQPO7IAnfmYAbZVAK5Yhmv7MxhvlSq2yrE208+H5yqJPy+prEJE6nzXhroSWFSb8oQak7kS2WjZ9Bv6qMR5sAetYYQr89gjxLIFKN4/px0LQHMSopyWa+53qthIvWEmpg1uGPcXEc8/4o1SZoQO+RKj1yx3NBYqE4l0JX0oosuwtSpU3HttdfWiDSgImiuueYaTJkyBRdddFHDB9kqrr/+euTzeZxzzjk1Iq1b8BMmYncb8SHecI9awPuJRWMykbAsLNULw1sPnIOzjl0U9PBc6cuk0FcVRWP59vapGRfXVBKDPWl87V0H4t2HzQVgfY1KZbP/wC/WMBGb0kfJEYrTwsYP1j6/hOH6btjZnkARu9RHp3el3zCRYszCRJwYqDpqgPvQa3mTyW/qozrLjQTDmB9WM+IkPs+rWvooh4mowVlGPL9w1OqUPor7yyNJnn9lJKxDJ12M/Dnrd6xUtxDICli6dClOOukkJJPOF5VUKoWjjz4ad911V+CDazV/+ctfAADHHHMMNmzYgJ/97Gd48cUXMXnyZLzuda/Dm970pkC9eVFClMd5OeHtXLcpfWlsGs413qMm3d2PnS0uCGo/jd8Uy0bpy6Ywmi/ZxnK3EvEaCXfxjCPmY69Zg7j1sbUW0SSuo0FSGa3x/LX3l1879srYow6WnjGQxaqtY9g+lne5V/MQPWry287pLe13sRqbHrU676XBnpSx8BeldXaITaZkQvMdRW3MuoqRoGgGTqWPcRrUXNOjJm0CqGsBY+B1dRevrFuv4xNFs4wyXywb6wV5w2JDG1NvSeegweqoxZFAQm18fBwbNmyoe7uNGzdiYqJ9DfN+eeaZZ4z/v+td78LOnTuN333961/HCSecgNtuuw1TpkxxfIxcLodcLmd8PzQ01LTjDULWR5yzXfz81L4MNg3nQnDUzPsXymXoJeCjP30Msyf34MI37YOpSvqWeVv7ReA+swcbOh6/DGRT2DycM+bOtAv14gpIu+xyj5pNmIhX5HTNerPj2K9mjyVdMZEwnid157lV2KU+OkXIF3y6pMUG+iE7iXr/vt5MCpOqpXRupY9GkEgq4XsjUJ7lRoIjXp9JvdYlUZwEsNHvLEofNXOOWl63fgaoYSJA5XMimxCfa5VzelJPCltG8iiVdZTLOnZI7wO6wMQL8mdbo/N7O5VApY8HHXQQHnjgAdx9992Ot7nnnntw//3346CDDgp8cK1m+/bKUOclS5bgoIMOwuOPP46hoSHcfffdWLRoEZYuXYoPf/jDro9x+eWXY/LkycZ/8+bNa8Whe8Z01DwINQdHDQgh9VG6e7Gk46m1O3Hvc5tx86NrcMlv/+V4PyE+ktWLyV2ffi2+9e6D8cb9ZjV0PH4RISWj7XbUqg3g2ZS1tA4wRe2Nj7yM/7tvBQBzQK4fjlw0DZ9/6z44fMFUnPaq3Wxvc8kp++M9R87Dq3ef7vvx44C8qE8mNCnUx7zwtHJRaDdHbc5k+3Jvv64Ce9Qq9KQSxiaHW4m0ESQSoPdCzLOqN6eNODNRKBnXw0m9aVz3wSOM92ecBLBanZGQHLWcWvoo4vml97j8OSGGXcubfIVyGTulCgJWyRMvyMF1dNR8cNFFF+Ed73gH3v72t+M//uM/cMYZZ2DBggUAKqmPv/jFL/Czn/0Muq7jc5/7XKgH7MZpp52GZ5991td9brjhBhx55JEAKtH8QKXH7g9/+AP6+ytBFK9//evxm9/8BgcddBB++ctf4vnnn3cMSLnooouwZMkS4/uhoaFIiTWjR83DTr6dUJvaV3G6wkx9LJTKRrokALwylLO5RwWxWy9q4xfPGsTiWa110wAYw7Yj46gla8sTS+VKycrFt//T+N2UXueIcCeSCQ3nvnYPnPta5wTXDxyz0PfjxpV0Uqtx1H79+Fp84bZ/4sr3HYbjF89s+jGUjNJHU2zMn96HK888DOf99DHLbf2Eiei6LvWRdrejVq/0sSedlOLLnZ/D8XzJuL1fnFL3iHfEsGtNAwYyKZyw9y74wtv2xZfu+Fe8HDWHeP5yWa9JFRXnteyoyUJNOB/ySIpCSbeUPtJRI16Q9wji6qgFEmqnnHIKfvjDH2LJkiW47rrrcP3111t+r+s6stksvve97+GUU04J5UC9sHLlSjz33HO+7jM2NmZ8PTAwgO3bt+Pd7363IdIEBxxwAI444gg8+uijuP/++x2FWjabRTab9X/wLcKMc64vtNR+o2RCM1KxGl0YlKUP6UJJt/TqTLj0z5lz1Nq7W9+XjYajJs+VEySlBm91oSEcUdJa5JTTZEIznG1x4VnyiycBAB++/h94/qtvadnxqKbXmw+Yjd2m9GKd1D/iJyBGPt26vUetnhDNphKGq+j2HAqxHkSombOu4rnT3Aj/XLcTdyxbh7cftCuAivsjXKSEUVIan+dVHvUCqD1q1muyeH7k97hcIi2u4bKjViyVLWEicRLBJDh01AIKNQA477zz8Na3vhXXXHMNHnzwQaxfvx4AMGfOHLzmNa/BBz/4QWO2WqtYtmxZQ/dfsGABtm/f7njcCxcuxKOPPopNmzY19HfaiVFy5WFnQn1TDGRThkAqNdhkLYvAYrlsFWoubp/hqLU5Uc7PPLpmUrbpPUtJAQPqrmUQR42ESzrp3KPmZQMlDMQiyS5cRl1A+Xmvywvbbk99dNNpyYSGVDJhfE65lY9OBJyhJv4OwEVvEN7+vco8zHuerVzPp0l90akYPq9qPL9R+liu/VwS552maUglNBSVCH/xudafTULTKhU0+VLZ0qMGVDaMOj2gjTQX2RTwUgnWjTQ0AGr+/Pm45JJLwjqWtnPooYdi2bJlRq+ayrZtlaHPAwMDrTysUPETz6+WPg72pFxLbX752FpM609j3tQ+XPfwKnz8xD0d+17kxy4UdWyXSh/dRGQxImVVmerz6CWUJQxyxRKSmlbjJBalcQUCY7ZSWa9ZaEzusw9pIc1FfhWcetRaidGjZvM+WjC9DxuHzBAoP2Ei8vnW7T1qbqWP4vUV70U3AW6EiaT9P19ymTMJxkvVwctiZAYgVSXE6Hl1LX1UrnNqim2xrFs2X8U5nU0nkU5Wkx+V0kegct524oYOBWbrkKuv4lr62N1XUp+IMs377ruv5ncjIyN4/PHHAVQEXaeSkZqk613c1cXFYE/acQd360gOn7n1SXzy58tw2g8fxs/+thoX3PyEp8culMvYLpVEjLsJtYgEFYhdx1Y4avliGYf/99143beW1vzOcNSka4a8G6yGDNBRaw+6UhIYldRHO7HxrX87GG85YDZ+8B+vAmDvBr24aQS/e2o9toxY+0nlhW27N1Oajdu/Twi1tBgI7NajVgjeo5Zg6mNoyJuKcRTAplCrnIfiElvS9Zrr3N5SyrJZ3lvrqPWkkkY/eaFUrkk/7cQ+tWVrduDw/74bv3xsbbsPJRZYQmpiWvrY0Gr3mWeewac//Wkce+yx2HvvvXHhhRcav3v44Yfx3e9+13ChOoGTTz4Z++67Lx5++GH88Ic/NH5eKpWwZMkSbNu2DQcccACOO+64Nh5lY8jpgPXcIPX3sydlHR01UdIwPFE0Zos9uWYnnJCb64slq6PmtmviNEet1Ri9fi344HhpywiGJ4pYs228tiytXOuoJd1KH9mj1nY0TTMGG08USzWvqdv5PzxRwG+eXN9wb6Qp8GvfR3On9uFHZx6Go3afBqDyXpd77MbzJbz5O/fj4zc9gY/f9LjlvnKZZJx71IToMgYCu7iSjQg1sUiOk6BoFrMtjlr8nlfXeH7lOnfwvCnG16aorXXUetIJI2m4UKp9nE5sATz/Z49j62gen7n1yXYfSiwolemoBS59vOKKK/C5z30OxaJITNKwZcsWy20+/elPI5vN4iMf+UhjR9kikskkbrrpJhx//PE4//zzcdVVV2HPPffEE088gZdeegnTp0/HTTfd1NGWtyzUcsWSayS0+qF64j67YG01ZEBtXh/3kX5YUty8Qsl7j1pUwkSyRihL8z84ZNejUCojmTBfs6Ih1Mzbywtk9TWkUIsGPVKYyA5l6PWGnRNYNKPf7m741M+X4Z7lm/C2A+fgB+99VeC/LwS820eZfB4Vy7qxObJzvGCcd2u2WYfWijJJTbMvq+wm/JQ+uqU+ih3jejMK7WCPWnjIpY9xTNP0MvD6TfvPwsVv389yP2McjNyjVjQ3H8zfl2sckU501Nwqfkj4yOcIHTUf/P73v8dnPvMZzJs3D7/+9a+xadMmy44rABxzzDGYOXMm7rjjjlAOtFUccsghWLZsGd7//vfjlVdewW9+8xvk83mcc845eOyxx3DggQe2+xAbIpVMGE3wdR01RYS8Yb9Zjhcwuw8vHfYfwmqaZEWomSURE8VSzfkkMGc0RSNMpBWOmvxPVZ/3so2jJotY9fgm97JHrR0cvnAq9p41iDftX5n3J4fRbBmxCrV128dr7i+4Z3kl+OD3T29o6HjEaeTmCsnnkbqxIhhRnL2ovD9bgds/UfQCm2Eizp8Tpivu/zmjUAuP2ZNqe9Ti9Lw6zVErlnTjuvM/px2IuVP7LPezC8zJGUmmCWSk39cItQ58ftX1C2ku8jnSrp7udhPIUbviiivQ39+PP//5z9h9990db3fIIYf4jsuPAosWLaoZOdBNZFNJjEtDPp2QF/lnH7sIcyb3OpaE2DlqTptlau9bsaxb5qiJhCix2JERO/btXgg2Uvo4li9i2ZodOHLhNE/OoOzgqgu+eo6a+hrTUWsP6WQCf/zUa4zXUpS5bRqawJu+c7/lttsVh60ZuPWoCSzR26WycczyOTWaK1oa68Vird09pK3AtUet6piK57DgsiBtRNzGMfSiWVh61GL4vNbE81ff03K5WdamPNeYFWhX+phKWgJ11Jj/ThzU7tZvSsLHLk00bgS6mj722GM4+uijXUUaAMyYMQMbN24MdGCkeWQ8RssLEfKWA2bjSydXyh2MhUfJi6Pm/riCiUIJQxPWJuOJvP2xFSNS+pjxkObmxHk/fRz/8eO/4cr7Vni6vSx41b8neo1SNj1qgJ2jRqHWLmTBLUTP31fVJsy2YnZTyWGOmkxaeo/JF0vraA3dck7Gy1HzUfro8llbbMBRE89zJy54o8acKXaOWnwWhkbpY9Ja+ihf2+3Kc9MiTMRm4HVPOmlxlbuh9DFOs/WigLX0MZ6OWqDVbj6fx+DgYN3bbdq0CalUQxMASBPwGg2eU2rWAecLmL2j5q30cWi8WOO+OQ29jlqYSJC5Hvc/vxkAcMNfX/Z0e9m9VHfzijbOiKVHTSlfTbdZ4JIKcq+oSqHY3MVLuawb7zc35yuZ0IweNnm3XBX/oznzHDNERwdGbvvFTViJ95mxSHV11KpVAgGeMzpq4ZBJJjCtT56jVj+ts9tQr/fimiILNbvrbsqmvFc8VjadMN4LhZJeu9HYgeetW78pCR9r6WM8RXKgVduiRYvw5JPuiTf5fB5PPfUUFi9eHOjASPPIGkEG9XrUrDtsgHNJSKOOmkAOWbCjkd3nMDGEWgP16mWPu4nyDp4qcsWFTl7kaZpmPD/yB9tuU+xn2pHWY5fwt9+cSQDCH3r91xVbsfS5Tcb38g5lvfdRSupTEajnoJxAGStHzeXfKJ5Xc5HaHEfNay/VlpEc3vyd+3HNgyt9/404MGty1vJ6xrlHTQ0TER8XmVTCNkjNCAux9LFWvk4nTaE2XijVbMh2oqNGWou81uxEYR8GgYTaKaecglWrVuGKK65wvM3Xv/51bN68Ge985zsDHxxpDv2ZistZL6lR/eAGnC9gYz5SH9VFiyzyxLE5iUjxpm23MxRGPL/XRYBsXqq7eXaOGmC+TvLx/XnJa4McJmkCWWW48QUn7ondZ1aSHsNsVi+Xdbznx4/grGv/jg07RWKr9wh9u/h3VUjKgSLi2Nu9kdIK1PfcPtJsKbGgTXmYo2aK2wADr6VkPje+d88LWL5xGJf97hnffyMOzJlk3cSK5Rw1NZ5fOR2zDtdcu8CcotRLLp5Lu5EicXp+iTNu6yhZnMW1ciDQavfCCy/Ebrvthv/6r//CGWecgZ///OcAgFdeeQW33XYb3v/+9+PLX/4yFi1ahI9//OOhHjBpnL5qJL+a2KZiJ9ScHDU7B8xp7aDa1+K+6aQ5CNgpAlcsBNu9Yy+CTloh1NwcNTMxznqflCLUZgxk0JdhGXJUUB21mYNZY4EUplCT32vLNwwD8DeUOmNTJl1b+mjnqHV/ia1cBTZ/Wh9+/bFjan7nZY6a02aLp2OQNs6cSs0BRorb0SNtlsgz1IB4lpSKoA+19FGQcSjXthtBUZIqPcSmqrzeEKXfbPci/3Pnszj00ruwasuo7e/l92BchX2gldvUqVNx99134/TTT8ett96KX/7ylwCAP/7xj/jjH/8IXdex33774fbbb/fUy0ZaS3+28rKP5f0LtWT1Q7ekhon4ctTsRV4yoUllmU49at3jqHmt+nDrUbMbeF35virUqov+IItA0jzUHrUZA1lLL0dYyO8jYwZiybtQ688ksXO8YHHM1eOTF2BRKU1uBXKp3MIZ/eiVxLd4P3qZoyYvav0iP8+lsh7oMeJKTzppVG7MUYWaFkNHzaH0UeAk1JI2mxHmhmrCcNzE50S6Kt5yxTJLHwkeenELRvMlPLthCAtt5ofKeQhxDXIJvMW+ePFiLFu2DL/97W9x1113YdWqVSiXy5g7dy7e+MY34l3veheSSedhyqR9iPJCu1IEGbHIz3roUfNT+qiKOrHbm0okjMWOk1BrZOZQmDSS+ijwepEqupSdicdIKkJMLPrFxbfdzxexojpqMwazSKdqy1UbRXZSXq7uWMoXO/W8UemrburYlTcK5Pd+I6Kj05Cfu6RmTfUUH5lG0IKbo1ZqvEcNqHwWOF3QNXT/6+EX+eO3Rqh5cEK7DeN679NRE0Js/Q5z/qO5oWo6amK9kU0ljRmEXvu0SfciEr+dRpiULI5aSw4pcgQSakuWLMHUqVNx8cUX49RTT8Wpp54a9nGRJiIctdGGetSc+8zqoc6JEruaKan00alHrdBAQlqYeE3OdMPrbq3VUVOEWsl+YayGidBRixY1pY8WR63+1cjryylveKzYPAJAiubX3AMxAMl9zzmXPlodtWiUJrcCWSSpIssIExFBCy5JnuaIjSDx/PZDyUl95Odr9mT2qDnF8wt6bOaaAsC20cpC+3/uXI6TD94Vcyb3Ss56wjhHxedEJpUwynTjGg5BTIYnKueF0ygMq1CLp1ILVD/2/e9/H0899VTYx0JaxEC28oFb11FrsEcNqNjaKltHrULNdNQ019RHr7HiraBtpY/K824uuq0XVbVHjY5atOhRSx999qh5Fd7yhseKzRVHzU8fWX+1n3VUKpNWXV27HjW1FLcbkR00NQ1PvD5pm2HAKkLcBnmPyk9znPqpwkB+n8lBMIBZkRCnOPB6pY89afv39Pxppsh9eesYAGnDJqkhk7KGiWRTCXPDt8MctTgJ91ag67oh1JwCl4ou65+4EOhqOnfuXJRjqmy7AVHOJM8/siNnzCzzkvpoL/ree/Xf8FJ1J1+wbURx1PJmj5rYtbMTan5CEJpNKKmPHi9Sbol7Rjy/w44+hVo0yUqOWiaVQH8m6atHzevLKTvdO8crO99+Su36MrWfFW5hIsZA+hicb3KbrFpCmgiU+tiYo+bmTtBQr0U879effWRNb8xA1mwPcAtp6Sac5qgJsjYjRQDgvOP3ML4W57lR+ig5auIzJJNKGI/dacKHoTzhMpYvGeeAkwgrWxy1zjpfwiKQUHvHO96B++67D8PDw2EfD2kB8kXIDTtHTSwma+eoOQuWlzZb03y2jeYs34vh1qlEAj0ZZ6Em17O3eyGYDSDUtozk8LbvPmB8732OmnOYiJEYpzwfaaWHLgbr5o5CdtQO2m0yNE0zxb+X0kePPUe5Qq3A8tPnKdx3eSNGdfxG7AZex+CEs/SoKf9e8flkF12uYgpn/5dj+c/Gdbc5CLquG8+XmF8oI66RZT0+i/O6pY8OQu3Q+VNx4G6TAZjOsfw5oKY+ZpKmo9Zp+/1jyprJ7X1N6iP60wDnzy86agGF2iWXXIL58+fjrW99K5544omwj4k0GSOe32vqowdHbdzlsdT1R03pY9VRSyUlR81GAEXSUfPxQf29e17Av9YPGd97L310ied36G8xetSkRE0SHVLSe+rgeVMASMORPYh/zz1qRTmtsXYRVQ8vYSLW0sf49KjJmyPq6yF+ZzcMWKURR00ebv9v//dXrNk25vsx4oh8/Urb9Dv3ZZLGa1pvjE23oDpqqkuslmvLiJ4+8dlVNKpxNOP5NUof05Kj1mFupRqapl7//aRfE7M/DXAWvZYetRATkTuJQGEip556KrLZLB566CEcfvjhmDNnDubPn4+enp6a22qahnvuuafhAyXhYQYE2F+Anl67E5+/7Wk8vW4nAG89am67jiNKiaUIE5nUk8LQRNG4b7JOj1opSkJN9DC4OIkqO8YLNT8rlsqWRbsd8udXjVAr2Ttq4nXKMZ4/8hy31wwAUj9Tk3rUimUd5bLuK7xiwBjl4Vz6+LO/vYzPvWUf9GdTxudCu8N+WoGbo5ZUetTEImTneAFXP/ASTj1kN+y5y0Dldw26kMmEhlJZx0ubR/H5257GjR86KtDjxIl6m36apmEgk8JwroiRiSJ2icGUIWPgtSh9VC5LTo4aUFtpUzBCrhKujlqnlbKNKhvShaIOZCpf3/zoanz+tqfxg/94Fd564Jw2HF3nMSStiZzOBVnMx9VRCyTUli5danyt6zrWr1+P9evX295WbbIm7affpu9E5ryfPoZ1UtRu1kvqo8tO0k5FoGyt9qjNmtSDoYkRo2wynUjUDLweyxfx7T8/j7ccOAcLp5t9BPVixZuNMQi4zqJ6zbYx/OlfG/GeI+fD7jNmrFDCpDpCzTqfxj5MhD1qnce33n0w1m4fxwmLZwIwz6nbl63HpN40Lj31AMf7eu5Rs9kB9tejVhs8lK/ef3JvGjvHCyjrwHUPr8L5r9szVgOv5c0RVTgbjprSo/alO/6JO5atx9UPrMSzl70ZQOMjR1IJDaJGYYvS/yvgZdhK0eKo2Z+rAz0VoVavl7tbEBtEzqWPzu9pdZNJdonFpo0QOdlU0ix97HBHLVcqAUgDAC769dMAgI/97HGs+n9va/WhdSQWR81JqFkGqcez1DSQUFu5cmXYx0FaSH+2NslNRhVWVkfNvjneTagNKY+3rVr6OHtyD17YNGIJExF/S8RZf+fuF/DjB1bixw+sxN+/8AYAlUVHvVjxZiOHiei67rghcfqVD+OVoRxe2jJqe1Eay5UwqSft+rdc4/nFIq9O6iMdtejxrsPmWr6XF4w3/PVlfOXk/Y3zXNd1i9D3ugE2oYyPKJTKvkrt7GYuinPqtEN3w5Nrd+CJ1TuwfGOlX7mRmWCdhvyeUt9fxhw1ZfC8SMGVKxAaSX1s5H5xRv4cdXofiMqT4VxtJUQ3UqgTJuLmqBklviJMRE59NOaoyWEilft1mqO2equ1tFiuLsgkEw3NVY0jlh41h+eOPWoehdoNN9yAPffcE8cccwwAYMGCBU09KNJc+uuEifSkExiR8j6y0vwUxx41l9JH+c2o67pR+iiGjArBmEpqSCesO3P/Wr/TuK+TKGkHWWmYe6GkGxHEKq8MVZ7IPz/zCg6bP7Xm9176H+QPp5rSR4fdeLGTbwg1LuYij7qzX9J1JFApazvl+w9aXBHvc9Ss50uhpBuLKC/nhPis2DlewJ/+tRFHLZpmnIM96SQ+dsKe+PAN/8DKLZVk13jNUav9eu9Zg3julWG845DdAJiLXvEeVnfkAbO0OehzJr/3vTyC28ZSXJArE5yE7oDHdORuoWAMqfYXJiLfR3w2GKWPiYThqI3YxPN30hy1YqmMHyx90fIz+TxaML0PL2yqfA7mi2XHAeHEZMiLo6bLjlrnnC9h4ulMOuuss3D11Vcb3+++++747Gc/27SDIs3F2CV3cMHUD+TBHlPPOw0CdRVqkqO2Y6xgfLjNnlQRamLxkpISosSCTx4U2+jOc5hkpTIQL7toIxNFi2AVOI01kClZhJpu+zungbvi2GLQMtTxqKEG4rXdNprHv9YP4Z/rzCAa7z1qSulj0aejVnXf731uMz5y42N4/08eNUukUgksqsaar9w8Cl3XjfeySG/tZpI2jtodHz8WSz9zAg5fOA2A+RyXyrrl+ZEpNfi5Jr+OzqeF+Yu47krLyO8BJ9E6YATpdL+jpuu6ca0whJqPMBG1F9MaJmLdNLTE83dQ6eOvn1iHlzaPYmpf2nY8z9S+jPH1CmUkEbFn2OKoOQg1OmrehFoikUCxaC4oV61ahc2bNzftoEhz6a8z8FoVapN7zdI8sZiQ3zDlsl6zcy8zNG7+nedeqZRI7TalF4PVkj8hViq7b9YSClkEifLkKAg1OQnTS0T/eKFklHzKeNmtLXlw1JzCRNij1jlkFEdNvMfsgnW8O2rOpY9+5qgJnlq7U0qD1TB/Wh8SWmXTZ/NwzvhMGcgEqqrvKOQFvnj/9aSTlplcclCQ03y8RgNY/L633Wa6xQXxOer23JlCrfsdNfl6Lj6H1GuK0xw1ADUzIO3i+Y3HT3VmmMiVS1cAAD56wh6YVhVl8vV4rGCucyjUvCGvDe1EmK7r1tTHDjpfwsSTUNtll13w9NNPN/tYSIvol5Lc7EoPel2EWsrmA1btg1GRe96Wb6i4AvvOmWTsSgmRV/lQV3bmpObRKDlqiYRmJivW+fcLRMmnzHjBn6Omfpg5uSNqmAh71KJPTeljddFj51YHdtQsPWr1P/7Fpo76GOJ4M6kE5k3rAwDc+fQGY1ErPmO6GflzyKkcW3ZJiw6N8KZwDlYqJf9t+TAeeGEzzrz6bzV9NU7HESfEc+4UJAKY5/DIRPfH88uCIy2V8cvneNZLPL8yAiSdTNRUCmRTSePzq1PCRMplHSu3VubBvuOQ3cwwMWmTdkwS9HbOOalFdtTsgkLU5WlcHTVPV9M3vOEN+OlPf4o99tjD6E/74x//iBNPPLHufRnPHz0GpEXUWKFk+R6oFWqDPXaOmvQBVedDSZT83fvcJnzlt88AAPadM1hTw51KmuLH2JmTdn/Fh3oUhBpQ2Rks5kueh15vH60tofFyX9cetepzoi7cxQIkR0etY0inVEet8trZvb+8vpyq050vln3FwffbOGPivSnev4cvmIaXt47hst8/ixP32QWAOSi7m5GfP6enUhbDdR21oD1q0kJYHoT+vmseBQD8563LsKeUL09Hzdumnyj5d6o86SbkFgNZvCY1DSVUfufWo5YxWhZ0iwuSSmg1G0LZVMJw6zole2N4omjMPZ3Sl7EtfZQ/p0cmiiiVdV536yD3qNl9PqqbSkF6GnVdx4ubRrDHzIGO7dX3JNSuuOIK7NixA3/4wx+wcuVKaJqGjRs3YuPGjXXvG/em5SiSraYulfXKLDVVqMn9V4PZlOXDRnzoyi6PXeLjohn9OHC3yfjNk+sNR+07f37e+P2+cybV7PanEpqxWBWCRC59bHRBEzaZVAJjPoSaXS9bzsN93QZeOz0njOfvPJx61OzeX14/V1U3zpL66KHUzs4Zy1cdZLGg+9LJ++FXj6+tzvIacbxft5GwKX1UkV9Tp/l4jcfzuztxG3ZOYI+ZA+Zx0FGTHB/n51y4yXEYeC1fm+RrSSIBoPoR4pr6mDSvN/LGYiqRqNmAyqYSRs90pzhqYg3Tm04ik0rUhKcA1hTtS3/3DO5Ytg63n38s18AuWB212nNB/agK4qj93/0v4f/9YTnOOmYhvnLK/r7vHwU81VrMmDEDv/nNbzA2NoZVq1ZB13WcfvrpWLlyZd3/XnrppWb/G4hPNE0zyzpsLkKyo6Yu5ux61Ox6aPabMwkfP3FPAGaYiDzj51Xzp9Y4aslEAumEuTMHWD8IjX6siHzwZZK15Q8q/XVCFbwINUs8rbLrVHZY5HHgdefhp0fNu6NWK9SKPt5HdqWPhqNWPd7JvWnsMbPSl7W12ocZB6FmSX10eC41zawSkN+78mdsoxtQ8t3sDkPXwT4PhaKUSujEQLZSSRIHoSbPUJOFhXxeu81RM8b2lMuW81xOchZkOjD1ccd45XNNtIGojppdUNCTa3c6uuikghw0Z1eSrf4syBy1b/zpOQCVWZ+diq+raTqdxvz58zF//nwsXLiQMf0dTH8mheGJom1ZlSzOnASAPITQ7jEm9aaN+WBDE0Xouo6Zg1ms2zGOi9++H2ZP7qlZmKaTWk2tu1yS4SetrhUI59Et9bE/m3JM1wS8CTX5Yqb+rXqpj7lCyfb3JHrU9KgJR62hHjXr+ZLzmfqohokA1vQ2gdht3zFWufCqLn034sVRAyqfp8Wybtl06pM2cEo+xiXYPr4kNuweoazrrps9ccRL+a8o341Tj5rqMMrnZE/KpfTRqITRLY5tKlkbJpLtwNRH4agZQk04iFLlj90GSKHEmH43LAOvbT6X1Oc0iKOWSSYwXu7snsFAV9NVq1aFfBik1biVdchvmNpBrrWOmt1CcuZAxtiBK5UrCwXxptu9mopW66hpUupjbZiIU8Jhu8gka+vUVQayKWwazjn+3m+PWrGkY+POCTy+ejvetP9sR6FmRCLTUesY1AWN2+ytoGEihZLuq9TOzhFWY7yB2rKoODhqbgOvZdKJBCZQtgi1Xul5Nd2d5qQ+6jqHxqoUHYSJzIDoUfMwQqXTMYSazTVZ4D7w2txglTdx5Tlqgk5MfawRakqLxphDMqhTuTOpYBl4bXMuqOdHkPMlm064jo/qBCj1Y4pYSF3+h+V4Zv2Q5XfyG0ZdgNjNUbProZkxmLUMypZr18VjZJUdulRCM3aqxOJFFjJRc9Qy1eP34ooJXrPXDDxz6Zvw7sPmVu/rP57/9d9aio/97HH8/O+rjR1JxzlqRo+a50MkbUIdmi6clkYuMjVCrVg2h1J76FFLJRM1aW/yHDWBWhZlVzLZbXgJEwHM51lOv5VDWhoNSUrWqX3UoVtKhopcPHpy1Kb1ZwFUevy6nXzRPgXTa+mj3LMlHDVNqzy/auVMTzpplj52mKM2yXDUrG0PTmLea/96XBmuM/C6xlELUA2gnn+dSOf/C0ggxELhyTU78NbvPmD5nbwL5CQA5PIGe0cta1nI5YtlY4EgHqM29TFhlPEUjB612tLHyDhqNslPKmrj/tS+DPoyKU/3FVhTH3WjlPK+5zY7uiOco9Z5ODlqEw1EPaujM+QSHa9x8Ko7Js4p2Y1Qk2JjUfoox/O7lj5WnmdRFgpYI9DNHrVgl2N5sWtf+mj9HGXfjLngc4vn33OXSgDLqi2jXe+MyD1qMl4dtbS0wWo8t9XzuU/5LOjPpMzSxw54Wh95aSu+cNs/AQBT+ipCTQ0TcUq+dmuLiDuFUtnyvNn1nzmNI/JD1mWDoVPo/H8BCYTbjrf8ZlDXDmIxoetm75STo5ZMaKazYzO/Sd2pTyXMHjW7OWpRc9SyHkof1R2gaf2VQZlZH25cWXHUBAlNq9ujJi4UTJ6KPjVCreRc+qh73ImWZxQC1jARr/OV1c8Ku0WdOgw3DqWPSc+lj5XfyQPvxdt42ZodeGlzZT5T0M0U+fOaYSLeKHiI5991cg/6M0kUyzpWbRlt1aG1BaceNTEjEfDqqEnR/NXHUkd19GclRy3i52K5rOPfr3rE+N4pTMRRqNFRc0Tt/bTbQAqjR02t3OpEKNRiittCykuPGiD10Dg4aoC1j0stfbTrUVN3qiyOmlEiFI3T1viwLjk7HuqHz9S+jPW+fnvUJOGqaVKYiMMcNcNRo1CLPH7CRLw24Yv3keg1yxfLUlKoR0ctY++oWUofUzF01DT56/qOmjzwvlzWsWMsj3f84CHzdgGFWj1HDbCGiTCe3wzDSrk4apqmYc9ZlflzL2waaclxtQu7vlMA2Ge2OX/PbcEr95YXlMoZda3Rn00FDhPZMZbHy1tbJ5qXPr/J8r2jUHNIBqV77YzcnwbYbyDV9qj5/+xyG9TeKXT+v4AEwi7NTSCLgfOO38PyO3kxUXIpzZoxWBVqKVHLXTJcMvEYdo6auFDY1SKbfQWOh95Ssh7ElhovO60/bbmv3x61h1dsNb62CDXOUet4/MTze62oKVRff7FYKpR0+I2DVxdaBZuysdoetRgINUvpo/Pt0jY9aiVdxytD1pChMBw1u3VhWbf2pTH10fxcVqPjVfaqlj++8Ep3CzW79zQA7DNnkvG1WwmZUfpYNjdkxWOpGz19mZTxfnFyd695cCXeeMV92DRs7Q885NI/4/hvLMXa7WP1/kmhsHzjsOX7SdWAGfP6LXrU6Kj5ZbjGUatf+hjMUTPP26g7uE5EZMlLWo1ajiAj3gz/9aa98e9HzLP8zuqoOYcdiB38jPSBpjZw2/aoiXh+m50Tv05As/HiiqmLoqn9Vkft2Q3D2FinWV0We3Kfi6Zpjk3xxuymiM2eI86ka8JEnEuLvTbhi17PXsNRK5ni3mPtY5+S/Ji3CRNRe9T6XPpZuoWkx3h+sWCVFyblsg4d1tfQS7iLHfLao2DzWVQTz09HzVOYCADMr5b+rd8x3vRjaifivFFTH8V8RKBej5q4FppjKFIOjtpANlU3TOSy3z2DFzaN4Hv3vGj7+ydW73A8ljApKdfvoep7WDwXYhNtzClMhD1qjsgz1ACHgdfK+RGoR01ygjs1wTUaK17Sctx2vMXu2oG7Ta7pbbJz1Ozqs8X97EofxYe66iCkEpo58NrNUYuI5sgou2p2qLtE0/pEj1rlvo+9vB1HX36P699x+nBKaJrxQaa6IzXpXXynR57aMJFqWY2to+a39LEBR03ZERez+Zzi+fszycgE/jQTa+qj+xw1wDoKpaTrlhmRQPDyZHkxY7crrfao0VHzFiYCmBsQ3b7gNvtOrefg/rtONr5223wxNwZr5zSqPa792aQUJuJ+LrY7xEV1cA6eNwWAeV6IHmCxBhKOm6Ddxx9lhhRHzc4tUz+rgjhq8sdqpw6v91Sfsnr16ob+yPz58xu6PwkfdfElI+qA7RZydj1qYlfp9MPm4k//3Ii3HjjHuI1cHlgv9TGZsA68VmOkzXlD0VAdakSvHeqFaHKftfTRC04XMw1mSZO6MK4Vat2/cO501Pebq6Pm8YIl3jPCFcuXykhXO5m8CoM+ZaElynxkp00ufYxD2SOglD66CbXq55XcPF8q6zW78GGUPtoLNd1a+khHzbOjJsr97MqPuwmnHrXJvWnc9enXQoN7P588V6yg9P9lU0loWmXDAKisPbzOUWv3ho/YBHnjfrNwxuHz8Nq9ZgAwN6ZENZH4jJ7Wn7EIEJY+OqP2qNmNDRHnhzh/gjhqstgbmSgCk11uHFE8XVEXLlwYODVO0zQUi52pYrsZL2Eidh/MmlZJcixJA6zFgmPRjH48dvEbLQLMDNyo3WmrmaOWTFjSoyaUDznRzxURnVa39FFXSo4AYHp1No+fJCKnXSRNM8ue1IWiKoJZ+hh91M9Y1x41j6WPYgEm3u/5YhkJrXLueRUGaeUNJ97Hsosmfz3QExOhJoeJuDyXYlNGXpiUy3pNyXjQDSh58bJq6xhuf2Id3nHobsbPagZe01HzNPAaqO1F6lacetQAYPGswZqfqRhjdUrmpoBcyqsBRqGvHCZSr4S73SFY4n0zf1of3rDfLOPnvUrpozg/JvdlgK1m/xwdNWdE6eNANoWRXNE+TKR6fmRTCUwUKmtIXdd96ZGc9BoMd7Oj9trXvrbmicnlcnjkkUps6dSpU7FgwQIAFfdt27Zt0DQNRx11FLLZbMiHTMJALkdQF2z1dhszyQTGyyXsGCtg1qQejFft/950skYgyI6a6JcRAlC9SFbCRMx4ftVJEIIoMo6aJELtkBdH//vvhwAAZk/usdxX4Pbh41b6WHSImVafWzpqnUepZN0IsfzOs6NWfW9W3a9CqWycG157opxEiNyXJsfzi/Lebscaz+98OyFiZaFWLOs1valeewZV1MXup25ZhmP2nG58r0MpfezQhvow8eqoidfOS+hTJ1NwcNS8Il+37cboaJKllkklJKHm/rjtvm45jQTqqX6eijWKWJuIVEgBHTVnNg9XwpRmT+7Bi5tGHOL5zVEwosy0rPtrf5H7dtWRAJ2CJ6G2dOlSy/fDw8M48cQTccABB+Ab3/gG3vSmN1l+f9ddd+HCCy9EoVDAn/70p9AOloSHLHYGlR3weruNhy2Yigdf3IL7nt+EvWcPGh9WaugAYHWd1A89TdOQSSUsyYRGzG9Zr3ESxK5Vu8shBMIVc/owlneu37DvLIuLqZY+lsq648LZ0VEDIKqY1AsaHbXOR7zuYiNExkuYSKmsGwuhfkmoFcuVc8PrIshp7ZZ1CBMRgTndjteB1+J5klMfNw3n8LlfP225XdB4frt9orGc+dmp67plZ5+7/JBcH3dhYjhqNu/BbsLoUUsFOwflSpit1XmBbhuq9VIfzduZx+N1dmSYiGu4uuYwHLWicNQq/68RanyvOfLKUGWjarcpvXhx04jtuSCe/2w6CVRFVrFcRjLhvSJJ/rwb7VBHLdD2ycUXX4wVK1bgL3/5S41IA4CTTjoJd999N1asWIEvfOELDR8kCR/55FWbhM2wAfvT46T9KyUAd/3rFQDAeKFy8ve6CTWHnbasdKFMJzUjLrlQKtcINdNRi4bokEcP2CEnV6oiTBVSbrvcavKUgWaWBtQPE4nGc0bcufjt+xlfG+MvbEofvbgilvd4xix9VAfP18Ou/KgnnbAsXuQetTg6am6lOMJt3DlWcLwN0EiPml2stfmzstLbwYHX5vunXjy/2Izr9tJHcW0N6qiJ69vT63biEzc/UX0s5+fW68Br+T3RjvPWKaxLfN6pjtoUOmqeEeNJdpvaC8C+d9Yss0/U/MwrhS4ofQz0rvz1r3+NE088ETNmzHC8zYwZM3DiiSfitttuC3xwpHnIA2nV814dTK1y3J6V1/2f63cCAIbGq0LNJhVKBG6MSju88gJRns2STJg9arpu3YEGpB61iLhD9eaoyY6a2uej9qi5ffgIMSZHJQOVi5y4H8NEuoMPHbcIx1bL1ozUR5vSR12vv8MsX6BEqXNeSn30+j6yc7DVqG554PW0gZgINY9hIj1Gj5r7IiH4wOvan41In7c18fzsUZNKHz06al1f+ugtBdMJu/u5uZVeB17Ln1HtKNl1ai1Qe9SEc6Y6ahx47YzsqAH2r69ol5HXS37PA/k1iJWjtnnzZk8BIcViEVu2bAnyJ0iTed0+u+DQ+VMA1J746hwUlcGeyodRrljG0EQBz2wYAgDsKw3HFAjnSF5syr0YckR/Skp9BFAzEDYXNUctWUeoVT/kNa12sVvjqLl8oAsx9jYpTRMwo4GB2ueEpY+dS1IaUaHrOrZXnRh1nEW9nUX5nOoz4vnLxi625x41m3NH3ZTpiWGPmvy0uK1v3QYFywTdTLFzJcakBYkO67nAHjUfYSLpuISJhNOjJuN2Pnt31Myv2+GoGTMnNXuhJgKBRGmsWlUUZpnxzvECPnLjP/CHpzeE9pjtRAi1ucJRc+lRszhqPsWvXH7aqQ5noHflwoULcc8992DNmjWOt1mzZg3uueceLFy4MOixkSaSTGj4xukHAagtnalXGiUuXroOLH1uM0plHXvM7Me86nBQmUx1J0SetSaLCllQpJKa5UKxccjabJ8r2u9utYu6YSJil9LmeVR71Nwis8XCSk3qlFPjaoSgcuGMyFNGPCDeH6WyjtF8ybi47DLJGsxUbzdaLBI0TRmT4TFIQTCpJ13zM1Wo9WbM8zkuPWry8+dW+tjjMeE1aEiSnfCS5wWp6bOM5/cRz5+yOifditMcNa/YCby0kvoo4+aoyeIt2W5HrXoNV4N+jDARxVFTN9PCFAbfvecF/Olfr+CjP3s8tMdsFyO5ojHmZdcpzqWPstMrTgX/jpr5uJ264RLoyvChD30Io6OjOP7443HDDTdgYsJcUOdyOdx44404/vjjMTY2hg996EOhHSwJF/FhqZ74Zjy//Ye2vPC4d/kmAMBrF8+0va1R+ig5ao5CLaFZfrdJEWr5qAq1OqWPds+j6ni5lj6WrRHrAlmosUetexCvVbGsY+tIxVXuTSct5cqAGSTjhCgbSScThlArOPSKunH2cQtx+IKplp+ppY9yacq0/lph143Ii0i30kevjlrQMFu7YBn587Yy5Lxs+T7ueB143RMTR81pjppX/G4ymHPUnI+lcjvzcb3OjgwTpx5wdeC1WANkUgmcdcxC43ZhholsGs7Vv1GHIBJvB3tSRpidW5hIOpGwbGD6QU597NQS5kDvyiVLluCMM87AqlWr8MEPfhD9/f2YPXs2Zs+ejb6+Ppx11llYtWoVTj/9dCxZsiTsYyYhIT5c1Q9AcVF3EmrppGbsbqzYPALAedaKECRy1L4sGuQFXjKRQDJhPvYrNY5aqeb+7aTejJ2Cy+DwWkfN+cNHfFipC3VZyKrlaeoFl6WPnYN5QSobCWrT+jM178e6jpoICEhoUipb2bHvwonBnjR++dFjsHC66Zj3KOJDFm5TY1L66DX1sdmOmp1Qk3vUAOvnr134SNzIey19FGEi3Z76WKwuiFPBzkG7tEj1HJQxSh9tzt2CRaiZP5evkV5Sb8PAKH1M2H/eTeStqY/ZVAJfOWV/nHn0fADhOmrtSL1sFmLtsstg1vjcs1sDydcqcwPT33Ma29LHRCKBm2++GTfffDOOO+44pNNpbNq0CZs2bUI6ncZxxx2Hm266CbfccgsSEZl5RWoRdr7lA1CK9HZaOGiaZgiNtdvHAVTecHZkjR61UvUxNUuZkOwsVQSgZpQKdkrpo5NQc9u19eOoiYuSujheJQ3WrNejFpXnjNTH6qhVhNqMgQz2mW3tAXU7Z9ZsG8Nnbn0SQGXxJc7BnJT66HeYrHweq70YltTHWJY+Ot/Os6MW8C1qt8AZU5rm5c8oOmrmsF27sl4ZOUykmxbKKo32qNmtFUakuYFfPe0AAMAnTtwTgFT6aHPuyotpeTOk1AahZpTIKu/Nmh41yVEDYNkYC4tuOvt2VN9/0/uzxtrFrkdNrkpKupwzTlRGk5i371Rn3NMcNSfOOOMMnHHGGSgWi9i6dSsAYPr06UilGnpY0iLsrGS3SHmZbCqJiUIZ26o7/jPrCrXKwqFm3pd0YRC/SyU15EvAJocwEb8LzGZRL0zECGWxeR7V1EdXR81DQlntwGsKtU5FvHalso5to5X3wLT+DL74tn2RSmj4+d8rvcFupUDn3vgYnq2G/KQSCWOn/IEXtuCoRdMA+B+wbBFqSumjvFCb0hsPoSa/pVwdNZs0XDvc+tzcsFuzuqWbMfUR2FEN6JncV0+oVV67sl75HK7nwHUqjfao2V3jhqWU0zOOmI8T9t7F2NB1m6MmL6zlc1t2Ulp1DpeMHjXr9VR8/hXLlRmFeUWo1WuLCEQXvW2NjZLelHHuuMXzp5MJywamV9RNqU51xkNRVKlUCrNmzQrjoUgLsetRkz843XpY1NI9J6GWsXHUZAakYdvid+L/qqNm9KhF5GJZt0fNJZSl1lFz/gAxyy+cj0Vd5KlNzSx97BzkC9KWqqM2fSCLKX0ZXP7OAw2h5lb6KEQaUFl8HbTbZOP7v63cBsB/eqp8zqriY5fBLI7efRp60klM6o3HRl3Ca49awHKyRnArO2Ppozn6RY1TV5Hd0FyxHNhxijK6ruP5V4YBBHfU1OsNYA20AYBZk3qMr8X7xc4Zk6+nTvP/Wlb66DRHTQpPmiiUjE1kIewzTXHUukepCRE/2JM2Sx9txHdBKn1MSRuYXlGf/07tUWvoilosFvH73/8ejz76KLZs2YKjjjoKZ599NgBg/fr12LJlC/bbbz86bBFF/vApl3UkEpplB8KtZ0JdqM0YcBBqSUWoKR/ocyabH97i76niThA5R61O6mPJpdcvSI9avZk/MmmlZ6AL1xddi+x0C8d6erWcUNM0JLTaIcauj5dMYOGMfix542Jc8efnjZ/7OZ8A62JMff8nEhp+fu6rfT1ep+M59dGjoxaUC16/F757zwuWn9nN3hMUfCx0uhWvQk0+5ycKpZo+4W7gyvtewuOrdwBoZOB17f3U67dMwqaaRyBfT502kVuVAOlUJp6pphDqeqX8scZRS7qvDYLQTZW3QxOi9Dhl2ZjUdd3yWWo6alKPmg83tVaodeYmVeDl24MPPog999wT73znO3H55Zfj6quvxoMPPmj8/q9//SsOPfRQ/OY3vwnlQEn4yM6U+OAL4qhN6884fsCrc9TUx5wzubfmeFSBKMpNcoVohok4lz46p+upO5BuHz5lh109Nxgm0rnIFySR+jhdGiIt3h9uQk1+j4j3z4Lp1vEZfh01WfzbDbePGxZHzUf1Qdh86vV74fcXHGf5mepmyBRDXDx2Kl6FWiKhGZ/VnbrIq8fX/rjc+DpomIjfklA3R01eXJcdhFqrZqo5jXHQNM1MfsyXDadGnCtpY20Q3nF2lVAzSh/TlnNHfVkL0ia1uF75cVNVodyp7+FA78pnnnkGb37zm7FhwwZ84hOfwC9+8YuaRtuTTz4ZfX19+NWvfhXKgZLwkRdq4oNPXMQTWu1sLhm5JGSmg5sGSEItZy+ydp0iO2pmj5rMLoOV20QtTESUOdSL57cTsepz661Hzfu/WxWCUXnOSH3E++Dbdz+Ph1ZUen+n9ZvvMbGP4SrUNFmoVe6gLkrd3t92uIWJxBFZqLk9ldkmi9pEQsP+u062/My1R42OmmehBkhDr7t0ltoMaRMozDlqbrg6anLpo27vorWqR00IRbuqGDlQRAgCca6w9NGdoWrp46SetGVtoj5fxmB6S+pj8B61Tk19DOTjX3bZZZiYmMCdd96Jk046yfY2mUwGr3rVq/DEE080dICkecgLjUojZ9Ioi7ErZZCRwzDUQbwyQqiJuT7qB/rsSbWlj2lLiVXCmLMh3mR+nYBmkZESwewo1Blz8JmTFuObd1VK0bz0qPn5dzP1sXORSxI3D5tz1Izfu+xGm4+hAdXTUryfpiix+b4dNZfSxzgiv6dcN7Xa0KM26lJ2FucwkYlCCWdc9YjhOKrvCTuyqSSGUYz8brxaNuaVPXcZwJaRSt+qHADih0wygd2m9GLdjkoKdCqh4eunH+R4+4TxGVb7u2g5as6bw0ZEv1z6WOOohXfOdNP+ihwmIl9X1Ne1KAlleWyNV9TnP1Y9avfeey+OPPJIR5Em2G233fDkk08GOjDSfOwctZJLuZ6MHMft1J8G1Pao1TpqUulj9XeyFT69P2sIHfEm8+sENIt6qY9GtKxDL9DHT9wLtz2xDis2j7ounsTFws+/m6WPnYudsJd/5rYbbdxeOlfEfacqCXd+xbs1TIRNj/JbzK1vth2ilo6aPQ+8sAVPrtlhfD+pp/4SqN68zCjwi7+vwf/743Jce9YROHjeFF/3zUibrkGFWiKh4fcXHIfV28aw75xJ0PXazUIZ8d6xS66Vy9Wc+tLqzZAMC7dRJqKqYNwSJlL5h2Wb4ah10dvW7FGzOmrqOkjuz2ePmk927NiBefPm1b3d6OgoCoVC3duR9pC0EWpuQ5plZEfNrXxElP2MG2EiSlmj5MYNV9+8srCZ1p8xHAbxAR41R82pYbjoYaiql34jr+JZRv2bdNQ6B7vXSn493YbFCmRRbzhqvY05anI5LXvU1NLHYI7asXtOD+14/vSp1xqhM+7x/J25WAmDmrESHkr2OqH08cJfPYVto3l86pZlvu87If273lsd1ByEKX0ZHDR3CtLJhKtIA6Q5avVSH/V2O2rObQdis0oOExHrItHPG2aYSDfl8w+NV0sfe9OW65Aa0S/cs3RS87RWUqlx1Do0nj+QUNtll13w4osv1r3ds88+60nQkfagaWbdr+GoeS59NH/vtispFndFh/I9WfCZwyLN20zrzxj3EW+yqLhD4ngLJd12Z9BLb5mXumtxsfIjtmodNc93JW3GTkDJmxfm4E9vjyHeT4M9KctgZr/iXX5fUqippY/Ot1NnJsrsOXMgtOPZe/Yg3n145XorSh+PWDgV7znSeg1u1SK3mRRLZdz73CZjZ94rQRwO8fp1wm58kNdWCNCr33+40Q/ebNR1h4zcV+QkzqLUozaRr019TNeptglCNzpqlWuSuQ59et1Oy+0K0hoqWI9ad5Q+BhJqJ554IpYtW4Z7773X8Ta33XYbXnzxRbzxjW8MfHCk+YhFnzj5Cx5dK4tQc3PUanqlak+5H733VTjv+D1w7B4zKn9bEhnTBzLGGzQX0R41wH7nrGjsBjm/zcQFwNVRc5nH5nhsLH3sWOwElN/SRztHLZHQLO63X6G2YHq/8TV71LzPUXMrE3UrGw+C0RNcddT6MikcPHeK5TZRKn1UQ8i88n/3v4QPXvt3nHPdP3zdbyKAKyauYUHu22qCfMxPVDdAW/medqsKKDiWPso/b41oFu8Vu+uneL5GckXjdtlmxvOH9kjtx+hR66lcj8TrfNa1f7fcTh547WWtpFIz8LoDNlvsCCTUPve5zyGTyeAd73gHfvSjH2Hjxo3G77Zv346f/OQn+NCHPoT+/n4sWbIktIMl4ePkqNVLcZI/1AfdHDVFqNmVAb7lwDn43Fv2MRaX1h41yVGLWI9aj7RTPm7TvO8Wzy/wskuUl9IuX7f3TE/Hlkholr/L0sfOwe58kd+PXsJELD1qksCfIgk1vxseHzpuEQ6dPwWaBiyeNejrvt1IIlErnu1wc9Tef8xCvGavGfjKyfuFckxZYxxK5fMonUzgwLnWRMioOGoPv7gFB19yF+5Yts73fW/622oAwKOrtvm633gAsSWEdqcu8uoxUb2utrLv1Ch9rJP6WHYofWz1HDW7TVLhqA1JfX2Go2ZU24QZJhKN922jFEtlw/G32+SXX2fZOGjEURMfz52a+hjonbnPPvvg5ptvRrlcxsc//nHstttu0DQN119/PWbMmIEPf/jDyOVy+NnPfoZFixaFfcwkRFKKUDPnVvhw1HqcHTVV8HkRDPJ9pvVnDYctiqmPU6oBDZuq6XwyRpiIm6NmNMjaf4Doum4sugayKXz3PYfif//9EE/HZ1ncR+Q5I/Wxc53tRLfXOWoZaf7ZZCnlzu/A6550Er887xg8+vk3YL9dJ/m6bzeS9Nij5rYAHsym/n975x3uRnWt/Vf99OaCbdxtMNUkFIOxjW1Ch2AM2JeQBENI4UJCAoSEJDe05H4kJBjSSQESEkoIJbQbCAQwHQymV4NtMLbB3afrqMz3h7RHe7b2SCNpJM3ReX/Pw4PPSCONpD0ze+13rXfhr2ceiNNnuXOfVJX0cDBl3f+LUz6F46aPBpBdB1ItvvSX5ejsj+Obt75c8L7FBpv9Uo3K77+4n6N9BlPqYzEIpbAaiprutuclM5HcNWqp70u0egAygZppJsI+alnIhjW6RX45uI1Li92ZlP8CXB/TryUa1Q/Wc7joJZQTTjgBr7/+Or7xjW9gt912Q11dHcLhMCZPnoyvfe1rePXVV3H88ce7eaykDIgm0/GkgXNuXoHP/+lZAPaW8oKIRVGzD9TU13ESZMlB4LCmjKImrtNeCjpEe4ENO/qyHotLhbB2iM/yq0few9qtvVmPR+NJ82bRGAmguS6E4/cZYz4+vqMBd519sPa15fdl6uPgIZ+iJuKrXCuLlvop6beXnR+LWfAI+H0Y0exuut5gRY5zcze8zlwr1do+t7MD1AwGoQQs+NTOmD01lVouJr/Fph26hQ/Ff/ZiFRURlBw3fTSO3HOUo30iedqwDHYyqY+VU9RyZQVYzESkebXFqt9DNWo7egcApFSbTC/YcvRRqw1Ea4xI0G/e127+yoHm43IwlbHn9xenqMXVQG1wnsNFnZmdnZ0AgAkTJuCaa67BG2+8gZ6eHvT19WHlypX47W9/iylTpgAAXn75ZdcOlriPuLD0xxK4/9UN5kW7sBo1+9THkF8/ccjFIbtm0vta60NZkyBPBWqtqUDt4x39WY/F8tjzA5kJ+JsbOnHS757Oerxbcm9rDKe+Z7lXzukHT8Snx7drX1uetHnpOyO5yVejJsZT3j5qaeRV6cnDM+YVXkkhHqzIilqub1JeMJk5xT2XRx3ZqebZ14B40sA5N63Acb96sqoTl1IyI4qtURJpfoWY4YhFycHqGJcPMQZypei6jV2drWEYeO2jjKFE0q7hdYVdH3ULncKeXyhq4aDfvDeL085N5U9eWKn2IkspiPEmvj8AmDk5c120KmqZsg9xDyzESMZU1NLKXSxheCb1uxCKCtSOO+449PdnT0xVXnzxRRx22GHFvAWpEOICpObu5wuo6opV1PIodQCweP+MS9nUkU1ZN3QvBR2jRaDWmX0+JBy0OpA/iy59sjudJtAYDmgn1rlskOU0KCpqgwdtHzW//Fum/p8z9dGnD9S++ZldsHNbPXw+YEJHgwtHO3SRz6lcp5e8sDJnl+HlPKSs1Ec5SJSL8e9/bQPeWN+JJ1duLuvx5CLg4F5gR/GKWuHGGYOhj1oh9McSWLWpG0Bqwl8dM5HU/9XFpn+8+BH+/sJa829LumMV7PkzNWr5Ux9199tyHecgjDVMxHiTF/t9Pp+2L23GM8GH+lAq2CqkzjSmpD6qrz9YKCpQe/LJJ7Fo0SIkEvZf2AsvvIDDDz/cVN+INxEXoF7FDCNXup68H5Dbnr+Yfl51oQCe/O583PrVgzBlRJO3FbWWVMPunIpazj5quT+LUNQaI/rvWJ2YyYSoqA1KHPdRc+AUqv67tSGER749F09+91BMHN6o25U4pBBF8uT9xmKfcW045YDi+1Q5QV24abG4fKZbpUgr0roFpkpRiIutSrH27Jl6LOfvPahcHx08Z9G1z+DQq5bh6fc2W4JPL5iJ/OZRa9unpI2KVg5FzTAMvLexS3nPjKKjUq8EanI5iJNrdOHHl/m3V+pMiyEa19dEhjUGLBl7fj+aImmXzQKasosawSZJTBiM6Y9FnZnnnnsu7r//fpx++unax59//nkcfvjh6O7uxs0331zK8ZEyI1Y1e5UGqfkm9vLJlFNRKyL1EQDGtjfgoLQcnqWoeUgdGtWaqtfRTXicmInk+56FzXaTTTB8wKQO232taU8534Z4CG0fNd1qbY70F7kgX53URIIB7NxWX+phDnkKWfz4+aJ9cPc5syzpPuVADdRGSPb/pnGRNMnb2Jmt4leK0lIf5Z5azietxRhn1JqZiOhVdfuKjyzpnNUxE7Fem1oVF0DrgpPkBlmGQO1PT6zGYUsfx4/uf1N6z9T/czW8zqmouZn6KFWpDeI4LaPgKqm2YjFSVrzEuR0K+Mw5UHfUeaAm7oP1oUyN22BU1OylkBxcc8012Lp1K2666Sa0t7fjl7/8pfnYs88+iyOPPBJ9fX249dZbceKJJ7p2sMR9RODUoyhq+Rpey4M9V/pdMWYiKqo7nZfUoVGt9oqauLGEchxvvsBVXJSaFEXthf85DFt7BjAphyoSYurjoETnxiiPISeuj3GbprHEPeQFI6+UjKjX4uHNsstn6njlgEOXbl0pnKTB2yEHmz3RBFobnK1EFROoZez5B99KfC4MI1OzF/D78rbkcRM7M5GsQM1iz5/ZXg5F7X//7y0AwA1PrcEln90z/Z725QuZGrWMOYYgl6tlsWQraoOzl6U4ByOKgiuuXbpFxqDfj6ZIamwUEqjFzEDPj0jQj96BxKBccCkqUAOAG264Adu2bcNvfvMbdHR04NJLL8VTTz2Fo48+GtFoFLfddhtOOOEEFw+VlANx/ekbsA7+fMXWUYdXoCx7/iJuzsWkT1aK4U2pidDm7oGsx+KSbG9Hvs9ipj6Grafq8KZI3ma5YU26HPE++RS1XM1iBXZNY4l7lJC5VzYiyvV2uEZRk1WUTV3VTH0s7prUH0tYanS6B+JobbDP6rDuW0yNWm0paoKkYWQC1xyLreXAZwZq2cdk+dtGUat0w+vc9vype7+8SOJ30OuyUOTXGsyKmjiPshW17Bq1uNRHTaQ+9hQRqIWDfoTNQG3wLbgUHagFAgH84x//wBFHHIEf/ehH2Lx5M/7617+aQdqCBQvcPE5SJoSio9aoNUbyBGoOHbDUm3EudckOL9eoiYu17uQ3C5FdqFGzS33MhcX1kYraoEH3UwU1rRZyrZUwUCs/XlSpsxQ1KVAT181+6VpVTUWt2Ou43LcKKKxmpagaNaGo1ZjrY9KQjB0qmPYIyGm41mvTth7rb5uosutjUlJ0VMRitqhFr7PUqKX+7+a1t1Zq1PIparFE9u8cDPjMrKKuggK11P7htKKWev/B992VtIxSV1eH++67D9OnT8fvfvc7DAwM4I477mCQNogQN0s1UGsI5w4Mjt4r1YNmfB7nODWFstAmu4CmRs2TgVr2yZ9rNU7guEbNxkwkF7Ka6fPgpJLo0eXQy20unKQ+Wm92g+/GNBjw4uJHVo1as6yoZQcc1axRKzbVLitQK2Di1m+zmp+LWu2jVk1FTWTWqLVm23utmSl2pkhuKlW5MO35NV+PqsrKWUjlVtQq1fC7HGRcHxUzEa2ilplDCUO1QhZmxGulUh8HrzLuaPZ344035nz81FNPxRtvvIETTjgBW7duzXr+aaedVvwRkrIiVup7ldTHxjxF7wdOHob/O3cOxnXkNiVQ0xbzuUnqUIO7UorQ3UbcxAfiSSSThsUJLpe1ryBfnYZpz59H4dSh66FEvE9M42gnj5NczWIzr0FFrdx4sQ+dPPnx+YD2Bl2NWibg2NqbnbJdKYq9Jm3vtQZqhaRC9Q/UtplIIQtyhhyoeURRU8ej1TRG/+9yknCgqAlklbYcro92rQoGGxnXRztFTWfPn1HECjnfB5QaNfn9BxOOArXTTz897wXAMAzcdtttuO222yzbfD4fAzUPI1Z+VDMROzt4mT3GtOR9jnqBK+bmrAYzXko5km9wA4kk6vyZv3NZ+wrym4mkfhdRSFsIE4Zl1M5iAmRSHQY0NxI52BdDxi79xzCMqqQJkeojZzgYhvXaI66jcupPOdzznFLsgpu6qFiYokZ7fkEsYZg9qSqd+mg2X5cm5f2xhDk2f3Li3rjoztdsVbRyBiry9CJXVkx92DqG6jSKmpvKV60YRNnViYqFZXlBJCaZuQjzloLMRISiFvSZqZY16/p48cUXM3WqRhE3y74iAjUnqAFCca6Pymt4KOiQnZ76YwnLxUdcTHOlSDlPfSz8Rvr9Y3ZHe0MYa7f2Yq+dWwven1SHAU3xmXz9FcG93SRbVeSmjWp28eiIzLF7j8a67X2eOb/qwwGMaa3Deo0LrU5RiycNc0G10hSrqKnKVkGBWjowyWeWJZMrvX0w89Cbn+ChNz8BUNkeakDmGiYHHNvSalrQ7zP7/yWrUKMmxoZ8fc1lJqL7O6OouXdctZIlYdaoKem2IvVR/pxxqRetKP8oxvUxPBRSHy+99NIyHwapFpkaNevgb3Cp34/P50PQ75OKQgu/IagntJcUtVAg1Z8jkTSyLgBmfnUZG17noi4UwHmH71rwfqS65FvxC9ikDQnkG90pB4zDRUfv5t7BEQu/+fy+1T6ELP64ZH8suvYZfP5Aa3Ntca3JdtoDqrH2Jd8L1LTxXKjnR0Gpj0WYZ2RSprw5wTNcUG0KqdlzA7HYKl/DtvakArW2hrC2Dtfu324gv54IuOJ5ArXs1MfsQM1NRa2cn7+SmK6PqqIW1NSoSamnzUX1UcukToY1it1gwR3ZhAxaMjVqiqKWx0yk0PfInHCFzwjUoNFp0+xKIfpzqKkxTmrU8rUrsOujRmqXmVOGA3jX9nGx+m2XiiWnyPzohL0q2h+JVJ89x7Ti5YuPyDIWsVOwEkmjKjWscraFmjaeCzVQq5zrozdTH3U1rXbYqfCVV9SyAzHh+NjeEDKzUOSPVs7Uv07JoEYYq8jvobuHZytqmobXLh3n/722ASs3dpt/D+ZAze4c1CtqmdRHsVjdE407zgKQ7fm9fh7noqiz85NPPsE999yD1atX2z5n9erVuOeee7Bx48aiD46Un4CtPb97gYHOsa4QVAdKj8VptqkxTvqo5Qtcu/pTN5DmIuz5yeBkvwntuOO/D7Z9XJwPqgoukFMnvWS8QyqHGqQB9gtc1Zr0ydfFQla57a6zTugrwjxDpEx5tbalEFfXmM1zK20mkskKyBzPB1t7AABj2+u1ZhzWPmrujlnZSVSoYPKxFauoAaXXgRqGgbNvWmHZNphdH0XqdZbrYzD1nQ1oUjyDAb+5WB1LZGcv2aE2vE69vzfP41wUNeVdunQpFi5ciP5++2aZfX19WLhwIX7xi18UfXCk/IhFzSx7/iJqouyQa8qKSX1UFbWwxxQCu2JzcTHNqajliTq39YpVxnDO55HaYr8J7baPiaJq9ZwViAlGOOBnbTExsavtrdakTz6aQpzYVLOdQlL/okU1vPb2BC8Wd/757dS39sbK3l90NWrvpRWjKSOatOnd1p5q7v4WcqAmfmf5LfRmIkqgJgUecl16qeeXrma5Uq6X5SBzDuoVtQGdmUjAZ8nyen71VkfvlalR8w3qGrWiZrz/+te/sOeee2L33Xe3fc4ee+yBPffcE/fff3/RB0fKjwgU+rLs+d1MfcwMM7XezAmqolZM8+dyYqeoJaQeIHbkUzy2dKf6HHVU+EZKvEtDerxd8/BKnPf3l7MeFxM3L5nukOpjd61JVGnSJxtFFNJMWr3OOhUsEknDnPQWYiZipkx51NZbVsnyfRUxm0nqmNY6F48oP7pATARqU0c2aRW1ctrTWwK19Fi0KGqaBS91LiO7QMrrr6Ueq07JrVQfuXKQcV7Vuz5aFLVEZrFbrmE97frnHbmwWvuoDV7Xx6ICtQ8++AC77prfpGCXXXbBhx9+WMxbkAohbt6qPb9bZiIAEJJOsGJSLNRj8Vq9lp2i5qQuL1cQF0sk0Zmuv2CgRgTy+XDXS+uwQ+krJfeOIURgd62pVkN0eQJbyCq3OtFyOmmVA63C7PlT51t/AcFkJYkX0Nw+plFnAGBMW+5+qG5j1qhJx75qUyr1cerIJq29fSKhD9rcYEtPpvG7GCdifPp9+p6JPp/PEvDbpj6WGFTpzo3B3HIl0/Ba30fNqqhZ+9iNlhYU3pNq9uyQzUS8vuCSi6Lu5ImEsw/q8/kQjUbzP5FUDWFm0as46bgZDJWqqKnNnpuL6ClWToSDmLoqnHDQRy1XfzNhV+zzpZywCAGAekVhVidn4m/2ziMytjVqVVqdlwO1Qla5i1XU5ECrEJfDOo9P8HR25nbYBcSVDtTEPVGogb0Dcazb3gfAmvpYKUVt7dY+899JI2VikauHmkCem8hjSnamLouiNogDtWgeRU1rJpK+l/3kpOnmY+9+0pX3vTJ91PwIB4ZY6uPkyZPxzDPPIB63d1uKx+N45plnMH78eNvnkOojJP3eWGVq1IpR1OpD1ompGrhVG1NRS1+Anl+9FY+/u0lqSVDchDnjghWuiisb8SaqwqzWMIjURypqRCaX62M1sCpqBdSoKePdaY2aMBIJB/yOWwEAGUUtljA86bYnBzD5HCBtFbXWCitqAasr4pbu1KJkJOhHe2MY4tKVsGly7X6g1mv5OxpPZvqg5hgr8gJqRFJprWYipR2bLlCrDUXNeh/TpSaqWUlzdx2B02ZOAAC84yRQk2vUTNfHIRKoffazn8XHH3+Miy66yPYi+b3vfQ8ff/wxjj/++JIOkJQXcQKoP6ObNWqy62Opilp9KFCUIUk5qZMUtWTSwOLfP4PTrn8em7pSanIuwxCb+yaATDpGe4O3FERSXbICNeVGHjMVNW+dJ6S62KVgV8uYQJ6EVyL10Wy0W6AVvXzP8mJ9S1xWIPKmPuq/q51aI64eUz6Euism4nJjYkBvb28XtLnBhzkCtVztgOR7s5wG6XfRTER3bgxmRc3uPMwoapKKmsj+DXbdqRkA8O7HzgM1q+ujN5XxXBQ1G7/gggtw44034uqrr8ZDDz2EM888E1OmTAEAvP/++7juuuvw+uuvY9SoUbjwwgtdPWDiLnarRYUUW+ejZEVNmpi62TbALWRFrU9SJjenjUBy1aglctxYRQPQYY2VvYkSb6Oa62QFanFrugghgH3PxmoZExSf+midaDlPfUztV+i9TQ7UovFElttftdFNbO2fq/+eVXWj3MgLxMmkkZV9onOFLGeN2kfb+ix/R+MJR6mPsqJWZwnUMs8pR+rjYFbUzIbXWfb82e6qCU1W0tSRTQCAVZt78r6XqFELB/2D2vWxqFlvR0cH/v3vf2PhwoV47bXXcN5551keNwwDu+66K+644w4MHz7clQMl5cFuMldIakg+QqXWqEkTUy/W3ciKmmyZLi4IuS706n0zmTTM735bOlCjkQiRURU19cYjJm5ea2NBqoutouaJ1MfCa9SCfh/iSaMARa1wa34gVWMd8PuQSDrv31RJ5ODLLhAT6KzeD9t9pOvHlA950SCeNMzjFtkyQkCRx0i5atQG4kls2KEEarGkOa5y3b9lRU0eVz6fD35fahGh1IWQAY0nxGDuo2bb8DqYXaMm2/MLWutT33lPNL8yplPUvKiK56NoeWL33XfHG2+8gTvvvBMPP/ww1q5dCwAYN24cDjvsMJx44okIBLy18kSy8Vegz5IcXBUTqNmlFHgFi6ImBWrdaYOWQhS1WDKJiD/1ebekA7VK97gh3kZd0c+qUWPqI9FQKzVq5op8KIDuaDwrbd92P5sJohMiQT96BxKOLMErTbyARtCqPf9/z5uCbx8xrSzHlQv5nphIGqYSKByiTTMRS7pjeRper9/eh6SRGhcN4SC29gw4rlFrtyhq1nEV8PuQdKGuUVdTVa2WGm5gV6MWUvqoJZOGeW7LqY+ZhfECAzWPmwLloqQ8skAggEWLFmHRokVuHQ+pMGoQcfa8KfjKnMkuv0f2SVYIsrqXpz90VaiTilR7YxmDHXGRyXWhV1ez5Yv6NjP1kYEayZC3Ro2pj0SDreujFwK1Agr8B5RAzali0RfTu805oS4UQO9AwqOKmlV1MgzDttG9WqM2prWuKkZV8nvGk0kp9TE1RgPaGjVI+7g3ZtduS9WnjW1vMN2vo/EEfBBpmM5SH9WU2tSisnPF146oRgUdrIpaImmgN92zN5+iJvcHlO9l4nvuK6CPWjjgNzNMvHgO58OD015SSVSji2mjml1XcOSTrNBCbhVd48lqY/bZiScsqY+CXMXI6iRJvpF2p6X9Zo81+CbVRQ3U1HSnmNQ7hhCB3XyzaoGaNNnUpeTZkQnUUuO74NTHIuqxTCMCDzrGqXVpuYIY9VqhtvqoFPI9MZ4wsmzYxeKs1ekxqf13qQhr/nHt9WarnYF40lQqc2Xx2KU+ApIqWOKhahW1QVqj9q/XN6B3IIHW+lBWS4hw+rcX57c8ruVgWZz3csqsHaaiFvTZtlEaDJR8lnZ1deH9999HV1eXrQPkIYccUurbkDKhrroXk5qYD3nCWMxNUsaLqY+yotavCdRy16gpN1npwmMWv3useJ1UF3VylaWoKQ5qhACpuhlR1yXjhRq1QlIKReqSWFkv1EykrojrqZcd42JqH8WEATvRUA2Iq1XzLd8S45KZiHCIFguy8m8bL5OZiHB8HN/RgA07+gGkVBdxX8+VmdAmBWrqInRA07S7GHSLGIM1ULvl+Q8BAGfMmpgV2JoNr4WiJn1uu6ys/lgi54KkuDcOSddHAHj99dfxrW99C4899ljePiZOG2STyqMGPuEyBGryakipipqbJiduIbsJaRW1HBf6XKmPZqpOhR25iLdpCOVOfSwlxYtUjmqsOQU0gZoXUh8LaREgpz4CzvuoiT6XdUXc47zsGKd+d7FkEvXQn/uqAlGt/pw+nw+hgM/sTRdTFDVxXHb1d27a04vUx3EdDXhp7XYAqcl8UKmX0yG7UKvXW50qWAw684vBGqiJfnn7jm/PekytUbv+qTUAgJHNkSyfA58vVVrSH0uiuc7+/WRjrYjGVXKwUFSgtnLlSsyePRudnZ2YNWsWNmzYgNWrV+OUU07BqlWrsGLFCsTjcRx//PFoa2tz+ZCJm6grauWw6ZWDwdIVtVKPxn0yiloiq3E4kPtCP7bdKv/H5EBtoPgVYFK75DMTEeOGSqy3qcalLOj3Iaps80KgpqpCuYiqqY8Ody3W9RGAp40I1OArV9CrTvqrWUoQ8KcCtXgymemXJWrUNGmD8gKDm4raR1szNWpyimtdMG0mkuM7kuvS1Bo1nSFKMejG3GAN1MS9SicIhM0+aqnn3LY8ZVD4/WN2t9Rc+nw+1AUD6IvlN/exuj5m0loHG0XJGz/+8Y/R1dWFG264AU888QTmzJkDALjpppvwzDPP4I033sDs2bPx5ptvYunSpa4eMHEXNT2qHIqaLP2XrKh5MPVRXm3tG4hnPZ6rGPmLMyfgzNmTzL/l1Me+Ivv+kNpGTU9WVwgz44apj15kwrAGAMAhu46o+HvrFo3yNUkuF5ZALV68olZow+tiXR8Bb9a3ZAdq9sfoFUUNsPZKE2NQVbHkuUOyTA2v16Z7qI3vaLDcy530UZswrNH8t5qCp2vaXQy1pKiZ5h6aeWZISX0U97G9x7ZmPVcsQuYyFJH784WDsuuj987hfBR1J3/kkUew++67Y8mSJdrHp06dirvvvhubNm3CD3/4w5IOkJQX9YQpR42anKZQqmK3++iWUg/HdcSNvz9mtecX5LrQR4IB/PC4PdCSNgyJa+o2GKgRGdXRTb2Rc9x4m1u+chC+fcSuWLr4UxV/76CmnqPQOK07Gse67X35n5gHeeJdSLAYzQrUnO3XX0JKsGwY5TWyUx9zKGrKc6t5P82kNxpmipoI1HRBjlqj5jTlNRfJpIGtaXflkS0RSx2TCBJzlS6MaI7gzrMPxgPfmpP1mDjVyhKoDVLXR/FZdPPMiFDU0os2ZrNrzfxJpC/nUtRklT4U8Emuj947h/NR1Kx848aN2GOPPcy/Q6FUQWV/f7+5ra2tDfPmzcN9991X4iGScqKeMOVIfZSDj2JX8O46+2B8bsZ4/PC4PfI/ucJYatQ0F45cro/mc9IXEflmlDEToTJC7FFv5L1m6iPdQr3ImLZ6fP3QXarSyN4NRW3OTx/BrJ88grXplDE7DMPAV298AV+87jn0xxL4w+Pv45K7X0dXfyz9vqW5PtYXWqNWwgKGbBjlNdTfL6eilv7uPj2+Dfd+fTbGdTSU9dhyEZRquOJK70d5nIqFXvXe6oaqJE/mZdVlIJ40e5XlSw/dd3w7dhuVHfBmDFFKTX3M/j2rZQBUKtEcgZqqqAn1V3fNEuUguoVxgeygHQr4M+fwUFHUOjo6EI1GLX8DwAcffJD13I0bNxZ5aKQShJXArBypj6VeqADg0+PbccWJe1dlcpOPUhQ1QVBTPE1TCGLHhUdmmtRm1ahRUSM26FanC53wbutNBVpPvrc55/P6Ygn8+81P8MTKzbj64Xfx//7vbfzlmQ9w7ysbst7XiZnIQDyJddv7zPFerD1/pARFzYuTPLU3mvq39bHU8U8a3qhNKask4r64uSuK51dvAyCZiUjBkVCPRF9RgRvBirzIpfbaylVP5QS3zUROP3gijt17NAB3zVQqSaavWfY5GFbMRDKKWvb3X2cq3Pbno/zbyjVqXlxsyUdRI3DSpEmWoOxTn/oUDMPA3//+d3Pb5s2b8dhjj2H8+PGlHyUpG5VIfSzE0Wswktf10UGgFtIoaqYpBCfcROGc+VPxuRnjAGhSHweoxBI9ukWjYieS+XoYdUcz9bofbc2kSm7vG8h631wqkOALf3oOs37yiJmqVrQ9fyk1ah5Mm1K/u1wKqfjNynGfLxRxXzz1T8+Ztu1iUi7PzcU42aoEaoWosHaoqotsES9+62KzjPyuKWriOPxm8DdYFLXH3tmIu176yPw7V/Bruj4mkjAMI2eNYL0jRS2jyAX8Pk+fw/koKjfmiCOOwI9//GN88MEHmDBhAj772c9i+PDhuPzyy/Hmm29i5513xp133okdO3bg61//utvHTFxEvWB7VVHzMhFZUSvQ9VF9jnyTFSvAdO8jOtQVSAEVNWKHG4qaIJ97Wm80cy3c0pPJwOlJB3AJS+pj/mN4fs1Wy9+FmIl0R+N4Kq0AFtXw2tOpj2ovzvw1arl6T1WKgKb2S7hQyypKImkgGk9YAn/AHfc+dTIvW8TnMr5wgmmIUuJhyschvrLBoKgZhoHTb1gOADhgYgcMI3POa10fgxnXR/njaWvUpDmXHZkeaqn9RcCdNFKLG7p6Xa9SVKD2xS9+EdFoFJ988gkmTJiAxsZG3HrrrVi8eDFuu+0283mHH344fvCDH7h2sMR9KmImUuuBmsX1sbA+aupzxE02nsikXnDCPTT57D5jcO8r67Fk5gTt42qDUIEZqLFGjSjoa9SKDNQKUNRkNaQnHcAVqqipRMwatfzP/frNK7A+3cy4FDMRL6Y+qr9DLqVTtiuvNiFNSpuYPFsUNcNAV09qLAX8PvggDEhK/y3UyXxIsojPVU/lBHGqlZz6KFSogB+B9BczGBS1fmlR44an1uC6J1ebf+ey5x+IJy0L1rr5k5gT5TQTUca67DgejQ+BQG3KlCm44oorLNsOPfRQfPDBB3jiiSewbds27Lrrrthvv/1cOUhSPrIDtfKaidQi8upOr8aev7AatdR3Jedes0ZtaPKzk6fjlAPG4YCJHdrHzUDNzkyE44Yo6CbohSykycYd+RSNHkugFsvaLjvXFTPpri9AUXvsnU2Z/YpICY44cJmrFqqClut+G4t7J1DT3RdD/uwaNdmZsb0hjL6BOOIDiYJaOtghB0FA5neOJQzXFLWSUx/N2kq/6SQ5GBa/d/Rlznk5SAOy20IBVkVNDm51NWpikSaXPb/628rvGY0n0RjJ+xE8Q1GB2j333INQKISjjz7asr2xsRFHHXWUKwdGKoO6WqQ2wHaDwSDTl4IIpOxr1By4PvqtzR6FMufzeaOegFSeulAAs6YOt31cFGSrq/x0CyV2aBW1AmqILU6N+QI1adFqW29GUesdSCCZNCxKWC5LeTucmomorpDFpT6m9umNJXDzcx/igInt2GWn5oJfpxyowWOu3zNmTl6r349UNxZNMxHpsYQUqHU0hrAxmQQGEi7VqFmDMTn1sXRFzR0zkahOURsEdf+d/THtdp9PP88U22IJwxKEa2vUTEXNfgx09aeuP83p1kd+v898j8HW9LqoQG3hwoU44ogjsgI1MviQL0KRoD+rR5Mb1LqiJq+26lZc89n7ApmLlLioy1bS5fhNyODHTlETQT6VWKJSao2arHzlV9Qy10L5Pbqj8aw+UMWkPorxn+/whUuloLjUx9R73fr8h+b7rfnJsQW/TjlQJ6u5zBJEcOOFtC9dSpsIRHw+H3y+VFprwjCwtVcEamHz93Qj9VEEBCJAkw0tMoFacddRXdPuYsgoe4FMb7ZBoKh19ukDtXBAP8+UlUtZKdNds+odKGrb0+OktSHjFP72j46uapP3YinqbB0xYgTa29vdPhZSBWSb1HIpN270O/EysqImT04EuqLprOf4M6tJAA0hSH7y1qhx7BAFretjAZM+2SUvn6LRE81OAweA3oF41j0hl6W8joZwwFwAy9dH7aNt1n5vxax7RRwGhdVAbcItq5cqYhGnwQMGVQFNpomstJh9yJLA1u6UGU1HY9jWRKkYBpQ6JjP9Lp5xfSw59bFURU1S9kTmTaLQLvVVYIddoGbzfcrpuKKExOfLtDmQcWImIs6DtvqQuW0wBmlAkYHavHnz8Pzzz7vSGZ5UF7nAUu2p5hZCeq5V5ABXJ/c7secXK5xiAkNVhOQjo6hZb1aZyVhtn3ekcHRp2IVkPBSiqKkufZntCU2gVtjEszESlOzPcz93rdQaACgubayY3muVQp2sbum2D9R6PXRt0N0X5fEpOyFvTasjHY1hKUXOPTORTOpjOjUukXF9rHbq44AUMGZes6SXrAh2qY9236dcQyYWG3WGM4AzM5Ed6THT3hCyfc5goagR+KMf/QibN2/Geeedh/7+frePqapEo1H89Kc/xb777oumpiZEIhFMmjQJX/nKV7Bq1apqH57ryCdHuRS1n5w0HbuPbsGvT/10WV6/2sjBlMiLlimm4XXGuc+7EwRSXSK05ycFolXUCpj1yUGOrh5XRpddkNqenfpY6KS7KRI0lbFcNWqGYeDW5R+afx83fTTmThtR0HsB+nujV2qvhdmE+D7UfmMyvZ5S1OzrlOTHk0lga7q9Q0dD2JKeWCqqM6BsaBF1zUyktGO0KGpmiYT3I7XOPv1Cjc5IBEgpZ2IeJBYb7eZOpplIjmuQqahJqY+DlaKWVW655RYcc8wx+NWvfoVbb70Vhx12GMaPH4+6urqs5/p8Pvzwhz8s+UArQX9/P+bPn49nn30WbW1tmDt3Lurq6rBixQr86U9/wt///nc8+uijNeVmGQ6WP1CbMqIJ//rmnLK8thcI+n3w+1IX5C7NKpKTGrWgaQtsrVErpjkrGRroUh8NwzADtTqaiRAFXV1QIQKTHFDpHG5l7B7vHYhnBTmFqlwN4YAjRe3BNz7BEytT/dO+c9Q0nD1vakHvI9DdG7sH4mipq/5qvbhX7NxWj4+29eUM1ITBixcCNa2ipkl9TBgGtvVkFLVMMFV6oKyaq2TSKg1JUSuyRs2lhtey6jeYFLVCUx/FY/GBTD9au2wkU1HLoepvT79/Ww0oao4CtcmTJ2PRokX46U9/CgC49NJL4fP5YBgGNm7ciJtvvtl238EUqP3hD3/As88+iwMOOAAPPfQQWltbAQCJRALf+ta38Otf/xrnn38+li1bVuUjdQ/5BuQFy97BiM/nQ10okHIzU67JfpscaxVhSyxWyjZ1pVYQqYoQO3RmItF40nTT49ghKlpFrYDVeTlQs1PMBPapj3Hc/fJ66+sWKDs0RoJmr61cJRibpNqmr8yZXNB7yOgm6zt6Y5ZA7cMtvRjbXu/oeu8mokZtTDpQ25JLUUv/Zo0RD6Q+auYbcupjOOgHoqnrm2iY3tEUyfQ6c6NGTWlXoDMTKVZREx+l9NTHzHEE/YNJUSsuUOsdSJjKr119f52pqNkvFonUR7lGbbDi6Gxds2YNNm3K9CG54YYbynZA1eTxxx8HAJx//vlmkAYAgUAAl19+OX79619j+fLl1Tq8siCfNDQXLJ5I+gKj4sSaH7Caiazb3ocLb38VAGvUiD26ono5Z5+BGlHRuz463z9mSX3MrajZmYn0x5K45J43rK+bZ9KtBmNNlho1+4lwNH0+zNlleEkLkbrMhu29MYxLtzi8bflafOeOV/G5GeNwxYnTi36fYhCujzu31QPInfrodUVNTn2sk+qQTHv+hoyZiCs1aqo9v2QmImrDis00cquPmvickaDfXAQYDK6PdopaLoVSnKMipdFOUWupT4UunZpSE8GQT31csmSJ28fhCSKR/B3whg0bVoEjqRzFrhYRK6mbiibt0eHqqrhAxRNJvLm+09y+fM1WV46P1B7i3JX7qInFgnDA7wkLbuIt5OuRSNcuVlGzU8wyj1sXrupCftu+R/E8x6AanjSEM21Lcu06IE1yS0E3udzelwmIrnzwHQDALc+vrUKgJhS1VOmJkxo1Lyhq+j5qUimG5Oy3VUp9DAUzhh+lIhYezBo1KQgsWVFzzUwko/plFDXvB2p2ZiK69GuB+P5789SodaSDr205xrqw56+F1EfeySWOOOIIAMDSpUuxY8cOc3sikcDFF18MADjzzDOrcmzlwq6wkxSGOhEQF3cnjo+A7HBlvQCfcsB4F46O1CK6GrU+1jaSHMgKvwg+inV9zGcmoipuHQ1h2+thvnojVT1JKWqpf+dW1EqbbAsiNoqaoJoO2JlALaWobUmne+oQKqdXFTV5m2hM3htLmOpIyvXRPXv+bDMR91wfAy4FVQNSMBmUmkJ7HTszkVwmPOI8zdSo6b97oZLlakWxfagrarXKF77wBTzwwAO49dZbMXHiRMyaNQt1dXV48cUX8cknn+DCCy8cNPV2TpEbD7KxcvGoKYqt9SFs6oo66qEGZFaZ4knD0vz1W4ft4t5BkppCV6PGtg4kF/IKdSTkR18sUZB7oRzU2aU22j3eGAmicSChTYnKl8amTkxle/5cMVKpTYsFusn6dulzlJreVgpCpRSBWmd/HLFEMivVM5E0zO+j0QP2/FpFTQ7U0sHxps6oGey0N4YyNWouBCuZ+q/U+4rXjsaS8PtKS310kprrhJiUnins6otpEF9p7FIfc6Vths3Ux9S1w05962gUgVoMhmFo567i/KwFe37HZ+vLL7+Myy+/vKg3EWqU1wkEAvjb3/6G8ePH48orr8T9999vPrbvvvviM5/5DAKB3Bf8aDSKaDSzotXZ2Znj2aRWUC/mIlBzqqhFpEm3KKyfOXlYTawGkfKgq1ETEzEGakSHfJ1KKRaxwhQ1aaz1DCRsJ0lAdmpkYySIxmhcO4HL5/qoBnKN4YAjRU3tk1UsejORzGp+NTPRhKK2U3Odmc66rXcAI5utLtyywtkQqf71Qe/6KI3P9DVs/Y5UH7ymSBCRYMBioV8qGddHa+rjO590mc8pNsh3y6HRHMMBv9RDbhAoajapj7nOdZHWmi/1UaQzJpIGOvvjaFUMQ3qicfM12hsH/xzKcaD2yiuv4JVXXinoxcVFvFKB2sKFC/HWW28VtM+NN96IGTNmAAC2bduGhQsXYvny5fjFL36Bk046CQ0NDXj88cfxjW98A8cccwxuvvlm/Nd//Zft611xxRW47LLLSvocZPChNkQVFw6nNWriZtAfS5irZblyuQmJaFIfoyUWwJPa5qDJw/CPFz8CkAleCknNkt0ZE0kDv33sfZwzX295r6ZGNtcF0R3VTznyTbrVyV1jJJipUcuV+ujS+aBV1KTUx2r2VBOBWkM4gIZwEN3RuLa/lDz59ULJQ0CT1qYzE1m/PRWoCRWlHGYipuuj5ncuvo9a6v+lGn/I6ZlBFz97ubFzfcx1vqo1anYL3XWhABrDAfQMJLCtZyArUFuzpQdASk3zQguNUnEcqE2ZMgWzZs0q57GUzOrVq/HOO+8UtE9vb6/57/POOw/Lli3D1VdfjXPPPdfcvmDBAuy8886YMWMGLrjgApx44okIhfQ//ve+9z2cf/755t+dnZ0YN25cgZ+kOjAsKB6dogY4d30UNRDReNKclLBdAsmF1p4/RkWN2HPkXqNwwT9SC65CYSkoUFPqgm5d/qFtoKYqau0NYVuji/ypj9bHGyyuj/b7uZX6qDufPJP6KKnodaFAKlCLZQdqcn2aF8ocQpqFSPl+KVIfN+zoB5BRRsR+bpiJmEYdQauiJlOy62MJQXwyaZiKdzjoN4+vEBW8GiSTBrpsUqNzXW9U10ddMC9oawijZ6AP23oHMBGNlsdWb04FapOGN+p2HXQ4DtRmz56N66+/vpzHUjIvv/xy0fsmEgnccsstAICTTz456/H9998fkyZNwqpVq7Bq1SpMmzZN+zqRSMSReySpLeQbud+XStMAnCtqdZKiFkvbmDlNmyRDE30fNSpqxJ6mSBAXHjkN976yHvtPbMffnv2woEmfcGdsqQuisz9u20vNMIysGrWOxjA+2qYfl4WbiWRSH3MZebiW+qgxE5FTCatllx5LJM2Jb13IbwY3uRQ1L9SnAXauj9mK2rq0ojbMDNTcNxMxUx8146T01MfCxsb9r25AfyyBk/YbawlGQwGfZCbibUWtKxq3rR3N9XWoZiK6YF7Q0RjGuu19WkOR1ZtEoNbk8Ii9De/maTZu3IiBgdQPLvdQkxHbt23bVrHjIoMDtXG4cNVyGmxlesZQUSPOkFdXxaqtqSDQ9ZHYcM78qXjgW4eYFteFTCSFA52onc3VK0192faGsG3AVKg9fzgQMCfCm7sH8P6mbsvjm7ujeG9jV1lTH+UgtVoCh9w3sS4UMHsn5lTUPFCfBtj1UcuuUduwPa2oNVgDNXdq1DJqlfr+guJTHws3E4klkjjn5hW44B+vYEt31PIZB1Pqo13aI5D7euPUnh/IKKyidYPM6nTq4+QRtaGo8W6epqOjA+Fw6od/4YUXsh7v7Ow00yonTJhQ0WMj3kdW1MIBP+rTgZrzGjWR+pgwL8KsUSO5kCcQYuVVpD6WmupFah+RVlSIGiTqZ0VqdypVO3vS2KNpht3eGLIdl7GEkVMZUyemAT8gsvfWbe/DZ65ahrVbM2UMM6/4Dw5b+jhWpVfWSw3UdClxsqJWLXt+uS9dJJi570Q1/eq8p6hlf6c6e34RdA5rSs3PIqaZiHuuj0K50Sk4Rac+FqGoyeO8J5qwfMZwwI+wcIf2uJmIneMjkCdQC2Z65wG5F7o70oYi23WKWjr1ceIwBmo1RSQSwVFHHQUAOP/887Fhwwbzsf7+fpx99tno7e3FrFmzMHr06GodZlnxQNr6oMWiqAX95s3QceqjrKilL2RO69vI0EQO1ISS1h9nHzXiDLEQlChg0icmknIT2d4c6o1MLkUNAH79yHtYv71Pqxaok3K/z2cqaoJXP0r1Pu2PZSa4KzemlLZSUx99Pl9W41wvKWqRoB8+n8+8j+gUNRGoeaGHGqBfiLQqatbfTFXU3Eh9VM1EdOOk6IbXoo9aIYpaPPNcny/zGYN+H/x+nzkn8LyiZuP4CORWGEOmopa6fuSaPwlVX1f3uqU7tW1Ua22UIXljacUjLF26FM899xxefvllTJs2DTNnzkR9fT2WL1+O9evXo6OjA9dee221D5N4EFlRCwV85sqmU1WsLpRR1OIJ6yofITrkVX5xQ6eiRpwiAp3CGl6nntsQDiDo9yGeNNAbTWQ5q6lGIkCqpiSXOnHVQ+/iqofexYxJHbjtazMtj6mqXWMkaFsDIytrAjfOh/EdDdjeu8P8W1YNS21qXCxRc2EmYPm/rkZNHG9jxBvTPqc1agK1Rs2V1EelhtFVMxFhdlNQarH1M6kNuUMuqonlxK7ZNeBMUcu4Ptp/9y11qXGsu9ZkUp5r4z7oaAQmk0nPG4m4wZQpU/DKK6/gvPPOw84774zHH38cDzzwABoaGnDOOefglVdewV577VXtwyQexK5GLZdrkYy4KX24tRdvf5zq4cLUR5ILny9js22mPtJMhDgkWGQNDWC9xunSHHUmI/kUNcHzq7dq3jdzjMdOH42Zk4dBnecLgU2kPcm4cT6Ma2+w/G1npFJJ+k2X19TnqxdmIjpFLT2hrfeKoqYJ1OTgTVXUhD2/6LVVDjMRN2vUTEWtgMOUA7V40pAUv3Rqpn9wmInkqlHLVTdmuj7G8teoNaQXHHTn4YDp9lob90FvLK14iJ122glLly7F0qVLq30oFWNEcwSbuqL4zG4jq30og5Z6pUatUDMRcUH5YEsvPtjSm963Ni4ypHyEg34MJJLmynC0xm5QpHyISdBj72xETzTuSGmRjY4aIynnx17NREkXvKUUteKCBDEx3W1UM35z6r4AYGsxL3ooybhxPoxssaZR9Wo+I5BSDJymvJeKSH0UC331Zgp99m/S2Z86XqFEVBvdd6QzExG0l6GPWkwx7nLT9VF8lIIWQqTgM55ImmmT4fQxCEXN6/b8dqmPu+7UhKsWf8p2P3GeCkU4V1ZRoxmo6RS12sos4d2c4N6vz8bPF+2Dcw7V98Mh+ZFXKYMBn5kK5HSCoOvTw9RHkg/Toj89aVEnboTYISbK23pjOPeWlxztI6/w51bUsre1NYSKDpiEK6Q8kbaLhVZv1qQ+ulCzOaJZDdQS2rQ2N5Qep5iKWnpCKu5DukBNGDy01HujAbBOvQpozEQE2amPpQcr0bg1tVDvRFncfTjgoCG7SpaiJlIz08cQHOSK2m8/vy92bqu33U98130OXB8bc1x/oi615fAK3lhaIVVlVGsdTt5vbLUPY1AjB2qhgB+H7DoCn5sxHsdNd2Y8ozN/CGpuZITIhJXCeipqxCnyJOg/b290tI9Q1IJpRQ3QK0u6QK0uFCh6XIpJuTyR9ttM4kTfLZlwoPSFixFN2cYEvbEE6pTPNBBPViy9MLMwkzqGiOKUKCMCtbb6cEWOLR+jWuqytskxjRpcm4qasjhVCmbqY/o1dSptsc3BM6mPBQRqFkXNyKQaK+0DvB6o2bk+5ssSUvuo5Xq+rKgZhmH+TnGpt2Ct3Adr41MQUmUalECtMRLEFSfujVlThzvaXyfRhyqUPkMGL+LGFlXNRKiokTwUk54n1/SYipom9bE7vW3W1GEAUimLQPETp0zLkvyKWkyjaLmhqH16fLv5bzF3743G0a+8n6gTrQTb0xPi5nQGhwgQ+wayv4PtvanntnpEUTtun+xFTFl9krMCgn6fmbLpbh+13MZdqtNnIZgNr4uoAQWAWDKJgbQLpFpDN1jt+fPV3ZufLx1o5VbUUuNhxYfbMf/nj6ErnW4pB/C10k+0Nj4FIVWmPpQRp3XOUfmgokaKwVxdNhU1mokQZxQVqCUzduFioqRT1IRxxbj2BrxyyRG49xuzARSfiiQmpvK1VVU6xF+6ibEb58PUkU248UszcN83ZpufvWcgkeWwKMygKsFH21JpnmPbU+lkuRpei3Q0rwRqkWAA93x9Fk7cd2dzm5xKKgdq7Y1h8/cWQZUbgVomtTB7fBw0uQNPfvfQol9bnF/bNQ2ZZfoGErjm4XfxxvodWYqa2j5ABDpuqInl5OPOVJPy4U1W9VaX7iqjXh9y1fg3So3b12zpxb9e+xiAVZUsZi7mRWrjUxBSZSypj8HCJ0A6RY2ujyQfqutjPxU14hCnRkcyos9TKOjP6brWLVnBt9aHzAmaep27+SsHOntfU1GTUh9tUtJ0zafdqlU5ZNcR2GvnVnOS2BONZ9WDnXb983hu1RZX3i8fH21LpXmO60g5UopALaoJ1Lb3pXpLlaISuc30sW1YamMuIRt0DZfSTsU176n3tmDhb5/Cqx9tL/r9Vft7mTGt9WgqoZWBGJ9/f2Et7lzxke3zHn7rE1zz8Er85F9vW+ru4smMSZRIfQwPEkVt/fZUoDZWcUrNd81RA6tccyDV/Mhs9p7+zgJ+X80sdtfGpyCkyqipj4WiNROh6yPJAxU1UiyyouZ0QipMPUJ+n1nMn6tGTZ1MyQHT/xy7Ow6e4iw1XHXnA+xTH3U1QW67v2XUxIRWvbrv1Q2uvp8dqqJWl8Oef4fHFDWZk/Ydi0+Na8N+EzLppXKWyR6jW8x/y2PgpQ+343N/eLbo9x0QSq3melnMfVxG3v38216xfZ5o2Pz+xm6LSijXqEUURU2ch14kmTSwYUdqAWF8hzVQC+W5FlhTRAAAbJ5JREFUL6nzoFztjdRrS3M6NVak/9eKmgbQTIQQV6gPlRqo6VIfqaiR3GQHajQTIc6QUwedTt6tfdQy6X8qwrK/KWKdeMnjshCVywwQcyhq4k+d2OC2+1uDpKjprt0t9ZWZWq3dmpoQZwK1/GYiXgzUrlq8T9Y2edK+z7hW89/qb6kbf07RKbV271MoAWl85mqJIBo2b+jsR5dkwhNPJjOpj0Hh+phxvJQNNLzE5u4oYgkDfh8wRnF4zLf4rKq9OVMfw2pQJ9JC04uVNVKfBlBRI8QV5NTHYlZy9KmPPD1JbiKmA1rq5lRr/WNI+ejuz0wKnVq2xyyuj8K4QmcmknptEcwJ5MlvIQtaGddHuUZN/1xd6qPbCxeZIDWu/fyiPUs5iSeSZi2QSDHLmIlYj6k/ljDTols9lPqYC9mef/rYNvPfpSpdMkJ91b2mLgAvBNmVNNd3LtRnw0ipaoJ4ImPPb/Z5k47Tq73UhOvqqJa6rPMu3+JzR4O1pi1nw2vl2iK+DzP9v4YWK2vnkxBSRRqUPmqFonOdousjyYdqz6/adRNix7begYL3kV3y6sMZVUlFKAHqZEleQAhrJp8CtT+ZrpYou0ZNb4fu9xVXj5cLkSraG9WnPjZVoKn0x539SCQNhAN+s3WA2fBacaIURiJ+H9AUHhyJVPJ3uPvoZvPf4Tw14G9t6MTVD71r25BcRowrOSA4Z/4UjGmtw1cPmVLoIVsIOFSs5fPnPTlQSxrmAkVYSX0EvFuntmFHavFgTFt91nmX7zwULRicPF9VPMX3MaC0XKgFBscZS4jHkVMfi5kS6FIYqKiRfNinPlJRI7mRLzlOHfTiUq2YCJp6NYqS2p9KIAduol5l2Xfm4fJ738S/Xv84s38yiYg/M4bjGht1NVATSpoqNISDftdTxMTC3MqNXVpzDl9Rd4HCEKplS33QVG/MQE35TbZLza7t+s95jY7GMK79wn5oqQsqAX7ua9vRv3gCQOpaeNHRu+V8rgjq5YDgwiN3w7ePmFbymJG/51wKa7dkxrNyY8YxNJZIYiBdc6z2UQNSAUk9vHedX59W1Ma01Vu+g6Dfl/c77VACtUABi96JpNKipobugZwJEuICcuqjWykJdr1dCBFk9VGL115+PikPXzhogqm8Og3UZLtwERTolAtdqiJgDdzC6evb6NZ6HLXXKMvzVLUgk3IpB2rW9xS2/KoaV44JmzBB+eMTq/H6+s6sxxMVMHsQDpzydxyxqVHLNLseHGmPgqP2GoWDlV6kTq9tb27I/l1U4kn9OHUjsJfHoVNFbaWS+qgqaiGLouZNQxGR+ji6rc4SADtJWVUXPQoxVBO/5UCi9sxEaueTEFJFLLnjLqUkqDcPQlRUe/5oDebnk/LQUhfCP752MAB9k2gdccl8QQRdMc31zq6RsJ2ZiFpvogaOutRHdTIt1JGkoQZq7p8Ln5sxDtPHpgwu3tYEBLrvxG1ETzu5HYxdHzVRj1iJlMxy43QCXufgdzcVtTIsispNn1WHQpkeaaFDHrqJpJEVdPh8PjNNsxJjrBiEorZzW70lpdTJdxwJBiwmIYX0ehS/pWhNUUuLlbXzSQipIvKkwS1FrZiGtGRooaY+ZmrUaiftg5SPTANdZ9esjE2+zwyadM134xo7fcDeTESdxKmT0LjG9EG9PIoATW14XY4Jm8/nQ3va+GB7X3ZDY12LALcxv2NpQU8opGrqo1Da62ogHUxXe6RThMU18KNtvbbqU1xTo+YWcqCmLh7IdGtqPIFUIB5LZAfjbjb8Lgeih9qY1vqCFTXAWqdWSG2pOB9q0fm4dj4JIR7BrR4nTH0k+aA9PykFMXlyOukTq/8N4WDOCaNdI2GdmYgOe0XNvkZN7CLmxAdMbMenxrXhG/N3sX2fUhCul9s1piyxSqQ+ar5j8W91sVBcF2rBYEH3GTo1wXIk6Mcjb3+C2T99FP990wrta8U1NWpuIQfwuQJ3nRkPoHd9BDKBuVddH+UatYC8GOPwO26XnB8Lq1FLpz6aY33wL0oIBv9ZS4jHcC31sYZyrEl5kAM1wzBoJkIKIlxooJaeVDZHgqbBQa5ATVXKwhozESDbbEG+hm7qiuKGp9akXs8vK2rW1xY1QWLCdtHRu+Of58zC4gPGOfhkhSPSNbf3ZgcJlXDk033HQhlSA4NaWsDRBWpCvZJbM0RCflz72CoAwENvfqJ9LTNQK8O9dofjQE3fBy7l+phdb5XrvKs2/bEEtqQbeO/cVn5F7YoT9zb/LRZHammsC2rnkxDiEYpV1O79+myrKxpTH0keIlL6WVSqM6I9P3GCSKlyGljItU5i7AlTC5mYTeqjpUZNemzf8W340qxJmf2la+h9r67P7CPtr/o9iJRHMSkud+q4qKXRBmoVTH0MalQL9R40UEOKmm4CLoIiyzUwGNC2TpDRuT66xWf3GZP1PjpE6qOqMMcTyYyZSDD7N376vc2uHatbCGv+hnAALfVByznoNEOoQzIUyVen/7kZ43HM3ikjooyilvrNa2GsC2rnkxDiEYpdTd17bCu+cehU828qaiQfsqLW2Z+arPh8QOMg6ZVEqotcZ6ZrFK3SlZ5UNuZR1HR2+oC9mYjP58PFn90Dw9Kr6fJryo25D91tpPlv1WZe1AGJz1Huda6GtEGEqNE7dvpojG6tA1AZR76M2pKtqCWNzPcQkxZxakFp16XMikCtSxoroaA/Zy81wzDKGtSfOmM8jps+GoB94G4YhqlSTx3ZZHksnsxkSOjSWy+990088/4W14+7FOS0R5/PZwmAnc5nRjRHpH3y/y4imGONGiHEMaWkJFhWzlijRvIgB2qm2hEODppeSaS6yBPAfCqQPKlsjgRzmokM2CpqmUBBN0TNGitpsUu8/pKZE7D76Bbb/c3URzNQq4yiJhjeGDYn5pUwE4lprOXlfyeSBi695w3se/lDWLUpZfteCyqDz+fLCtZEoCbXeyWSBvo0Pf4E8ngvh6IW8PswZ5dUawG1ZYQgGk+axzG+o8F6fAnDpg4xc6zPrPJWoLYtXa8p+qFZXB8dfseThmcCVicBdFBJ962lRQnB4D9rCfEYpVgghzVFw4TYIcZLNJE0V5Oba8CCm1QG+XqTb4GpL5Ywm0k31eU2ExGpd1mKmpSSqxPwgprXNCdeipNptpmIteF1uQM1taVAKOA3VYNKWKfHdTVqcp+tpIE/P70GXdE4bnruQwC1ozKoAacI7GUHxVgiid4cqY9yMF2u7JVAHuMPObAc01ZveSwuuT5aF3Dl2k5vXesHFDVLXjhwWqM2eUSj+W8nwV3ATPe1monUylgHGKgR4hq/OXVfTB/biv89Ye/8T7ZBdiqiokbyIcbLQFwO1AZXU1tSPeRrjK7WTEYotn5fql9XOEdQEtOkbAHKQpRm4qZ7TbMvkjLxyq5RS/0/WakatYg1cAwH/WZdcUUaXmuMJgLSl9KvCVJqQVEDsseCCLrk1Md4wkBvFRU1+XXtFFZhJFIfCmBYU9jyWExyfZTTW+XXasrRn60aDChqlnwO1jtsGSMHak5qPcU1TCxciFYUtRSoeetXJmQQc+z00Tg2nfpSLNY+QwzUSG4sqY/RVPpPLTS1JZVBnqDqUhhluqX6NJ9P6qOmaZZtpuUpwZjf78O3DtsF23tjmDi8MWs/ddIlH5c68bJzfRS1auUu8dUpauKYYpVIfTTNRLJr1ADggy29WfvUSjpYlqKW/r5lhSqeTGrHpiAhLQaUK6j325i7CES7i8ZIAG0NqvOpvrbQUofnsTp2tT5M/l5bG5wtII5oytSordvWl/f5Q0FR4x2dEA9hdXeqnQsNKQ/iPp00DHQy9ZEUiKj3GUgk87rVdkv1aUDGJlwX4On6ngm+ddiutu8hrnnya0ZjesfCrNRHxfXRV+4aNY2iJqiEmUgm9VHfq2rV5u6sfWpFUVM/h1Aw5dTHgTwKsTzey62o2Z1awpWyPhxAW71VUYsnM4qanDLc1Z9xGfWaRb/qLip/r631zgI1+bx1sugorhlqjVqtjHWAgRohniJCMxFSAGKymkgaTH0kRREK+DCQcJ762CgCNamezDAMc4KVSBpm/VmhdbYi+JPNROzMAdS5tZioifcOVLhGLRzwm2peJez5Tet2KVDz+33w+VLfwepNPVn71IrKoP624rvoUhS1XMSlFNlyBfWBPIqaWISIBANoVxW1ZFKbxie3IPBcoJbIoag5DNQA4OYvH4j7XtuA02ZOzPvcoKKo0UyEEFJWrKmPPD1JboJSsbrp+uixugXibXIpYzJCrRCr3CJAMAxr3Yw8eQwVGBiENBNbu5oTdXJdeddHJVALZsxEKtLwOv0dqWqQ+Pv9zbUbqKkkdKmPeX6DeAVqGcVvseLD7bh22ftZLTDksa2mBsYTRl51aKAC46wQRD1pKYoaABw8dTj+38K9Hd3LhIGOUFVrqWegoHY+CSE1QNgmjYUQHWbqY9IwU2K85gRGvE3INPBwGKilJ0/yREg2/5Bfp9BrWMbyP1tRy059tO6rpj6WO3O8QUl9DAX8pspYCXt+XcNrIBOgbtieXd9TM4Ga8tuLoEvuuScvPOiGoahRK+d9Vm6T8pN/vY2n3rPa6cv1VG0N2amPsuKmw2uKWlQxuJGDYLUGzy3sFDUGaoSQsiCvArPhNcmHmfpoGFkTaUKcIFQsp4GaqIGUFX95UiwHbYVmBejMROxTH/WKmhAtqqGoBRx+l26ga3gNZCaunf3ZzZ5rZfKq/rJCTRH91ADrGNI5DgrVthKKmmC9EjzLQUVW6mMiaWukI4jlMEupBmpNnVy+UYiiVggBpeF13EZpHszUxllLSI0gX1vo+kjyIW6ESUuNGgM14hyRnqgLLm5bvhaX3fuGpf2DCFDkiZC8r5gg+32FT4LDGnVPdZIT2JqJGOVPaQN0iprPrMmrZI2auqAnPnenFLQIaqVuR017Fd/31p4Bc1uf1J6gQbN4JVTPcpYY5BuD8iKEGkzGkobUmkL/u+VLV640ZuAZEPb8Us+3MgVqqqImArZaKh3hHZ0QDyGnStD1keRDTFbjSQOd6dRHmomQQghpepcJvnPHqwCsioSoUZMdI+XAasB0fCz8+pVpeJ05Fp3zXer9rfuKQxCGHmUW1NCgTKwjQb+0ql+dhtepv1Pf0w5NoFYripqKCLo2d0fNbdt7M5+/IZwd6IgxVl5Fzfp9G7CvUcsKPiV7fvl3mz11OJ58bzMA7wVqWYpakTVqhRBUatRiNufFYKY2z1pCBilU1EghiElGImlkmT0Q4gQnNWq/fex9fNKZmgQ3S+qE6fwoOUaWsqKdMePINhMJ29RiCQzDSP+X+rvcro/BgN+i8oUCfm3qZrnQNbwGMteEqCYtrlZq1NRfVgTGWyRFTQ7UdCNBBHflTJHLdwqYNWjpoH/GxA7zsUTS0KrJ135xP+y1cwuA/E6tlSajqJXm+lgIWYpaBX7XSlMbZy0hNYJIGQDK3weIDH7EZDRpMPWRFEdYstnPekyaIN6x4iMAwDCpIW3GMTKTZparh1r+Y8lW99TJrEBnzy8LWeVOfQSs6nU46Le4sJYbs6m4otrkClBrRVHLVlOzUx+392X+rfs5KlGjFsiTFaMGYrd89SBc8tk9AKTOAVNxk9TkpkgQn9ltp/RzvKaoWV0f5d+prcw1aomsQK02xjrA1EdCPMWeY1pw+B47YUxrXbUPhQwCAtJqolhBblfcwwjJhem0qFmd1036h8uBmmZfu9opJ4hV8FiyuBo12W2xEgtdLfVBM92u0oqaXeqjGngE/T5z8lorNWoq8YSBeCKJbb2Z4Kw/lvkNkkb22K5EjZqq6qiHobaeCPh96GhMXb8H4knzXFJ/t3COutJqMqCcqz3RzAJO5WrUai/1kYEaIR7C7/fhj6ftX+3DIIMEM80pljRXk0c2R3LtQoiFXKmPCc0Ed1hTZiFAZ/5hl5Ln6FjEBFQK/NRVeoEahyWThmVCXglFTU7nCgcra8+va3gNZE9Qx7bXY82WXvMYawGfksx4/VOrsb1vICsQEiQ1v0clatTyOY/qHE3F8fRKZijq7ybG2YDHXB/VmjoRdALlC4jNxcr0dYdmIoQQQjyDMJ/Z2NUPILW6SEWNFEIwR+qjboJrVdSy9zXtsYtY0dY3vNYraqpiljCsgVolSlRa5NTHgN9Mw9IZs7iNnWmCGniM62gwA7VaqVHTceeKdbaP6eLmStSoqb+NehiZtF65f6pQozLtFdTfLdNv0FuBmqqoTRreiF+c8ins1FK+DCF1ccSuEfxghoEaIYQMUsxUsfTEcERzxOIcSkg+dKqYQKeoDW/KXiWXJ4wiDbIUMxExng3DsO2jlnWsSauSVe4+aoBGUdMEmuXCruG1PEH1+4DRUhp9zShqBf60utTHytSo6dsICNTURyATePSmAzW/Lzvo8GzqYyL7XF3wqZ3L+p4BpS7U7rwYzNTOJyGEkCGGOhkdwbRHUiB29vyyg6I8zNTgRN23lIazahqmHADmCzKSiplIpQO1UMBvqRktN3YNr+XP3RgOoqMxc02oZUVN0KTpmZarRq2cE3r1HJBrF19YsxU3PfchAOvYFmOoZyDTQ01Vj3O11KgmQiGs5IKA+I4Tao1aDS1Y1v5ZSwghNYq6Ysv6NFIodg2vZXWqLqh3ozUnjHFNjVoRk7WQYsYh1+DkCzIShmFJ1axEjVpLfSYoCAf9UnuB6rk+yul2DZEA2huyA+taZqeW7Gug3vWx/KmP6mKBfI6dfO0z5r9lBUqcUyL1Ufeb5VLBq0lGUavcOMvUqInURxGAM1AjhBBSZdTJ6IhmuoWSwhDB0bOrtuCB1zeY2+W0x3pNw2DAzkyk+AlwJpUy9RrRAgI11Uyk0jVqoYCvomYi9q6Pme+pMRJEu2ToUCuuj7kcPUdpHJO1qY+VaHgdUAM1/biQx7bqYqgb9+I80fXKqyYDmgbd5SaopBtXws2z0tTOJyGEkCEGFTVSKiJ978E3PsFZf1uB9dv7AABymZXduAoF0+5zGtfH4mrUrIqa7CKXz24/YRhmcOnzVcaev1FKs4sEAub5WAmlw85dUw6QmyJBdDTIgVptTPn2n9Bu+5jOuEIXOJeSousU9fps59JoCdSU4E42GhHoTHy8QNTGobWcqOnGMaY+EkII8Qpqn6vhDNRIgRyyywjL3zv6Uv34ZEXtJydNx+jWOlxx4t6W52b6qEmujyXYY4cUYwDTRc7BayWSmZq6XE2f3aReasIdCvrMz1yZGjV9jZUcHDSEA2hvtDpT1gLfPXo3nH/4rvjSrElZj43SBGo62/7K1KhZX9susJKbuav76FRQr5qJODX+cRNxziUUM5FaUtTo+kgIIYMUdcVWnjgS4oSZU4ZZ/haTLVmF2GN0C5753mey9tWZGgyYilrhwZK6Om664mlUBRXDyBxzJYxEAGtKaFg2E6lEw+uk/nuWg9SmSNBs1xEO+GvGEbYpEsS5n9kFty1fm/WY49THCtSoqQsGtoFaLkUtV42apkl9NVH7qFUCtUatEm6elaZ2Qk5CCBliqDejWkr3IJWhLhTAVw+ZbP4dTTfadWLMoatRK8UeWzUTMftMOVihT0g1av4KzWzkQC3g92UpguUkZtMGQZ7oN0aCmDisEYfvsRM+f9D4sh9TpdGNS8epjxWoUQsUUaMmO4kC+qDHzgComhiGkalRq6CaJbs+GoYhKc21cy+kokYIIYOUrECthm5OpHJ8/5jd8eTKzXhzQyf6haLmwJhDVytjVzvlBLWPWiEr9CnXR3G8lTkPxrTWm//2+XyZGrtKBGo2NVbyNaE+FIDf78MfT9u/7MdTDXTXO+epj+WvZVJf265BtbwQMbq1HkG/b9CZichBqBMF3C0CkpmIHJCHKrVaUwEYqBFCyCBFnZCq9Q2EOEVMrlRFzZ/DmEPX8Dpm40bohIC5Om6153digpFMZsxEKlWjNm1UMy757B5m/8JgBVMfTdMW5buRg4NaMQ+xQ3e9k1MfG8MB9Awkcqc+llH9ybLn16QVA9l91HZur8cHW3oB6NVkL5qJiDRloMKKmrQ4Ii+Q1NKiJQM1QggZpDD1kbiF6JWmKmq5UsNMUwOpViZWipmIokh1R1PGJg027QFkEkYm9bFCcRoA4AzJ0EJM+pNGKnAsZ02YaZqgBCvy7xWp8ZpV3dgcJrUjGNEcQc+WXos6LEhUoEZNfW0RWMWT1gBLDajHtTdIgVr2eRTxYOqjbChUyUBNtKOIJ6yBWi2ZidTOJyGEkCEGUx+JW6iKmhNjjpC2Rq0Ee/70pOuJlZux4DdPYdXmHgBAR2N+N9NEMtPwulpGAvL7ljv90a4WJzCkFLXs3zkY8JupukLpNIxUDZVMvAJjRQ3UxW+mNkSvU1IFx3VkUmq1NWoaE59qMyClPFfStEaMgXXb+3DML57I2l4LUFEjhJBBSraiVtsTM1I+VEVNLPo7UdT0fdQKnyjJk6tX1m7HK2u3A7CqJHYkDQMiNqpUjZqK/JnjySTCZVwLt+tXJze8rhU7fjtUs47TD54IIBWsDcSTZqAGpII1eVjESxinxSLOEzVQU9Mbx7Y32D4G6FOOq40w/qmk4yNgXaj4cGuv+W+6PhJCCKk6ai0OFTVSLFmKmoN6LxEIyGlPAyWkPtrVC3U05Q/UEkkjowIOAUXNLtCw1KhV0NShGsifde6uI3Dp8XsCAELp7SOaMoGamv5YCUVNRQTXsTypj7uNajb/rVOG5P6FqlJYLban+y8211VW/9Gqqn5fRRreV4raPosJIaSGYY0acQuhqKl91HIFPWL1XHaf64nGAaSMHArFbqHBkaKWzPTLqpSZiIpcL6aqJm4QSySxYUcfegfi6BlIBdS5Gl5XsvFwNZA/q7wwIL6T4VKgphqKZGrUKjcNjtkoauo5Nn/aSJx+8ESEA34cOLkj63VkpbQSDqNOEGrWOEkNrAQBze9XawuWTH0khJBBSnaNGtfeSHFkuT4WYCYyoAnUmopYWbdbaOhwEKjJZiLVWq/w+33w+1JmIqphRCkkkgYuvP0VPPj6x2aAJgjlWKypdBpapZGDrHAwO2hTUx9lqqKopU135JrO3UY1o6PBOr79fh8uPX5P/ODY3bXKtPy7xhJJTxhnrE0HamOl+rpKoFUca6wEgIEaIYQMUtR7FBU1Uix1IWuNmlj1z1XvFdbUynSJQC0S0u6TCzt1w1GglpRq1Kp4HgT9fgwkkq4qau983IU7V6zL2t4QDlhUI8D62WvdTEQOsuSxI9JB5UBNtcQ3FbVq1Kil37sxHMB935htO17tAjA53TUWN4D8p0fZ+WhbdRQ13e9Xa4pabZ/FhBBSw/h8PkuwVms3KFI5xKQ+W1Gz3yejqGVUnu7+dOpjxL3URyeBmmEYjpwqy43Z18nFQK13IK7d/o+zZmZN8oNDKPUxaJP6uO/4drTUBbHnmFZzm5r6aPb7q0KNmqgvDAf9RWVBBPw+0xjFiaHIexu7TMWrXIjUx/EdlU59zP79dOmQgxkqaoQQMogRK/ipfzNQI8VhKmoxa41aTjMRXY1aOqgoxlTArdTHajq+ifd2M/VR/CYq9Zo+aYEhlPpo/ayZf//61E8jGk9aHldLuczxXYUatUxrheLe2+fzIZR2tswXqG3rGcBhSx8HAKz5ybFFvZ8T1m7tAwCMq3CgplPhK+nkWQlq+ywmhJAaR75P0Z6fFIupqMWtro+50ggjmho1oagVk/pol+o1zFEfNZh91Kpp+CY+Q6EmD29/3IlfPLxSq571xRKaPTLBtUxwCKU+ygqsfO3z+XyoCwUsympS+T3iFWh4rWL2UUsH8Wp9YSGIfeN5ArXVW3qKfg+nxBNJrNsuArXK1qjpFmVqLbOkts9iQgipcWTFo9ZuUKRyRBRFzUnzaJ09f6ZGrXBFTfdekaAf9Q4cJJNJw1FLgXIT9BeX+njUNU/g6offxa8feS/rsX6bQE2vqGWmdTUfqNmkPgrk4ZTl+mjTMLyciPNEBGxqH7hCEGrcFf/3Np5fvTXvewLZTb/dYsOOfiSSBsIBP3ZqrivLe9gxFLJIavssJoSQGsdvU1BPSCFkKWoOUh+FU6ScfpVR1AoP1HQpS06CNCClAIp5aFVr1EpMfXxjfWfWtkIUNTleqf3UR8lAJJj9m/t8mVouVeAUvcyqWaNWijuhOFceeONjLP79M7bPkwO1cjn5r00biYxtr6+4kY8u0E6UoTVGNants5gQQmoci/MZFTVSJFk1ag5SH8OB1D4DUu81EVQUZ8+fPSVxqgolPdDwGsgoHTGbyeKOvhjueukjs42Bii64EgYvLcp3qvturIraEDITsQl6RNCuqknVrFFzw3HSqSV/1BKolSeAyVjzV7Y+DbD2lBPEPNJbzi0YqBFCSI0wFNJASHlQFTUhCDkxExGBWrcUfBTj+qhPfXSuqDlxqiw34hxU7eAFX795Bc77+yu46M7XtI/rAjUR/LZLpiqRoF8bkFpq1EK1PcWTAx27wEV8HQklSBGpqZW8Zpqpjy4023Ya5A1UJFBL16e1V7Y+DUippuo5Y3fuDVZq+ywmhJAaR773suE1KZaiFDXF9VEEauGAvyg1RzfZdqqopfqoecmeX5/6+MTKzQCAe19Zr31c93nFb9ImNUa2Swm1OCHW+PVA/qy61EcgMxbUufv6HangwomjqFuYZiIi9bEURc1hkBeVWmeUKU4zUx8rbc0viCjjPJ/BymCjts9iQgipceSUHipqpFiyFTUHfdSUhtcina+YtEdArxI4rbNKJg1TBaxmoCZS6Qp1fRToAjVTUWvIOGnW2QTCgaGkqEnBit21zwzUpN8jmTTw7sddAIDdRjWX8QitDCSSMAyjZHv+1L6FK2rlUppED7VKW/ML1GtEseeeV6nts5gQQmoc+ZbEQI0USyl91MRksKsEIxFAP34dK2pGxvWxmqeBUEkKMROJSQqATlXsNwO1/IqarJrUeo2aHJTaBefiKXLa37rtfegZSCAc8GPi8MayHqNKLGGYY6OU67WaNikHZJbtifKnPu7ojQEAhlVQnZRRrxEM1AghhHgG+d5bzUa/ZHATUdIYi+mj1l2CNT+gVxgc16glnbUUKDdi8m1nJqJDNhbRpSvqAjW7tLmEFCAOJXt+O8T4lefub6fVtCkjmxybcrjFQCJp1seV8t4h5bfV9d8DgGis/K6P4poR0biQVgLWqBFCCPEscuqjr5qdfsmgRihqwmEwWYCiJtIlS7HmB2wUNYfpe0nDMCei1TwPhNJRyGSx28YBUiBUTjn10Q5ZTRhKNWr2ipoI1DLfy8qNqUBt2k5NZTw6Pf2xhKmgluT6qJwrPQP6Fg6yolauPmoiUKvWeGOgRgghxLPU1i2JVIu6dEDUH0/AkNMIHTS8ThqpAn5RS+W095lKKamPScMjDa8DQlFznvrYE81MsnX79aUn4a0OAjV5klrNNgWVIGgJ1PTPMVMfpe9lR18qVW9Ec6Rsx2ZHfyxhBtNuuj722gT7UakHX7nil4H0Qk21aiJrPcWXgRohhAxiyuXkRYYWbfWptLpYwkB3NF5QjRqQWrkXQUaxjZYLtee/+Lg9zH8nkoapGFSz77v4DMUqagOalEkzAHaQWlZrakIu5PFip6IGNKmP/QPOv89Suffrs3HO/CnmokZ/LOmO66OiXtkpatEKmIl4TVGrNWr70xFCSI1TrgJxMrSoDwfMlMVNXVGpJ1n+GjUgVacWK3ECqpts51LUvjR7EpZdOA+A0vC6ioqamEDHi6xR01mL9xegVNaakUIu5PFiN0x9mtTH3nRQU1ek8lsIe49txYVH7oa2tBqaSn0svZZSDdTsatT6YrI9v7tj45POfvzv/W9KNWpVCtRqPMW3uERyQgghnmDoTMtIuRnRHEF3NI5NXVGIeCHXZDIY8MPvS6kVA/GkaSripkFDvsmfCMoSUo2aF8xECgmY5EBNl/ooAjU7S36ZQgLEWsJOUTMbXku/RyEKpVuYNaDxhOn6WMp5oqYJ90b1ilrfQPlSH8++aQVe/GCb+XckUJ0UxFpvQ1Hbn44QQmqdoTkvI2VgRFOqZmdz94BjB0W56bUITlwN1PIEJ2Zqm+T66ImG1wXY88tpazq3SGEm4kRRG6oKu91PLlJ35a+lvyqBmpT6aNaouZn6qFfU+uPls+eXgzSAilq5qO1PRwghNY7BSI24hDBX2NTVL/UkyxOoSU2vY/HSa29U8pmJmDVhlj5q1Xd9LNaeX2smIhQ1B4FFIQFiLeGD/jcXStul975h9vsq1fSmGCyKWhkaXvfa1KhZFbXy3itYo1YeavvTEUJIjTOESlJImRnelDIU2dQdzZiJ5JklhNOKl7VGzb2pRb5JmJn6mDTMiWg1zQ6DppmI84Cp22nqowPFohQnwcGMXWwuvo4XP9iGK/71FoBM8OIk8HULkbZaNjMRO9fHuBSolTGGDwV8VXMZVVX30a11VTmOcjE0z2hCCKkRytUbhww9MoqaMzMRwNr0esCFRr7Zr597Mi0HL2KyWtUaNdOev1hFrTTXx7PnT8Gk4Y246OjdHL9/LWD3k8uupe9+kuqf1idSSSsYqEXM1McEYi7Y86tBXrUVtWqmH8qLOZ8a14abvnxg1Y6lHNBMhBBCBjEM04hbyIGaUwdFMUkaSMhKgZuBWu7Xaq4LoTkSRFc0jnXb+gBUN/UxUETDa1GDBugVtWj6cScK0MjmOjz67XmO33uw0xgOoGcggYOnDNc+7rc4Q6b+XYiLpluI384tRU0N8uxq1PpilQnUIhUMerPeW7pGXHnydEweUflG5uWEihohhAxiKKgRtxieNhOxpj46q1GLxkq359fhxKBgTFs9AGCtCNSqqKiJz66z2bdDritTA7VE0sBAIhOo7b1zKwDgxH3HlnqoNcFzPzgMT3xnPsZ1NGgfl2N2Eaj1VbCPmiATqGXs+dU6s0LIbnitV9T6C2x4/dR7m/Hhlt6Cj6eaipocqLm5SOQVqKgRQgghxOz1tKMvlkl9dKyoJaqS+ggAY9rq8M4nXfhoW2qCWd0atbSZSAGKmpzuqKY+yoFbKODD3758IFZ8uA1zpuoVpKFGUyRo9v/TIS80CBGqEHMWt6hLnyf9kj1/KamPamBkl/ooq7X50uRfWbsdn//Tc/D7gFVXHJvzueqCQjUt8uXUx1KcNL0KAzVCCCGEoLkuFah198fNPmr51KmwVKNWjdRHQFLUtqYUtXzBZTkRSkchqY+y+qZT1AShgB91oQDmTxtZ4lEOHeTURxG0VdP1MZX6KBY03FPU7Nw+ZUUtkSdQe271FgAp5a0/lsgZyO7oi1n+9oqiVosOkLX3iQghhBBSMM11qbXbrv64Y0UtIvVRK0vqYwGBmpiAVzP1Uazo62rN7JCbY6v7yQ2sq2mSMljxKTVqiaRhNmavqJmIOE8sqY+lNLy27mvX6HxA7qOWZ0h292fq3NZs6cn53O291kCtmoqavDBUi6mPtfeJCCGEEFIwQlGLJw3TMt6popYK1NxPfQw5CNR2TgdqAm/Y8xeS+igratb9ZKWkFtO6yo08FP0+n0VhqlaNWib1sRR7fuu+AzYLA/IiQD4zkdVSbdr7G/MFagOWv6upqMmqaSl1f16FgRohhBBC0BgOmEGOWDHPN/+SFbWBMqQ+OkljHKX0TaquPb99w2u7GiE5qFNNSGRTF18VUzoHK2rqo+yC6EStdYs6054/k/pYWqCmKmr6QE1Od8xnPLV6c7f57/c2dud4ZraiVk0lSz4tqhkwlgvWqBFCCCEEPp8PTZEgOvvj2NGXWjHPFygJpSAaS7hiO559TPmfI0xQMvt4oUYte+IcV1S2c25egYZQwBLUDahmIg7dN4kea+qj3OzaX9EUWfM8iSdMBbWk1Ec1ULNRcOVFgFw1aoZhYPWmjIr2/qbcgZraDqCa5sPyb1yLqjMDNUIIIYQASKU/dvbHzRXzfJPZuqCYgJYn9dGH/BOv1nproFZVMxG/sOfPnrqq9Wf3v7oBQKpJr91zEi6oL0MZec1ATn2sZNojkOkz1h9LmsFTKQsa6r66mkjDMCyBWq7Ux+5oHD2Sc+R2xSxERR3f+Rwly4l8atTiggYDNUIIIYQAyBiKmKmP+cxEzJSuRFlSH53Mu1rqrIGaV+35Y3H9ZHabVO+jprC5Uc80lLFLfax0oCbb82cCtfKaiah1krmCKXX/WDy384j62gWUZLqO/BvXYnowAzVCCCGEAMgEasJ+O98KdSalK1mW1MfRilGIjoZwAEG/z0z/qqrrY47Ux5iN7d7Wnkygpta2iQlxKWlyQxl5Eu/3+zKpjxW05gesZiJiGJTSx001zdApamo6ZK5gSn1uPtfS7Nf2hqJWi/DMJ4QQQgiAjPOjcHXLn/qYUdTcTn385ec+jUnDG/M+z+fzWdIf/VVNfbQ3E7Gb/HZJtugDiaRF+RCvU4spXZXAp6Q+Vk1Rk1Ifo/HUMZRiZqKaZujGW5bqlSNSU59r5yKZeb718Woqao4KWQcxDNQIIYQQAiCjqIl6lfypjxmlIOZi6uPwpgiO32eM4+fLgVpVXR9z2PPb9bpSsRhAiDQ5BmpFIQ/fgA9Vq1Grk1KEo+m0wkjQPUVN1/BaVb1ymYmo+w/kSX1UX9srNWq1CAM1QgghhADIBGqCfIqa3p6/+JlTQzolbf8J7QXt1+wVRS1g3/A6n0ohkNURMYEO1GB/qEogxx9+vw/9sdSGUtIOi0FOERaBWl0JTaKd1KipClquWEpdWMib+pjwTuqjbMZTi7BGjRBCCCEAspUGp/b8/bGEOXlz0qTajnu/MRt3vPgRvjJnckH7WVMfi377khG1ZLqJs1NFbSCRRD1S36tQLtSJOXGGrCL5fT4z7bCUIKkY2tMtJLZ0R9EYSU29S1HUnDS8LqSOrNDUx6zXdrYGURb2HNOKW796EMa05q9nHYwwUCOEkEFMW0MI23tjFZ94kNpk/fZ+y99OG173x5KZ1McSgoopI5rwnaN2K3g/OVBrqLBRhEyu1Md8KoVAdn6Ms0atJOTfIeDLKGqlBEnFMCodRHT2xzOpj6UoalkNrx3UqBWiqNk4lGaer9aoVbNIDTho8rCqvn854Z2dEEIGMTd/+SDM3XUEbj/r4GofCqkBTp810fJ3XjMRTSPfULDyQUVrfWbdec+dWyv+/gIRqOkcHp0GanLqo+n6yECtKNQAxA0jj2JoigTRnFbSMjVqxR+DOhz0NWrOg6nB7PpY6zBQI4SQQcweY1rwly/NwF5VnJyS2uGAiR340Ql7mX87T30sT8Nrp8jzxE+Nbav4+wsy9vw6Rc3ZZFaeJIuATzWPIM6Qf4d40kA0VrqaVSyjWussf7up6rnu+pjDTORvz36A3y9bZdm27/jCakqJc5j6SAghhBCTkc0R89/5Uu4iFnv+0lMfi2XNlh7z3+2N4Yq/vyCXPb/azNoOOVBLmKmPXFcvBlnpSRqGK46LxTKqtQ4rN3abf5eSrq4KWG73UbOrUYsnkviff75u/j131xHYZ2wrvnxIYTWlxDk88wkhhBBiIjs/5nNQFIraQDxZ1dRHYT6y8NM7V/y9ZYTyFU8ksaM3ZrEtL871kfb8pRBXWh1UK/URQJbZhZvBorMatVyKWmpsCjMhu9RHNaDbbVQzzj9iGlrqQtrnk9JhoEYIIYQQE3nSVZiiVr3Ux3nTRuKxb8/DlSdPr/h7ywhFbeXGbuxz+b/xswffAQA88PoG/OetjY5eQ54km/b8DNSKQk73S8iKWoXt+YHs1MdwCcGiGnLpaiLV4C1XrzPxXGHEkzT0CrAa/DElt/ww9ZEQQgghJhZFzaGZSHc0bm6rRqAGABOHN1blfWXUietvH3sfX5o9CWf9bYXj17CkPgozEU6Ii0K2508mDbPhdTUUNTlQCwV8rgbfhpEaK/JrqgpaTtfH9HPl/nKxhAFV9FMVNabklh9+w4QQQggxaS4gjUnU2XRZArWhG1To3Bl7pO8mF5m0Myn1McE+aqUQT6ipj6U7LhZLm9RCotS0xxkTO9DeEMJuo5rNbWq6ohpU6Qxu1MfqpdYWulRd1ZCEbqTlh2c+IYQQQkyaIhlFrX8gkfO5YsIpL95XS1HzArqASu15ZYdIO9MqapwQF0WWmUiseqmP8gJIqYFifTiA575/GO46e5a5TQ3UCul1JoI62eBE5/yYrahxXJYbpj4SQgghxESunekZyK0G6ZzrhnJQoUtRdPpt1GmMHGKsUSuJhGImkkhWL/WxRer1V+dCoBgO+q3tB5SatOwaNfvXSkjKbTjgx0AiqTUUyapR47gsOzW77NXT04O//vWv+MY3voEDDzwQkUgEPp8Pl156ad59P/roI5xxxhkYM2YM6urqsOuuu+KSSy5Bf39/+Q+cEEII8Qi9eRQ1dcIZCvjgy+MUWcvoJq5OmwFnFDVNw+shnE5aCpZAzXCn2XSxtLioqAkCfh/E6aYaihTi+iiUsoDfZ6Yu6y3/rducqsWkeGpWUVu5ciVOO+20gvd77733MHPmTGzevBl77bUX5syZgxdeeAGXX345/vOf/+A///kPIpFI/hcihBBCBjn56qvCykRtKKc9AvrPrzHk01KvSX1kjVppyKl6SalGzQ1Fq1Bkkx43FdKQP6WAZSlqBfRRS0iBWjjoR89AQpv6SEWt8tTsmd/c3IwzzzwT1157LV588UVcfvnljvY7/fTTsXnzZpx77rl47bXX8Pe//x3vvPMOFi5ciKeeegpXXHFFmY+cEEII8Qb5FDV/emInGOqBmm4C7lRR06U+CgWDE+LiSCqpj9XsoybXqNn1KSsGOwUsS1HLEanJ40ycwzozEfU1mZJbfmr2ijplyhT86U9/wte+9jXsu+++CIXyu1g9//zzeOqppzBy5EhceeWV5vZgMIjf/e53CIVC+OUvf4l43JmDEyGEEDIYOW3mBAT8Ppw9b0re59YxUDNpDGcnKiUKDtSyG15zQlwc8nefMAz0CzMRF5tNO0Ve0NCpVcUi0g9jiqJWSOqjeCwgBWrq6+lekwsI5WdoX1EV7r//fgDAZz/72az0xp122glz5szBtm3b8OSTT1bj8AghhJCKcPmCvfDGZUdil52a8z5XdtAbytb8QMowQjVYyaVkyITT353caNg0eRjiAXCxZKc+phU1jQlOJdGpVcUizjm1fqyQ1MdMiq3PVBvp+ugNeOZLvPLKKwCAfffdV/u42P7qq69W7JgIIYSQauC0jqdB6r3UGKnZ0ndH+Hw+jG6tt2xzGKdJSobs+kh7/lKwpD7K9vxVSH2UEcfhBqJ+Ua1RK0RRy9So+bXj0O41h7qCXgn4DUt8+OGHAICxY8dqHxfbP/jgg4odEyGEEOJlZDc72TBhqDK8KWz+W7VQBwA7U8xMbZBcV0V7/lKQUx9lM5FqpD7KRF1MfQwFU2NDVelUhc1w5Ppo/3ry8wQcl+WHV1SJ7u5uAEBDQ4P28cbGRgBAV1eX7WtEo1FEo1Hz787OThePkBBCCPEWcnAmGyYMVWRVMRLwZykZzZEgOvuza92DmtRHMTEe6imlxSJ/9QmjumYiMq6mPjpU1NS/dY+JPmqAPvWRNWqVx7OB2sKFC/HWW28VtM+NN96IGTNmlOmInHHFFVfgsssuq+oxEEIIIZWCipoVOVBLGkZ2oFYX0gZqYU3KmahRC9Cev2Tiiera85cLXYAPFFijZumj5jz1kYpa+fHsFXX16tV45513Ctqnt7e3pPdsamrK+To9PT0AUtb/dnzve9/D+eefb/7d2dmJcePGlXRchBBCiFeRg7MWBmpolgK1WMLImtzaBbO61Mc4a9RcIxpPmgpbtc1E3ETUqMXyKGi5a9Qy9vzCndJJw2vWqJUfz15RX3755Yq/5/jx4/HSSy/ho48+0j4utk+YMMH2NSKRCBtiE0IIGTK01MuKGlMfh0k1agOJZNYEucXmO9KnPrJGzS3k5u3VSn089zO74Jf/WYlvH7Gra68ZEoFVPLeilqtLhBhyAb+voNRHjsvyw1BYYp999gEArFixQvu42D59+vSKHRMhhBDiZSw1akPc9REAvjx7MoY3ZRZsB+LWyW2TjaKmTX1kjVpJ/OVLmXKYPql5e7hKStB5h+2Cx749D+fMn+raa4b8ent+tS2EI0Ut4NMquwI1+KPSW34YqEkce+yxAIB7773XYggCAJ988gmeeOIJtLe3Y9asWdU4PEIIIcRzsEbNSntjGMsunGf+3R9PWB7Pl/ooNxqOsUatJObuOgJ3/PfBAIDeWMZIxGdnvVlmfD4fJg5vdPX9hRKrNqhWg6pcjdctNWo2Ch2QHfxRUSs/PPMlZsyYgVmzZmHjxo347ne/a26Px+M4++yzEYvFcO655yIUYmoHIYQQAtD1UYdcu6P2zLIL1DIT7mxFjcpF8YhgQnyX1XZ8dBsx1lRFLZFlz2//GmYfNZ+U+ujAnj9Ipbfs1PTS18KFC7FhwwYAwPr16wEAf/rTn/DAAw8AAEaPHo277rrLss8NN9yAmTNn4he/+AUeeeQR7LHHHli+fDlWrVqFgw8+GN/73vcq+yEIIYQQD2OtUavpaYVj5FTFaJaipg9mdW57ppkIJ8RFE1DUq1pyfAQyQXwsnltRU9Uw3XMDfj/CwWT69VL/f29jF/7r989iTFs93v3E2p4qSKW37NT0FfWll17Kak69bt06rFu3DoDeFGSXXXbBSy+9hIsvvhgPPPAA7rrrLowfPx4//OEP8f3vf59GIYQQQoiENfWRihqQSnELBXyIJQzHilpYk/oojEWoqBWPGkvUkuMjIAX4qqKWUGvU7F8jIS0IhJLWBYOX1+7Alp4BbOkZyNqPqY/lp6YDtTVr1hS137hx43DDDTe4ezCEEEJIDWJNfazpaUVBhAJ+xBIJTY1abtdHnaLGGrXiUYOJSLC2FDUz9TFPjVpuM5FMjZpYMIimx6Han02GSm/54RWVEEIIIUXTKqU+2lnPD0VSE+gE+mPWQK3BJvVOl/qYYOpjyaipj7VWo6YL8IFC+6hlaiFD4dT4FC6Zan82y3tTUSs7DNQIIYQQUjQN4UzgUR+uLbWiFETgpaY+2gVd2tRHmomUjD9LUautQC1kY/5RiKIm9+sTNXw90VSglktRo9JbfhioEUIIIaRoOhrDmLPLcBgGMFxq9jzUCacDsqhicy47Qu4zrg2vrN2OLxw0Xp/6mGDD61LJVtRqazGhLqRfEFADM0c1an4fGtOLLb0DcctjOriAUH4YqBFCCCGkaHw+H/565oHVPgzPIfpRqa6PctB1yC7DceMZM9BSH8R9r6ZcqrWuj1QuikYNcutqzEykLh14qrWQas1aTkUt/Vy/34eGcCo06BGpj5rG1wKm5JYfBmqEEEIIIS4jlLN+SemYPLzRYt0f8PvQ2hCyPF+eGLNGrXSyUx9rS1GL2ChqxfRRC/p9aIykA7VoSlHLnfrIcVluamtZgRBCCCHEA4i0MGEmMml4I/7vm3MsdT1yWp4I4OKa1EemmBVPVupjjSpqqnKr1qjlSmGU3UUbI6JGLRWo5TYTqa3v0ovwGyaEEEIIcZmwmfqYCrbGttejLhRASAq6AgE5UBOmENlmIlQuiierj1qNmYmIwLM/S1ErwPXRyChqIvWxdyC/mQiV3vJTW6OVEEIIIcQDZFIfUxNeX1rZkYMuq6Jmb88vG5CQwqh9M5HcippQanOmPiYyCwJCUaOZiDfgmU8IIYQQ4jIhxfVRiA9BKeiSgzZd6mOMro8lk93wuramvqaZiI2iJoL83Pb8sutjSlHrFqmP6SAurPneOC7LT22NVkIIIYQQD2D2UUsrHf60siOrENZALdtMRPw7xBSzoskK1GqsRi2T+mhV1NRALZcylpD6qAkzkf5YEomkYfZYa9T0SGSNWvnhN0wIIYQQ4jJhxfVRuA/KdT1BTaAmNy4W6Wdi8kwKJ8uev8ZSHyOmmUg+Rc3+NeRaSLmBfe9A3FwsELVrMhTUyg8DNUIIIYQQl8koaiL10WfZDlit43Wpj139qUCtiYFa0fhr3PXRTlETSphI9TRymYlIgVok6DeD296BhDke6zWKms/HSK3c1NZoJYQQQgjxAGbD6/QEWmSJyQqPTlETCkYskTSDPAZqxZNdo1ZbilpdHkVNKLjOatT88Pl8ZprjP15Yi7tfXg8AFqWNVA4GaoQQQgghLqOaiQhlJyTV9chqjwjsROqj6GMFMPWxFLJdH2tr6ltnq6g5T31MKm0gxHj7+b/fNcdjfYiBWjWordFKCCGEEOIBwoo9vwjK5N5pcr2a6K8mUs1E2mNdyE97/hLw17qZiFPXRwcNr4XCq1PPqKhVh9oarYQQQgghHiCo2vP7haKWCRz8mj5qSSM1yRb26Ex7LB05/bHmUh9DVndRQVzY6jtIfTRr1NLP1Y05nZkIKT8M1AghhBBCXEYEYcLUwa9peK1LfQRS9Wk9DNRcQ05/rKsxRc1seG2jqIn+Z05cHzOKWvaYU81ElsycUNwBk4Lg2U8IIYQQ4jJmoJZWNkR8FrRJY6wL+uH3pSbUO/pi6BKBWh2naqXi9wNIC061pqhFpNrGRNIwFwI6+2MAgPp00JVbUbM6k+pqIuXUx+8dvRu+NneKC0dP8lFbywqEEEIIIR4go6hZjRpkp0d56hwM+DGyuQ4AsH57H7ppze8asqJWe2YimQBqIJ1mG40n8N7GbgDA7qObAQA54rSsMdpaH8p6jqyoqU6apHzU1mglhBBCCPEA6lxW9JySDUTU3lZj2lKB2oYd/axRcxF/DdeoyYGnMK559+NuxJMG2hpCGNtWDyCTCqlDtfJva8gO1BpCmXHIQK1yMFAjhBBCCHEZ1W1QZDzK9vwqo9OT6vXb+1ij5iIWM5Eaq1ELBvymSiuMa95YvwMAsNeYVnMcOjITSY9NnaImpz4GGahVjNoarYQQQgghHkBp32Wm38kBnDp33tkM1PpNe37WqJVOLac+Apn0R6GovbWhEwCw55gW87PnMhMxAzWffepjQ0ROfay979Cr8JsmhBBCCHEZtdGyT43cABiwzp5Ht4rUxz4z9ZHNrkunllMfgUzw2Z+26N+wox8AMLajwayVVNNsZXoHUvvVh6moeQ0GaoQQQgghLuP3qamPmkBNmTuPbk0rajv6zdTHZgZqJSO+SyDbZr4WUC36N3dHAQAjmsKmsmuX+mgYhvn9iEWBVk2NWr1kWgLGaRWDgRohhBBCiMuocZmTQG1UWlHb1NmfsednoFYyQjEC9GrRYEfU3YnUx03pQG14U8RcMEjYCGrReNJ0fRRjTe/6mBmHyVx5lMRVGKgRQgghhLiMaiaiyXyEOt0VzZij8ST608GFbL9OSuOgyR3VPoSyINI5+9NmIpu7BgCkA7X0TN8u9bFbUhsbw/aBWqOkRDJOqxwM1AghhBBCXCYr9VETqTVFrEFYOG0NOSCpHHYNsolzpu2U6iX29fm7VPlIyoMIonqjcfRE4+hLK2vDmzOKml3qo+jX1xgOmIsLbZpATV4wyOUgSdyFejohhBBCiMuomY5y4HbZ8XvipQ+34fA9RlmeE06bQkQTSdOJLxRgQVCp/PXMGVi7rQ/7TWiv9qGUhZZ0YNXVHzfr0+pC/lTwJQK1pH5fnWlNiyZQC0tumQzUKgcDNUIIIYQQl1FTH+W/lxw8EUsOnpi1j5gMD8STiCVSM2s2Fy6dkS11GNlSV+3DKBvN6RYOnf0xM1Ab3hSBz+fLr6hFs9tAhDQqrjwOWaNWOainE0IIIYS4jJPUR5VIIJNeJowhguxZRfLQUpdSwDr7YtiUrk8b0RwBkFF27QI1p43V5UbtdsYkxH149hNCCCGEuEx26mP+feT0MuFUyJ5VJB8t9UJRi1sUNSDTv89OBOu2CdT+duaBlr+DUgpurp5sxF0YqBFCCCGEuIyqqKmpkDp0gVqANWokD6ai1h8zAy+RDilSFvOlPqqN1WfvMhznzJ9i/i0HagmmPlYMBmqEEEIIIS6TFag5SH0M+H2mgtY7kJpAh5j6SPLQbKY+xjGQtuiPpIP+TOqjfl/h+qhLfZTTbuVxOLIlUvIxE2fQTIQQQgghxGWyG1472y8c9CM+kECPUNSY+kjykEl9jJmBmmj1IBYI7NIVc9WoyWMvEPDh91/cD8+t2orj99nZvYMnOWGgRgghhBDiMlmujw4UNSAVqPUOJMwJd5CpjyQPspmIcAsVabRi2NmlK3bZpD4C1kAt5PfjyD1H4cg9R2U9j5QP6umEEEIIIS5TTOojkFFCBDQTIfmQ+6hF49ZAzZ/HTKRHqWmTkYcsFwyqAwM1QgghhBCXybLndxhwyYYiAO35SX5apD5qA2lFTfRCM81EbCI100wkHMh6TG4pwQWD6sCznxBCCCHEZYqx5wc0gRqVDJIHYSbSHY2b/ffEOGpNq21bewe0+3ZHU8/XpT7Kiw0+h4owcRcGaoQQQgghLlOMPT/A1EdSOCJt0TCAbT2pgEyMo9GtdQCAzd1Rs+5RRjy/ozGc9ZjTMUvKBwM1QgghhBCXUSe5AYeKRERR1Oj6SPJRFwqYCtqWdOAlxlFHYxjhgB+GAWzs6s/ad0u6QbYuUKOYW30YqBFCCCGEuEx26mNxNWohp77+ZEgTSY8TUXOWcX30YVRaVft4hzVQMwzDDOyGN2X3RqOiVn149hNCCCGEuEzRqY9U1EgRiFrG3qi1Rg0ARrWkArUNSqDWM5AwXSKHNWlSH1mXVnUYqBFCCCGEuEx2HzVn+7FGjRRDMD1uegZSipqsxNopaiLtsT4UQEM420xkbHt9WY6VOIcNrwkhhBBCXEaNr4q252fqI3FAKD2+egfSipo0boShiKqobclhJAIAc3cdgQuPnIY9Rre4frzEGQzUCCGEEEJcpuiG10FrPyumPhInhNIBfiLdL00O+Ec0p+rPVDORLd2iPk0fqPl8Ppwzf6rrx0qcw2UaQgghhBCXKdpMJKCaiTBQI/lRU2TlQC0SSgX/8YS16bVIfRymMRIh3oCBGiGEEEKIy6iBmdMMRpqJkGJQ3UHlNg+iNUQ8qQRqeVIfSfVhoEYIIYQQ4jJqoOYrso9a0M+pGslPUFFe5cBNqG2JpLXhtUh91Dk+Em/As58QQgghxGXU+Mppw2tZUfP5qKgRZ6gBvTyOxBhSMh+xpSeV+ji8kamPXoWBGiGEEEKIy2SnPhauqNGanzhFrWWUax0DNora1h4qal6HgRohhBBCiMtkpz462083wSYkH04UNdVMZHM3a9S8DgM1QgghhBCXKVZRkyfYIdanEYeoNWphjTKbNPSuj8Pp+uhZeAUghBBCCHEZN2rUArTmJw7Jcn0MZPrx+f3Zro+GYTD1cRDAQI0QQgghxGWKdX20KiGcphFnqPWMoaAv67FE0jAbYnf2xc3AjamP3oVXAEIIIYQQlyk69VFjq05IPlRFTVfr+OpHO/Cpy/+N19ftwOa042NzJIhIMADiTRioEUIIIYS4jBpjOY256sOZSTPNRIhT5Bo1vw8I2pjSdPXHcdLvnmba4yCBgRohhBBCiMv4lSBL/duOBilQUy3XCbFDTpMNK03T1YA/Gk9i9aYeAMAwGol4GgZqhBBCCCEuk5X66LBGrT4UzOxDRY04RA7qw0oapK7W8eWPtgMA2huoqHkZBmqEEEIIIS6TnfpYuKJGMxHiFLlGLazUnAU0w0hY8zdFWJ/mZXgFIIQQQghxGTUwcxpzNUoTZ7U3FiF2BC2Kmmpkkz34tqSbXcs1kcR7MFAjhBBCCHGZrEDNaepjOJP6SNdH4hSroqbUqGnG3pa0mUhdiIGal2GgRgghhBDiMlkNr52aiUgTZ6e91wiRg/p8ZiJAJvWxgYqap2GgRgghhBDiMsX2UZNT0QbiSVePidQuwRyKmi6FtrM/DgCop6LmaRioEUIIIYS4jBqohRwWqUWkSfZAgoEacUZIWghokJxDgdxpt0x99DYM1AghhBBCXEYV0AIOjUHkdEcqasQpsqKmGoTkqnVsCAdtHyPVh4EaIYQQQojLZCtqhdebxaioEYfIfdTUurNcabf1YYYCXoa/DiGEEEKIy6iT46CumVUeqKgRp8iqmaqo5QzUmProaRioEUIIIYS4jFoW5NRMRIaBGnGKvBCgKmq5Uh/rmfroaRioEUIIIYS4TFbqYxHNq6NMfSQOkceXqpJRURu8MFAjhBBCCHGZYu35ZaioEacE/bKZiFUlY6A2eGGgRgghhBDiMqobv1N7fkKKIUgzkZqEvw4hhBBCiMvIiprPB/iLUNQIcUooZ42a/XSfNWrehoEaIYQQQojLyIFaIEfDYULcwOL6qKQz5hJzmfrobRioEUIIIYS4jBycqfVqhLhNKCgralaVLKeixkDN0zBQI4QQQghxGZ80wyq0PO0vX5qBYY1h/Om0/d09KFKzyDWQaupjrqzbuhBDAS/DxFRCCCGEEJfxl6Cozd11BF74n8PgoxJHHCKbidQpKpndOAoH/BxjHodhNCGEEEKIy5Sa+sgJNCmEUA7XRzsG2KfP8zBQI4QQQghxGTnOYsxFyk0wR+qjyt47t5b7cIhLMFAjhBBCCHEZi+sjrflJmZFTH+vzBGrfO3o3AMDMycPKekykdFijRgghhBDiMnJsRtdHUm7kMaa6PqocPHU4HvzWIRjeFC73YZESYaBGCCGEEOIysopGQY2Um3jCMP/tpEZt2qjmch4OcQmmPhJCCCGEuIyPfdRIBYklM8YgkSCn97UCFTVCCCGEkDLCQI2Umz3HtGBUSx1GtdbRMbSGYKBGCCGEEFJGmPpIyk0kGMAT351vaQtBBj8M1AghhBBCyggVDlIJQgGmPNYa/EUJIYQQQsoI7fkJIcXAQI0QQgghpIwwTiOEFAMDNUIIIYSQMkIzEUJIMTBQI4QQQggpI4zTCCHFwECNEEIIIaSMUFEjhBQDAzVCCCGEkDLCQI0QUgwM1AghhBBCyoifbiKEkCJgoEYIIYQQUkYYpxFCioGBGiGEEEJIGWHqIyGkGBioEUIIIYSUEaY+EkKKoWYDtZ6eHvz1r3/FN77xDRx44IGIRCLw+Xy49NJLbff55JNPcN1112HhwoUYO3YswuEw2traMHfuXPzlL3+BYRiV+wCEEEIIqQkYpxFCiiFY7QMoFytXrsRpp51W0D4XXHABbrrpJgSDQey///6YPXs21q1bhyeffBKPP/447rvvPtx6660IBAJlOmpCCCGE1BpMfSSEFEPNKmrNzc0488wzce211+LFF1/E5ZdfnnefYcOG4X//93+xfv16PPPMM7j11lvxxBNP4Nlnn0VLSwtuv/12XHfddRU4ekIIIYTUClTUCCHFULOK2pQpU/CnP/3J/Pvf//533n1+8YtfaLcfcMABuOiii/D9738ft9xyC7761a+6dpyEEEIIqW2oqBFCiqFmFTW32WeffQAA69evr/KREEIIIWQwwUCNEFIMDNQcsmrVKgDAqFGjqnwkhBBCCBlM+DnbIoQUAS8dDojFYvjtb38LAFiwYEGVj4YQQgghgwkqaoSQYqjZGjU3+eEPf4i33noLkyZNwllnnZXzudFoFNFo1Py7s7Oz3IdHCCGEEA/DQI0QUgyeDdQWLlyIt956q6B9brzxRsyYMcPV47j11ltx5ZVXoq6uDjfffDMaGhpyPv+KK67AZZdd5uoxEEIIIWTwQtdHUm3OnjcFv33sfVz62T2qfSikADwbqK1evRrvvPNOQfv09va6egyPPPIITj/9dPj9ftxyyy046KCD8u7zve99D+eff775d2dnJ8aNG+fqcRFCCCFk8EBFjVSbC4+chtNmTsSo1rpqHwopAM8Gai+//HJV33/58uVYsGABBgYGcN111+GEE05wtF8kEkEkEinvwRFCCCFk0OCnpEaqjM/nY5A2CPFsoFZN3nzzTRx99NHo7u7G1VdfjTPOOKPah0QIIYSQQQrjNEJIMdD1UWHNmjU44ogjsGXLFlx66aX41re+Ve1DIoQQQsgghqmPhJBiYKAmsXHjRhxxxBFYt24dLrjgAlxyySXVPiRCCCGEDHJGt9ZX+xAIIYMQn2EYRrUPolwsXLgQGzZsAACsX78ea9euxc4774yxY8cCAEaPHo277rrL8vx//vOfaGhowKJFi7SvOXz4cPz85z93fAydnZ1obW3Fjh070NLSUsKnIYQQQshg4pG3P8E/X1qPHy/cCy11oWofDiHEAxQSG9R0oDZx4kR88MEHto9PmDABa9asMf+eN28eli1blvM11X3ywUCNEEIIIYQQAhQWG9S0mUghARUAPPbYY2U5DkIIIYQQQggpBNaoEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeIxgtQ+g1jEMAwDQ2dlZ5SMhhBBCCCGEVBMRE4gYIRcM1MpMV1cXAGDcuHFVPhJCCCGEEEKIF+jq6kJra2vO5/gMJ+EcKZpkMon169ejubkZPp+v2oeDzs5OjBs3DmvXrkVLS0u1D4cQE45N4lU4NomX4fgkXoVjU49hGOjq6sKYMWPg9+euQqOiVmb8fj/Gjh1b7cPIoqWlhScN8SQcm8SrcGwSL8PxSbwKx2Y2+ZQ0Ac1ECCGEEEIIIcRjMFAjhBBCCCGEEI/BQG2IEYlEcMkllyASiVT7UAixwLFJvArHJvEyHJ/Eq3Bslg7NRAghhBBCCCHEY1BRI4QQQgghhBCPwUCNEEIIIYQQQjwGAzVCCCGEEEII8RgM1IYAfX19uPjii7Hrrruirq4OY8aMwZe+9CWsW7eu2odGaoDe3l7885//xJlnnolp06ahrq4OjY2N2GeffXD55Zeju7vbdt8///nPmDFjBpqamtDR0YFjjjkGTz/9dM73e+qpp3DMMcego6MDTU1NmDFjBm688Ua3PxapUbZs2YKRI0fC5/Nh6tSpOZ/L8UkqxaZNm/Dtb38b06ZNQ319PTo6OrDvvvviwgsv1D7/3nvvxdy5c83+VPPmzcP999+f8z3eeOMNLFq0CCNGjEB9fT323ntvXHPNNUgmk+X4SKQGWL58ORYvXowxY8YgFAqhra0Nc+bMwQ033ACdxUUikcDVV1+NvffeG/X19RgxYgQWL16Mt956K+f7FDOehwwGqWn6+vqMgw46yABgjB492li8eLExY8YMA4AxYsQI4/3336/2IZJBzh//+EcDgAHA2H333Y1FixYZRx55pNHc3GwAMHbbbTfjk08+ydrvm9/8pgHAqK+vNxYsWGAceeSRRjAYNAKBgHHXXXdp3+v22283AoGA4fP5jLlz5xonnXSS0dbWZgAwLrjggjJ/UlILLFmyxPD5fAYAY8qUKbbP4/gkleKFF14whg0bZgAw9txzT+O//uu/jKOPPtqYMGGCEQgEsp5/9dVXGwCMYDBoHHXUUcaCBQuM+vp6A4Dxq1/9SvseTz/9tPmcGTNmGIsXLzZGjRplADAWLVpkJJPJcn9MMsgQ1zMAxr777mssXrzYmD9/vhEMBg0Axqmnnmp5fiKRMBYuXGgAMNra2oyTTjrJmDt3ruHz+YyGhgbjueee075PMeN5KMFArcb5wQ9+YAAwZs6caXR1dZnbr7rqKgOAMXfu3OodHKkJ/vznPxtf/epXjTfffNOyff369canP/1pA4Dxuc99zvLYQw89ZAAwhg0bZrz77rvm9qefftoIh8NGW1ubsW3bNss+W7ZsMVpaWgwAxh133GFu//jjj42pU6caAIxHH33U9c9HaoeHH37YAGB89atfzRmocXySSrFx40Zj+PDhRkNDg3H33XdnPa5Obt9++20jEAgYkUjEePrpp83t77zzjjFs2DAjGAwaK1eutOwzMDBgTJo0yQBgLF261Nze1dVlzJw50wBg3HDDDe5+MDKoicVixsiRIw0Axk033WR57M033zQ6OjoMAMYjjzxibheLtrvssovx8ccfm9tvv/12A4AxdepUIxaLWV6rmPE81GCgVsNEo1GjtbXVAGCsWLEi6/Hp06cbAIwXXnihCkdHhgJPP/20AcCIRCJGNBo1tx999NEGAOPqq6/O2ufcc881ABg///nPLdt/+tOfGgCMBQsWZO1z5513GgCM4447zu2PQGqE3t5eY8qUKcYee+xhvPvuuzkDNY5PUin++7//2wBg/OY3vyno+d/85jezHlu6dKkBwPj6179u2f73v//dAGDss88+Wfu8+OKLBgBjr732KubwSY3y2muvGQCMadOmaR8X18Gf/vSn5rbdd9/dAKDNODj++OMNAMbtt99u2V7MeB5qsEathnnqqaewY8cOTJkyBZ/+9KezHj/55JMBpHKDCSkH++yzDwAgGo1iy5YtAFI1k4888giAzBiUsRuXIl9dt8+xxx6Luro6PPzww+jv73fvA5Ca4bLLLsOqVatw7bXXIhQK2T6P45NUir6+Pvztb39DY2MjzjjjDEf75BpnxYzNfffdF5MnT8brr7+ONWvWFHL4pIZx2qB62LBhAIDVq1fjrbfeQn19PY499tis5xUzNjlHTcFArYZ55ZVXAKQuxDrE9ldffbVix0SGFqtWrQIAhEIhdHR0AADeeecdRKNRjBgxAmPHjs3ax25c5hrP4XAYe+21F/r7+/Huu++6+hnI4OfVV1/FVVddhTPOOANz5szJ+VyOT1IpXnjhBXR1deHTn/406uvr8a9//Qvnn38+zj77bFxzzTVYv3695fnbt2/Hhx9+CADaxddx48Zh+PDh+OCDD9DZ2Wlu51yAFMrkyZMxZcoUvPPOO7j55pstj7311lv429/+hvb2dixcuBBAZozttdde2oUw3RgrdjwPNRio1TDiBNBNNuTtH3zwQcWOiQwtfvGLXwAAjjrqKHOFLt+4bGxsRFtbG7Zt24auri4AQGdnJ3bs2JFzP45noiOZTOLLX/4y2tracOWVV+Z9PscnqRRvvvkmAGDkyJE44YQTcMwxx+Dqq6/G7373O5x33nmYOnUqbrnlFvP5Ymy2t7ejsbFR+5q6cca5ACmUQCCAv/zlL2hra8PnP/957LfffjjllFNw6KGHYvr06Rg7diz+85//mAuwxYyxYsfzUIOBWg0jbNEbGhq0j4sTQ0w2CHGT//u//8N1112HUCiEH/3oR+b2fOMSyB6bssU/xzMphF/96ldYvnw5fvazn5lpOrng+CSVYtu2bQCAe+65Bw888AB+85vfYOPGjVizZg2+/e1vo6+vD0uWLMHLL78MoLix6WQ/jk2iY9asWVi2bBkmT56MFStW4O9//zseffRR+P1+HH744Zg8ebL53GLGWLHjeajBQI0Q4jpvv/02vvCFL8AwDPzsZz8za9UIqSQffvgh/ud//gdz587F6aefXu3DIcSC6F8Wj8dx+eWX4+yzz8aIESMwYcIE/OxnP8OiRYsQi8Xws5/9rMpHSoYit9xyC2bMmIFx48bhueeeQ3d3N959912cfvrpuOqqq3DooYciGo1W+zBrHgZqNUxTUxOAVENiHT09PQCA5ubmih0TqX3WrVuHo446Ctu2bcP555+Pb37zm5bH841LIHtsin1y7cfxTFTOOeccDAwM4Nprr3W8D8cnqRTyuNGZiYhty5Ytszy/kLHpZD+OTaKycuVKLFmyBMOHD8d9992HGTNmoLGxEbvssgt+//vf47jjjsOKFStw/fXXAyhujBU7nocaDNRqmPHjxwMAPvroI+3jYvuECRMqdkykttm6dSuOOOIIfPDBBzjjjDPw85//POs5+cZlT08Ptm/fjvb2dvPi3NLSgtbW1pz7cTwTlfvuuw8NDQ0466yzMG/ePPO/U045BUBqUUFs+/jjjwFwfJLKIcZCQ0MDRowYkfX4xIkTAQAbN24EkBmb27ZtMyewKrpxxrkAKZRbb70VsVgMRx11lGVBQbB48WIAwOOPPw6guDFW7HgeajBQq2FEutmKFSu0j4vt06dPr9gxkdqlu7sbRx99NN58802ceOKJ+OMf/wifz5f1vGnTpiESiWDTpk1Yt25d1uN24zLXeI7FYnj99ddRV1eHXXfd1Y2PQ2qE7du3Y9myZZb/nnvuOQBAf3+/uU3Y5nN8kkohnO76+vq0KWRbt24FkFEe2trazMntSy+9lPX8tWvXYvPmzZgwYQJaWlrM7ZwLkEIRAZJYgFIR20WdpRhjr7/+OmKxWNbzdWOs2PE81GCgVsPMmjULra2teP/9981iZJnbb78dAPDZz362wkdGao1oNIoFCxbg+eefx5FHHolbbrkFgUBA+9z6+noceuihAIB//OMfWY/bjUvRm0U8LnPfffehv78fhx12GOrq6kr6LKR2MAxD+9/q1asBAFOmTDG3CfWC45NUivHjx2OfffaBYRhmeqOM2CZbl+caZ8WMzZdeegmrVq3CXnvtZZ4DhIwaNQpAqoWEjuXLlwPIqL6TJk3C7rvvjr6+PrM3mkwxY5Nz1DTV6rRNKsMPfvADA4Bx8MEHG93d3eb2q666ygBgzJ07t3oHR2qCeDxuLFy40ABgzJkzx+jp6cm7z0MPPWQAMIYNG2a8++675vann37aiEQiRltbm7Ft2zbLPlu2bDFaWloMAMYdd9xhbv/kk0+MqVOnGgCMRx991K2PRWqY1atXGwCMKVOmaB/n+CSV4qabbjIAGHvvvbexfv16c/tLL71kdHR0GACM2267zdz+9ttvG4FAwIhEIsYzzzxjbn/33XeNYcOGGcFg0Fi5cqXlPQYGBoxJkyYZAIylS5ea27u7u42ZM2caAIwbbrihfB+SDDpefPFFA4ABwPjtb39reeyZZ54xGhsbDQDGQw89ZG7/4x//aAAwdtllF+OTTz4xt99xxx0GAGPq1KlGLBazvFYx43mowUCtxunr6zMOPPBAA4AxevRoY/HixebfI0aMMN5///1qHyIZ5FxzzTXmBX3hwoXGkiVLtP9t2rTJst83v/lNA4DR0NBgLFiwwDj66KONYDBoBAIB46677tK+1+233274/X7D5/MZ8+fPN04++WSjra3NAGCcf/75Ffi0pBbIF6gZBscnqRxLliwxABhtbW3GMcccY8yfP9+IRCIGAOMrX/lK1vOXLl1qADCCwaBx9NFHGwsWLDDq6+sNAMYvf/lL7Xs89dRT5nMOPPBAY/Hixcbo0aMNAMbJJ59sJJPJcn9MMsj49re/bd7b99xzT2PRokXGrFmzDL/fbwAwvvrVr1qen0gkzEXb9vZ24+STTzbmzZtn+Hw+o76+3nj22We171PMeB5KMFAbAvT29ho//OEPjSlTphjhcNgYNWqUcfrppxtr166t9qGRGuCSSy4xL+a5/lu9enXWvjfccIOx3377GQ0NDUZbW5tx1FFHGU899VTO93vyySeNo446ymhrazMaGhqM/fff3/jzn/9cpk9HahEngZphcHySypBMJo0//OEP5lhrbGw0Zs6cmXPc3HPPPcacOXOMpqYmo6mpyZgzZ45x77335nyf119/3TjppJOMYcOGGXV1dcaee+5pLF261EgkEm5/JFIj3HnnncYRRxxhqlvt7e3G/PnzjZtvvln7/Hg8blx11VXGnnvuadTV1RnDhg0zTj75ZOONN97I+T7FjOehgs8wDMPtdEpCCCGEEEIIIcVDMxFCCCGEEEII8RgM1AghhBBCCCHEYzBQI4QQQgghhBCPwUCNEEIIIYQQQjwGAzVCCCGEEEII8RgM1AghhBBCCCHEYzBQI4QQQgghhBCPwUCNEEIIIYQQQjwGAzVCCCEAAJ/PZ/nP7/ejtbUVBx10EK655hrEYrFqH6KWP//5z/D5fLj00kurfSgF4/P5MHHixGofRtk4/fTT4fP58Nhjj1X7UAghZNDBQI0QQoiFJUuWYMmSJfj85z+P6dOnY/ny5TjvvPNw9NFHIx6PV/vwPMfEiRPh8/lce73HHnsMPp8Pp59+umuvWS7c/uyEEEIyBKt9AIQQQrzFn//8Z8vfzz33HObNm4f//Oc/uPXWW/GFL3yhOgdWg7z11lsIhULVPoyyccUVV+Ciiy7C+PHjq30ohBAy6KCiRgghJCcHHnigqe48+OCD1T2YGmO33XbDlClTqn0YZWP06NHYbbfd0NDQUO1DIYSQQQcDNUIIIXnZc889AQAbN27MeswwDNxyyy049NBD0d7ejrq6Ouy+++649NJL0dvbm/X89957D5deeilmzpyJUaNGIRwOY+zYsTjttNPw7rvv2h7DU089hcMOOwzNzc1oa2vDkUceieeeey7ncW/ZsgUXXnghdtllF9TV1aGjowNHHXUU/v3vf2c9d82aNfD5fJg3b572tS699FL4fD5TcRQpih988AEAa42f07oz9bmnn3465s+fDwD4y1/+YnlNtQZv7dq1+PrXv44pU6aYn+24447D008/nfU+cjrlxx9/jC9/+csYO3YsgsEgrrnmGgDAhg0bcOWVV2Lu3LnYeeedEQ6HMWrUKJx44olYvny59vXyffZcNWpr167F1772NUyYMAGRSAQjR47Uvhdg/W36+vpw0UUXmftNnToVP/3pT2EYhoNvnBBCBg9MfSSEEJKXrq4uAMDIkSMt25PJJL7whS/glltuQVNTE/bff3+0t7fjhRdewGWXXYZ//etfeOyxx1BfX2/u86c//QlXXnkl9tprLxxwwAGIRCJ488038de//hV33303nnjiCUyfPt3yPvfddx8WLlyIeDyOGTNmYPLkyXjllVdwyCGH2NZyrVu3DocccghWrVqF8ePH44QTTsCmTZvw8MMP48EHH8TSpUtx3nnnFf2djBo1CkuWLMHtt9+Onp4eLFmyxHxs+PDhRb3m7Nmz8fHHH+PBBx/ElClTMHv2bPOxT33qU+a/n3nmGRx77LHYtm0bpk2bhmOPPRabNm3Cgw8+iAceeAA33XQT/uu//ivr9Tdt2oQDDjgA8Xgcs2fPRn9/v6l23X333fjud7+LadOmYfr06WhpacHKlStx11134b777sN9992HI444wpXP/tprr+HQQw/F5s2bMW3aNJx44on48MMPcdddd+Hee+/FzTffjEWLFmXtNzAwgCOOOAJvvvkm5s2bh56eHixbtgwXXXQRurq68OMf/9jxd00IIZ7HIIQQQgzDAGDY3RYOOeQQA4Dxt7/9zbL9yiuvNAAY8+bNMzZs2GBuj0ajxplnnmkAML773e9a9nnmmWeMVatWZb3H9ddfbwAw5s+fb9ne2dlpjBgxwgBgXH/99eb2ZDJpfPe73zWP+5JLLrHsd9xxxxkAjFNPPdWIRqPm9ieeeMJoaGgwAoGA8dJLL5nbV69ebQAw5s6dq/0OLrnkEgOAccMNN1i2T5gwwfZ7ywcAY8KECZZtjz76qAHAWLJkiXafHTt2GKNHjzYCgUDW77F8+XKjvb3daGpqMjZu3Jj1mgCMhQsXGn19fVmv++qrrxqvv/561vYHHnjACIfDxpQpU4xkMml5LN9nX7JkiQHAePTRR81tyWTS2HvvvQ0Axne+8x3La95+++2G3+83mpqajPXr15vbxW8jfp8dO3ZYPnMgEDAaGhqMrq4u22MhhJDBBlMfCSGEaEkmk3j//ffx3//933j88cexYMECi0oTj8dx5ZVXorGxEbfeeitGjRplPhYOh/GrX/0Ko0aNwh/+8Ackk0nzsYMOOgiTJk3Ker8zzjgDs2bNwmOPPYYdO3aY22+//XZs2rQJhxxyCM444wxzu8/nw49+9COMHTs267VWrVqF++67D01NTfjVr36FcDhsPjZ79mycddZZSCQS+M1vflP8F1Qlrr/+emzYsAHf+ta38PnPf97y2P77748f/vCH6O7uxt/+9resfSORCH71q1+hrq4u67G9997bTHGVOfLII7Fo0SK8//77eP3110s+/sceewyvvfYaxo8fjx//+McW18iTTjoJJ5xwArq7u3H99ddn7ev3+/H73/8eLS0t5rb9998fRx99NHp7e/HCCy+UfHyEEOIVmPpICCHEgs5u/Stf+Qp+//vfWx5bsWIFNm/ejMMPPxw77bRT1j719fXYb7/9cP/992PlypWYNm2a+Vh3dzfuvfdevPzyy9i6davZo23Dhg0wDAPvv/8+9t13XwDAE088AQA45ZRTst4jFArh5JNPNuusBE8++SQA4KijjkJHR0fWfl/84hexdOlS87UHE6K+7sQTT9Q+PmfOHADA888/n/XYvvvui5133tn2taPRKB544AE8//zz2LRpEwYGBgCkUhUBYOXKldh7771LOn7xnS9evFjrePnFL34Rd955p/a3mTBhgmUcCXbddVcAqfFDCCG1AgM1QgghFkS9UX9/P1555RW8/fbb+OMf/4iDDz7YUg+2Zs0aAMBDDz2Ut5eWqEUCgEceeQSnnHIKNm3aZPt8URMHAOvXrweQmqTr0Bl3iH3sTD3E9nXr1uU8bi8ivvdZs2blfN7mzZuztuWyyX/ttddw/PHHm6+vQ/5diqWU30anngJAc3MzgFSgSQghtQIDNUIIIRbUPmo/+9nP8J3vfAfnnHMO5s+fbwZMIp1x6tSpeYOGYcOGAUgpaYsXL8bWrVtx8cUX45RTTsGECRNQX18Pn8+HU089FbfcckvZHfyKadIsp29WE3EcJ598MhobG22ft9tuu2Vt06U8AinnzsWLF2PNmjU466yzcNZZZ2Hy5MloamqCz+fD97//fVxxxRUVcVbM9dv4/azYIIQMHRioEUIIycmFF16Ihx9+GP/+979x2WWXmbVDQt3YbbfdsoI7O5544gls2bIFJ598Mi677LKsx1etWpW1bfTo0QBgWsGr6LaPGTMm5z5CNZLTAEUdW3d3t3aftWvXardXmrFjx+Kdd97BRRddhP3228+V13z77bfx9ttvY//998fvfve7rMd1v0uxFPPbEELIUIRLU4QQQvLyk5/8BADw17/+1ZxgH3DAAWhtbcWyZcuwdetWR6+zbds2APoUtvfeew8rVqzI2i5qrm677basx+LxOO64446s7cLW/oEHHsD27duzHhdGG+K1gZStfDAYxOrVqxGPxy3Pj8ViWLZsmfYziQBP3adY8r3e4YcfDgC46667XHk/IPfvsm3bNjz00EPa/Yr57OI7/8c//oFEIpH1uO63IYSQoQgDNUIIIXn59Kc/jRNOOMF0egRSDoLf+c530NXVhRNPPFGruqxbtw5//etfzb+F6cOdd95pqVHbvn07zjzzTNNURGbRokUYNmwYHnvsMfzlL38xtxuGgUsuuQQffvhh1j6TJ0/Gsccei66uLnzzm9+0vO4zzzyD3/3udwgEAjjnnHPM7eFwGDNnzsTWrVstbpDxeBwXXHABVq9erf1uhEL0zjvvaB8vlHyv97WvfQ0jR47ElVdemeWoKY73wQcfLMihcerUqfD7/XjkkUewcuVKc3t/fz/OOuss20C8mM8+b9487L333lizZg0uvvhiSzrlXXfdhTvvvBNNTU340pe+5Pg1CSGkFmGgRgghxBGXXnopfD4frr/+enz88ccAgIsuughf/OIXsWzZMuy+++446KCD8LnPfQ4nnXQS9tprL4wbNw5XXXWV+Rr7778/Dj/8cHz44YfYddddsXDhQixcuBCTJk3C+vXrsWDBgqz3bW5uxnXXXYdAIIDTTz8dBx10EE499VTstdde+NnPfoavfOUr2uP9/e9/j0mTJuHGG2/ELrvsgs997nM47LDDMGfOHPT09ODKK6+0NJEGgIsvvhh+vx/f+ta3cPDBB+PEE0/ElClTcMstt1iaOsscf/zxAIDPfOYz+NznPocvf/nLuOiii4r5igGkzDSmT5+OF154ATNmzMAZZ5yBL3/5y7jnnnsAAG1tbbj77rvR2tqKr33ta5g4cSKOOeYYfP7zn8dnPvMZjBgxAkcddRTee+89x+85cuRInHnmmejs7MQ+++yD4447DosWLcLEiRPxyCOP2DYVL+az+3w+3HTTTRg2bBj+3//7f9hzzz1x6qmnYvbs2TjxxBPh9/tx3XXXmSmvhBAyVGGgRgghxBH77LMPFi5ciP7+fixduhRAytzhxhtvxN13343DDz8cq1evxh133IEnn3wSdXV1uPDCC7P6Yd199934wQ9+gBEjRuBf//oXXnzxRZxyyil49tln0dbWpn3vBQsW4NFHH8X8+fPx+uuv4/7778fo0aOxbNkyHHzwwdp9dt55ZyxfvhwXXHABgsEg7rzzTrz44ov4zGc+gwcffBDnn39+1j6HHXYY7rnnHhxwwAFYsWIFli1bhoMOOgjLly+3dSk899xz8T//8z9oamrCHXfcgeuuuw633nqr8y9Wwx133IETTjgBq1atwo033ojrrrvOkhZ60EEH4bXXXsN3vvMdtLS0YNmyZfjnP/+JDz74AHPnzsWf//xnHHbYYQW95+9+9ztcddVVmDRpEv7zn//giSeewGGHHYYXXnjB1nGz2M++9957Y8WKFfjKV76C7u5u3H777XjnnXdwwgkn4KmnnsLixYsLOnZCCKlFfEYlLJwIIYQQQgghhDiGihohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeAwGaoQQQgghhBDiMRioEUIIIYQQQojHYKBGCCGEEEIIIR6DgRohhBBCCCGEeIz/D0jrjnoZz5COAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timing 8: 1822.928 s\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "better_sleep(1800)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "start,stop = 2,None\n",
    "print(\"Timing 0: %.3f s\"%(time.time()-start_time))\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[start:stop]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "print(\"Timing 1: %.3f s\"%(time.time()-start_time))\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "print(\"Timing 2: %.3f s\"%(time.time()-start_time))\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 60, range = (0,200))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "print(\"Timing 3: %.3f s\"%(time.time()-start_time))\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3.2,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "    probs.append(prob)\n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "print(\"Timing 4: %.3f s\"%(time.time()-start_time))\n",
    "########################## kmeans 4d #########################################\n",
    "probs_4d, kmeans_4d_model = kmeans_4d(data)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(8,7), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(N_ROcycle_array)>1:\n",
    "    for i in range(4): \n",
    "        for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "        ax[i].set_ylabel('Probability')\n",
    "        ax[i].set_xlabel('Number of readout pulses')\n",
    "        ax[i].legend()\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].grid(True)\n",
    "else:\n",
    "    for i in range(4):\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "        \n",
    "        bar = ax[i].bar(labels,probs_4d[:,0,i].flatten().round(3), color=None, edgecolor = 'k', alpha = 0.2, linestyle='--')\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].bar_label(bar)\n",
    "        ax[i].set_ylabel(\"$P_N$\")\n",
    "print(\"Timing 5: %.3f s\"%(time.time()-start_time))\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\"\"\"\n",
    "plt.figure(figsize=(6,5))\n",
    "plot_2d_sweep(\n",
    "    prep,\n",
    "    x=labels,\n",
    "    y=labels,\n",
    "    xlabel = \"Prepared state\",\n",
    "    ylabel = \"Measured state\",\n",
    "    clabel = \"Probability\",\n",
    "    vmin = 0,\n",
    "    vmax = 1,\n",
    "    cmap = \"Blues\"\n",
    ")\"\"\"\n",
    "\n",
    "plt.figure()\n",
    "delta_freq = 1e-3*res.delta_freq.fetch_all()['value']\n",
    "plt.plot(delta_freq[4*start:])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "print(\"Timing 6: %.3f s\"%(time.time()-start_time))\n",
    "filename_kmean = directory+filename+'kmean.model'\n",
    "pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "print(\"Timing 7: %.3f s\"%(time.time()-start_time))\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "print(\"Timing 8: %.3f s\"%(time.time()-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2161,
   "id": "6f709a90-e35a-41f2-9110-30982ead6f14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T14:50:27.047454Z",
     "iopub.status.busy": "2024-04-01T14:50:27.047454Z",
     "iopub.status.idle": "2024-04-01T14:50:27.313485Z",
     "shell.execute_reply": "2024-04-01T14:50:27.312483Z",
     "shell.execute_reply.started": "2024-04-01T14:50:27.047454Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 2161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(delta_freq)//4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4e41dd6-520b-4033-9029-7c4b8b5338df",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "#centre_freq += delta_freq[-1]\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "##################### Raman pulse parameters #####################\n",
    "\n",
    "raman_detuning_a = -int(809.791e3/2-10e3)\n",
    "raman_detuning_b = -int(809.791e3/2+32.5e3)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.771e3)\n",
    "nuclear_spin_freq_b  = int(810.469e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.70e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.40e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle_array = np.linspace(250, 250, 1)\n",
    "N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "pulses = np.linspace(0,250,6, dtype = int)\n",
    "for pulses_after_tracking in pulses:\n",
    "    print(pulses_after_tracking)\n",
    "    ####################### Save params #######################\n",
    "    \n",
    "    experiment_name='nuclear_4stateprep'\n",
    "    time_stamp=get_timestamp()\n",
    "    filename=time_stamp+'%s'%(experiment_name)\n",
    "    directory = make_exp_directory(path,experiment_name) \n",
    "    \n",
    "    shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "    shutil.copy('config.py',directory+filename+'_config.py')\n",
    "    with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "        n = declare(int)           # Index for repeated preparation pulses\n",
    "        m = declare(int)           # Index for repeated preparation pulses\n",
    "    \n",
    "        j = declare(int)           # Index for Ramsey sweep\n",
    "        k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "        N_ROcycle_set = declare(int)\n",
    "        freq_set  = declare(int)\n",
    "        \n",
    "        \n",
    "        preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "        \n",
    "        rabi_stream  = declare_stream()\n",
    "        timing_stream = declare_stream() # Stream for Ramsey index\n",
    "        \n",
    "        #### Frequency Tracking variables ####\n",
    "        delta_freq = declare(int) \n",
    "        assign(delta_freq,0)\n",
    "        delta_freq_stream = declare_stream()\n",
    "        Y=declare(fixed)\n",
    "        angle=declare(fixed)\n",
    "        delta_freq_acc = declare(int) \n",
    "        assign(Y,0)\n",
    "        assign(delta_freq_acc,0)\n",
    "    \n",
    "        \n",
    "        click_acc=declare(int)\n",
    "    \n",
    "        with for_(j, 0, j < N_repetition, j + 1):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "                \n",
    "                with for_(k, 0, k < 4, k + 1):\n",
    "                    \n",
    "                    save(0, timing_stream)\n",
    "                    \n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "                    wait(int(10e6//4))\n",
    "                    \n",
    "                    align()\n",
    "                    chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                    align()\n",
    "                    wait(int(20e6/4))\n",
    "                    align()\n",
    "    \n",
    "                        \n",
    "                    save(0, timing_stream)\n",
    "                    \n",
    "                    amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                    gaussian_pulse_length = 5000//4\n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                    save(delta_freq, delta_freq_stream)\n",
    "                    \n",
    "                    align()\n",
    "                    save(0, timing_stream)\n",
    "                    \n",
    "                    chirped_pumping(centre_freq, delta_freq, pump_steps = int(pulses_after_tracking), enable_fsv_trigger=False)\n",
    "                    align()\n",
    "                    wait(int(50e6/4))\n",
    "                    align()\n",
    "                    save(0, timing_stream)\n",
    "            \n",
    "                    ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                    # align()\n",
    "                    \n",
    "                    # If k==0, we flip a and b\n",
    "                    with if_(k==0):\n",
    "                        Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                        Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                    \n",
    "                    # If k==1, we flip a\n",
    "                    with if_(k==1):\n",
    "                        align()\n",
    "                        Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                    \n",
    "                    # If k==2, we flip b\n",
    "                    with if_(k==2):\n",
    "                        \n",
    "                        align()\n",
    "                        Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                        \n",
    "                    ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                    save(0, timing_stream)\n",
    "                \n",
    "                    align()\n",
    "                    wait(int(5e6//4))\n",
    "                    nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_set, readout_freqs, delta_freq, enable_fsv_trigger = True)\n",
    "    \n",
    "                    save(0, timing_stream)\n",
    "                \n",
    "        with stream_processing():\n",
    "            \n",
    "            delta_freq_stream.save_all('delta_freq')\n",
    "    \n",
    "            rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "            timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "            \n",
    "    \n",
    "    qmm = QuantumMachinesManager()\n",
    "    qm = qmm.open_qm(config)\n",
    "    job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "    res = job.result_handles\n",
    "    #better_sleep(total_measurement_time)\n",
    "    start_time = time.time()\n",
    "    better_sleep(1.5*3600)\n",
    "    res = job.result_handles\n",
    "    ###########################################################\n",
    "    print(\"Timing 0: %.3f s\"%(time.time()-start_time))\n",
    "    timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()[0:]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "    #data=np.transpose(data,axes = [0,2,3,1])\n",
    "    print(data.shape)\n",
    "    sigmoid_length = 5\n",
    "    x = N_ROcycle_array\n",
    "    print(x)\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    print(\"Timing 1: %.3f s\"%(time.time()-start_time))\n",
    "    ############################### extract populations #################################\n",
    "    pops = []\n",
    "    deltas = []\n",
    "    for i in range(len(N_ROcycle_array)):\n",
    "        data_nro = data[:,i]\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans)\n",
    "        pops.append([p0,p1,p2,p3])\n",
    "        deltas.append([delta_x,delta_y])\n",
    "        \n",
    "    pops = np.array(pops)\n",
    "    print(\"Timing 2: %.3f s\"%(time.time()-start_time))\n",
    "    ############################### Plot raw histograms ##############################\n",
    "    \n",
    "    for k in range(len(N_ROcycle_array)):\n",
    "        plt.figure(figsize=(10,10))\n",
    "        for j in range(4):\n",
    "            plt.subplot(2,2,j+1)\n",
    "            for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 60, range = (0,200))\n",
    "            plt.xlabel(\"SMPD counts\")\n",
    "            plt.ylabel(\"counts\")\n",
    "            plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "        plt.tight_layout()\n",
    "        save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "    print(\"Timing 3: %.3f s\"%(time.time()-start_time))\n",
    "    ############################### Plot clouds ###############################\n",
    "    rows = int(np.floor(len(x)/5.01)+1)\n",
    "    if len(x)<5:columns = (len(x))//rows\n",
    "    else: columns = 5\n",
    "    fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3.2,2.5+rows*2), tight_layout=True)\n",
    "    axs = np.array([axs]).flatten()\n",
    "    fig.suptitle(\"K-means\")  \n",
    "    \n",
    "    probs = []\n",
    "    for i, delta in enumerate(deltas):\n",
    "        prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "        probs.append(prob)\n",
    "    probs = np.array(probs)\n",
    "    \n",
    "    save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "    print(\"Timing 4: %.3f s\"%(time.time()-start_time))\n",
    "    ########################## kmeans 4d #########################################\n",
    "    probs_4d, kmeans_4d_model = kmeans_4d(data)\n",
    "    \n",
    "    ##############################################################  \n",
    "    fig,ax=plt.subplots(2,2,figsize=(8,7), tight_layout=True)\n",
    "    ax = ax.flatten()\n",
    "    # fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "    if len(N_ROcycle_array)>1:\n",
    "        for i in range(4): \n",
    "            for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "            ax[i].set_ylabel('Probability')\n",
    "            ax[i].set_xlabel('Number of readout pulses')\n",
    "            ax[i].legend()\n",
    "            ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "            ax[i].set_ylim([0,1])\n",
    "            ax[i].grid(True)\n",
    "    else:\n",
    "        for i in range(4):\n",
    "            ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "            bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "            \n",
    "            bar = ax[i].bar(labels,probs_4d[:,0,i].flatten().round(3), color=None, edgecolor = 'k', alpha = 0.2, linestyle='--')\n",
    "            ax[i].set_ylim([0,1])\n",
    "            ax[i].bar_label(bar)\n",
    "            ax[i].set_ylabel(\"$P_N$\")\n",
    "    print(\"Timing 5: %.3f s\"%(time.time()-start_time))\n",
    "    save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plot_2d_sweep(\n",
    "        prep,\n",
    "        x=labels,\n",
    "        y=labels,\n",
    "        xlabel = \"Prepared state\",\n",
    "        ylabel = \"Measured state\",\n",
    "        clabel = \"Probability\",\n",
    "        vmin = 0,\n",
    "        vmax = 1,\n",
    "        cmap = \"Blues\"\n",
    "    )\"\"\"\n",
    "    \n",
    "    plt.figure()\n",
    "    delta_freq = 1e-3*res.delta_freq.fetch_all()['value']\n",
    "    plt.plot(delta_freq)\n",
    "    plt.xlabel(\"Readout iteration\")\n",
    "    plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "    print(\"Timing 6: %.3f s\"%(time.time()-start_time))\n",
    "    filename_kmean = directory+filename+'kmean.model'\n",
    "    pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "    print(\"Timing 7: %.3f s\"%(time.time()-start_time))\n",
    "    plt.show()\n",
    "    ############################### Save ###############################\n",
    "    \n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'x': x,\n",
    "                'pulses_after_tracking': pulses_after_tracking,\n",
    "                #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "                #'raman_detuning': raman_detuning,\n",
    "                'gauss_duration': gauss_duration_prep,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle_prep': N_ROcycle_prep\n",
    "                }\n",
    "    \n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    print(\"Timing 8: %.3f s\"%(time.time()-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b472076e-9cba-4cd0-92b7-d025c2999eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "\n",
    "delta_timing=((timing[:,1:]-timing[:,:-1])*1e-6).T\n",
    "label=['chirped pumping','Ramsey tracking','second pumping', 'Pulse sequence','RO']\n",
    "bar_colors = [colors[0],colors[1],colors[0],colors[2],colors[3]]\n",
    "\n",
    "print(delta_timing.mean(1))\n",
    "sizes = delta_timing.mean(1)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize = (7,6))\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    return f\"{pct:.1f}%\\n({absolute:d} ms)\"\n",
    "\n",
    "bar = ax.bar(label,sizes,color = bar_colors, width = 0.8)\n",
    "ax.bar_label(bar, fmt='%i')\n",
    "ax.set_xticklabels(label, rotation = 90)\n",
    "ax.set_ylabel(\"Time (ms)\")\n",
    "#ax.set_yscale(\"log\")\n",
    "#ax.pie(sizes, labels=label, autopct=lambda pct: func(pct, sizes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b034547-d2f6-49a4-a0b9-a1e9db0fc4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "timing.mean(0)[0]/1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b39a2ac4-1830-4f81-8630-0d8aeb65a7e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(np.linspace(1,6,6), timing.mean(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b077dca-c0cf-409f-bb69-9da7eda5ed37",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_shape = data.shape[:-1]\n",
    "X=data.reshape(np.cumprod(original_shape)[-1], 4)\n",
    "kmeans = KMeans(n_clusters=4, random_state=0, init=init, n_init=1)\n",
    "\n",
    "Z = kmeans.fit_predict(X).reshape(original_shape)\n",
    "probs=np.array([(Z==j).mean(0) for j in range(4)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22047d38-57b1-4e3c-ad93-83cf34176340",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_4d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c45828-bebf-4dd3-9417-bbbe8111c385",
   "metadata": {},
   "source": [
    "#### Sweeping chirped pulse params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec6576da-975c-44b4-907a-a3258ada5159",
   "metadata": {},
   "outputs": [],
   "source": [
    "chirped_pump_amplitudes = np.linspace(0.01,0.3,5)\n",
    "chirped_pump_durations = np.linspace(300e3,10e6,5)\n",
    "\n",
    "print(chirped_pump_durations)\n",
    "print(chirped_pump_amplitudes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d3dadf0-426a-4631-a2b0-2b33d6ac3aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linspace(10e6,30e6,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a29db4-744a-40d2-8555-231304a6f2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitudes = np.linspace(0.1,0.1,1)\n",
    "chirped_pump_durations = np.linspace(3.5e6,3.5e6,1)/4\n",
    "\n",
    "for chirped_pump_amplitude in chirped_pump_amplitudes:\n",
    "    for chirped_pump_duration in chirped_pump_durations:\n",
    "        \n",
    "        print(chirped_pump_amplitude,chirped_pump_duration)\n",
    "        \n",
    "        chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "        ####################### Define the readout frequencies #######################\n",
    "\n",
    "        readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "        readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "        freq_electron = readout_freqs[-1]\n",
    "        \n",
    "        ####################### Save params #######################\n",
    "        \n",
    "        experiment_name='nuclear_4stateprep'\n",
    "        time_stamp=get_timestamp()\n",
    "        filename=time_stamp+'%s'%(experiment_name)\n",
    "        directory = make_exp_directory(path,experiment_name) \n",
    "        \n",
    "        shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "        shutil.copy('config.py',directory+filename+'_config.py')\n",
    "        \n",
    "        ####################### Readout parameters #######################\n",
    "        \n",
    "        N_ROcycle_array = np.linspace(250, 250, 1)\n",
    "        N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "        \n",
    "        ####################### Define preparation parameters #######################\n",
    "        switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "        prep_ro_freq = readout_freqs[-1]\n",
    "        \n",
    "        #readout_freqs = [readout_freqs[0]]\n",
    "        \n",
    "        ####################### Measurement time estimate #######################\n",
    "        N_repetition = int(1e9)\n",
    "        \n",
    "        ####################### Run program #######################\n",
    "        \n",
    "        with program() as Spin_detection_linewidth_angle:\n",
    "        \n",
    "            n = declare(int)           # Index for repeated preparation pulses\n",
    "            m = declare(int)           # Index for repeated preparation pulses\n",
    "        \n",
    "            j = declare(int)           # Index for Ramsey sweep\n",
    "            k = declare(int)           # Index for Ramsey sweep\n",
    "        \n",
    "            N_ROcycle_set = declare(int)\n",
    "            freq_set  = declare(int)\n",
    "            \n",
    "            \n",
    "            preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "            \n",
    "            rabi_stream  = declare_stream()\n",
    "            #prepare_stream = declare_stream()\n",
    "            timing_stream = declare_stream() # Stream for Ramsey index\n",
    "            preparation_flag_stream = declare_stream()\n",
    "            \n",
    "            #### Frequency Tracking variables ####\n",
    "            delta_freq = declare(int) \n",
    "            assign(delta_freq,0)\n",
    "            delta_freq_stream = declare_stream()\n",
    "            Y=declare(fixed)\n",
    "            angle=declare(fixed)\n",
    "            delta_freq_acc = declare(int) \n",
    "            assign(Y,0)\n",
    "            assign(delta_freq_acc,0)\n",
    "        \n",
    "            \n",
    "            click_acc=declare(int)\n",
    "        \n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "        \n",
    "                with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "                    \n",
    "                    with for_(k, 0, k < 4, k + 1):\n",
    "                        \n",
    "                        save(0, timing_stream)\n",
    "                        \n",
    "                        align()\n",
    "                        chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=True)\n",
    "                        align()\n",
    "                        wait(int(20e6/4))\n",
    "                        align()\n",
    "        #                 chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=True)\n",
    "        \n",
    "                        \n",
    "        #                 click_acc = nuclear_spin_RO(\n",
    "        #                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "        #                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "        #                 )\n",
    "                        \n",
    "        #                 raman_preparation_a(prepare_stream, click_acc, freq_electron+delta_freq, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep_blue+delta_freq+centre_freq*1e3)\n",
    "            \n",
    "                        save(preparation_flag, preparation_flag_stream)\n",
    "                            \n",
    "                        save(0, timing_stream)\n",
    "                        \n",
    "                        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                        gaussian_pulse_length = 5000//4\n",
    "                        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                        save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "                        save(0, timing_stream)\n",
    "                        \n",
    "            \n",
    "                        ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                        # align()\n",
    "                        \n",
    "                        # If k==0, we flip a and b\n",
    "                        with if_(k==0):\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                            wait(int(1e6//4),spin_element)\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                            wait(int(1e6//4),spin_element)\n",
    "                            align()\n",
    "                        \n",
    "                        # If k==1, we flip a\n",
    "                        with if_(k==1):\n",
    "                            align()\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                            wait(int(1e6//4),spin_element)\n",
    "                            align()     \n",
    "                        \n",
    "                        # If k==2, we flip b\n",
    "                        with if_(k==2):\n",
    "                            \n",
    "                            align()\n",
    "                            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                            wait(int(1e6//4),spin_element)\n",
    "                            align()\n",
    "                            \n",
    "                        ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                        save(0, timing_stream)\n",
    "                    \n",
    "                        align()\n",
    "                        wait(int(5e6//4))\n",
    "                        nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_set, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "        \n",
    "                        save(0, timing_stream)\n",
    "                    \n",
    "            with stream_processing():\n",
    "                \n",
    "                delta_freq_stream.save_all('delta_freq')\n",
    "        \n",
    "                rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "                timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "                \n",
    "                preparation_flag_stream.save_all('preparation_flag')\n",
    "        \n",
    "        qmm = QuantumMachinesManager()\n",
    "        qm = qmm.open_qm(config)\n",
    "        job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "        res = job.result_handles\n",
    "        #better_sleep(total_measurement_time)\n",
    "        better_sleep(60)#0.45*3600)\n",
    "        res = job.result_handles\n",
    "        ###########################################################\n",
    "        # timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "        data = np.array([item[0] for item in res.clicks.fetch_all()[10:]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "        #data=np.transpose(data,axes = [0,2,3,1])\n",
    "        print(data.shape)\n",
    "        sigmoid_length = 5\n",
    "        x = N_ROcycle_array\n",
    "        print(x)\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "        ############################### extract populations #################################\n",
    "        pops = []\n",
    "        deltas = []\n",
    "        for i in range(len(N_ROcycle_array)):\n",
    "            data_nro = data[:,i]\n",
    "            p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans)\n",
    "            pops.append([p0,p1,p2,p3])\n",
    "            deltas.append([delta_x,delta_y])\n",
    "            \n",
    "        pops = np.array(pops)\n",
    "        ############################### Plot raw histograms ##############################\n",
    "        \n",
    "        for k in range(len(N_ROcycle_array)):\n",
    "            plt.figure()\n",
    "            for j in range(4):\n",
    "                plt.subplot(2,2,j+1)\n",
    "                for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "                plt.xlabel(\"SMPD counts\")\n",
    "                plt.ylabel(\"counts\")\n",
    "                plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "            plt.tight_layout()\n",
    "            save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "        \n",
    "        ############################### Plot clouds ###############################\n",
    "        rows = int(np.floor(len(x)/5.01)+1)\n",
    "        if len(x)<5:columns = (len(x))//rows\n",
    "        else: columns = 5\n",
    "        fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3,3+rows*2), tight_layout=True)\n",
    "        axs = np.array([axs]).flatten()\n",
    "        fig.suptitle(\"K-means\")  \n",
    "        \n",
    "        probs = []\n",
    "        for i, delta in enumerate(deltas):\n",
    "            prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "            probs.append(prob)\n",
    "        probs = np.array(probs)\n",
    "        \n",
    "        save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "        \n",
    "        ##############################################################  \n",
    "        fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "        ax = ax.flatten()\n",
    "        # fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "        if len(N_ROcycle_array)>1:\n",
    "            for i in range(4): \n",
    "                for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "                ax[i].set_ylabel('Probability')\n",
    "                ax[i].set_xlabel('Number of readout pulses')\n",
    "                ax[i].legend()\n",
    "                ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "                ax[i].set_ylim([0,1])\n",
    "                ax[i].grid(True)\n",
    "        else:\n",
    "            for i in range(4):\n",
    "                ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "                bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "                ax[i].set_ylim([0,1])\n",
    "                ax[i].bar_label(bar)\n",
    "                ax[i].set_ylabel(\"$P_N$\")\n",
    "        \n",
    "        save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "        \"\"\"\n",
    "        plt.figure(figsize=(6,5))\n",
    "        plot_2d_sweep(\n",
    "            prep,\n",
    "            x=labels,\n",
    "            y=labels,\n",
    "            xlabel = \"Prepared state\",\n",
    "            ylabel = \"Measured state\",\n",
    "            clabel = \"Probability\",\n",
    "            vmin = 0,\n",
    "            vmax = 1,\n",
    "            cmap = \"Blues\"\n",
    "        )\"\"\"\n",
    "        \n",
    "        plt.figure()\n",
    "        plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "        plt.xlabel(\"Readout iteration\")\n",
    "        plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "        \n",
    "        filename_kmean = directory+filename+'kmean.model'\n",
    "        pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "        \n",
    "        plt.show()\n",
    "        ############################### Save ###############################\n",
    "        \n",
    "        fullpath=directory+filename+'.hdf5'\n",
    "        datasets= {\n",
    "                    'click_array': data,\n",
    "                    'x': x,\n",
    "                    #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                    #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                    #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                    #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                    #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                    #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                    #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                    #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                    'readout_freqs': readout_freqs,\n",
    "                    'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "                    #'raman_detuning': raman_detuning,\n",
    "                    'gauss_duration': gauss_duration_prep,\n",
    "                    'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                    't_wait_prep': t_wait_prep,\n",
    "                    'N_ROcycle_prep': N_ROcycle_prep\n",
    "                    }\n",
    "        \n",
    "        save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c061315-e746-4e06-b54d-88f61c88ae13",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# qmm = QuantumMachinesManager()\n",
    "# qm = qmm.open_qm(config)\n",
    "# job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "# res = job.result_handles\n",
    "# #better_sleep(total_measurement_time)\n",
    "# better_sleep(0*3600)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "# timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[10:]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)#, kmeans = kmeans)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure()\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "    probs.append(prob)\n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(N_ROcycle_array)>1:\n",
    "    for i in range(4): \n",
    "        for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "        ax[i].set_ylabel('Probability')\n",
    "        ax[i].set_xlabel('Number of readout pulses')\n",
    "        ax[i].legend()\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].grid(True)\n",
    "else:\n",
    "    for i in range(4):\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].bar_label(bar)\n",
    "        ax[i].set_ylabel(\"$P_N$\")\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\"\"\"\n",
    "plt.figure(figsize=(6,5))\n",
    "plot_2d_sweep(\n",
    "    prep,\n",
    "    x=labels,\n",
    "    y=labels,\n",
    "    xlabel = \"Prepared state\",\n",
    "    ylabel = \"Measured state\",\n",
    "    clabel = \"Probability\",\n",
    "    vmin = 0,\n",
    "    vmax = 1,\n",
    "    cmap = \"Blues\"\n",
    ")\"\"\"\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "filename_kmean = directory+filename+'kmean.model'\n",
    "pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4861cb80-592e-4647-b795-cdac90f533b9",
   "metadata": {},
   "source": [
    "#### vs number of pumping steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ad8a27f-bbc0-4704-9f5f-df60ce7b1109",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_steps = sinhspace_asymm(0,300,13,nonlinearity=0)\n",
    "chirped_pump_steps = [int(x) for x in chirped_pump_steps]\n",
    "\n",
    "print(chirped_pump_amplitude,chirped_pump_duration)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_red_prep_vs_pumping_steps_chirped'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    pumping_steps = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "\n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(pumping_steps, chirped_pump_steps):\n",
    "            \n",
    "            ################# Scramble the initial state #################\n",
    "            # align()\n",
    "\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "\n",
    "            ############## Pump into red ##########################\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps =pumping_steps, enable_fsv_trigger=True)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ##############  Track ###########################\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,state_list=[0,0,0,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(chirped_pump_steps)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de5f79dd-d4d1-469b-a203-54ae72f8196c",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "x = chirped_pump_steps\n",
    "plt.plot(x, data.mean(0)[:,-1])\n",
    "plt.grid()\n",
    "plt.ylabel(r\"${|\\downarrow\\downarrow\\rangle}$ Mean counts\")\n",
    "plt.xlabel(\"Number of preparation pulses\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ad1fec-1c2b-4fcf-8b7d-dd45b1225dda",
   "metadata": {},
   "source": [
    "#### vs pumping steps AFTER tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f12f3d7-3ffd-4dbc-8e8b-69dc9ab4975a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_steps = sinhspace_asymm(0,80,5,nonlinearity=0)\n",
    "chirped_pump_steps = [int(x) for x in chirped_pump_steps]\n",
    "\n",
    "print(chirped_pump_amplitude,chirped_pump_duration)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_red_prep_vs_pumping_steps_chirped'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    pumping_steps = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "\n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(pumping_steps, chirped_pump_steps):\n",
    "            \n",
    "            ################# Scramble the initial state #################\n",
    "            # align()\n",
    "\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "\n",
    "            ############## Pump into red ##########################\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_chirped, enable_fsv_trigger=True)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ##############  Track ###########################\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            ## Pump again after tracking\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pumping_steps, enable_fsv_trigger=True)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,state_list=[0,0,1,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(chirped_pump_steps)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8cf9923-ee36-446d-acff-4da7988aabd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "\n",
    "###########################################################\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "x = chirped_pump_steps\n",
    "\n",
    "print(data.shape)\n",
    "print(x)\n",
    "\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(x)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "\n",
    "probs = np.array(pops)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5), tight_layout=True)\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(x)>1:\n",
    "    # for j in range(len(readout_freqs)): ax.semilogy(x, 1-probs[:,j], \"o-\", label = labels[j])\n",
    "    # ax.set_ylim([1e-2,1])\n",
    "    for j in range(len(readout_freqs)): ax.plot(x, probs[:,j], \"o-\", label = labels[j])\n",
    "    ax.set_ylim([0.9,1])\n",
    "    ax.set_ylabel('1-Probability')\n",
    "    ax.set_xlabel('Number of readout pulses')\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    \n",
    "    ax.grid(True)\n",
    "else:\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    bar = ax[i].bar(labels,probs[:,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,:].flatten().round(3))\n",
    "    ax.set_ylim([0,1])\n",
    "    ax.bar_label(bar)\n",
    "    ax.set_ylabel(\"$P_N$\")\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(x)):\n",
    "    #print(f\"Number of pump steps {x[k]}\")\n",
    "    plt.figure(figsize=(5,5))\n",
    "    for i  in range(4): plt.hist(data[:,k,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "    plt.xlabel(\"SMPD counts\")\n",
    "    plt.ylabel(\"counts\")\n",
    "    plt.title(\"Preparing \"+labels[-1]+f\" {x[k]} pulses\", color=colors[-1])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "    \n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a0c704-3dcf-443e-a6c5-fb9edc56be29",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "better_sleep(3*36)\n",
    "\n",
    "###########################################################\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "x = chirped_pump_steps\n",
    "\n",
    "print(data.shape)\n",
    "print(x)\n",
    "\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(x)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "\n",
    "probs = np.array(pops)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5), tight_layout=True)\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(x)>1:\n",
    "    for j in range(len(readout_freqs)): ax.semilogy(x, 1-probs[:,j], \"o-\", label = labels[j])\n",
    "    ax.set_ylabel('1-Probability')\n",
    "    ax.set_xlabel('Number of readout pulses')\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    ax.set_ylim([1e-2,1])\n",
    "    ax.grid(True)\n",
    "else:\n",
    "    ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "    bar = ax[i].bar(labels,probs[:,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,:].flatten().round(3))\n",
    "    ax.set_ylim([0,1])\n",
    "    ax.bar_label(bar)\n",
    "    ax.set_ylabel(\"$P_N$\")\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(x)):\n",
    "    #print(f\"Number of pump steps {x[k]}\")\n",
    "    plt.figure(figsize=(5,5))\n",
    "    for i  in range(4): plt.hist(data[:,k,i], alpha = 0.5,bins = 40, range = (0,200))\n",
    "    plt.xlabel(\"SMPD counts\")\n",
    "    plt.ylabel(\"counts\")\n",
    "    plt.title(\"Preparing \"+labels[-1]+f\" {x[k]} pulses\", color=colors[-1])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "    \n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0bea47-b40b-45a0-9bfc-ef4c5e5e4cf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "x = chirped_pump_steps\n",
    "\n",
    "full_probs = []\n",
    "N = 100\n",
    "for i in range(N):\n",
    "    PrintStatic(f'bootstrapping {i+1} out of {N}')\n",
    "    mask = np.random.randint(0,data.shape[0],data.shape[0])\n",
    "\n",
    "    pops = []\n",
    "    for i in range(len(x)):\n",
    "        data_nro = data[mask,i]\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "        pops.append([p0,p1,p2,p3])\n",
    "\n",
    "    full_probs.append(pops)\n",
    "full_probs = np.array(full_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb3e2fdb-3fed-4d34-9c54-f7b5e25f716c",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = full_probs.mean(0)\n",
    "dprobs = full_probs.std(0)\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.gca()\n",
    "for j in range(len(readout_freqs)): ax.errorbar(x, 1-probs[:,j], yerr=dprobs[:,j],  label = labels[j])\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('1-Probability')\n",
    "ax.set_xlabel('Number of readout pulses')\n",
    "ax.legend()\n",
    "ax.set_title(\"Preparing \"+labels[-1], color=colors[-1])\n",
    "ax.set_ylim([0.01,1])\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10911dd7-9358-46de-9b3d-30ae9a80b80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "# timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = np.array(wait_times_RO)*4\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(x)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True, accumulated=True)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(x)):\n",
    "    fig = plt.figure()\n",
    "    fig.suptitle(r'wait time=%d ms'%(x[k]*1e-6))\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 20, range = (20,170))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3,3+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    probs.append(kmeans_plot(delta, h=3, ax=axs[i], title=r'wait time=%d ms'%(x[i]*1e-6))) \n",
    "probs = np.array(probs)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'RO_clouds.pdf')\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(10,8), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "\n",
    "for i in range(4): \n",
    "    for j in range(len(readout_freqs)): ax[i].plot(x*1e-6, probs[:,i,j], \"o-\", label = labels[j])\n",
    "    ax[i].set_ylabel('Probability')\n",
    "    ax[i].set_xlabel('Wait time (ms)')\n",
    "    ax[i].legend()\n",
    "    ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "    ax[i].set_ylim([0,1])\n",
    "    ax[i].grid(True)\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'N_ROpulses_sweep.pdf')\n",
    "\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d65ba5b-6fac-444b-9087-0f2af5b92c3a",
   "metadata": {},
   "source": [
    "#### Pi/2 B hacked 4 testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e9ab3e-3c94-43fb-a2f6-d60ba2b11a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep) # Detuned Sideband frequency\n",
    "update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep)                             # Detuned Electron frequency\n",
    "\n",
    "with switch_(l):\n",
    "    with case_(0): # XX\n",
    "        # FSV trigger\n",
    "        play('ON',fsv_trigger)\n",
    "        # Pi/2 X A\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "        align()\n",
    "\n",
    "        wait(100)\n",
    "        align()\n",
    "\n",
    "        # Pi/2 X B\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0984a823-a51e-48bf-ad1d-e2d601c6d090",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "#centre_freq += delta_freq[-1]\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_4stateprep'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "##################### Raman pulse parameters #####################\n",
    "\n",
    "raman_detuning_a = -int(809.791e3/2-10e3)\n",
    "raman_detuning_b = -int(809.791e3/2+32.5e3)\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.777e3)\n",
    "nuclear_spin_freq_b  = int(810.466e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.70e6//4) # in ns \n",
    "raman_pi_duration_b = int(5.40e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.025\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.025\n",
    "detuned_sideband_amplitude_b = 0.05\n",
    "\n",
    "ramp_time       = int(0.4e6/4)\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle_array = np.linspace(250, 250, 1)\n",
    "N_ROcycle_array = [int(N) for N in N_ROcycle_array]\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    N_ROcycle_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    \n",
    "    preparation_flag = declare(int) # This flag will hold an integer whose prime decomposition gives us what preparations were carried out.\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep number of readout pulses\n",
    "\n",
    "        with for_each_(N_ROcycle_set, N_ROcycle_array):\n",
    "            \n",
    "            with for_(k, 2, k < 3, k + 0):\n",
    "                \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "                wait(int(10e6//4))\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "                \n",
    "                align()\n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(50e6/4))\n",
    "                align()\n",
    "                save(0, timing_stream)\n",
    "        \n",
    "                ################# Now prepare each of the 4 nuclear spin states in succession and read out #################\n",
    "                # align()\n",
    "                \n",
    "                # If k==0, we flip a and b\n",
    "                with if_(k==0):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                \n",
    "                # If k==1, we flip a\n",
    "                with if_(k==1):\n",
    "                    align()\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning_a, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "                \n",
    "                # If k==2, we flip b\n",
    "                with if_(k==2):\n",
    "                    \n",
    "                    update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep) # Detuned Sideband frequency\n",
    "                    update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep)                             # Detuned Electron frequency\n",
    "\n",
    "                    play('ON',fsv_trigger)\n",
    "                    wait(100)\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                    \n",
    "                    \n",
    "                    # align()\n",
    "                    # Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning_b, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "                    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_set, readout_freqs, delta_freq, enable_fsv_trigger = True)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(4).buffer(len(N_ROcycle_array)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39cbca72-a5f8-4142-a63f-1945db2d2e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "###########################################################\n",
    "print(\"Timing 0: %.3f s\"%(time.time()-start_time))\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()[0:]]) #data_lunch_dont_delete_me_or_overwrite_me_pls_jaime # \n",
    "#data=np.transpose(data,axes = [0,2,3,1])\n",
    "print(data.shape)\n",
    "sigmoid_length = 5\n",
    "x = N_ROcycle_array\n",
    "print(x)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "print(\"Timing 1: %.3f s\"%(time.time()-start_time))\n",
    "############################### extract populations #################################\n",
    "pops = []\n",
    "deltas = []\n",
    "for i in range(len(N_ROcycle_array)):\n",
    "    data_nro = data[:,i]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data_nro, frequency_domain = True,accumulated=True, kmeans = kmeans_standard)\n",
    "    pops.append([p0,p1,p2,p3])\n",
    "    deltas.append([delta_x,delta_y])\n",
    "    \n",
    "pops = np.array(pops)\n",
    "print(\"Timing 2: %.3f s\"%(time.time()-start_time))\n",
    "############################### Plot raw histograms ##############################\n",
    "\n",
    "for k in range(len(N_ROcycle_array)):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for j in range(4):\n",
    "        plt.subplot(2,2,j+1)\n",
    "        for i  in range(4): plt.hist(data[:,k,j,i], alpha = 0.5,bins = 60, range = (0,200))\n",
    "        plt.xlabel(\"SMPD counts\")\n",
    "        plt.ylabel(\"counts\")\n",
    "        plt.title(\"Preparing \"+labels[j], color=colors[j])\n",
    "    plt.tight_layout()\n",
    "    save_fig_manustyle(directory+filename+'_'+'raw_histograms_%i.pdf'%k)\n",
    "print(\"Timing 3: %.3f s\"%(time.time()-start_time))\n",
    "############################### Plot clouds ###############################\n",
    "rows = int(np.floor(len(x)/5.01)+1)\n",
    "if len(x)<5:columns = (len(x))//rows\n",
    "else: columns = 5\n",
    "fig, axs = plt.subplots(rows,columns,figsize = (2+columns*3.2,2.5+rows*2), tight_layout=True)\n",
    "axs = np.array([axs]).flatten()\n",
    "fig.suptitle(\"K-means\")  \n",
    "\n",
    "probs = []\n",
    "for i, delta in enumerate(deltas):\n",
    "    prob, kmeans = kmeans_plot(delta, h=3, ax=axs[i], title=r'$N_{RO}=$%d'%x[i])\n",
    "    probs.append(prob)\n",
    "probs = np.array(probs)\n",
    "\n",
    "\n",
    "########################## kmeans 4d #########################################\n",
    "probs_4d, kmeans_4d_model = kmeans_4d(data)\n",
    "\n",
    "##############################################################  \n",
    "fig,ax=plt.subplots(2,2,figsize=(8,7), tight_layout=True)\n",
    "ax = ax.flatten()\n",
    "# fig.suptitle(\"K-means Readout vs N_RO\")  \n",
    "if len(N_ROcycle_array)>1:\n",
    "    for i in range(4): \n",
    "        for j in range(len(readout_freqs)): ax[i].plot(x, probs[:,i,j], \"o-\", label = labels[j])\n",
    "        ax[i].set_ylabel('Probability')\n",
    "        ax[i].set_xlabel('Number of readout pulses')\n",
    "        ax[i].legend()\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].grid(True)\n",
    "else:\n",
    "    for i in range(4):\n",
    "        ax[i].set_title(\"Preparing \"+labels[i], color=colors[i])\n",
    "        bar = ax[i].bar(labels,probs[:,i,:].flatten().round(3), color = colors, alpha = 0.7, label = probs[:,i,:].flatten().round(3))\n",
    "        \n",
    "        bar = ax[i].bar(labels,probs_4d[:,0,i].flatten().round(3), color=None, edgecolor = 'k', alpha = 0.2, linestyle='--')\n",
    "        ax[i].set_ylim([0,1])\n",
    "        ax[i].bar_label(bar)\n",
    "        ax[i].set_ylabel(\"$P_N$\")\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "delta_freq = 1e-3*res.delta_freq.fetch_all()['value']\n",
    "plt.plot(delta_freq)\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Tracked frequency (kHz)\")\n",
    "print(\"Timing 6: %.3f s\"%(time.time()-start_time))\n",
    "filename_kmean = directory+filename+'kmean.model'\n",
    "pickle.dump(kmeans, open(filename_kmean, 'wb'))\n",
    "print(\"Timing 7: %.3f s\"%(time.time()-start_time))\n",
    "plt.show()\n",
    "############################### Save ###############################\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "            #'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            #'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            #'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            #'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            #'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            #'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            #'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            #'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse_prep,\n",
    "            #'raman_detuning': raman_detuning,\n",
    "            'gauss_duration': gauss_duration_prep,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle_prep': N_ROcycle_prep\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "print(\"Timing 8: %.3f s\"%(time.time()-start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02367006-d1f9-4131-9814-a8aa7efd21ab",
   "metadata": {},
   "source": [
    "## Ramsey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "374f10e6-6c93-477b-bde8-87aae2f36124",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "shift_ac =  89.5e-9\n",
    "\n",
    "t0 = 100//4\n",
    "tf = 40_000_100//4 \n",
    "dt = 2_000_000//4\n",
    "ramsey_times = np.arange(t0, tf + dt//2, dt)*4 \n",
    "ramsey_detuning=0.025e-6 + 0*shift_ac#GHz 0.2e-4\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_a_prep, detuned_electron_amplitude_a_prep,\n",
    "                                       detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            wait(100)\n",
    "            update_frequency(spin_sticky_element, freq_electron + raman_detuning_prep + nuclear_spin_freq_a_prep+int(137), keep_phase=True)  # Detuned Sideband frequency\n",
    "            wait(100)\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_element, duration = 100) \n",
    "            # play(spin_sticky_pulse*amp(-0.001), spin_sticky_element, duration = 100) \n",
    "            wait(100,spin_sticky_element)\n",
    "            align()\n",
    "            wait(t,spin_sticky_element)\n",
    "            wait(100,spin_sticky_element)\n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_element)\n",
    "            wait(100,spin_sticky_element)\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_element, duration = 100)\n",
    "            # play(spin_sticky_pulse*amp(-0.001), spin_sticky_element, duration = 100)\n",
    "            wait(100,spin_sticky_element)\n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset(nuclear_spin_freq_a_prep, freq_electron, raman_detuning_a_prep, detuned_electron_amplitude_a_prep, \n",
    "                                       detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep, keep_phase=True)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b18e03e-c7b4-4a83-918b-f6090ebf6dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-6*np.array(ramsey_times)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))*np.exp(-t/T)\n",
    "\n",
    "############################### subplot 0 ###############################\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey time (ms)')\n",
    "ax[0].legend(loc = \"lower right\")\n",
    "\n",
    "############################### subplot 1 ###############################    \n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [0.25*(ramsey_detuning-shift_ac)*1e6, 1e3, 0.5,1,i/2*np.pi]\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Ramsey time (ms)')\n",
    "ax[1].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = \"Artificial detuning: %.1f Hz\\nFitted oscillation rate: %.1f Hz\\nEstimated stark shift: %.1f Hz\"%(ramsey_detuning*1e9,abs(est[0]*1e3),abs(est[0]*1e3)-ramsey_detuning*1e9)\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "############################### subplot 2 ###############################\n",
    "\n",
    "ax[2].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'timing': timing,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9251131d-187d-4f9f-ade7-a14e164b6f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "element1 = spin_sticky_extra2_element\n",
    "element2 = spin_sticky4_element\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "shift_ac =  89.5e-9\n",
    "\n",
    "t0 = 100//4\n",
    "tf = 1_000_100//4 \n",
    "dt =  50_000//4\n",
    "ramsey_times = np.arange(t0, tf + dt//2, dt)*4 \n",
    "ramsey_detuning=1e-6 + 0*shift_ac#GHz 0.2e-4\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            reset_frame(element1)\n",
    "            reset_frame(element2)\n",
    "            align()\n",
    "            Raman_pulse_cos_no_phase_reset(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_b_prep, detuned_electron_amplitude_b_prep,\n",
    "                                           detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = element1, element2 = element2)\n",
    "            wait(100)\n",
    "            update_frequency(element1, freq_electron + raman_detuning_b_prep + nuclear_spin_freq_b_prep+int(343), keep_phase=True)  # Detuned Sideband frequency\n",
    "            wait(100)\n",
    "            play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "            wait(100,element1)\n",
    "            align()\n",
    "            wait(t,element1)\n",
    "            wait(100,element1)\n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), element1)\n",
    "            wait(100,element1)\n",
    "            play(spin_sticky_pulse*amp(0.0), element1, duration = 100)\n",
    "\n",
    "            wait(100,element1)\n",
    "            align()\n",
    "            Raman_pulse_cos_no_phase_reset(nuclear_spin_freq_b_prep, freq_electron, raman_detuning_b_prep, detuned_electron_amplitude_b_prep, \n",
    "                                           detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, keep_phase=True, element1 = element1, element2 = element2)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96fd8b6-d2cd-40b3-a2de-a2800397b250",
   "metadata": {},
   "outputs": [],
   "source": [
    "1e-3*(nuclear_spin_freq_b_prep+int(343))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd6ce244-f5b0-469b-839c-f5d0ad9043b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "1e-3*(nuclear_spin_freq_a_prep+int(137))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "112afd6d-a90b-4614-8c24-85a8f3c70420",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-6*np.array(ramsey_times)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))*np.exp(-t/T)\n",
    "\n",
    "############################### subplot 0 ###############################\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey time (ms)')\n",
    "ax[0].legend(loc = \"lower right\")\n",
    "\n",
    "############################### subplot 1 ###############################    \n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1*(ramsey_detuning-shift_ac)*1e6, 1e3, 0.15,1,i/2*np.pi]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Ramsey time (ms)')\n",
    "ax[1].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = \"Artificial detuning: %.1f Hz\\nFitted oscillation rate: %.1f Hz\\nEstimated stark shift: %.1f Hz\"%(ramsey_detuning*1e9,est[0]*1e3,est[0]*1e3-ramsey_detuning*1e9)\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "############################### subplot 2 ###############################\n",
    "\n",
    "ax[2].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'timing': timing,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13161992-a2b0-4cc9-ada5-c667df1f411c",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Using Pauli function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bab695b-0bf5-408f-bc20-fe63ca7a7784",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e23d5f-9b0e-4cda-bfe1-3096bab74336",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "t0 = 100//4\n",
    "tf = 10_000_100//4 \n",
    "dt = 500_000//4\n",
    "ramsey_times = np.arange(t0, tf + dt//2, dt)*4 \n",
    "ramsey_detuning=0.1e-6\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            # update_frequency(spin_sticky_element, freq_electron + raman_detuning_prep + nuclear_spin_freq_a_prep, keep_phase=False)  # Detuned Sideband frequency\n",
    "\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            Pauli2('aY90',0.239)\n",
    "\n",
    "            align()\n",
    "            wait(t,spin_sticky_element)\n",
    "            align()\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_element, duration = 100) \n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_extra_element, duration = 100) \n",
    "            wait(100,spin_sticky_element)\n",
    "            wait(100,spin_sticky_extra_element)\n",
    "            \n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_element)\n",
    "            \n",
    "            wait(100,spin_sticky_element)\n",
    "            wait(100,spin_sticky_extra_element)\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_element, duration = 100) \n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_extra_element, duration = 100) \n",
    "\n",
    "            align()\n",
    "            \n",
    "            Pauli2('aX90',0.239)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26e3afd7-718b-4d4b-86d3-b35d9c9eddcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*300)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-6*np.array(ramsey_times)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))*np.exp(-t/T)\n",
    "\n",
    "############################### subplot 0 ###############################\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey time (ms)')\n",
    "ax[0].legend(loc = \"lower right\")\n",
    "\n",
    "############################### subplot 1 ###############################    \n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1*(ramsey_detuning)*1e6, 1e3, 0.15,1,i/2*np.pi]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Ramsey time (ms)')\n",
    "ax[1].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = \"Artificial detuning: %.1f Hz\\nFitted oscillation rate: %.1f Hz\\nEstimated stark shift: %.1f Hz\"%(ramsey_detuning*1e9,est[0]*1e3,est[0]*1e3-ramsey_detuning*1e9)\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "############################### subplot 2 ###############################\n",
    "\n",
    "ax[2].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'timing': timing,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35cb3dc3-b23d-4757-a0d7-b0059ffa4519",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-18T11:03:55.380487Z",
     "iopub.status.busy": "2024-03-18T11:03:55.380487Z",
     "iopub.status.idle": "2024-03-18T11:03:55.565049Z",
     "shell.execute_reply": "2024-03-18T11:03:55.565049Z",
     "shell.execute_reply.started": "2024-03-18T11:03:55.380487Z"
    }
   },
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2bfd9f1-b23d-4756-9aaf-00b19acec9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "t0 = 100//4\n",
    "tf = 20_000_100//4 \n",
    "dt = 1000_000//4\n",
    "ramsey_times = np.arange(t0, tf + dt//2, dt)*4 \n",
    "ramsey_detuning=0.1e-6\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_(t, t0, t < tf + dt/2, t + dt):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            # update_frequency(spin_sticky_element, freq_electron + raman_detuning_prep + nuclear_spin_freq_a_prep, keep_phase=False)  # Detuned Sideband frequency\n",
    "\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            Pauli2('bX90',-0.431)\n",
    "\n",
    "            align()\n",
    "            wait(t,spin_sticky_extra2_element)\n",
    "            align()\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_extra2_element, duration = 100) \n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky4_element, duration = 100) \n",
    "            wait(100,spin_sticky_extra2_element)\n",
    "            wait(100,spin_sticky4_element)\n",
    "            \n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_extra2_element)\n",
    "            \n",
    "            wait(100,spin_sticky_extra2_element)\n",
    "            wait(100,spin_sticky4_element)\n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky_extra2_element, duration = 100) \n",
    "            play(spin_sticky_pulse*amp(0.0), spin_sticky4_element, duration = 100) \n",
    "\n",
    "            align()\n",
    "            \n",
    "            Pauli2('bX90',-0.431)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "789b93fd-c3eb-4d8d-8e44-0b25f5b79342",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*300)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,12))\n",
    "sigmoid_length = 5\n",
    "x = 1e-6*np.array(ramsey_times)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))*np.exp(-t/T)\n",
    "\n",
    "############################### subplot 0 ###############################\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey time (ms)')\n",
    "ax[0].legend(loc = \"lower right\")\n",
    "\n",
    "############################### subplot 1 ###############################    \n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1*(ramsey_detuning)*1e6, 1e3, 0.15,1,i/2*np.pi]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Ramsey time (ms)')\n",
    "ax[1].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = \"Artificial detuning: %.1f Hz\\nFitted oscillation rate: %.1f Hz\\nEstimated stark shift: %.1f Hz\"%(ramsey_detuning*1e9,est[0]*1e3,est[0]*1e3-ramsey_detuning*1e9)\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "############################### subplot 2 ###############################\n",
    "\n",
    "ax[2].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'timing': timing,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e0084d-60c4-4de0-a0a8-2e530c73e043",
   "metadata": {},
   "source": [
    "## Ramsey + loop wait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e03479-6d7c-45bf-86f0-4d1b1e03ce48",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[3]\n",
    "# readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_detuning = 1400e3\n",
    "nuclear_spin_freq_a  = int(808.844e3)\n",
    "nuclear_spin_freq_b = int(810.717e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.38e6//4) # in ns \n",
    "raman_pi_duration_b = int(19.25e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.1636\n",
    "detuned_electron_amplitude_b = 0.2\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.4\n",
    "detuned_sideband_amplitude_b = 0.8\n",
    "\n",
    "ramp_time       = int(0.6e6/4)\n",
    "\n",
    "raman_pi_half_duration_a = int(4.23e6//4) # in ns\n",
    "###################### ramsey params  #######################\n",
    "shift_ac =  0# 0.1427e-6\n",
    "\n",
    "ramsey_times = 1+np.linspace(0,20,21)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ramsey_detuning= 2e-9 + shift_ac # GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_ramsey'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(t, ramsey_times):\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            click_acc = nuclear_spin_RO(\n",
    "                    prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse_prep, gauss_duration_prep, \n",
    "                    readout_freqs[-1]+delta_freq, waiting_time_spin_prep, N_readout_prep, waiting_time_SMPD_prep, waiting_after_spinreadout_prep\n",
    "            )\n",
    "\n",
    "            raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold_prep, N_ROcycle_prep, readout_freqs[-1]+delta_freq, freq_sideband_prep+delta_freq+centre_freq*1e3)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            ################# Now play the Raman sideband sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning + nuclear_spin_freq_a )  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning)                         # Detuned Electron frequency\n",
    "            \n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_half_duration_a, ramp_time)\n",
    "            \n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "                \n",
    "                \n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_half_duration_a, ramp_time)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "ramsey_times = np.array(ramsey_times)\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e8e0c70-6ca9-4e41-98c2-f674271cd769",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(5,15))\n",
    "sigmoid_length = 5\n",
    "x = np.array(ramsey_times)\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "ramsey_freq = 1e-3*fft_x[np.argmax(fft_y)]\n",
    "\n",
    "print(ramsey_freq)\n",
    "############################### Fit #################################\n",
    "\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [ramsey_freq, 1e3, 0.5,1,0*np.pi]\n",
    "\n",
    "print(guess)\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Ramsey time (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "print(guess)\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min([np.min(np.concatenate(data)),np.min(data_prep)]),np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[2].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "print(guess)\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], \"o\",label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "print(guess)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Ramsey time (ms)')\n",
    "ax[1].legend()\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p3)\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\", alpha = 0.2)\n",
    "ax[1].plot(fine,data_fit)\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - (ramsey_detuning-shift_ac)*1e6):.4f} kHz \\n T$_2^*$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except Exception as e:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker:\", e)\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "            'raman_pulse_durations': raman_pulse_durations,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle,\n",
    "            'shift_ac': shift_ac\n",
    "            }\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb2eceec-7edc-4313-ba8f-2efa884e8564",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = 'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_ramsey\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04e972ff-ec9a-46a8-88f3-80d92b69ff33",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk = 12\n",
    "plot_guess = 0\n",
    "plt.figure(figsize=[5,3*data.shape[0]//chunk])\n",
    "Ramsey_freqs = []\n",
    "Ramsey_freq_errors = []\n",
    "T2stars = []\n",
    "T2star_errors = []\n",
    "\n",
    "for i in range(data.shape[0]//chunk):\n",
    "\n",
    "    y = (data[i*chunk:(i+1)*chunk,:,0]>85).mean(0)\n",
    "    plt.subplot(data.shape[0]//chunk,1,i+1)\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x,y)\n",
    "    T2stars.append(est[1])\n",
    "    Ramsey_freqs.append(est[0])\n",
    "    Ramsey_freq_errors.append(std[0])\n",
    "    T2star_errors.append(std[1])\n",
    "    \n",
    "    plt.plot(x, y,\"o\")\n",
    "    \n",
    "    if plot_guess: plt.plot(fine,ramsey_fit(fine, *guess))\n",
    "    plt.plot(fine,data_fit)\n",
    "    plt.ylabel(r\"$P_{~|\\downarrow\\downarrow\\rangle}$\")\n",
    "    plt.xlabel('Ramsey time (ms)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21fb1884-cd33-44d9-9553-48e7189c4db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.errorbar(np.linspace(0,len(Ramsey_freqs)-1,len(Ramsey_freqs)),np.multiply(1e3,Ramsey_freqs),yerr = np.multiply(1e3,Ramsey_freq_errors), fmt = \"o\", capsize = 10)\n",
    "plt.ylabel(\"Ramsey frequency (Hz)\")\n",
    "plt.xlabel(\"Chunk no.\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a642cc0-6f0d-4746-822d-0457a0973334",
   "metadata": {},
   "source": [
    "## Echo + loop wait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1373,
   "id": "f06b1f9a-853b-4b4e-89dd-867abce0278e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T09:38:44.670992Z",
     "iopub.status.busy": "2024-03-28T09:38:44.670992Z",
     "iopub.status.idle": "2024-03-28T09:38:48.752647Z",
     "shell.execute_reply": "2024-03-28T09:38:48.751642Z",
     "shell.execute_reply.started": "2024-03-28T09:38:44.670992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights loaded\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[3]\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "ramsey_times = 1+sinhspace_asymm(0,4000,21, nonlinearity = 3)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ramsey_detuning= 0*1e-9 # GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_echo'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(t, ramsey_times):\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ############## Pump into red ##########################\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            ################# Now play the Raman sideband sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning_b_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                \n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "                \n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "ramsey_times = np.array(ramsey_times)\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1491,
   "id": "263bf3bc-d267-417f-a703-84de7350ab4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T15:08:53.998146Z",
     "iopub.status.busy": "2024-03-28T15:08:53.998146Z",
     "iopub.status.idle": "2024-03-28T15:08:55.964191Z",
     "shell.execute_reply": "2024-03-28T15:08:55.963189Z",
     "shell.execute_reply.started": "2024-03-28T15:08:53.998146Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(184, 21, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\raman_echo\\\\20240328103845_raman_echo.hdf5'"
      ]
     },
     "execution_count": 1491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 1\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    \n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,T,a,b):\n",
    "    return a*np.exp(-t/T) + b\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "##############################################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        if i==3: guess = [4e3, 0.4, 0.5]\n",
    "        else: guess = [4e3, -0.5, 0.5]\n",
    "        ax[1].errorbar(x, pops[i], yerr=np.sqrt(pops[i]*(1-pops[i])/data.shape[0]), fmt='o', label = labels[i])\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "else:\n",
    "    guess = [0.5*ramsey_detuning*1e6, 5e3, -0.5,-1,0*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend(loc = \"upper right\")\n",
    "plt_label = f'T$_2$$_E$ = {est[0]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "#ax[1].set_ylim(0,1)\n",
    "\n",
    "##############################################################\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1696d7a5-12f9-4f83-b20b-7df434542ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*360)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "    \n",
    "def exp_decay(t,T,alpha,beta):\n",
    "    return alpha*np.exp(-t/T)+beta\n",
    "\n",
    "\n",
    "# if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "\n",
    "plt.errorbar(x, (data).mean(0)[:,-1] , (data).std(0)[:,-1]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\", color = colors[3])\n",
    "guess = [2e3,40,40]\n",
    "est,std,fine,data_fit = fit_function(guess, exp_decay, x, (data).mean(0)[:,-1])\n",
    "plt.plot(fine,data_fit, \"k--\",label = f\"T2 = {np.round(est[0]/1e3,3)} s\")\n",
    "plt.ylabel(\"Mean counts\")\n",
    "plt.xlabel(r'$2\\tau$ (ms)')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c700cb66-fa51-4f0d-8ea9-713359d84cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    \n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "##############################################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        guess = [0.5*ramsey_detuning*1e6, 2e3, 0.5*(-1)**i,(-1)**(i),0*np.pi]\n",
    "        ax[1].plot(x,pops[i], 'o', label = labels[i])\n",
    "        est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "else:\n",
    "    guess = [0.5*ramsey_detuning*1e6, 5e3, -0.5,-1,0*np.pi]\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend(loc = \"upper right\")\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_2$$_E$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "#ax[1].set_ylim(0,1)\n",
    "\n",
    "##############################################################\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd9d5ec7-581a-40df-ba97-a06237a32a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[3]\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "ramsey_times = 1+sinhspace_asymm(0,4000,21, nonlinearity = 3)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ramsey_detuning= 0*1e-9 # GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_echo'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = 1e9\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    t = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(t, ramsey_times):\n",
    "                \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ############## Pump into red ##########################\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            \n",
    "            ################# Now play the Raman sideband sequence #################\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning_b_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "                \n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "                \n",
    "            align()\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(n_step_duration).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "ramsey_times = np.array(ramsey_times)\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22d3b5fe-0f49-4ab8-9caa-bc03d2b03c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2echo_data_b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dab032d-39b8-4dff-92a7-8df0698d9ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29dc75df-e4c0-4789-8fe7-60e3fe4420cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# better_sleep(4*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(10,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    \n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "##############################################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        guess = [0.5*ramsey_detuning*1e6, 2e3, 0.5*(-1)**i,(-1)**(i),0*np.pi]\n",
    "        ax[1].plot(x,pops[i], 'o', label = labels[i])\n",
    "        # est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "        # ax[1].plot(fine,data_fit, color = colors[i])\n",
    "        if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess),\"--\", color = colors[i],alpha = 0.5)\n",
    "else:\n",
    "    # guess = [0.5*ramsey_detuning*1e6, 5e3, -0.5,-1,0*np.pi]\n",
    "    # est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend(loc = \"upper right\")\n",
    "plt_label = f'f = {est[0]:.5f}$\\pm${std[0]:.5f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_2$$_E$ = {est[1]:.0f}$\\pm${std[1]:.0f} ms'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "#ax[1].set_ylim(0,1)\n",
    "\n",
    "##############################################################\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Ramsey time (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'timing': timing,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018a1341-77af-4ecb-a769-fdc9ff410fae",
   "metadata": {},
   "source": [
    "## SEDOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc1fcdd-e255-405a-adf4-eb04a920b80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Raman_pulse_cos_no_phase_reset_no_frequpdate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faa5a9c2-c677-4845-b8a1-f206d28a08b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%%write_and_run temp.py\n",
    "\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "ramsey_times = 1+np.linspace(0,2000,41)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ramsey_detuning= 2*2*1e-9 # GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='SEDOR'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            ################# Now play the SEDOR sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            # # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            # # Long wait\n",
    "            # align()\n",
    "            # # interpulse delay between Ramsey pulses\n",
    "            wait(int(raman_pi_duration_b_prep+2*ramp_time_prep))\n",
    "            align()\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                 wait(int(1e6//4))\n",
    "            align()\n",
    "            \n",
    "            # Pi A\n",
    "            frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            frame_rotation_2pi(-0.25, spin_sticky_element)\n",
    "            align()\n",
    "            # wait(int(1e6//4))\n",
    "    \n",
    "            # Pi B\n",
    "            align()\n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep,\n",
    "                                                         detuned_sideband_amplitude_b_prep,\n",
    "                                                         raman_pi_duration_b_prep,\n",
    "                                                         ramp_time_prep,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "            \n",
    "            # Long wait\n",
    "            align()\n",
    "            # interpulse delay between Ramsey pulses\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "                \n",
    "            align()\n",
    "            \n",
    "            # Pi/2 A\n",
    "            \n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            \n",
    "            wait(int(1e6//4),spin_element)\n",
    "            align()\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8859627-ddec-40fa-815d-86b184dac5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(6*3600)\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = 2*np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p1\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "guess = [0.5*ramsey_detuning*1e6, 3e3, 0.5, 1, 0.5*np.pi]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], 'o-', label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "ax[1].plot(fine,data_fit)\n",
    "plot_guess = 0\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$2\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Ramsey time (kHz)\")\n",
    "plt.show()\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a212eda-4411-4ce9-8f0a-8324864ed895",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## DFS - Decoherence Free Subspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "097ab99a-6cf7-481b-8ba5-04c91916b233",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "ramsey_times = 1+np.linspace(0,1000,21)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ramsey_detuning= 265*1e-9 # GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='DFS'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            ################# Now play the SEDOR sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            # CNOT B\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                     detuned_sideband_amplitude_cnot,\n",
    "                                                     raman_pi_duration_cnot,\n",
    "                                                     ramp_time_cnot,\n",
    "                                                     element1 = spin_sticky_extra2_element,\n",
    "                                                     element2 = spin_sticky4_element)\n",
    "            align()\n",
    "            \n",
    "            # Pi A\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "\n",
    "            align()\n",
    "            \n",
    "            # Pi A\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            # CNOT B\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                     detuned_sideband_amplitude_cnot,\n",
    "                                                     raman_pi_duration_cnot,\n",
    "                                                     ramp_time_cnot,\n",
    "                                                     element1 = spin_sticky_extra2_element,\n",
    "                                                     element2 = spin_sticky4_element)            # # Long wait\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20b418f5-a3b8-43f4-ad79-05e0b9b47b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=p1\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], 'o-', label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "guess = [0.02, 3e3, 0.2, 1.7, 0*np.pi]\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(est[0] - ramsey_detuning*0.5e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(0,1)\n",
    "\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Ramsey time (kHz)\")\n",
    "plt.show()\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba78c5cb-e859-4784-9d70-5099152f0cfd",
   "metadata": {},
   "source": [
    "### DFS (pi/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77195961-8369-4ca3-8f79-960b2e7fb397",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "\n",
    "t0 = 100//4\n",
    "tf = 100_100//4 \n",
    "n_step_duration = 2\n",
    "\n",
    "ramsey_times = np.linspace(t0, tf, n_step_duration) \n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "\n",
    "ac_shift = +0.200e3*1e-9\n",
    "ramsey_detuning= 0.05e3*1e-9 + ac_shift# GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='DFS'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #phase_stream = declare_stream()\n",
    "    #phase = declare(fixed)\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                         detuned_sideband_amplitude_cnot,\n",
    "                                                         raman_pi_duration_cnot,\n",
    "                                                         ramp_time_cnot,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "            align()\n",
    "             \n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "            \n",
    "\n",
    "            align()\n",
    "            wait(t)\n",
    "            align()\n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_element)\n",
    "            #assign(phase, Cast.mul_fixed_by_int(ramsey_detuning*4, t))\n",
    "            #save(phase, phase_stream)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 A\n",
    "            #Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            #Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "410adfec-3b0a-4962-8884-467d9053e2bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = np.array(ramsey_times)*4e-6\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=pops[2]-pops[1]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "h2 = ax[2].hist(np.concatenate(data)  , bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(4): ax[1].plot(x,pops[i], 'o-', label = labels[i])\n",
    "    p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "    #ax[1].plot(x,p_data, 'o-', color='g')\n",
    "    #ax[1].plot(np.linspace(x[0],x[-1],100),0.5*np.cos(np.linspace(x[0],x[-1],100)*ramsey_detuning*2*np.pi*1e6), 'o-', color='gray', label = 'expected oscillation', alpha=0.2)\n",
    "    #ax[1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    \n",
    "guess = [rabi_freq*0.5e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - (ramsey_detuning-ac_shift)*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Ramsey time (kHz)\")\n",
    "plt.show()\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862f0654-c064-49ea-a03e-c5b2cdaef30a",
   "metadata": {},
   "source": [
    "### DFS (pi/2 & ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c16d0bc-218f-4941-8b3b-96a28fce6b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "# t0 = 1\n",
    "tf = 4000 #in ms\n",
    "ramsey_times = 1+np.linspace(0,tf,11)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "\n",
    "ac_shift = +(0.200e3-1.4)*1e-9\n",
    "ramsey_detuning= 0.5*1e-9 + ac_shift# GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='DFS'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #phase_stream = declare_stream()\n",
    "    #phase = declare(fixed)\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                         detuned_sideband_amplitude_cnot,\n",
    "                                                         raman_pi_duration_cnot,\n",
    "                                                         ramp_time_cnot,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "            align()\n",
    "             \n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "            \n",
    "\n",
    "            align()\n",
    "            #wait(t)\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "            align()\n",
    "            #align()\n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_element)\n",
    "            #assign(phase, Cast.mul_fixed_by_int(ramsey_detuning*4, t))\n",
    "            #save(phase, phase_stream)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        phase_stream.buffer(len(ramsey_times)).save('phase')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe598aa9-afb1-4b1a-8996-c0e4f12e4935",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=pops[2]-pops[1]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "for i in range(4):\n",
    "    h2 = ax[2].hist(np.concatenate(data[:,:,i])  , bins=bins   , label = labels[i], alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    #for i in range(4): ax[1].plot(x,pops[i], 'o-', label = labels[i])\n",
    "    #ax[1].plot(x,p_data, 'o-', color='g')\n",
    "    #ax[1].plot(np.linspace(x[0],x[-1],100),0.5*np.cos(np.linspace(x[0],x[-1],100)*ramsey_detuning*2*np.pi*1e6), 'o-', color='gray', label = 'expected oscillation', alpha=0.2)\n",
    "    ax[1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    \n",
    "guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - (ramsey_detuning-ac_shift)*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Ramsey time (kHz)\")\n",
    "plt.show()\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d18c46f-986d-48b8-9aca-6e713e64ae52",
   "metadata": {},
   "source": [
    "### DFS with pi pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59153d6c-9aa1-459b-bdd8-8c8b879a8f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "# t0 = 1\n",
    "tf = 1000 #in ms\n",
    "ramsey_times = 1+np.linspace(0,tf,11)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "\n",
    "ac_shift = +(-14)*1e-9\n",
    "ramsey_detuning= 2*1e-9 + ac_shift# GHz \n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='DFS'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #phase_stream = declare_stream()\n",
    "    #phase = declare(fixed)\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            \n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "            \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            # CNOT\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                         detuned_sideband_amplitude_cnot,\n",
    "                                                         raman_pi_duration_cnot,\n",
    "                                                         ramp_time_cnot,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "            align()\n",
    "            \n",
    "            # Pi A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "            \n",
    "\n",
    "            align()\n",
    "            #wait(t)\n",
    "            with for_(k, 0, k < t , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                frame_rotation_2pi(ramsey_detuning*1e6, spin_sticky_element)\n",
    "            align()\n",
    "            #align()\n",
    "            frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, t), spin_sticky_element)\n",
    "            #assign(phase, Cast.mul_fixed_by_int(ramsey_detuning*4, t))\n",
    "            #save(phase, phase_stream)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "            wait(100)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        phase_stream.buffer(len(ramsey_times)).save('phase')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a2fd8b2-3f3e-45bd-8ad1-d9318a15c731",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(4,1,figsize=(8,15))\n",
    "sigmoid_length = 5\n",
    "x = np.array(ramsey_times)\n",
    "############################### Fit #################################\n",
    "\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data=pops[2]-pops[1]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    p_data = (data[:,:,-1]>80)\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    \n",
    "def ramsey_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[0].legend()\n",
    "\n",
    "p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "##############################################################    \n",
    "\n",
    "bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "for i in range(4):\n",
    "    h2 = ax[2].hist(np.concatenate(data[:,:,i])  , bins=bins   , label = labels[i], alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    #for i in range(4): ax[1].plot(x,pops[i], 'o-', label = labels[i])\n",
    "    #ax[1].plot(x,p_data, 'o-', color='g')\n",
    "    #ax[1].plot(np.linspace(x[0],x[-1],100),0.5*np.cos(np.linspace(x[0],x[-1],100)*ramsey_detuning*2*np.pi*1e6), 'o-', color='gray', label = 'expected oscillation', alpha=0.2)\n",
    "    ax[1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "    \n",
    "guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "if plot_guess: ax[1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel(r'$\\tau$ (ms)')\n",
    "ax[1].legend()\n",
    "plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - (ramsey_detuning-ac_shift)*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "ax[1].set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax[3].plot(fft_x, fft_y)\n",
    "ax[3].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[3].set_xlabel(\"Frequency (Hz)\")\n",
    "ax[3].set_ylabel(\"FFT\")\n",
    "ax[3].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"Ramsey time (kHz)\")\n",
    "plt.show()\n",
    "\n",
    "#display.display(plt.gcf())\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            }\n",
    "\n",
    "plt.tight_layout()\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a831d8ea-e406-4d04-9245-58a85ef55001",
   "metadata": {},
   "source": [
    "### Interleaved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b202c670-70eb-4d25-9e2d-7f8644007c38",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "###################### ramsey params  #######################\n",
    "# t0 = 1\n",
    "tf = 4000 #in ms\n",
    "ramsey_times = 1+np.linspace(0,tf,41)\n",
    "ramsey_times = [int(ramsey_time) for ramsey_time in ramsey_times]\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "\n",
    "ac_shift_0 = +(0.200e3-1.4+0.1)*1e-9\n",
    "ac_shift_1 = +(-14+0.6)*1e-9\n",
    "ramsey_detuning= 1*1e-9\n",
    "ramsey_detuning_0 = ramsey_detuning + ac_shift_0\n",
    "ramsey_detuning_1 = ramsey_detuning + ac_shift_1\n",
    "\n",
    "n_step_duration=len(ramsey_times)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='DFS'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Readout parameters #######################\n",
    "\n",
    "N_ROcycle = int(250)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "#readout_freqs = [readout_freqs[0]]\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    #phase_stream = declare_stream()\n",
    "    #phase = declare(fixed)\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t, ramsey_times):\n",
    "            with for_(l, 0, l<2, l + 1):\n",
    "                ################# Raman experiment #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "\n",
    "\n",
    "                ################# Now play the Ramsey sequence #################\n",
    "                # Update pulses for spin b\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "\n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "\n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "\n",
    "\n",
    "                # Pi/2 A\n",
    "                Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                align()\n",
    "\n",
    "                # CNOT\n",
    "                Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                             detuned_sideband_amplitude_cnot,\n",
    "                                                             raman_pi_duration_cnot,\n",
    "                                                             ramp_time_cnot,\n",
    "                                                             element1 = spin_sticky_extra2_element,\n",
    "                                                             element2 = spin_sticky4_element)\n",
    "                align()\n",
    "\n",
    "                # Pi A interleaved\n",
    "                with switch_(l):\n",
    "                    with case_(0):\n",
    "                        wait(100)\n",
    "                    with case_(1):\n",
    "                        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "                    with default_():\n",
    "                        # Should never get here\n",
    "                        wait(100)\n",
    "\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "\n",
    "                align()\n",
    "                #wait(t)\n",
    "                with for_(k, 0, k < t , k + 1):\n",
    "                    wait(int(1e6//4))\n",
    "                    with switch_(l):\n",
    "                        with case_(0):\n",
    "                            frame_rotation_2pi(ramsey_detuning_0*1e6, spin_sticky_element)\n",
    "                        with case_(1):\n",
    "                            frame_rotation_2pi(ramsey_detuning_1*1e6, spin_sticky_element)\n",
    "                        with default_():\n",
    "                            # Should never get here\n",
    "                            wait(100)\n",
    "                align()\n",
    "                #align()\n",
    "\n",
    "                #save(phase, phase_stream)\n",
    "                align()\n",
    "\n",
    "                # Pi/2 A\n",
    "                Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "                align()\n",
    "                wait(100)\n",
    "                align()\n",
    "\n",
    "                # Pi/2 B\n",
    "                Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        phase_stream.buffer(len(ramsey_times)).save('phase')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(2).buffer(len(ramsey_times)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c2f34c-2edd-46c4-92e0-e7a9ecc9987a",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing = np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data_total = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "delta_f = res.delta_freq.fetch_all()['value']\n",
    "print('Data shape:', data_total.shape)\n",
    "differential_pops = []\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "\n",
    "for j in range(2):\n",
    "    data = data_total[:,:,j] \n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,2,figsize=(14,8))\n",
    "    fig.suptitle(plt_labels[j])\n",
    "    sigmoid_length = 5\n",
    "    x = np.array(ramsey_times)\n",
    "    ############################### Fit #################################\n",
    "\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data=pops[2]-pops[1]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        p_data = (data[:,:,-1]>80)\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    def ramsey_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    ##############################################################\n",
    "    for i in range(len(readout_freqs)): ax[0,0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0,0].set_ylabel(\"Mean counts\")\n",
    "    ax[0,0].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,0].legend()\n",
    "\n",
    "    p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "    differential_pops.append(p_data)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "    ##############################################################\n",
    "    if len(readout_freqs)==4:\n",
    "        ax[0,1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "    else:\n",
    "        ax[0,1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "    guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "    if plot_guess: ax[0,1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    try: est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    except: est=guess; std=guess;fine=x;data_fit=p_data\n",
    "    \n",
    "    plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - ramsey_detuning*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "    ax[0,1].plot(fine,data_fit)\n",
    "    ax[0,1].set_ylabel(\"P\")\n",
    "    ax[0,1].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,1].legend()\n",
    "    ax[0,1].set_title(plt_label, fontsize = \"small\")\n",
    "    ax[0,1].set_ylim(-1,1)\n",
    "    \n",
    "    ##############################################################    \n",
    "\n",
    "    bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "    for i in range(4):\n",
    "        h2 = ax[1,0].hist(np.concatenate(data[:,:,i])  , bins=bins   , label = labels[i], alpha=0.5)\n",
    "    ax[1,0].legend()\n",
    "    ax[1,0].set_ylabel(\"instances\")\n",
    "    ax[1,0].set_xlabel('counts')\n",
    "\n",
    "    ##############################################################\n",
    "    ax[1,1].plot(fft_x, fft_y)\n",
    "    ax[1,1].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[1,1].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[1,1].set_ylabel(\"FFT\")\n",
    "    ax[1,1].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    save_fig_manustyle(directory+filename+'_'+'nuclear_spin_%i.pdf'%j)\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "plt.plot(1e-3*delta_f)\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"$\\Delta_{f}$ (kHz)\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'frequency_drift.pdf')\n",
    "plt.show()    \n",
    "    \n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data_total,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'differential_pops': differential_pops,\n",
    "            'delta_f_Hz': delta_f,\n",
    "            'timing': timing,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "678a76da-3d73-492d-92dc-1c88bc9ed434",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "plot_guess = 0\n",
    "\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing = np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data_total = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "delta_f = res.delta_freq.fetch_all()['value']\n",
    "print('Data shape:', data_total.shape)\n",
    "differential_pops = []\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "\n",
    "for j in range(2):\n",
    "    data = data_total[:,:,j] \n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,2,figsize=(14,8))\n",
    "    fig.suptitle(plt_labels[j])\n",
    "    sigmoid_length = 5\n",
    "    x = np.array(ramsey_times)\n",
    "    ############################### Fit #################################\n",
    "\n",
    "    if len(readout_freqs)==4:\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        pops = [p0,p1,p2,p3]\n",
    "        p_data=pops[2]-pops[1]\n",
    "        labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    else:\n",
    "        pops = []\n",
    "        p_data = (data[:,:,-1]>80)\n",
    "        labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "\n",
    "    def ramsey_fit(t,f,T,a,b,phi):\n",
    "        return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "    ##############################################################\n",
    "    for i in range(len(readout_freqs)): ax[0,0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "    ax[0,0].set_ylabel(\"Mean counts\")\n",
    "    ax[0,0].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,0].legend()\n",
    "\n",
    "    p_data =(pops[2]+pops[1])-(pops[3]+pops[0])\n",
    "    differential_pops.append(p_data)\n",
    "    fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "    fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "    ##############################################################\n",
    "    if len(readout_freqs)==4:\n",
    "        ax[0,1].plot(x,p_data, 'o-', color='k', label = labels[2]+'+'+labels[1]+'-'+labels[3]+'+'+labels[0])\n",
    "    else:\n",
    "        ax[0,1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "    guess = [rabi_freq*1e-3, 3e3, -1, 0, 0.25*np.pi]\n",
    "    if plot_guess: ax[0,1].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    try: est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, p_data)\n",
    "    except: est=guess; std=guess;fine=x;data_fit=p_data\n",
    "    \n",
    "    plt_label = f'f = {est[0]*1e3:.3f}$\\pm${std[0]*1e3:.3f} Hz, $\\delta $f = {(abs(est[0]) - ramsey_detuning*1e6)*1e3:.3f} Hz \\n T$_2^*$ = {est[1]*1e-3:.1f}$\\pm${std[1]*1e-3:.1f} s'\n",
    "    ax[0,1].plot(fine,data_fit)\n",
    "    ax[0,1].set_ylabel(\"P\")\n",
    "    ax[0,1].set_xlabel(r'$\\tau$ (ms)')\n",
    "    ax[0,1].legend()\n",
    "    ax[0,1].set_title(plt_label, fontsize = \"small\")\n",
    "    ax[0,1].set_ylim(-1,1)\n",
    "    \n",
    "    ##############################################################    \n",
    "\n",
    "    bins=np.arange(np.min(np.concatenate(data)),np.max(np.concatenate(data)),5)\n",
    "    for i in range(4):\n",
    "        h2 = ax[1,0].hist(np.concatenate(data[:,:,i])  , bins=bins   , label = labels[i], alpha=0.5)\n",
    "    ax[1,0].legend()\n",
    "    ax[1,0].set_ylabel(\"instances\")\n",
    "    ax[1,0].set_xlabel('counts')\n",
    "\n",
    "    ##############################################################\n",
    "    ax[1,1].plot(fft_x, fft_y)\n",
    "    ax[1,1].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "    ax[1,1].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[1,1].set_ylabel(\"FFT\")\n",
    "    ax[1,1].set_title(f\"Max frequency = {rabi_freq:.2f} Hz\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    save_fig_manustyle(directory+filename+'_'+'nuclear_spin_%i.pdf'%j)\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "plt.plot(1e-3*delta_f)\n",
    "plt.xlabel(\"Readout iteration\")\n",
    "plt.ylabel(\"$\\Delta_{f}$ (kHz)\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'frequency_drift.pdf')\n",
    "plt.show()    \n",
    "    \n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data_total,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'ramsey_times': ramsey_times,\n",
    "            'ramsey_detuning': ramsey_detuning,\n",
    "            'differential_pops': differential_pops,\n",
    "            'delta_f_Hz': delta_f,\n",
    "            'timing': timing,\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078760d0-6bcd-4e7b-b49e-645eb17a4ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "\n",
    "for j in range(2):\n",
    "    data = data_total[:,:,j]\n",
    "    ############################### Plotting ###############################\n",
    "    fig,ax=plt.subplots(2,2,figsize=(12,7), tight_layout=True, sharex = True)\n",
    "    plt.subplots_adjust(hspace=0, bottom = 0, top = 0.01)\n",
    "    fig.suptitle(plt_labels[j])\n",
    "    ax = ax.flatten()\n",
    "    ############################### Fit #################################\n",
    "\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "    for i in range(4): \n",
    "        ax[i].plot(x,pops[i], 'o-', label = labels[i], color=colors[i])\n",
    "        ax[i].set_ylabel(\"P\")\n",
    "        ax[i].legend()\n",
    "        ax[i].set_ylim(0, 0.5)\n",
    "    for i in [-1,-2]: ax[i].set_xlabel(r'$\\tau$ (ms)')\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'raw_populations.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "032be4b0-8bab-4043-b14d-ea158016ea49",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "x = np.array(ramsey_times)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "plt_labels = [\"Decoherence free\", \"Free decoherence\"]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i,differential_pop in enumerate(differential_pops):\n",
    "        \n",
    "    fft_y = np.abs(np.fft.rfft(p_data - p_data.mean()))\n",
    "    rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "    \n",
    "    guess = [rabi_freq*1e-3, 1e3, -1, 0, 0.25*np.pi]\n",
    "\n",
    "    est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, differential_pop)\n",
    "\n",
    "    plt.plot(fine,data_fit,color = colors[i])\n",
    "    plt.plot(x,differential_pops[i],\"o\", color = colors[i], label = plt_labels[i]+f\", T$_2^*$ = {est[1]*1e-3:.2g}$\\pm${std[1]*1e-3:.2g} s\")\n",
    "\n",
    "plt.xlabel(r'$\\tau$ (ms)')\n",
    "plt.ylabel(r\"$P_{|\\uparrow\\downarrow\\rangle}+P_{|\\downarrow\\uparrow\\rangle}-P_{|\\uparrow\\uparrow\\rangle}-P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.legend(fontsize = \"small\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(directory+filename+'_'+'decoherence_comparison.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c8c860-804e-4ed4-bf82-ef22705f66b0",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Nuclear T1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f48226-0cdf-4be3-bdba-8f7eb411fd1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Electron Spin params #####################\n",
    "\n",
    "electron_amplitude = 0.0045\n",
    "electron_duration = 80_000//4\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.84e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.01\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 350e3\n",
    "ramp_time = int(3e6/4)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "\n",
    "N=20\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 1700 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "intermeasure_wait = 300 # in ms\n",
    "N_iterations = 60*60/(intermeasure_wait*1e-3 + 0.7/200*N_ROcycle)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep    \n",
    "    \n",
    "    freq_set=declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    end_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < 100, j + 1):\n",
    "        \n",
    "        click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        with while_(click_acc<threshold):\n",
    "\n",
    "            ################# Preparation #################\n",
    "            # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "            align()\n",
    "            # If we're not in the right state, try flipping nuclear spin a\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not right after this, we try flipping nuclear spin b\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "    update_frequency(spin_sticky_element, prep_ro_freq+delta_freq)\n",
    "    with for_(j, 0, j < N_iterations, j + 1):\n",
    "        click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        align()\n",
    "        with for_(k, 0, k < intermeasure_wait, k + 1):\n",
    "            wait(int(1e6//4))\n",
    "    \n",
    "    \n",
    "    with for_each_(freq_set, readout_freqs):  \n",
    "        click_acc = nuclear_spin_RO(end_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        align()\n",
    "        wait(int(5e6//4)) \n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.timestamps().save_all('times')\n",
    "        rabi_stream.save_all('clicks')\n",
    "        end_stream.save_all('end_clicks')\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e77379-790b-44b7-a7b4-604e4f3b0d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "for i in range(12):\n",
    "    better_sleep(1*3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    time_data= np.array([item[0] for item in res.times.fetch_all()])\n",
    "    delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    end_state = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    print(data.shape)\n",
    "\n",
    "    time_data -= time_data[0]    # Re-zero the time\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig, ax = plt.subplots(2,1,figsize=[10,5],tight_layout=True)\n",
    "\n",
    "    ax[0].plot(measure_time/60, data, '.')\n",
    "    ax[0].set_xlabel('Time (min)')\n",
    "    ax[0].set_ylabel('Integrated counts')\n",
    "\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    if np.any(end_state):\n",
    "        for i in range(4): ax[1].bar([i], end_state[i], color=colors[i]) \n",
    "        ax[1].text(1, 0.5, 'Nuclear spin readout at\\nthe end of the measurement')\n",
    "    else:\n",
    "        ax[1].text(1, 0.5, 'No data yet')\n",
    "    plt.xticks([0,1,2,3], labels=labels)\n",
    "    ax[1].set_xlabel('State')\n",
    "    ax[1].set_ylabel('Counts')\n",
    "\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c2d1b6-bf28-4807-a0ae-d42fd25e7307",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Electron Spin params #####################\n",
    "\n",
    "electron_amplitude = 0.0045\n",
    "electron_duration = 80_000//4\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.84e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.01\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 350e3\n",
    "ramp_time = int(3e6/4)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "\n",
    "N=20\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 1700 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "intermeasure_wait = 300 # in ms\n",
    "N_iterations = 22*60*60/(intermeasure_wait*1e-3 + 0.7/200*N_ROcycle)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep    \n",
    "    \n",
    "    freq_set=declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    end_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < 100, j + 1):\n",
    "        \n",
    "        click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        with while_(click_acc<threshold):\n",
    "\n",
    "            ################# Preparation #################\n",
    "            # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "            align()\n",
    "            # If we're not in the right state, try flipping nuclear spin a\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not right after this, we try flipping nuclear spin b\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "    update_frequency(spin_sticky_element, prep_ro_freq+delta_freq)\n",
    "    with for_(j, 0, j < N_iterations, j + 1):\n",
    "        click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        align()\n",
    "        with for_(k, 0, k < intermeasure_wait, k + 1):\n",
    "            wait(int(1e6//4))\n",
    "    \n",
    "    \n",
    "    with for_each_(freq_set, readout_freqs):  \n",
    "        click_acc = nuclear_spin_RO(end_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        align()\n",
    "        wait(int(5e6//4)) \n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.timestamps().save_all('times')\n",
    "        rabi_stream.save_all('clicks')\n",
    "        end_stream.save_all('end_clicks')\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0834e5-d5fa-4e63-a19b-45b7a9c5e43c",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b937c175-b5ad-434e-845a-cc4ccaafa2dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f92a11-da98-4867-b412-42d628fd3adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "for i in range(20):\n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    time_data= np.array([item[0] for item in res.times.fetch_all()])\n",
    "    delta_freq = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "    end_state = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    print(data.shape)\n",
    "\n",
    "    time_data -= time_data[0]    # Re-zero the time\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig, ax = plt.subplots(2,1,figsize=[10,5],tight_layout=True)\n",
    "\n",
    "    ax[0].plot(measure_time/60, data, '.')\n",
    "    ax[0].set_xlabel('Time (min)')\n",
    "    ax[0].set_ylabel('Integrated counts')\n",
    "\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    if np.any(end_state):\n",
    "        for i in range(4): ax[1].bar([i], end_state[i], color=colors[i]) \n",
    "        ax[1].text(1, 0.5, 'Nuclear spin readout at\\nthe end of the measurement')\n",
    "    else:\n",
    "        ax[1].text(1, 0.5, 'No data yet')\n",
    "    plt.xticks([0,1,2,3], labels=labels)\n",
    "    ax[1].set_xlabel('State')\n",
    "    ax[1].set_ylabel('Counts')\n",
    "\n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_%i_nuclear_spin.pdf'%i)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'_%i.hdf5'%i\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5b28bb-6b40-4286-95a8-692c720812fe",
   "metadata": {},
   "source": [
    "### Measure state every 10 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5062b6a-ae97-4ee9-960f-ef0ebde87e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Electron Spin params #####################\n",
    "\n",
    "electron_amplitude = 0.0045\n",
    "electron_duration = 80_000//4\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.84e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.01\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 350e3\n",
    "ramp_time = int(3e6/4)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "\n",
    "N=20\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 1700 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "intermeasure_wait = 300 # in ms\n",
    "wait_time_4state = 10 * 60\n",
    "N_iterations = wait_time_4state / (intermeasure_wait*1e-3 + 0.7/200*N_ROcycle)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep    \n",
    "    \n",
    "    freq_set=declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    end_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < 10, j + 1):\n",
    "        \n",
    "        click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "        with while_(click_acc<threshold):\n",
    "\n",
    "            ################# Preparation #################\n",
    "            # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "            align()\n",
    "            # If we're not in the right state, try flipping nuclear spin a\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not right after this, we try flipping nuclear spin b\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "            # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "            with if_(click_acc<threshold):\n",
    "                Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                wait(int(1e6//4),spin_element)\n",
    "                align()\n",
    "                click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle_prep, spin_element,  amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "\n",
    "\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "    update_frequency(spin_sticky_element, prep_ro_freq+delta_freq)\n",
    "    \n",
    "    with while_(True):\n",
    "        with for_(j, 0, j < N_iterations, j + 1):\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, prep_ro_freq+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[1]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            \n",
    "            align()\n",
    "            with for_(k, 0, k < intermeasure_wait, k + 1):\n",
    "                wait(int(1e6//4))\n",
    "\n",
    "\n",
    "        with for_each_(freq_set, readout_freqs):  \n",
    "            click_acc = nuclear_spin_RO(end_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            align()\n",
    "            wait(int(5e6//4)) \n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.timestamps().save_all('times')\n",
    "        rabi_stream.save_all('clicks')\n",
    "        end_stream.buffer(4).save_all('end_clicks')\n",
    "        end_stream.timestamps().buffer(4).save_all('end_clicks_times')\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "683df1b9-9a2e-4e2b-a5a6-cb31be70a1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "for i in range(10):\n",
    "    better_sleep(3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    data           = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    time_data      = np.array([item[0] for item in res.times.fetch_all()])\n",
    "    end_state      = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    time_end_state = np.array([item[0] for item in res.end_clicks_times.fetch_all()])\n",
    "    delta_freq     = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    \n",
    "    if np.any(time_end_state): time_end_state = (time_end_state[:,0] - time_data[0])*1e-9\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    fig, ax = plt.subplots(2,1,figsize=[10,5],tight_layout=True,sharex=True)\n",
    "\n",
    "    ax[0].plot(measure_time/60, data, '.')\n",
    "    ax[0].set_ylabel('Integrated counts')\n",
    "\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    if np.any(end_state):\n",
    "        \n",
    "        for ii, end in enumerate(end_state):\n",
    "            ax[0].vlines(time_end_state[ii]/60, min(data), max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "        \n",
    "        for i in range(4): ax[1].plot(time_end_state/60, end_state[:,i], color=colors[i], label=labels[i]) \n",
    "\n",
    "    else:\n",
    "        ax[1].text(1, 0.5, 'No data yet')\n",
    "\n",
    "    ax[1].set_xlabel('Time (min)')\n",
    "    ax[1].set_ylabel('Counts')\n",
    "    ax[1].legend()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_%i_nuclear_spin.pdf'%i)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'_%i.hdf5'%i\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9e85fb-87ff-47da-a790-c99e17d07b2c",
   "metadata": {},
   "source": [
    "### Prepare upup and measure other states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd31d594-e644-4ca7-84e1-757b20826a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Electron Spin params #####################\n",
    "\n",
    "electron_amplitude = 0.0045\n",
    "electron_duration = 80_000//4\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.84e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.01\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 350e3\n",
    "ramp_time = int(3e6/4)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "\n",
    "N=20\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 1700 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "intermeasure_wait = 9300 # in ms\n",
    "wait_time_4state = 5 * 60 * 60\n",
    "N_iterations = wait_time_4state / (intermeasure_wait*1e-3 + 0.7/200*N_ROcycle)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep    \n",
    "    \n",
    "    freq_set=declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    end_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    # Initial preparation of the state\n",
    "    with for_(j, 0, j < 30, j + 1):\n",
    "        \n",
    "        raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "\n",
    "        # After each preparation pulse we perform a frequency tracking to make sure that we are still aligned\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "    update_frequency(spin_sticky_element, prep_ro_freq+delta_freq)\n",
    "    \n",
    "    '''\n",
    "    # Prepare state upup (blue)\n",
    "    align()\n",
    "    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "    align()\n",
    "    wait(int(5e6//4))\n",
    "    align()\n",
    "    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "    align()\n",
    "    wait(int(5e6//4))  \n",
    "    '''\n",
    "    \n",
    "    # Main measurement scheme, measure the state of the system with 4 state readout and then proceed to measure states where the transition is improbable.\n",
    "    with while_(True):\n",
    "        with for_each_(freq_set, readout_freqs):  \n",
    "            click_acc = nuclear_spin_RO(end_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            align()\n",
    "            wait(int(5e6//4)) \n",
    "            \n",
    "        with for_(j, 0, j < N_iterations, j + 1):\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[1]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[2]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[3]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            \n",
    "            align()\n",
    "            with for_(k, 0, k < intermeasure_wait, k + 1):\n",
    "                wait(int(1e6//4))\n",
    "\n",
    "\n",
    "\n",
    "    with stream_processing():\n",
    "        rabi_stream.buffer(3).save_all('clicks')\n",
    "        rabi_stream.timestamps().buffer(3).save_all('times')\n",
    "        \n",
    "        end_stream.buffer(4).save_all('end_clicks')\n",
    "        end_stream.timestamps().buffer(4).save_all('end_clicks_times')\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c8bd419-6610-40fd-8fb8-27f843efabcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(2):\n",
    "    better_sleep(1*3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    data           = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    time_data      = np.array([item[0] for item in res.times.fetch_all()])\n",
    "    end_state      = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    time_end_state = np.array([item[0] for item in res.end_clicks_times.fetch_all()])\n",
    "    delta_freq     = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    \n",
    "    if np.any(time_end_state):\n",
    "        time_end_state = (time_end_state[:,0] - time_data[0,0])*1e-9\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    \n",
    "    fig, ax = plt.subplots(4,1,figsize=[10,10],tight_layout=True,sharex=True)\n",
    "    ax[0].plot(measure_time[:,0]/60, data[:,0], '.', color=colors[1])\n",
    "    ax[0].set_ylabel(f'{labels[1]}')\n",
    "    ax[1].plot(measure_time[:,0]/60, data[:,1], '.', color=colors[2])\n",
    "    ax[1].set_ylabel(f'{labels[2]}')\n",
    "    ax[2].plot(measure_time[:,0]/60, data[:,2], '.', color=colors[3])\n",
    "    ax[2].set_ylabel(f'{labels[3]}')\n",
    "    \n",
    "\n",
    "    if np.any(end_state):\n",
    "        \n",
    "        for ii, end in enumerate(end_state):\n",
    "            ax[0].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[1].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[2].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "        \n",
    "        for i in range(4): ax[3].plot(time_end_state/60, end_state[:,i], 'o-', color=colors[i], label=labels[i]) \n",
    "\n",
    "    else:\n",
    "        ax[3].text(1, 0.5, 'No data yet')\n",
    "\n",
    "    ax[3].set_xlabel('Time (min)')\n",
    "    ax[3].set_ylabel('Counts')\n",
    "    ax[3].legend()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_%i_nuclear_spin.pdf'%i)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'_%i.hdf5'%i\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a794d0-a426-4d39-9020-2ba9f52cec58",
   "metadata": {},
   "source": [
    "### Purcell enhanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48af29a5-d543-43e4-a54f-83719fb338fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "####################### Electron Spin params #####################\n",
    "\n",
    "electron_amplitude = 0.0045\n",
    "electron_duration = 80_000//4\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.84e3)\n",
    "nuclear_spin_freq_b = int(810.49e3)\n",
    "\n",
    "raman_pi_duration_a =  int(6.52e6//4) # in ns \n",
    "raman_pi_duration_b = int(6.28e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.01\n",
    "detuned_electron_amplitude_b = 0.05\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 350e3\n",
    "ramp_time = int(3e6/4)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "\n",
    "N=20\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 1700 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated/N)*N  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "intermeasure_wait = 9300 # in ms\n",
    "wait_time_4state = 5 * 60 * 60\n",
    "N_iterations = wait_time_4state / (intermeasure_wait*1e-3 + 0.7/200*N_ROcycle)\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_freq = readout_freqs[-1]\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep    \n",
    "    \n",
    "    freq_set=declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    end_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    \n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    # Initial preparation of the state\n",
    "    with for_(j, 0, j < 1, j + 1):\n",
    "        \n",
    "        raman_preparation_a(prepare_stream, click_acc, freq_electron, threshold, N_ROcycle, readout_freqs[-1]+delta_freq)\n",
    "\n",
    "        # After each preparation pulse we perform a frequency tracking to make sure that we are still aligned\n",
    "        amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "        gaussian_pulse_length = 5000//4\n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "        delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "        save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "    update_frequency(spin_sticky_element, prep_ro_freq+delta_freq)\n",
    "    \n",
    "    '''\n",
    "    # Prepare state upup (blue)\n",
    "    align()\n",
    "    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a, ramp_time)\n",
    "    align()\n",
    "    wait(int(5e6//4))\n",
    "    align()\n",
    "    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron+delta_freq, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b, ramp_time)\n",
    "    align()\n",
    "    wait(int(5e6//4))  \n",
    "    '''\n",
    "    \n",
    "    # Main measurement scheme, measure the state of the system with 4 state readout and then proceed to measure states where the transition is improbable.\n",
    "    with while_(True):\n",
    "        with for_each_(freq_set, readout_freqs):  \n",
    "            click_acc = nuclear_spin_RO(end_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            align()\n",
    "            wait(int(5e6//4)) \n",
    "            \n",
    "        with for_(j, 0, j < N_iterations, j + 1):\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "                \n",
    "            align()\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[1]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[2]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element, amplitude_readout_pulse, gauss_duration, readout_freqs[3]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            \n",
    "            align()\n",
    "            update_frequency(spin_sticky_element, freq_electron + delta_freq + raman_detuning + nuclear_spin_freq_b)  # Detuned Sideband frequency\n",
    "            Raman_pulse_ms(k, 0, detuned_sideband_amplitude_b_prep, intermeasure_wait, ramp_time_prep)\n",
    "            align()\n",
    "            wait(int(10e6//4))\n",
    "\n",
    "    with stream_processing():\n",
    "        rabi_stream.buffer(3).save_all('clicks')\n",
    "        rabi_stream.timestamps().buffer(3).save_all('times')\n",
    "        \n",
    "        end_stream.buffer(4).save_all('end_clicks')\n",
    "        end_stream.timestamps().buffer(4).save_all('end_clicks_times')\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        \n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3112e9c5-cb59-486a-96b8-9774e3cf6bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(18):\n",
    "    better_sleep(1*3600)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    data           = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    time_data      = np.array([item[0] for item in res.times.fetch_all()])\n",
    "    end_state      = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    time_end_state = np.array([item[0] for item in res.end_clicks_times.fetch_all()])\n",
    "    delta_freq     = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    \n",
    "    if np.any(time_end_state):\n",
    "        time_end_state = (time_end_state[:,0] - time_data[0,0])*1e-9\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    \n",
    "    fig, ax = plt.subplots(4,1,figsize=[10,10],tight_layout=True,sharex=True)\n",
    "    ax[0].plot(measure_time[:,0]/60, data[:,0], '.', color=colors[1])\n",
    "    ax[0].set_ylabel(f'{labels[1]}')\n",
    "    ax[1].plot(measure_time[:,0]/60, data[:,1], '.', color=colors[2])\n",
    "    ax[1].set_ylabel(f'{labels[2]}')\n",
    "    ax[2].plot(measure_time[:,0]/60, data[:,2], '.', color=colors[3])\n",
    "    ax[2].set_ylabel(f'{labels[3]}')\n",
    "    \n",
    "\n",
    "    if np.any(end_state):\n",
    "        \n",
    "        for ii, end in enumerate(end_state):\n",
    "            ax[0].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[1].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[2].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "        \n",
    "        for i in range(4): ax[3].plot(time_end_state/60, end_state[:,i], 'o-', color=colors[i], label=labels[i]) \n",
    "\n",
    "    else:\n",
    "        ax[3].text(1, 0.5, 'No data yet')\n",
    "\n",
    "    ax[3].set_xlabel('Time (min)')\n",
    "    ax[3].set_ylabel('Counts')\n",
    "    ax[3].legend()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_%i_nuclear_spin.pdf'%i)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'_%i.hdf5'%i\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6088bb7d-7949-4c14-b905-532483a79e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(1):\n",
    "    better_sleep(0)\n",
    "    res = job.result_handles\n",
    "    plot_guess = 0\n",
    "\n",
    "    ###########################################################\n",
    "    data           = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "    time_data      = np.array([item[0] for item in res.times.fetch_all()])\n",
    "    end_state      = np.array([item[0] for item in res.end_clicks.fetch_all()])\n",
    "    time_end_state = np.array([item[0] for item in res.end_clicks_times.fetch_all()])\n",
    "    delta_freq     = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "    print(data.shape)\n",
    "    \n",
    "    if np.any(time_end_state):\n",
    "        time_end_state = (time_end_state[:,0] - time_data[0,0])*1e-9\n",
    "    measure_time = (time_data - time_data[0])*1e-9\n",
    "\n",
    "    ############################### Plotting ###############################\n",
    "\n",
    "    \n",
    "    fig, ax = plt.subplots(4,1,figsize=[10,10],tight_layout=True,sharex=True)\n",
    "    ax[0].plot(measure_time[:,0]/60, data[:,0], '.', color=colors[1])\n",
    "    ax[0].set_ylabel(f'{labels[1]}')\n",
    "    ax[1].plot(measure_time[:,0]/60, data[:,1], '.', color=colors[2])\n",
    "    ax[1].set_ylabel(f'{labels[2]}')\n",
    "    ax[2].plot(measure_time[:,0]/60, data[:,2], '.', color=colors[3])\n",
    "    ax[2].set_ylabel(f'{labels[3]}')\n",
    "    \n",
    "\n",
    "    if np.any(end_state):\n",
    "        \n",
    "        for ii, end in enumerate(end_state):\n",
    "            ax[0].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[1].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "            ax[2].vlines(time_end_state[ii]/60, np.min(data), np.max(data), linestyle='dashed', color = colors[np.argmax(end)]) \n",
    "        \n",
    "        for i in range(4): ax[3].plot(time_end_state/60, end_state[:,i], 'o-', color=colors[i], label=labels[i]) \n",
    "\n",
    "    else:\n",
    "        ax[3].text(1, 0.5, 'No data yet')\n",
    "\n",
    "    ax[3].set_xlabel('Time (min)')\n",
    "    ax[3].set_ylabel('Counts')\n",
    "    ax[3].legend()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+'_%i_nuclear_spin.pdf'%i)\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "    plt.show()\n",
    "\n",
    "    fullpath=directory+filename+'_%i.hdf5'%i\n",
    "    datasets= {\n",
    "                'click_array': data,\n",
    "                'time_data': time_data,\n",
    "\n",
    "                'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "                'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "                'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "                'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "                'raman_pi_duration_a': raman_pi_duration_a,\n",
    "                'raman_pi_duration_b': raman_pi_duration_b,\n",
    "                'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "                'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "                'raman_detuning': raman_detuning,\n",
    "\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'prep_freq': prep_freq,\n",
    "                'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "                'electron_amplitude': electron_amplitude,\n",
    "                'electron_duration': electron_duration,\n",
    "                'intermeasure_wait': intermeasure_wait,\n",
    "                'gauss_duration': gauss_duration,\n",
    "                'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "                't_wait_prep': t_wait_prep,\n",
    "                'N_ROcycle': N_ROcycle\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad604a0-aa92-4f99-9562-d432fd756870",
   "metadata": {
    "tags": []
   },
   "source": [
    "### With chirped pumping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1696,
   "id": "427b587b-3092-4ca2-9638-aefbdf27d7d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T17:41:08.796081Z",
     "iopub.status.busy": "2024-03-28T17:41:08.795084Z",
     "iopub.status.idle": "2024-03-28T17:41:12.406973Z",
     "shell.execute_reply": "2024-03-28T17:41:12.404976Z",
     "shell.execute_reply.started": "2024-03-28T17:41:08.796081Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time per avg = 0.00 hrs\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='nuclear_T1'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Frequency sweep params #######################\n",
    "freq_sweep_avgs = 200\n",
    "\n",
    "span=0.3e6\n",
    "freq_final = Photon_IF + centre_freq*1e3 + span/2\n",
    "freq_init  = Photon_IF + centre_freq*1e3 - span/2\n",
    "freq_step = 0.01e6\n",
    "n_steps = int((freq_final-freq_init)/freq_step)+1\n",
    "freq_range = np.linspace(freq_init,freq_final,n_steps)\n",
    "\n",
    "####################### T1 wait parameters #######################\n",
    "t1_waits = [1] # [int(6*10**f) for f in np.arange(4)] # N*10ms\n",
    "time_per_avg = np.sum(np.array(t1_waits)*10e-3)\n",
    "print(f\"Time per avg = {time_per_avg/3600:.2f} hrs\")\n",
    "\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    j = declare(int)\n",
    "    k = declare(int)\n",
    "    l = declare(int)\n",
    "    \n",
    "    click=declare(int)\n",
    "    click_acc=declare(int)\n",
    "    assign(click_acc, 0)\n",
    "    \n",
    "    t1_wait = declare(int)\n",
    "    \n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    \n",
    "    timing_stream = declare_stream()\n",
    "    \n",
    "    freq_sweep_stream = declare_stream()\n",
    "        \n",
    "    chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    align()\n",
    "    # Before the first average run a frequency tracking to make sure first frequency sweep is centered\n",
    "    amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "    gaussian_pulse_length = 5000//4\n",
    "    chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "    delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "    align()\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(t1_wait, t1_waits):\n",
    "\n",
    "            save(0, timing_stream)    \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            with for_(k, 0, k < t1_wait , k + 1):\n",
    "                wait(int(1e6//4))\n",
    "                #wait(int(10e6//4))\n",
    "                align()\n",
    "                \n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            # Run frequency sweep\n",
    "            with for_(l, 0, l < freq_sweep_avgs , l + 1):\n",
    "                with for_(freq_set, freq_init, freq_set < freq_final+freq_step//2, freq_set + freq_step):\n",
    "                    assign(click_acc, 0)\n",
    "                    nuclear_readout_block(click, click_acc, freq_set, measure=True)\n",
    "                    save(click_acc,freq_sweep_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "  \n",
    "\n",
    "\n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        freq_sweep_stream.buffer(n_steps).buffer(freq_sweep_avgs).buffer(len(t1_waits)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(4).save_all('timing') #????\n",
    "                                 \n",
    "        # if stream_I:\n",
    "        #     I_stream.buffer(N_iterations).save_all('I')\n",
    "        # #p_stream.boolean_to_int().buffer(N).buffer(N_iterations//N).buffer(n_step_duration).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        # p_stream.boolean_to_int().buffer(N_iterations//N).buffer(N).buffer(n_steps).map(FUNCTIONS.average(2)).save_all('clicks')\n",
    "        # p_stream.timestamps().buffer(N_iterations).save('timestamp')\n",
    "        # index_stream.save('interation')\n",
    "\n",
    "start_time_freqsweep = time.time()\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1700,
   "id": "eecad74e-e72a-4a52-8928-4dcef61ef3fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T17:43:38.272067Z",
     "iopub.status.busy": "2024-03-28T17:43:38.272067Z",
     "iopub.status.idle": "2024-03-28T17:43:38.912632Z",
     "shell.execute_reply": "2024-03-28T17:43:38.911629Z",
     "shell.execute_reply.started": "2024-03-28T17:43:38.272067Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13, 1, 31)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "delta_freq_array = np.array([item[0] for item in res.delta_freq.fetch_all()])\n",
    "\n",
    "avg_data = data.mean(2)\n",
    "print(avg_data.shape)\n",
    "plot_2d_sweep(avg_data[:,0,:], x =np.round((freq_range-Photon_IF)*1e-3,0), cmap = 'Blues')\n",
    "# x =freq_range\n",
    "# plt.figure(figsize = (7,5))\n",
    "# for i in range(len(t1_waits)):\n",
    "#     plt.plot(x,avg_data[i])\n",
    "    \n",
    "# save_fig_manustyle(directory+filename+'_'+'frequency_sweeps.pdf')\n",
    "\n",
    "# fullpath=directory+filename+'.hdf5'\n",
    "# datasets= {\n",
    "#             'click_array': data,\n",
    "#             'delta_freq_array':delta_freq_array,\n",
    "#             'freq_range': readout_freqs,\n",
    "#             'timing':timing,\n",
    "            \n",
    "# }\n",
    "\n",
    "\n",
    "# save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2e39ff-340e-472f-909e-607d623c6e2c",
   "metadata": {},
   "source": [
    "# Rabi frequency vs preparation state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4a7ef7-9813-4141-b9b6-3afc1960c450",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[3]\n",
    "\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_freq_a  = int(808.77e3)\n",
    "nuclear_spin_freq_b = int(810.4e3)\n",
    "\n",
    "raman_pi_duration_a =  int(3.24e6//4) # in ns\n",
    "raman_pi_duration_b = int(4.18e6//4) # in ns\n",
    "\n",
    "detuned_electron_amplitude_a = 0.004\n",
    "detuned_electron_amplitude_b = 0.02\n",
    "\n",
    "detuned_sideband_amplitude_a = 0.15\n",
    "detuned_sideband_amplitude_b = 0.15\n",
    "\n",
    "raman_detuning = 150e3\n",
    "ramp_time       = int(3e6/4)\n",
    "raman_pulse_durations = np.linspace(2,20,10)\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "threshold = 85\n",
    "prep_freq = Photon_IF-0.790e6+centre_freq*1e3\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "prep_ro_index = -1\n",
    "\n",
    "####################### Electron spin readout parameters #######################\n",
    "\n",
    "waiting_time_SMPD = 1000      # in ns\n",
    "waiting_time_spin= 120000//4 # in ns\n",
    "waiting_after_spinreadout = 1_000_000//4 # in ns\n",
    "amplitude_readout_pulse = 0.0045\n",
    "gauss_duration = 80_000//4      # frequency selective pi pulse\n",
    "cycle_time_estimated = 17  #in us Need to change this to something better\n",
    "Integration_time = 2000 #in us\n",
    "N_readout = int(Integration_time/cycle_time_estimated)  #30000\n",
    "N_ROcycle=200\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "total_measurement_time = 3600*2\n",
    "one_measurement_time = ((Integration_time*1e-6 + waiting_time_spin*4e-9)*N_ROcycle*2  +  t_wait_prep*4e-9*N_preparation  +  5e-3) * len(raman_pulse_durations)\n",
    "N_repetition = int(total_measurement_time//one_measurement_time)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    duration_set = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            '''\n",
    "            raman_preparation_a(\n",
    "                threshold, click_acc, freq_electron, raman_detuning,\n",
    "                \n",
    "                nuclear_spin_freq_a, nuclear_spin_freq_b, \n",
    "                detuned_electron_amplitude_a, detuned_electron_amplitude_b, \n",
    "                detuned_sideband_amplitude_a, detuned_sideband_amplitude_b,\n",
    "                raman_pi_duration_a, raman_pi_duration_b,\n",
    "\n",
    "                prepare_stream, spin_gauss_pulse, N_ROcycle, spin_element,  \n",
    "                amplitude_readout_pulse, gauss_duration, \n",
    "                readout_freqs[-1]+delta_freq, \n",
    "                waiting_time_spin, N_readout,\n",
    "                waiting_time_SMPD, waiting_after_spinreadout\n",
    "            )\n",
    "            '''\n",
    "            with while_(click_acc<threshold):\n",
    "                \n",
    "                ################# Preparation #################\n",
    "                # Prepare the nucleus in desired state using coherent raman pulses\n",
    "\n",
    "                align()\n",
    "                # If we're not in the right state, try flipping nuclear spin a\n",
    "                with if_(click_acc<threshold):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()\n",
    "                    click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, readout_freqs[prep_ro_index]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                \n",
    "                # If we're still not right after this, we try flipping nuclear spin b\n",
    "                with if_(click_acc<threshold):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_b, freq_electron, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b,ramp_time)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()\n",
    "                    click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, readout_freqs[prep_ro_index]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                \n",
    "                # If we're still not correct, we must now be in a state where we only need to flip nuclear spin a again\n",
    "                with if_(click_acc<threshold):\n",
    "                    Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "                    wait(int(1e6//4),spin_element)\n",
    "                    align()\n",
    "                    click_acc = nuclear_spin_RO(prepare_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, readout_freqs[prep_ro_index]+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            \n",
    "            update_frequency(spin_sticky_element      , freq_electron + delta_freq + raman_detuning + nuclear_spin_freq_a )  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element, freq_electron + delta_freq + raman_detuning)                         # Detuned Electron frequency\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            ################# Now play the Raman sideband sequence #################\n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b, freq_electron, raman_detuning, detuned_electron_amplitude_b, detuned_sideband_amplitude_b, raman_pi_duration_b,ramp_time)\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a, freq_electron, raman_detuning, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, raman_pi_duration_a,ramp_time)\n",
    "            \n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_ms(k, detuned_electron_amplitude_a, detuned_sideband_amplitude_a, duration_set, ramp_time)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            with for_each_(freq_set, readout_freqs):  \n",
    "                click_acc = nuclear_spin_RO(rabi_stream, click_acc, spin_gauss_pulse, N_ROcycle, spin_element,  amplitude_readout_pulse, gauss_duration, freq_set+delta_freq, waiting_time_spin, N_readout, waiting_time_SMPD, waiting_after_spinreadout)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4a0b32b-c659-4a08-a20c-71abeb9e6086",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "print(data.shape)\n",
    "\n",
    "#timing = timing_downdown\n",
    "#data = data_downdown\n",
    "#data_prep = data_prep_downdown\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "x = np.array(raman_pulse_durations)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft( p_data.mean(0)- p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "sigmoid_length = 5\n",
    "\n",
    "############################### Fit #################################\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi))\n",
    "\n",
    "guess = [1e-3*rabi_freq,0.5,1,1*np.pi]\n",
    "\n",
    "try:est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "T_half_period = 0.5/est[0]\n",
    "T_min = 0.5/(est[0]+std[0])\n",
    "T_max = 0.5/(est[0]-std[0])\n",
    "T_err = T_half_period-T_min\n",
    "\n",
    "\n",
    "# T_pi = fine[np.argmin(data_fit[:int(len(fine)*T_half_period*2/max(fine))])]\n",
    "# T_pio2 = T_pi-T_half_period/2\n",
    "T_pi_list=(-est[3]+np.arange(-5,5)*np.pi)/(2*np.pi*est[0])\n",
    "T_pi_list_p=T_pi_list[T_pi_list>0]\n",
    "#T_pi = T_pi_list_p[np.argmin(T_pi_list_p)]\n",
    "#T_pio2 = T_pi_list_p[np.argmin(T_pi_list_p)]-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): ax[1].plot(x,pops[i], label = labels[i])\n",
    "else:\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "plt_label = \"$T_{hp} = %.2f(%i)$ ms \"%(T_half_period,1e2*T_err)+ r\"$T_\\pi = %.2f(%i)$ ms \"%(T_pi,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))+r\"$T_{\\pi/2} = %.2f(%i)$ ms\"%(T_pio2,1e2*np.sqrt((std[3]/(2*np.pi*est[0]))**2+T_err**2))\n",
    "ax[1].plot(fine,data_fit)\n",
    "ax[1].set_ylim(0,1)\n",
    "#ax[1].set_xlim(0,200)\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "\n",
    "\n",
    "ax[2].plot(fft_x,fft_y,\"o-\")\n",
    "ax[2].set_ylim(0,None)\n",
    "ax[2].set_xlim(0,None)\n",
    "ax[2].axvline(rabi_freq,linestyle = \"--\", color = \"black\", alpha = 0.5)\n",
    "ax[2].set_xlabel(\"Rabi frequency (Hz)\")\n",
    "ax[2].set_ylabel(\"Population\")\n",
    "ax[2].set_title(\"Max FFT freq = %i Hz\"%rabi_freq, fontsize = \"small\")\n",
    "\n",
    "bins=np.arange(20,np.max([np.max(np.concatenate(data)),np.max(data_prep)]),5)\n",
    "ax[3].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[3].hist(data_prep  , bins=bins   , label = r\"$preparation$\", alpha=0.5)\n",
    "ax[3].legend()\n",
    "ax[3].set_ylabel(\"instances\")\n",
    "ax[3].set_xlabel('counts')\n",
    "\n",
    "\n",
    "ax[4].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[4].set_xlabel(\"Readout iteration\")\n",
    "ax[4].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'click_array_prep': data_prep,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            'prep_freq': prep_freq,\n",
    "            'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'raman_pulse_durations': raman_pulse_durations,\n",
    "            'gauss_duration': gauss_duration,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafae0fa-eec9-4637-bd57-ae51c344c028",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# State tomography"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce27253-c97e-48f8-af29-afde3acd3ee8",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5e168a4-555a-45c9-82d8-c62f9e02ded4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq,pump_steps = 50, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = 10, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin b\n",
    "            # update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            # update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            # play('ON',fsv_trigger)\n",
    "\n",
    "            # Pi/2 A\n",
    "            #Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            #align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            #Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            #align()\n",
    "\n",
    "            # CNOT\n",
    "            # Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "            #                                              detuned_sideband_amplitude_cnot,\n",
    "            #                                              raman_pi_duration_cnot,\n",
    "            #                                              ramp_time_cnot,\n",
    "            #                                              element1 = spin_sticky_extra2_element,\n",
    "            #                                              element2 = spin_sticky4_element)\n",
    "            # \n",
    "            # Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "            \n",
    "            # reset_frame(spin_sticky_element)\n",
    "            # reset_frame(spin_sticky_extra_element)\n",
    "            # reset_frame(spin_sticky_extra2_element)\n",
    "            # reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "                    align()\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, 150, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b04f3d3-fd3c-4e29-862f-f979fc74ceaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb709922-165a-4f5f-a870-c91044fcf56d",
   "metadata": {},
   "source": [
    "### Pure states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e3c87e-aa5b-4885-9886-f0d2097b8460",
   "metadata": {},
   "source": [
    "#### Down-down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8dedc5-2c2d-481f-9575-5334643645a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_a = Photon_LO -(freq_electron + raman_detuning_a_prep)\n",
    "drive_b = Photon_LO -(freq_electron + raman_detuning_b_prep)\n",
    "drive_b2 = Photon_LO -( freq_electron+ raman_detuning_b_prep + nuclear_spin_freq_b_prep)\n",
    "\n",
    "print(\"Drive A = %.3f MHz\\nDrive B2 = %.3f MHz\\nDelta drive B = %.3f kHz\\n\"%(drive_a*1e-6,drive_b2*1e-6,(drive_b-drive_b2)*1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1944c4c7-5ce3-41c1-8887-898525d2a0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "1e-6*raman_pi_half_duration_b_prep*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02504442-497a-4ed9-a2be-9c61f4bfe190",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            # play('ON',fsv_trigger)\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "            \n",
    "            # reset_frame(spin_sticky_element)\n",
    "            # reset_frame(spin_sticky_extra_element)\n",
    "            # reset_frame(spin_sticky_extra2_element)\n",
    "            # reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc33ff9-14cc-425e-b7a9-0c963ac3bd96",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(1*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "print('standard errer: ', np.sqrt(0.25/len(data)))\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "259941a1-c59f-4d4d-bd8d-b0155e7fd56b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df85d3cd-a35a-4f5d-9e37-f4f7baaab760",
   "metadata": {},
   "source": [
    "#### Up-down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1701,
   "id": "46423f68-2897-4b12-8f4f-59155cd6b7d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T17:47:32.085110Z",
     "iopub.status.busy": "2024-03-28T17:47:32.085110Z",
     "iopub.status.idle": "2024-03-28T17:47:40.681078Z",
     "shell.execute_reply": "2024-03-28T17:47:40.679077Z",
     "shell.execute_reply.started": "2024-03-28T17:47:32.085110Z"
    }
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()           \n",
    "            \n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            # play('ON',fsv_trigger)\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "            \n",
    "            ################# Pi-Pulse on Spin A #################\n",
    "            \n",
    "            # Pi X A\n",
    "            Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1710,
   "id": "6666ecab-8d78-448c-b63a-cafab63a9e6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T23:17:03.798187Z",
     "iopub.status.busy": "2024-03-28T23:17:03.798187Z",
     "iopub.status.idle": "2024-03-29T02:18:50.764692Z",
     "shell.execute_reply": "2024-03-29T02:18:50.763691Z",
     "shell.execute_reply.started": "2024-03-28T23:17:03.798187Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 10800 s                                                      \n",
      "\n",
      "Data shape: (1300, 3, 3, 4)\n",
      "standard errer:  0.013867504905630728\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240328184732_Tomography.hdf5'"
      ]
     },
     "execution_count": 1710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "print('standard errer: ', np.sqrt(0.25/len(data)))\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45dd202-fa28-4c50-8fa0-4ac8d11bf500",
   "metadata": {},
   "source": [
    "#### Down-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46032251-5807-4af1-b36e-d7ce306e2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()           \n",
    "            \n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            # play('ON',fsv_trigger)\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "            \n",
    "            ################# Pi-Pulse on Spin B #################\n",
    "            \n",
    "            # Pi X B\n",
    "            Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc8eb29d-bf3d-4c8f-9e2a-69baec4bd06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(5*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "print('standard error: ', np.sqrt(0.25/len(data)))\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,                             \n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b42521-2472-47c3-b62b-2a666b31d189",
   "metadata": {},
   "source": [
    "#### Up-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d21be42-0cba-41fc-aafa-97c542eadd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()           \n",
    "            \n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            # play('ON',fsv_trigger)\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "            \n",
    "            ################# Pi-Pulse on Spin A #################\n",
    "            \n",
    "            # Pi X A\n",
    "            Pauli_swept('aX',    delta_freq)\n",
    "            \n",
    "            ################# Pi-Pulse on Spin B #################\n",
    "            \n",
    "            # Pi X B\n",
    "            Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69609048-1e24-49bb-b2f9-e4103b58ff4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "print('standard error: ', np.sqrt(0.25/len(data)))\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,                             \n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45af1495-9a5c-41d0-8c1c-56f7561996ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-20T10:18:04.941820Z",
     "iopub.status.busy": "2024-02-20T10:18:04.940820Z",
     "iopub.status.idle": "2024-02-20T10:18:05.252719Z",
     "shell.execute_reply": "2024-02-20T10:18:05.251354Z",
     "shell.execute_reply.started": "2024-02-20T10:18:04.941820Z"
    }
   },
   "source": [
    "### Up-up + down-down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "654c47b0-6294-4490-89a8-53d6f6a3b130",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            # Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep)\n",
    "            # align()\n",
    "\n",
    "            # CNOT\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                         detuned_sideband_amplitude_cnot,\n",
    "                                                         raman_pi_duration_cnot,\n",
    "                                                         ramp_time_cnot,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "            \n",
    "            # # Pi B\n",
    "            # Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, \n",
    "            #                                              detuned_sideband_amplitude_b_prep, \n",
    "            #                                              raman_pi_duration_b_prep, \n",
    "            #                                              ramp_time_prep, \n",
    "            #                                              element1 = spin_sticky_extra2_element,\n",
    "            #                                              element2 = spin_sticky4_element)\n",
    "            \n",
    "            # Pi A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(1): # XY\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(2): # XZ\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(4): # YY\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(5): # YZ\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(8): # ZZ\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7a422f1-6652-416b-bef2-10ac10da152b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21286b1-fd63-4f41-8a7d-18e16c07f152",
   "metadata": {},
   "source": [
    "### CZ entangled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e43fe129-73fb-4b98-82b8-9a41a580c0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 A #################\n",
    "            Pauli_swept('aX90',    delta_freq)\n",
    "            \n",
    "            ################# Pi/2 B #################\n",
    "            Pauli_swept('bX90',    delta_freq)\n",
    "            \n",
    "            ################# CZ B #################\n",
    "            align()\n",
    "            CZ_gate(delta_freq, CZ_phase_correction_b = CZ_phase_correction_b_prep)\n",
    "            \n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68221558-f0d2-4eb6-ad7b-426b477528af",
   "metadata": {},
   "source": [
    "### Uniform superposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e06f48b6-c1dd-4a63-ac8c-7ab4be64693e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 A #################\n",
    "            Pauli_swept('aX90',    delta_freq)\n",
    "            \n",
    "            ################# Pi/2 B #################\n",
    "            Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74b04a65-5577-49be-afc6-c4bc36b84bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64f8ff77-9f7b-473a-a347-9d3462c2c77e",
   "metadata": {},
   "outputs": [],
   "source": [
    "timing_bell_state= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "\n",
    "delta_timing=((timing_bell_state[:,1:]-timing_bell_state[:,:-1])*1e-6).T\n",
    "label=['chirped pumping','Ramsey tracking','second pumping', 'Pulse sequence','RO']\n",
    "bar_colors = [colors[0],colors[1],colors[0],colors[2],colors[3]]\n",
    "\n",
    "print(delta_timing.mean(1))\n",
    "sizes = delta_timing.mean(1)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize = (7,6))\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    return f\"{pct:.1f}%\\n({absolute:d} ms)\"\n",
    "\n",
    "bar = ax.bar(label,sizes,color = bar_colors, width = 0.8)\n",
    "ax.bar_label(bar, fmt='%i')\n",
    "ax.set_xticklabels(label, rotation = 90)\n",
    "ax.set_ylabel(\"Time (ms)\")\n",
    "#ax.set_yscale(\"log\")\n",
    "#ax.pie(sizes, labels=label, autopct=lambda pct: func(pct, sizes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef93de0-26ad-43c1-b5eb-8590be18b78e",
   "metadata": {},
   "source": [
    "#### Pi A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90fc1cbf-2b8d-4154-8fd7-cee64b9533bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_cnot + nuclear_spin_freq_cnot ) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_cnot)                             # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_a_prep)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_prep)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            # Pi/2 A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "            align()\n",
    "            \n",
    "            # Pi/2 B\n",
    "            # Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep,element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "            # align()\n",
    "\n",
    "            # CNOT\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_cnot,\n",
    "                                                         detuned_sideband_amplitude_cnot,\n",
    "                                                         raman_pi_duration_cnot,\n",
    "                                                         ramp_time_cnot,\n",
    "                                                         element1 = spin_sticky_extra2_element,\n",
    "                                                         element2 = spin_sticky4_element)\n",
    "            \n",
    "\n",
    "            \n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "            \n",
    "            # Pi A\n",
    "            Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, \n",
    "                                                         detuned_sideband_amplitude_a_prep, \n",
    "                                                         raman_pi_duration_a_prep, \n",
    "                                                         ramp_time_prep, \n",
    "                                                         element1 = spin_sticky_element,\n",
    "                                                         element2 = spin_sticky_extra_element)\n",
    "            \n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(1): # XY\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(2): # XZ\n",
    "                    # Pi/2 X A\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(4): # YY\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    wait(100)\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(5): # YZ\n",
    "                    # Pi/2 Y A\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_half_duration_a_prep, ramp_time_prep)\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    frame_rotation_2pi(0.25, spin_sticky_extra2_element)\n",
    "                    Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_half_duration_b_prep, ramp_time_prep, element1 = spin_sticky_extra2_element, element2 = spin_sticky4_element)\n",
    "\n",
    "                with case_(8): # ZZ\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c92960e-a242-40c6-b6ae-57d64ad289d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a244bcd-dd8c-41af-9c8c-6c176dfdbff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76b92a1a-db52-4c04-b3d1-3fa420989dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qutip\n",
    "from qutip import *\n",
    "from scipy.sparse import dia_matrix\n",
    "\n",
    "def generate_S(S: float) -> (Qobj, Qobj, Qobj):\n",
    "    mS = np.arange(S, -S-1, -1)\n",
    "    nS = len(mS)\n",
    "    S_plus = Qobj(dia_matrix((np.sqrt(S*(S+1)-mS*(mS+1)),1), shape = (nS, nS)).A)\n",
    "    S_minus = Qobj(np.transpose(S_plus))\n",
    "    Sx = Qobj(0.5*(S_plus+S_minus))\n",
    "    Sy = Qobj(-0.5*1j*(S_plus-S_minus))\n",
    "    Sz = Qobj(dia_matrix((mS,0), shape = (nS, nS)).A)\n",
    "\n",
    "    return (Sx,Sy,Sz)\n",
    "\n",
    "S = 1/2\n",
    "nS = int(2*S+1)\n",
    "(Sx,Sy,Sz) = generate_S(S)\n",
    "pauli = [2*Sx,2*Sy,2*Sz]\n",
    "\n",
    "states = [qutip.tensor(qutip.basis(2, i//2), qutip.basis(2, i%2)) for i in range(4)]\n",
    "oper_labels = ['X', 'Y', 'Z']\n",
    "state_labels = ['^^', '^v', 'v^', 'vv']\n",
    "\n",
    "for k in range(3):\n",
    "    for l in range(3):\n",
    "        oper = qutip.tensor(pauli[k], pauli[l])\n",
    "        print(oper)\n",
    "        for i in range(4):\n",
    "            state = oper*states[i]\n",
    "            pi = (state.dag()*oper*state).full().flatten()\n",
    "            print(f'<{state_labels[i]}|{oper_labels[k]}x{oper_labels[l]}|{state_labels[i]}> = {pi[0]}')\n",
    "        print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3b25e25-bf2d-41e5-b61e-0ab6fbb46edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "qutip.tensor(pauli[2], pauli[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b6d0fef-8f1d-4b6d-9039-a12f6cb6e229",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Pauli(pulse_identifier):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "    will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "    \"\"\"\n",
    "    spin = pulse_identifier[0]\n",
    "    axis = pulse_identifier[1]\n",
    "    \n",
    "    if pulse_identifier[-2:] == \"90\": angle = 'pihalf'\n",
    "    else: angle = \"pi\"\n",
    "    \n",
    "    print(spin,axis,angle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413bd7e5-3a83-4a30-89eb-da80cf3455aa",
   "metadata": {},
   "source": [
    "# All XY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "94e726d4-4855-4d45-a8a7-060480bc2e58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:22.764163Z",
     "iopub.status.busy": "2024-04-01T18:35:22.763163Z",
     "iopub.status.idle": "2024-04-01T18:35:22.978170Z",
     "shell.execute_reply": "2024-04-01T18:35:22.976165Z",
     "shell.execute_reply.started": "2024-04-01T18:35:22.764163Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Pauli_OLD(spin,axis,angle):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes spin = \"a\", \"b\"\n",
    "    axis = \"X\", \"Y\"\n",
    "    angle = \"pi\", \"pihalf\", \"zero\"\n",
    "    \"\"\"\n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "\n",
    "    if angle!='zero':\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element1)    \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_pi_half_duration_a_prep,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element1)    \n",
    "        align()\n",
    "\n",
    "def Pauli(pulse_identifier, phi = 0):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "    will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "    you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "    phi is the additional Z rotation applied after the pulse in units of 2pi\n",
    "    \"\"\"\n",
    "    \n",
    "    if pulse_identifier[0] == \"I\": return()\n",
    "    \n",
    "    spin = pulse_identifier[0]    \n",
    "    axis = pulse_identifier[1]\n",
    "        \n",
    "    if pulse_identifier[-2:] == \"90\": \n",
    "        angle = 'pihalf'\n",
    "    elif len(pulse_identifier) == 2:\n",
    "        angle = \"pi\"\n",
    "    else:\n",
    "        print(\"error: must specify angle 90 or leave blank for pi rotation\")\n",
    "        return()\n",
    "    \n",
    "    if spin!=\"a\" and spin!=\"b\": \n",
    "        print(\"error: must specify spin a or b\")\n",
    "        return()\n",
    "        \n",
    "    if axis!=\"X\" and axis!=\"Y\": \n",
    "        print(\"error: must specify axis X or Y\")\n",
    "        return()\n",
    "    \n",
    "    \n",
    "    \n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + raman_detuning_a_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "        \n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + raman_detuning_b_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "\n",
    "    if angle!='zero':\n",
    "        \n",
    "        wait(100,element1)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element1)\n",
    "        \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element1)   \n",
    "        wait(100,element1)\n",
    "        \n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element1)\n",
    "        frame_rotation_2pi(phi, element1)\n",
    "        wait(100,element1)\n",
    "        \n",
    "        \n",
    "        align()\n",
    "    \n",
    "    ############# Another dummy pulse to enforce timings #############\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "    ############# Now update element 2 such that the nuclear spin frame evolves at the bare undriven frequency #############\n",
    "    if spin == 'a':\n",
    "        update_frequency(element2, freq_electron + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift , keep_phase=True)  # Detuned Sideband frequency\n",
    "\n",
    "\n",
    "    elif spin == 'b':\n",
    "        update_frequency(element2, freq_electron + raman_detuning_b_prep + nuclear_spin_freq_b_starckshift  , keep_phase=True)  # Detuned Sideband frequency\n",
    "    \n",
    "    ############# Last dummy pulse #############\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element1, duration = 100)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100)\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "# def Pauli_VZ(pulse_identifier, phi):\n",
    "#     \"\"\"Plays a pauli rotation with additional virtual z_phi gate\n",
    "#     takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "#     will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "#     you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "#     \"\"\"\n",
    "    \n",
    "#     if pulse_identifier[0] == \"I\": return()\n",
    "    \n",
    "#     spin = pulse_identifier[0]    \n",
    "#     axis = pulse_identifier[1]\n",
    "        \n",
    "#     if pulse_identifier[-2:] == \"90\": \n",
    "#         angle = 'pihalf'\n",
    "#     elif len(pulse_identifier) == 2:\n",
    "#         angle = \"pi\"\n",
    "#     else:\n",
    "#         print(\"error: must specify angle 90 or leave blank for pi rotation\")\n",
    "#         return()\n",
    "    \n",
    "#     if spin!=\"a\" and spin!=\"b\": \n",
    "#         print(\"error: must specify spin a or b\")\n",
    "#         return()\n",
    "        \n",
    "#     if axis!=\"X\" and axis!=\"Y\": \n",
    "#         print(\"error: must specify axis X or Y\")\n",
    "#         return()\n",
    "    \n",
    "#     if spin=='a':\n",
    "#         detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "#         detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "#         element1 = spin_sticky_element\n",
    "#         element2 = spin_sticky_extra_element\n",
    "        \n",
    "#         if angle=='pi':\n",
    "#             raman_duration=raman_pi_duration_a_prep\n",
    "#         elif angle=='pihalf':\n",
    "#             raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "#     elif spin=='b':\n",
    "#         detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "#         detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "#         element1 = spin_sticky_extra2_element\n",
    "#         element2 = spin_sticky4_element\n",
    "        \n",
    "#         if angle=='pi':\n",
    "#             raman_duration=raman_pi_duration_b_prep\n",
    "#         elif angle=='pihalf':\n",
    "#             raman_duration=int(raman_pi_half_duration_b_prep)\n",
    "\n",
    "#     if angle!='zero':\n",
    "#         wait(4*28//4)\n",
    "#         if axis=='Y':\n",
    "#             frame_rotation_2pi(0.25, element1)   \n",
    "#         update_frequency(element1)\n",
    "    \n",
    "#         Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "#                                                      detuned_sideband_amplitude,\n",
    "#                                                      raman_duration,\n",
    "#                                                      ramp_time_prep,\n",
    "#                                                      element1 = element1,\n",
    "#                                                      element2 = element2)\n",
    "        \n",
    "#         update_frequency(element1)\n",
    "        \n",
    "#         # frame_rotation_2pi(0.5*phi, element1)\n",
    "#         if axis=='Y':\n",
    "#             frame_rotation_2pi(-0.25+0.5*phi, element1)\n",
    "#         wait(4*28//4)\n",
    "#         align()\n",
    "\n",
    "\n",
    "# def Pauli2Q(pulse):\n",
    "#     \"\"\"take input of the form of tuples of strings: pulse=('X','X90') qnd it play the pulses on spin a and b accordingly\"\"\"\n",
    "\n",
    "#     for j,pulse_1Q in enumerate(pulse):\n",
    "#         if j==0:\n",
    "#             qubit='a'\n",
    "#         elif j==1: \n",
    "#             qubit='b'\n",
    "            \n",
    "#         if pulse_1Q=='X':\n",
    "#             Pauli(qubit,'X','pi')\n",
    "#         if pulse_1Q=='X90':\n",
    "#             Pauli(qubit,'X','pihalf')\n",
    "\n",
    "#         if pulse_1Q=='Y':\n",
    "#             Pauli(qubit,'Y','pi')\n",
    "#         if pulse_1Q=='Y90':\n",
    "#             Pauli(qubit,'Y','pihalf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "636ff02a-8a6f-4ba2-95c9-6fe20439d16d",
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    "execution": {
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   "source": [
    "def Pauli2(pulse_identifier, phi = 0):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "    will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "    you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "    phi is the additional Z rotation applied after the pulse in units of 2pi\n",
    "    \"\"\"\n",
    "    \n",
    "    if pulse_identifier[0] == \"I\": return()\n",
    "        \n",
    "    spin = pulse_identifier[0]    \n",
    "    axis = pulse_identifier[1]\n",
    "        \n",
    "    if pulse_identifier[-2:] == \"90\": \n",
    "        angle = 'pihalf'\n",
    "    elif len(pulse_identifier) == 2:\n",
    "        angle = \"pi\" \n",
    "\n",
    "    else:\n",
    "        print(\"error: must specify angle 90 wait (w) or leave blank for pi rotation\")\n",
    "        return()\n",
    "    \n",
    "    if spin!=\"a\" and spin!=\"b\": \n",
    "        print(\"error: must specify spin a or b\")\n",
    "        return()\n",
    "        \n",
    "    if axis!=\"X\" and axis!=\"Y\" and axis!='W': \n",
    "        print(\"error: must specify axis X or Y, or wait (W)\")\n",
    "        return()\n",
    "    \n",
    "    \n",
    "    \n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "        \n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "\n",
    "    if axis!='W':\n",
    "        \n",
    "        wait(100,element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element2)\n",
    "        \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element2)   \n",
    "        wait(100,element2)\n",
    "        \n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        frame_rotation_2pi(-phi, element2)\n",
    "        wait(100,element2)\n",
    "     \n",
    "    elif axis=='W':\n",
    "        wait(100,element2)\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(0,\n",
    "                                                     0,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "\n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        wait(100,element2)\n",
    "     \n",
    "    \n",
    "    align()\n",
    "    \n",
    "    ############# Another dummy pulse to enforce timings #############\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "    ############# Now update element 2 such that the nuclear spin frame evolves at the bare undriven frequency #############\n",
    "    if spin == 'a':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift , keep_phase=True)  # Detuned Sideband frequency\n",
    "\n",
    "\n",
    "    elif spin == 'b':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_starckshift  , keep_phase=True)  # Detuned Sideband frequency\n",
    "    \n",
    "    ############# Last dummy pulse #############\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    # play(spin_sticky_pulse*amp(0.0), element1, duration = 100)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100)\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2190,
   "id": "1e5065d0-8e7b-4f79-8076-02ceefa85652",
   "metadata": {
    "execution": {
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   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<21, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            # pulse_list_1 = ['aX','aY','aX90','aY90']\n",
    "            # pulse_list_2 = ['I','aX','aY','aX90','aY90']\n",
    "            \n",
    "            pulse_list_1 = ['bX','bY','bX90','bY90']\n",
    "            pulse_list_2 = ['I','bX','bY','bX90','bY90']\n",
    "            \n",
    "            pulse_list = [(p1,p2) for p1 in pulse_list_1 for p2 in pulse_list_2]\n",
    "            pulse_list.insert(0,('I','I'))\n",
    "\n",
    "            with switch_(l):\n",
    "                for i, pulse_ in enumerate(pulse_list):\n",
    "                    with case_(i):\n",
    "                        Pauli2(pulse_[0],-0.431)\n",
    "                        Pauli2(pulse_[1],-0.431)\n",
    "                        \n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
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    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\1294548278.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Data shape:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "better_sleep(0*600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "idx = [0,2,8,3,7,11,16,15,19,13,17,5,9,12,4,18,10,1,6,14,20]\n",
    "data = data[:,idx]\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "x = np.linspace(1,len(p3),len(p3))\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[idx][:,0]\n",
    "p2 = np.array(pulse_list)[idx][:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xticks(x,[p1[i]+\" \"+p2[i] for i in range(len(p1))], rotation = 90)\n",
    "#plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "248cf5d5-04bd-4a07-823d-31254bc2cf33",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<21, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            pulse_list_1 = ['aX','aY','aX90','aY90']\n",
    "            pulse_list_2 = ['I','aX','aY','aX90','aY90']\n",
    "            \n",
    "            # pulse_list_1 = ['bX','bY','bX90','bY90']\n",
    "            # pulse_list_2 = ['I','bX','bY','bX90','bY90']  \n",
    "            \n",
    "            pulse_list = [(p1,p2) for p1 in pulse_list_1 for p2 in pulse_list_2]\n",
    "            pulse_list.insert(0,('I','I'))\n",
    "\n",
    "            with switch_(l):\n",
    "                for i, pulse_ in enumerate(pulse_list):\n",
    "                    with case_(i):\n",
    "                        Pauli(pulse_[0],0.239)\n",
    "                        Pauli(pulse_[1],0.239)\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "720cd482-0f2f-4edc-80c2-e3783691c7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*6*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "idx = [0,2,8,3,7,11,16,15,19,13,17,5,9,12,4,18,10,1,6,14,20]\n",
    "data = data[:,idx]\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "x = np.linspace(1,len(p3),len(p3))\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[idx][:,0]\n",
    "p2 = np.array(pulse_list)[idx][:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xticks(x,[p1[i]+\" \"+p2[i] for i in range(len(p1))], rotation = 90)\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f0bba7-99cb-4fd2-a3f8-ce34bf6727b2",
   "metadata": {},
   "source": [
    "## Adjusting z rotation on pulses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4999cde6-211d-4d4a-86db-481ac3a63fff",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd105372-f06b-476e-9895-be80ac71c1c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:26.866224Z",
     "iopub.status.busy": "2024-04-01T18:35:26.865222Z",
     "iopub.status.idle": "2024-04-01T18:35:30.178789Z",
     "shell.execute_reply": "2024-04-01T18:35:30.176793Z",
     "shell.execute_reply.started": "2024-04-01T18:35:26.866224Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('aX90','aY90'),('aY90','aX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "phis = sinhspace(0.229-0.5,0.229+0.5,21, nonlinearity = 3)\n",
    "phis = [round(float(phi),4) for phi in phis]\n",
    "#phis = np.linspace(-0.5,0.5,20)\n",
    "# phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dec3de61-c491-4882-85fd-d523807931b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:49:48.001444Z",
     "iopub.status.busy": "2024-04-01T18:49:48.001444Z",
     "iopub.status.idle": "2024-04-01T18:49:48.923507Z",
     "shell.execute_reply": "2024-04-01T18:49:48.922506Z",
     "shell.execute_reply.started": "2024-04-01T18:49:48.001444Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (17, 21, 2, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0.*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x-phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(f\"phi/2pi = {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "        \n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e7873c-af5c-4857-8895-84452930cd6f",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c250a834-0599-4853-86c5-965d06521969",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:50:00.928360Z",
     "iopub.status.busy": "2024-04-01T18:50:00.926360Z",
     "iopub.status.idle": "2024-04-01T18:50:04.304617Z",
     "shell.execute_reply": "2024-04-01T18:50:04.303623Z",
     "shell.execute_reply.started": "2024-04-01T18:50:00.928360Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('bX90','bY90'),('bY90','bX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "phis = sinhspace(-0.432-0.5,-0.432+0.5,11, nonlinearity = 1)\n",
    "phis = [round(float(phi),4) for phi in phis]\n",
    "# phis = np.linspace(-0.5,0.5,10)\n",
    "# phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb7c52c4-9206-427f-8c06-7b0ae3df5640",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T18:49:43.772334Z",
     "iopub.status.idle": "2024-04-01T18:49:43.773333Z",
     "shell.execute_reply": "2024-04-01T18:49:43.773333Z",
     "shell.execute_reply.started": "2024-04-01T18:49:43.773333Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(0.3*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x-phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,-np.pi]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(r\"$\\phi/2\\pi$\"+f\"= {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "    \n",
    "\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "\n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcb214db-54dc-46be-b880-466fd6f8053b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x+phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(f\"phi/2pi = {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "        \n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d31ca265-4fec-43b0-a032-cbadc75491e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c3168f8-740b-4093-84e1-f9f124f11770",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('bX90','bY90'),('bY90','bX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "# phis = [round(float(phi),4) for phi in phis]\n",
    "phis = sinhspace(0.35,0.45,11, nonlinearity = 2)\n",
    "phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "\n",
    "####################### Run program #######################\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f827e02-aefe-492f-985f-5dae7dfa04f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "plt.plot(x,p3,\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4766cfd0-5224-438f-9f3f-71790a1533ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linspace(-0.5,0.5,41)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5561bd8a-bdf1-4cfc-b727-a9df09cdec43",
   "metadata": {},
   "source": [
    "# (old) All XY "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d46da09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:22.764163Z",
     "iopub.status.busy": "2024-04-01T18:35:22.763163Z",
     "iopub.status.idle": "2024-04-01T18:35:22.978170Z",
     "shell.execute_reply": "2024-04-01T18:35:22.976165Z",
     "shell.execute_reply.started": "2024-04-01T18:35:22.764163Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Pauli_OLD(spin,axis,angle):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes spin = \"a\", \"b\"\n",
    "    axis = \"X\", \"Y\"\n",
    "    angle = \"pi\", \"pihalf\", \"zero\"\n",
    "    \"\"\"\n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "\n",
    "    if angle!='zero':\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element1)    \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_pi_half_duration_a_prep,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element1)    \n",
    "        align()\n",
    "\n",
    "def Pauli(pulse_identifier, phi = 0):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "    will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "    you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "    phi is the additional Z rotation applied after the pulse in units of 2pi\n",
    "    \"\"\"\n",
    "    \n",
    "    if pulse_identifier[0] == \"I\": return()\n",
    "    \n",
    "    spin = pulse_identifier[0]    \n",
    "    axis = pulse_identifier[1]\n",
    "        \n",
    "    if pulse_identifier[-2:] == \"90\": \n",
    "        angle = 'pihalf'\n",
    "    elif len(pulse_identifier) == 2:\n",
    "        angle = \"pi\"\n",
    "    else:\n",
    "        print(\"error: must specify angle 90 or leave blank for pi rotation\")\n",
    "        return()\n",
    "    \n",
    "    if spin!=\"a\" and spin!=\"b\": \n",
    "        print(\"error: must specify spin a or b\")\n",
    "        return()\n",
    "        \n",
    "    if axis!=\"X\" and axis!=\"Y\": \n",
    "        print(\"error: must specify axis X or Y\")\n",
    "        return()\n",
    "    \n",
    "    \n",
    "    \n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + raman_detuning_a_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "        \n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + raman_detuning_b_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "\n",
    "    if angle!='zero':\n",
    "        \n",
    "        wait(100,element1)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element1)\n",
    "        \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element1)   \n",
    "        wait(100,element1)\n",
    "        \n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element1)\n",
    "        frame_rotation_2pi(phi, element1)\n",
    "        wait(100,element1)\n",
    "        \n",
    "        \n",
    "        align()\n",
    "    \n",
    "    ############# Another dummy pulse to enforce timings #############\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "    ############# Now update element 2 such that the nuclear spin frame evolves at the bare undriven frequency #############\n",
    "    if spin == 'a':\n",
    "        update_frequency(element2, freq_electron + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift , keep_phase=True)  # Detuned Sideband frequency\n",
    "\n",
    "\n",
    "    elif spin == 'b':\n",
    "        update_frequency(element2, freq_electron + raman_detuning_b_prep + nuclear_spin_freq_b_starckshift  , keep_phase=True)  # Detuned Sideband frequency\n",
    "    \n",
    "    ############# Last dummy pulse #############\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    play(spin_sticky_pulse*amp(0.0), element1, duration = 100)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100)\n",
    "    wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "# def Pauli_VZ(pulse_identifier, phi):\n",
    "#     \"\"\"Plays a pauli rotation with additional virtual z_phi gate\n",
    "#     takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "#     will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "#     you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "#     \"\"\"\n",
    "    \n",
    "#     if pulse_identifier[0] == \"I\": return()\n",
    "    \n",
    "#     spin = pulse_identifier[0]    \n",
    "#     axis = pulse_identifier[1]\n",
    "        \n",
    "#     if pulse_identifier[-2:] == \"90\": \n",
    "#         angle = 'pihalf'\n",
    "#     elif len(pulse_identifier) == 2:\n",
    "#         angle = \"pi\"\n",
    "#     else:\n",
    "#         print(\"error: must specify angle 90 or leave blank for pi rotation\")\n",
    "#         return()\n",
    "    \n",
    "#     if spin!=\"a\" and spin!=\"b\": \n",
    "#         print(\"error: must specify spin a or b\")\n",
    "#         return()\n",
    "        \n",
    "#     if axis!=\"X\" and axis!=\"Y\": \n",
    "#         print(\"error: must specify axis X or Y\")\n",
    "#         return()\n",
    "    \n",
    "#     if spin=='a':\n",
    "#         detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "#         detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "#         element1 = spin_sticky_element\n",
    "#         element2 = spin_sticky_extra_element\n",
    "        \n",
    "#         if angle=='pi':\n",
    "#             raman_duration=raman_pi_duration_a_prep\n",
    "#         elif angle=='pihalf':\n",
    "#             raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "#     elif spin=='b':\n",
    "#         detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "#         detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "#         element1 = spin_sticky_extra2_element\n",
    "#         element2 = spin_sticky4_element\n",
    "        \n",
    "#         if angle=='pi':\n",
    "#             raman_duration=raman_pi_duration_b_prep\n",
    "#         elif angle=='pihalf':\n",
    "#             raman_duration=int(raman_pi_half_duration_b_prep)\n",
    "\n",
    "#     if angle!='zero':\n",
    "#         wait(4*28//4)\n",
    "#         if axis=='Y':\n",
    "#             frame_rotation_2pi(0.25, element1)   \n",
    "#         update_frequency(element1)\n",
    "    \n",
    "#         Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "#                                                      detuned_sideband_amplitude,\n",
    "#                                                      raman_duration,\n",
    "#                                                      ramp_time_prep,\n",
    "#                                                      element1 = element1,\n",
    "#                                                      element2 = element2)\n",
    "        \n",
    "#         update_frequency(element1)\n",
    "        \n",
    "#         # frame_rotation_2pi(0.5*phi, element1)\n",
    "#         if axis=='Y':\n",
    "#             frame_rotation_2pi(-0.25+0.5*phi, element1)\n",
    "#         wait(4*28//4)\n",
    "#         align()\n",
    "\n",
    "\n",
    "# def Pauli2Q(pulse):\n",
    "#     \"\"\"take input of the form of tuples of strings: pulse=('X','X90') qnd it play the pulses on spin a and b accordingly\"\"\"\n",
    "\n",
    "#     for j,pulse_1Q in enumerate(pulse):\n",
    "#         if j==0:\n",
    "#             qubit='a'\n",
    "#         elif j==1: \n",
    "#             qubit='b'\n",
    "            \n",
    "#         if pulse_1Q=='X':\n",
    "#             Pauli(qubit,'X','pi')\n",
    "#         if pulse_1Q=='X90':\n",
    "#             Pauli(qubit,'X','pihalf')\n",
    "\n",
    "#         if pulse_1Q=='Y':\n",
    "#             Pauli(qubit,'Y','pi')\n",
    "#         if pulse_1Q=='Y90':\n",
    "#             Pauli(qubit,'Y','pihalf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "189aa343",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:23.367198Z",
     "iopub.status.busy": "2024-04-01T18:35:23.366182Z",
     "iopub.status.idle": "2024-04-01T18:35:23.585226Z",
     "shell.execute_reply": "2024-04-01T18:35:23.583221Z",
     "shell.execute_reply.started": "2024-04-01T18:35:23.367198Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Pauli2(pulse_identifier, phi = 0):\n",
    "    \"\"\"Plays a pauli rotation\n",
    "    takes (for example) pulse_identifier = \"aX90\", \"bY\", etc.\n",
    "    will only do pi or pi/2 along x or y for nuclear spin a or b\n",
    "    you can also pass \"I\" as the pulse identifier, in which case it will return nothing\n",
    "    phi is the additional Z rotation applied after the pulse in units of 2pi\n",
    "    \"\"\"\n",
    "    \n",
    "    if pulse_identifier[0] == \"I\": return()\n",
    "        \n",
    "    spin = pulse_identifier[0]    \n",
    "    axis = pulse_identifier[1]\n",
    "        \n",
    "    if pulse_identifier[-2:] == \"90\": \n",
    "        angle = 'pihalf'\n",
    "    elif len(pulse_identifier) == 2:\n",
    "        angle = \"pi\" \n",
    "\n",
    "    else:\n",
    "        print(\"error: must specify angle 90 wait (w) or leave blank for pi rotation\")\n",
    "        return()\n",
    "    \n",
    "    if spin!=\"a\" and spin!=\"b\": \n",
    "        print(\"error: must specify spin a or b\")\n",
    "        return()\n",
    "        \n",
    "    if axis!=\"X\" and axis!=\"Y\" and axis!='W': \n",
    "        print(\"error: must specify axis X or Y, or wait (W)\")\n",
    "        return()\n",
    "    \n",
    "    \n",
    "    \n",
    "    if spin=='a':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_a_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_a_prep\n",
    "        \n",
    "        element1 = spin_sticky_element\n",
    "        element2 = spin_sticky_extra_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_a_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_a_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        wait(100,element2)\n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "        \n",
    "    elif spin=='b':\n",
    "        detuned_electron_amplitude = detuned_electron_amplitude_b_prep\n",
    "        detuned_sideband_amplitude = detuned_sideband_amplitude_b_prep\n",
    "        \n",
    "        element1 = spin_sticky_extra2_element\n",
    "        element2 = spin_sticky4_element\n",
    "        \n",
    "        wait(100,element2)\n",
    "        \n",
    "        if angle=='pi':\n",
    "            raman_duration=raman_pi_duration_b_prep\n",
    "        elif angle=='pihalf':\n",
    "            raman_duration=raman_pi_half_duration_b_prep\n",
    "\n",
    "        \n",
    "        ############# Update frequency on element 2 to the correct frequency for pulses #############\n",
    "        \n",
    "        # Sandwich between dummy pulses to enforce the timing of the update_frequency\n",
    "        # All dummy pulses are in turn sandwiched between waits to keep the OPX happy\n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        update_frequency(element1, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep,                            keep_phase=True)  # Detuned Sideband frequency      \n",
    "        \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "        play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "        # wait(100,element1)\n",
    "        wait(100,element2)\n",
    "        \n",
    "        #############################################################################################\n",
    "\n",
    "    if axis!='W':\n",
    "        \n",
    "        wait(100,element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(-0.25, element2)\n",
    "        \n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(detuned_electron_amplitude,\n",
    "                                                     detuned_sideband_amplitude,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "        if axis=='Y':\n",
    "            frame_rotation_2pi(0.25, element2)   \n",
    "        wait(100,element2)\n",
    "        \n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        frame_rotation_2pi(-phi, element2)\n",
    "        wait(100,element2)\n",
    "     \n",
    "    elif axis=='W':\n",
    "        wait(100,element2)\n",
    "        Raman_pulse_cos_no_phase_reset_no_frequpdate(0,\n",
    "                                                     0,\n",
    "                                                     raman_duration,\n",
    "                                                     ramp_time_prep,\n",
    "                                                     element1 = element1,\n",
    "                                                     element2 = element2)\n",
    "\n",
    "        ############# Additional frame rotation corrects for excess Z axis rotation during the pulse itself #############\n",
    "        wait(100,element2)\n",
    "        wait(100,element2)\n",
    "     \n",
    "    \n",
    "    align()\n",
    "    \n",
    "    ############# Another dummy pulse to enforce timings #############\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    # play(spin_sticky_pulse*amp(0.0), element1, duration = 100) \n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100) \n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "\n",
    "    ############# Now update element 2 such that the nuclear spin frame evolves at the bare undriven frequency #############\n",
    "    if spin == 'a':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_starckshift , keep_phase=True)  # Detuned Sideband frequency\n",
    "\n",
    "\n",
    "    elif spin == 'b':\n",
    "        update_frequency(element2, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_starckshift  , keep_phase=True)  # Detuned Sideband frequency\n",
    "    \n",
    "    ############# Last dummy pulse #############\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)\n",
    "    # play(spin_sticky_pulse*amp(0.0), element1, duration = 100)\n",
    "    play(spin_sticky_pulse*amp(0.0), element2, duration = 100)\n",
    "    # wait(100,element1)\n",
    "    wait(100,element2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2190,
   "id": "846f9a9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:20:39.114621Z",
     "iopub.status.busy": "2024-04-01T18:20:39.113620Z",
     "iopub.status.idle": "2024-04-01T18:20:54.918809Z",
     "shell.execute_reply": "2024-04-01T18:20:54.916795Z",
     "shell.execute_reply.started": "2024-04-01T18:20:39.114621Z"
    }
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<21, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            # pulse_list_1 = ['aX','aY','aX90','aY90']\n",
    "            # pulse_list_2 = ['I','aX','aY','aX90','aY90']\n",
    "            \n",
    "            pulse_list_1 = ['bX','bY','bX90','bY90']\n",
    "            pulse_list_2 = ['I','bX','bY','bX90','bY90']\n",
    "            \n",
    "            pulse_list = [(p1,p2) for p1 in pulse_list_1 for p2 in pulse_list_2]\n",
    "            pulse_list.insert(0,('I','I'))\n",
    "\n",
    "            with switch_(l):\n",
    "                for i, pulse_ in enumerate(pulse_list):\n",
    "                    with case_(i):\n",
    "                        Pauli2(pulse_[0],-0.431)\n",
    "                        Pauli2(pulse_[1],-0.431)\n",
    "                        \n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2191,
   "id": "ed7a572f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:20:54.920807Z",
     "iopub.status.busy": "2024-04-01T18:20:54.919806Z",
     "iopub.status.idle": "2024-04-01T18:20:55.199355Z",
     "shell.execute_reply": "2024-04-01T18:20:55.197353Z",
     "shell.execute_reply.started": "2024-04-01T18:20:54.920807Z"
    }
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\1294548278.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Data shape:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "better_sleep(0*600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "idx = [0,2,8,3,7,11,16,15,19,13,17,5,9,12,4,18,10,1,6,14,20]\n",
    "data = data[:,idx]\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "x = np.linspace(1,len(p3),len(p3))\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[idx][:,0]\n",
    "p2 = np.array(pulse_list)[idx][:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xticks(x,[p1[i]+\" \"+p2[i] for i in range(len(p1))], rotation = 90)\n",
    "#plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93fc93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<21, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            align()\n",
    "\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            pulse_list_1 = ['aX','aY','aX90','aY90']\n",
    "            pulse_list_2 = ['I','aX','aY','aX90','aY90']\n",
    "            \n",
    "            # pulse_list_1 = ['bX','bY','bX90','bY90']\n",
    "            # pulse_list_2 = ['I','bX','bY','bX90','bY90']  \n",
    "            \n",
    "            pulse_list = [(p1,p2) for p1 in pulse_list_1 for p2 in pulse_list_2]\n",
    "            pulse_list.insert(0,('I','I'))\n",
    "\n",
    "            with switch_(l):\n",
    "                for i, pulse_ in enumerate(pulse_list):\n",
    "                    with case_(i):\n",
    "                        Pauli(pulse_[0],0.239)\n",
    "                        Pauli(pulse_[1],0.239)\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a6d398",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*6*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "idx = [0,2,8,3,7,11,16,15,19,13,17,5,9,12,4,18,10,1,6,14,20]\n",
    "data = data[:,idx]\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "x = np.linspace(1,len(p3),len(p3))\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[idx][:,0]\n",
    "p2 = np.array(pulse_list)[idx][:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xticks(x,[p1[i]+\" \"+p2[i] for i in range(len(p1))], rotation = 90)\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880ad972-9eac-4de2-8df5-5a8d465e6c53",
   "metadata": {},
   "source": [
    "## Adjusting z rotation on pulses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b39048-ad11-4ba7-a435-b5e5f58ca947",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ae8d3b97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:35:26.866224Z",
     "iopub.status.busy": "2024-04-01T18:35:26.865222Z",
     "iopub.status.idle": "2024-04-01T18:35:30.178789Z",
     "shell.execute_reply": "2024-04-01T18:35:30.176793Z",
     "shell.execute_reply.started": "2024-04-01T18:35:26.866224Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('aX90','aY90'),('aY90','aX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "phis = sinhspace(0.229-0.5,0.229+0.5,21, nonlinearity = 3)\n",
    "phis = [round(float(phi),4) for phi in phis]\n",
    "#phis = np.linspace(-0.5,0.5,20)\n",
    "# phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f413363",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:49:48.001444Z",
     "iopub.status.busy": "2024-04-01T18:49:48.001444Z",
     "iopub.status.idle": "2024-04-01T18:49:48.923507Z",
     "shell.execute_reply": "2024-04-01T18:49:48.922506Z",
     "shell.execute_reply.started": "2024-04-01T18:49:48.001444Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (17, 21, 2, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(0.*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x-phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(f\"phi/2pi = {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "        \n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa85e96d-5c29-4f30-8a1d-ebdf0cf2584c",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3b4e583",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T18:50:00.928360Z",
     "iopub.status.busy": "2024-04-01T18:50:00.926360Z",
     "iopub.status.idle": "2024-04-01T18:50:04.304617Z",
     "shell.execute_reply": "2024-04-01T18:50:04.303623Z",
     "shell.execute_reply.started": "2024-04-01T18:50:00.928360Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('bX90','bY90'),('bY90','bX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "phis = sinhspace(-0.432-0.5,-0.432+0.5,11, nonlinearity = 1)\n",
    "phis = [round(float(phi),4) for phi in phis]\n",
    "# phis = np.linspace(-0.5,0.5,10)\n",
    "# phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11dfc01",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T18:49:43.772334Z",
     "iopub.status.idle": "2024-04-01T18:49:43.773333Z",
     "shell.execute_reply": "2024-04-01T18:49:43.773333Z",
     "shell.execute_reply.started": "2024-04-01T18:49:43.773333Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(0.3*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x-phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,-np.pi]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(r\"$\\phi/2\\pi$\"+f\"= {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "    \n",
    "\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "\n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01e9f8e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "#plt.plot(x,p3,\"o-\")\n",
    "\n",
    "def sine_fit(x,a,b,f,phi):\n",
    "    return a*np.sin(2*np.pi*f*x+phi)+b\n",
    "\n",
    "for i in range(p3.shape[-1]):\n",
    "    plt.errorbar(x,p3[:,i],yerr=np.sqrt(p3[:,i]*(1-p3[:,i])/data.shape[0]),fmt=\"o\")\n",
    "    guess = [1,0,1,1]\n",
    "    try:\n",
    "        est,std,fine,data_fit = fit_function(guess, sine_fit, x, p3[:,i])\n",
    "        plt.plot(fine,data_fit, color = colors[i], alpha = 0.5)\n",
    "    except:\n",
    "        print(\"fit failed\")\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "save_fig_manustyle(directory+filename+'_'+'populations.pdf')\n",
    "plt.show()\n",
    "plt.figure(figsize = (6,5))\n",
    "diff_data = p3[:,0]-p3[:,1]\n",
    "\n",
    "plt.errorbar(x,diff_data,yerr=np.sqrt((p3[:,0]*(1-p3[:,0])+p3[:,1]*(1-p3[:,1]))/data.shape[0]),color = colors[3],fmt=\"o\")\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, sine_fit, x, diff_data)\n",
    "    plt.title(f\"phi/2pi = {np.round(est[3]/(2*np.pi),3)}±{np.round(std[3]/(2*np.pi),3)}\")\n",
    "    plt.plot(fine,data_fit, color = colors[3], alpha = 0.5)\n",
    "except:\n",
    "    print(\"fit failed\")\n",
    "        \n",
    "        \n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "#plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/2\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population_diff.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8dad896",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67c15a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "pulse_list = [('bX90','bY90'),('bY90','bX90')]\n",
    "# phis = np.linspace(-1,1,5)\n",
    "# phis = [round(float(phi),4) for phi in phis]\n",
    "phis = sinhspace(0.35,0.45,11, nonlinearity = 2)\n",
    "phis = [float(phi) for phi in phis]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='AllXY'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "\n",
    "####################### Run program #######################\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    phi_set  = declare(fixed)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(phi_set, phis):\n",
    "        # with for_(phi_set, 0, phi_set < 0.55, phi_set + 0.1):\n",
    "            with for_(l, 0, l<len(pulse_list), l + 1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "    \n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "    \n",
    "                save(0, timing_stream)\n",
    "                \n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "    \n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "    \n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "    \n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "    \n",
    "                with switch_(l):\n",
    "                    for i, pulse_ in enumerate(pulse_list):\n",
    "                        with case_(i):\n",
    "                        \n",
    "                            align()\n",
    "                            Pauli(pulse_[0],phi_set)\n",
    "\n",
    "                            align()\n",
    "                            Pauli(pulse_[1], phi_set)\n",
    "\n",
    "                            align()\n",
    "                            \n",
    "                    with default_():\n",
    "                        pass\n",
    "    \n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "    \n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "    \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(pulse_list)).buffer(len(phis)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bb965f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*3600)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "\n",
    "x = phis\n",
    "\n",
    "plt.figure(figsize = (6,5))\n",
    "\n",
    "plt.plot(x,p3,\"o-\")\n",
    "\n",
    "p1 = np.array(pulse_list)[:,0]\n",
    "p2 = np.array(pulse_list)[:,1]\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.ylabel(r\"$P_{|\\downarrow\\downarrow\\rangle}$\")\n",
    "plt.xlabel(r\"$\\phi/\\pi$\")\n",
    "plt.tight_layout()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'pulse_list': pulse_list,\n",
    "}\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1db4f056",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linspace(-0.5,0.5,41)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1749c99f-3730-4a16-91fe-b406908e3e4a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Recalibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa632b47-fefd-429f-b4d8-02f3ef5c615d",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Pi A pulse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcd6fab-3943-4338-ae89-221d6c450be2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-19T17:41:16.889850Z",
     "iopub.status.busy": "2024-02-19T17:41:16.888849Z",
     "iopub.status.idle": "2024-02-19T17:41:17.075852Z",
     "shell.execute_reply": "2024-02-19T17:41:17.074851Z",
     "shell.execute_reply.started": "2024-02-19T17:41:16.889850Z"
    }
   },
   "source": [
    "### Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02558df-6f42-4281-982c-e938596cfc9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_pulse_duration = int(5.8e6//4) #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_a_prep + sinhspace(-1e3,1e3,16,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc755978-8d5c-46cc-898c-afcff943db3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0.3)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1090bd1f-6278-4a99-8b6e-917d14d35861",
   "metadata": {},
   "source": [
    "#### At low amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd189b69-11e0-4b3e-9258-d686992b06e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "raman_pulse_duration = raman_pi_duration_a_prep*4  #raman_pi_duration_b_prep #int(0.58e6//4)\n",
    "nuclear_spin_frequencies = int(808.8e3) + sinhspace(-0.5e3,0.5e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "raman_pulse_amp = 0.025\n",
    "\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "\n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_prep, raman_pulse_amp, raman_pulse_amp, raman_pulse_duration, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)#, state_list=[False, False, True, True])\n",
    "            #nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65148373-7391-447b-bf8a-4b247384d4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(300)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b14245-8024-4b43-99dc-5557dde03550",
   "metadata": {},
   "source": [
    "### Rabi Freq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc56a837-9347-49e6-9f73-1f3da344ee01",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,1,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d9ea73-85bd-4395-b39e-d646df30c53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "threshold = 50\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc6220c-c342-456e-a3ec-a33d63cb8c80",
   "metadata": {},
   "source": [
    "#### At low amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1522ad-c41c-436f-8a66-79bb445288e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,7,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "raman_pulse_amp = 0.025\n",
    "raman_pulse_freq = int(808.79e3)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_a'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(raman_pulse_freq , freq_electron + delta_freq, raman_detuning_prep,raman_pulse_amp, raman_pulse_amp, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34bbc4ac-0b34-4571-9bdf-0da5d96d2abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(1800)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "threshold = 50\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data =data[:,:,-1]# (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9a6903-1146-4055-8988-a588a1c90a80",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Pi B pulse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7ce682-8417-4a9c-8d20-cabd488d3814",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-19T17:41:16.889850Z",
     "iopub.status.busy": "2024-02-19T17:41:16.888849Z",
     "iopub.status.idle": "2024-02-19T17:41:17.075852Z",
     "shell.execute_reply": "2024-02-19T17:41:17.074851Z",
     "shell.execute_reply.started": "2024-02-19T17:41:16.889850Z"
    }
   },
   "source": [
    "### Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd2dc001-419a-402a-aa67-119e75bbe2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_b_prep + sinhspace(-0.3e3,0.3e3,16,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ad7703-ae39-4274-8bf5-47ee3f55fc13",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0.3)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value']\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8121763-dd16-4788-9ac4-8d17364e83e2",
   "metadata": {},
   "source": [
    "### Rabi Freq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbab9f60-79f5-452e-9286-2dd9253b4692",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,7,11))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, duration_set, ramp_time_prep)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "796a20ca-e088-4cbe-9685-e6a956a7dee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0.5)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2262b023-5c05-4ce0-bfe3-b50e70d2883e",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## CNOT gate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667b6aa4-f5e8-468e-8c8c-95d02abb1192",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-19T17:41:16.889850Z",
     "iopub.status.busy": "2024-02-19T17:41:16.888849Z",
     "iopub.status.idle": "2024-02-19T17:41:17.075852Z",
     "shell.execute_reply": "2024-02-19T17:41:17.074851Z",
     "shell.execute_reply.started": "2024-02-19T17:41:16.889850Z"
    }
   },
   "source": [
    "### Spectroscopy on B (A down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e3f30af-42d6-4c4d-8185-dec7d2f53802",
   "metadata": {},
   "outputs": [],
   "source": [
    "nuclear_spin_freq_cnot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "828cdf92-1ec5-41e8-b940-a1a4c39a95a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "# nuclear_spin_frequencies = nuclear_spin_freq_cnot + sinhspace(-0.3e3,0.3e3,16,nonlinearity=1)\n",
    "nuclear_spin_frequencies = np.linspace(809.9e3, 810.7e3, 25)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot, detuned_sideband_amplitude_cnot, raman_pi_duration_cnot, ramp_time_cnot)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eb2a133-49c6-4e5f-9109-101886a42ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*4.5)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "skip_points = 2\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()][skip_points:])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p3\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value'][skip_points*len(x):]\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57c5660-baaa-4262-930a-9f817be04f83",
   "metadata": {},
   "source": [
    "### Spectroscopy on B (A up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e3bce5-7504-4849-88ba-d81057c0b8b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "# nuclear_spin_frequencies =810.42e3 + sinhspace(-0.2e3,0.2e3,11,nonlinearity=2)#nuclear_spin_freq_b + sinhspace(-0.3e3,0.3e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = np.linspace(809.9e3, 810.7e3, 25)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Pi Pulse on Spin A #################\n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot, detuned_sideband_amplitude_cnot, raman_pi_duration_cnot, ramp_time_cnot)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b665f3-602e-4e31-9a45-2614d89cfc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*5)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "skip_points = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()][skip_points:])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p1\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value'][skip_points*len(x):]\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536be050-dfc4-46ea-bd17-af41c56a97f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-19T17:41:16.889850Z",
     "iopub.status.busy": "2024-02-19T17:41:16.888849Z",
     "iopub.status.idle": "2024-02-19T17:41:17.075852Z",
     "shell.execute_reply": "2024-02-19T17:41:17.074851Z",
     "shell.execute_reply.started": "2024-02-19T17:41:16.889850Z"
    }
   },
   "source": [
    "### Spectroscopy on A (B down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63c61c1b-8194-44d2-bd86-5ed653575d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_cnot_a + sinhspace(-0.3e3,0.3e3,16,nonlinearity=1)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            # Raman_pulse_cos(nuclear_spin_freq_a_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_a_prep, detuned_sideband_amplitude_a_prep, raman_pi_duration_a_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot_a, detuned_sideband_amplitude_cnot_a, raman_pi_duration_cnot_a, ramp_time_cnot)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85106727-c8e4-489e-b1ab-759479b21a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "skip_points = 2\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()][skip_points:])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p2\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value'][skip_points*len(x):]\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e3e055-ee65-4f76-988d-ba151dde6a57",
   "metadata": {},
   "source": [
    "### Spectroscopy on A (B up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01d42c15-088d-4977-a172-588dc38d8d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "spectroscopy_b = False\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "\n",
    "####################### Raman pulse params #######################\n",
    "\n",
    "nuclear_spin_frequencies = nuclear_spin_freq_cnot_a + sinhspace(-0.2e3,0.2e3,11,nonlinearity=0)#nuclear_spin_freq_b + sinhspace(-0.3e3,0.3e3,16,nonlinearity=2)\n",
    "nuclear_spin_frequencies = [int(freq) for freq in nuclear_spin_frequencies]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_spectroscopy'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Define preparation parameters #######################\n",
    "\n",
    "N_repetition = 1e6\n",
    "N_ROcycle=250\n",
    "switch_duration_extra = (adiabatic_spin_sigma*(adiabatic_spin_Nsigma+0.5)*2)//16*4 # Ensures this value is a multiple of 4\n",
    "#readout_freqs = [readout_freqs[-1]]\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(freq_set, nuclear_spin_frequencies):\n",
    "            \n",
    "            ################# Raman experiment #################\n",
    "            # Sweep number of readout pulses\n",
    "    \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            \n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "        \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            ################# Pi Pulse on Spin A #################\n",
    "            \n",
    "            align()\n",
    "            Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron+delta_freq, raman_detuning_prep, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time_prep)\n",
    "\n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "                        \n",
    "            Raman_pulse_cos(freq_set, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot_a, detuned_sideband_amplitude_cnot_a, raman_pi_duration_cnot_a, ramp_time_cnot)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(nuclear_spin_frequencies)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df7722b9-3d0c-48d1-bcb3-c23590ac356d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "skip_points = 0\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()][skip_points:])\n",
    "# data_prep = np.array([item[0] for item in res.clicks_prep.fetch_all()])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "sigmoid_length = 5\n",
    "x = 1e-3*np.array(nuclear_spin_frequencies)\n",
    "\n",
    "############################### extract populations #################################\n",
    "\n",
    "if len(readout_freqs) == 4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans = kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    p_data = p2\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "############################### Fit ###############################\n",
    "\n",
    "guess = [x[np.argmin(p_data)],(max(x)-min(x))/8, min(p_data)-max(p_data), max(p_data)]\n",
    "try: \n",
    "    est,std,fine,data_fit = fit_function(guess, lorentz, x, p_data)\n",
    "    fit_success = 1\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    fit_success = 0\n",
    "    fine = np.linspace(x[0],x[-1],len(x)*100)\n",
    "\n",
    "############################### Plotting ###############################\n",
    "\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,15))\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel('Nuclear drive frequency (kHz)')\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "\n",
    "ax[1].plot(x, p_data, \"o\", label = r\"${|\\downarrow\\downarrow\\rangle}$\") \n",
    "if fit_success: ax[1].plot(fine,data_fit, label = f\"centre freq = {np.round(est[0],3)} kHz $\\sigma$ = {np.round(est[1],2)} kHz\")\n",
    "if plot_guess: ax[1].plot(fine,lorentz(fine,*guess))\n",
    "ax[1].set_ylabel(\"P\")\n",
    "ax[1].set_xlabel('Pulse Duration (ms)')\n",
    "ax[1].legend()\n",
    "\n",
    "delta_freq = res.delta_freq.fetch_all()['value'][skip_points*len(x):]\n",
    "ax[2].plot(1e-3*delta_freq)\n",
    "ax[2].set_xlabel(\"Readout iteration\")\n",
    "ax[2].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'nuclear_spin_frequencies': nuclear_spin_frequencies,\n",
    "            'click_array': data,\n",
    "\n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "\n",
    "            'readout_freqs': readout_freqs,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b01f74-e3c3-46ca-afe1-8d7c60866eb7",
   "metadata": {},
   "source": [
    "### Rabi Freq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb685984-d547-47a0-bca3-ec1542dd5369",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Raman sweep params #######################\n",
    "\n",
    "raman_pulse_durations = (1e3+1e6*np.linspace(0,10,21))//4\n",
    "raman_pulse_durations = [int(duration) for duration in raman_pulse_durations]\n",
    "print(raman_pulse_durations)\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='raman_rabi_b'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    duration_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    prepare_stream = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "    \n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        with for_each_(duration_set, raman_pulse_durations):\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse,gaussian_pulse_length,interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            \n",
    "            #Raman_pulse_cos(nuclear_spin_freq_b_prep, freq_electron + delta_freq, raman_detuning, detuned_electron_amplitude_b_prep, detuned_sideband_amplitude_b_prep, raman_pi_duration_b_prep, ramp_time)\n",
    "            #wait(int(5e6//4))\n",
    "            \n",
    "            ################# Now play the Raman spectroscopy sequence #################\n",
    "            \n",
    "            play('ON',fsv_trigger)\n",
    "            align()\n",
    "            \n",
    "            Raman_pulse_cos(nuclear_spin_freq_cnot, freq_electron + delta_freq, raman_detuning_cnot, detuned_electron_amplitude_cnot, detuned_sideband_amplitude_cnot, duration_set, ramp_time_cnot)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        #prepare_stream.save_all('clicks_prep')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(raman_pulse_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n",
    "#better_sleep(total_measurement_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86dc3a78-4b21-4ce3-b368-ef34db4c4f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0)\n",
    "res = job.result_handles\n",
    "plot_guess = 0\n",
    "###########################################################\n",
    "#timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(data.shape)\n",
    "############################### Plotting ###############################\n",
    "fig,ax=plt.subplots(5,1,figsize=(10,25))\n",
    "sigmoid_length = 5\n",
    "x = 4e-6*np.array(raman_pulse_durations)\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "\n",
    "############################### Fit #################################\n",
    "p_data = (data[:,:,-1]>80)\n",
    "if len(readout_freqs)==4:\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "else:\n",
    "    pops = []\n",
    "    labels = [r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "def rabi_fit(t,f,T,a,b,phi):\n",
    "    return a*(b-np.cos(2*np.pi*f*(t)+phi)*np.exp(-t/T))\n",
    "\n",
    "#guess = [0.05,100,0.5,1,1*np.pi]\n",
    "guess = [1e-3*rabi_freq,100,0.4,0.9,1*np.pi]\n",
    "\n",
    "try:\n",
    "    est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  p_data.mean(0))\n",
    "    print(guess)\n",
    "except: \n",
    "    print(\"fit failed\")\n",
    "    est = guess\n",
    "    std = np.multiply(guess,0.1)\n",
    "    fine = x\n",
    "    data_fit = rabi_fit(x,*guess)\n",
    "\n",
    "\n",
    "T_half_period = 0.5/est[0]\n",
    "T_pi        = fine[np.argmin(data_fit)]\n",
    "T_pio2      = T_pi-T_half_period/2\n",
    "\n",
    "std[3]/(2*np.pi*est[0])\n",
    "##############################################################    \n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "for i in range(len(readout_freqs)): ax[0].errorbar(x, (data).mean(0)[:,i] , (data).std(0)[:,i]/np.sqrt(len(data)), label = labels[i], fmt = \"o-\")\n",
    "ax[0].set_ylabel(\"Mean counts\")\n",
    "ax[0].set_xlabel('Pulse Duration (ms)')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlim([0,None])\n",
    "\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)):\n",
    "        try:\n",
    "            guess = [1e-3*rabi_freq,30,(-1)**(i//2+1)*0.5,1,np.pi]# [1e-3*rabi_freq,0.5,1,1*np.pi,1*np.pi]\n",
    "            # guess = [0.05,100,0.4,1,1*np.pi]\n",
    "            est,std,fine,data_fit = fit_function(guess, rabi_fit, x,  pops[i])\n",
    "\n",
    "            ax[1].plot(fine,data_fit, color = colors[i])\n",
    "            if i==1: target_pop  = data_fit[np.argmax(data_fit)]\n",
    "        except: print(\"fit failed\")\n",
    "        ax[1].plot(x,pops[i],\"o\", label = labels[i], color = colors[i])\n",
    "        \n",
    "else:\n",
    "    pass\n",
    "    ax[1].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o-\") \n",
    "\n",
    "\n",
    "    \n",
    "if plot_guess: ax[1].plot(fine,rabi_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "try: \n",
    "    plt_label = \"$T_{hp} = %.2f$ ms \"%(T_half_period)+ r\"$T_\\pi = %.2f$ ms \"%(T_pi)+r\"$T_{\\pi/2} = %.2f$ ms \"%(T_pio2)+r\"$F_\\pi = %.2f$\"%(target_pop)#(est[2]*2)\n",
    "    ax[1].set_title(plt_label, fontsize = \"small\")\n",
    "except: pass\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlim(0,x[-1])\n",
    "ax[1].set_xlabel(\"Rabi duration (ms)\")\n",
    "ax[1].set_ylabel(\"Population\")\n",
    "ax[1].axhline(target_pop, linestyle = \"--\",color = colors[1])\n",
    "ax[1].legend(fontsize = \"small\", loc = \"upper right\")\n",
    "\n",
    "ax[1].grid()\n",
    "\n",
    "bins=np.arange(20,np.max(np.concatenate(data)),5)\n",
    "ax[2].hist(np.concatenate(data,axis = None), bins=bins   , label = r\"$measurement$\", alpha=0.5)\n",
    "ax[2].legend()\n",
    "ax[2].set_ylabel(\"instances\")\n",
    "ax[2].set_xlabel('counts')\n",
    "\n",
    "ax[3].plot(1e-3*res.delta_freq.fetch_all()['value'])\n",
    "ax[3].set_xlabel(\"Readout iteration\")\n",
    "ax[3].set_ylabel(\"Tracked frequency (kHz)\")\n",
    "\n",
    "p_data = (data[:,:,-1]>threshold)\n",
    "fft_x = 1e3*np.fft.rfftfreq(len(x),d=x[1]-x[0])\n",
    "fft_y = np.abs(np.fft.rfft(p_data.mean(0) - p_data.mean(0).mean()))\n",
    "rabi_freq = fft_x[np.argmax(fft_y)]\n",
    "\n",
    "ax[4].plot(fft_x, fft_y)\n",
    "ax[4].vlines(rabi_freq, min(fft_y), max(fft_y)*1.1, linestyle='dashed', color='k')\n",
    "ax[4].set_xlabel(\"Frequency (kHz)\")\n",
    "ax[4].set_ylabel(\"FFT\")\n",
    "ax[4].set_title(f\"Max frequency = {rabi_freq:.2f} kHz\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "############################### Save ###############################\n",
    "try:\n",
    "    plt.savefig(directory+filename+'_'+'nuclear_spin.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "plt.show()\n",
    "#display.display(plt.gcf())\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'x': x,\n",
    "    \n",
    "            'detuned_electron_amplitude_a': detuned_electron_amplitude_a,\n",
    "            'detuned_electron_amplitude_b':detuned_electron_amplitude_b,\n",
    "            'nuclear_spin_freq_a': nuclear_spin_freq_a,\n",
    "            'nuclear_spin_freq_b': nuclear_spin_freq_b,\n",
    "            'raman_pi_duration_a': raman_pi_duration_a,\n",
    "            'raman_pi_duration_b': raman_pi_duration_b,\n",
    "            'detuned_sideband_amplitude_a': detuned_sideband_amplitude_a,\n",
    "            'detuned_sideband_amplitude_b': detuned_sideband_amplitude_b,\n",
    "    \n",
    "            'readout_freqs': readout_freqs,\n",
    "            #'amplitude_readout_pulse': amplitude_readout_pulse,\n",
    "            'raman_detuning': raman_detuning,\n",
    "            'amplitude_prep_pulse': amplitude_prep_pulse,\n",
    "            't_wait_prep': t_wait_prep,\n",
    "            'N_ROcycle': N_ROcycle\n",
    "            }\n",
    "\n",
    "print(fullpath)\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab3b0dd-5ddb-423d-9d1d-8984b01e42e2",
   "metadata": {},
   "source": [
    "# IQ mixer calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52a3b70-2838-4e35-b3ae-33a49ad42785",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "amplitude = 0.05\n",
    "duration  = int(40e6//4)\n",
    "ramp_time = ramp_time_prep\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Play flattop pulse #################\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "        align()\n",
    "\n",
    "        play(\"ON\", spin_switch_element, duration=duration+int(2.1*ramp_time))\n",
    "        # update_frequency(spin_sticky_element, freq_electron)\n",
    "        flattop_pulse3_cos(amplitude, duration, switch_duration_extra, element = spin_sticky_element, ramp_time=ramp_time)\n",
    "        align()\n",
    "        ramp_to_zero(spin_sticky_element, duration=4)\n",
    "        align()\n",
    "        wait(100)\n",
    "        align()\n",
    "\n",
    "\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed085dca-bc9c-4f18-8976-58886618ef65",
   "metadata": {},
   "outputs": [],
   "source": [
    "Photon_LO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5a173e7-96be-4ea1-9242-b351cbaf64af",
   "metadata": {},
   "outputs": [],
   "source": [
    "Buffer_freq/1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14b269a0-82f2-4036-b567-28e4b151f613",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "amplitude = 0.05\n",
    "duration  = int(40e6//4)\n",
    "ramp_time = ramp_time_prep\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Play flattop pulse #################\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "        align()\n",
    "\n",
    "        play(readout_pulse,readout_element) \n",
    "        align()\n",
    "        wait(100)\n",
    "        align()\n",
    "\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e0f7d9-7e9e-46e8-96cf-ad431d74df36",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "\n",
    "N_repetition = int(1e9)\n",
    "amplitude = 0.05\n",
    "duration  = int(40e6//4)\n",
    "ramp_time = ramp_time_prep\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "    \n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Play flattop pulse #################\n",
    "\n",
    "        play('ON',fsv_trigger)\n",
    "        align()\n",
    "\n",
    "        play(pump_pulse,pump_element) \n",
    "        align()\n",
    "        wait(100)\n",
    "        align()\n",
    "\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaa2e95-e14d-4956-ad26-9e26ac962304",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "# CZ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29258211-2cfa-492d-87d0-381162db5e03",
   "metadata": {},
   "source": [
    "## Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f7aa663-2bc0-4625-8b23-593f380fcb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "CZ_gate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711eb016-0120-49d0-8684-f070463bb0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "amplitude_factor = 1.5\n",
    "correction_phase = 0.720\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "CZ_durations = [int(CZ_duration//4) for CZ_duration in np.linspace(100,7e6,21)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    CZ_duration = declare(int)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(CZ_duration, CZ_durations):\n",
    "\n",
    "            with for_(k, 0, k < 2, k+1):\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                ################# Now play the Ramsey sequence #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                play('ON',fsv_trigger)\n",
    "\n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "\n",
    "\n",
    "                align()\n",
    "                # Pi A\n",
    "                with if_(k==1):\n",
    "                    Pauli_swept('aX', delta_freq)\n",
    "                #Pi/2 B\n",
    "                Pauli_swept('bX90', delta_freq)\n",
    "                #CZ pulse\n",
    "                CZ_gate(CZ_duration=CZ_duration)\n",
    "                #Pi/2 B\n",
    "                Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                with if_(k==1):\n",
    "                    Pauli_swept('aX', delta_freq)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[0,0,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(2).buffer(len(CZ_durations)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "428c6c9f-03dd-442f-8632-270d978f89d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "CZ_duration_prep*4e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d77a838-2372-4082-856d-1b9363ef63e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(0*300)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(3,1,figsize=(10,12))\n",
    "plot_labels = [\"Spin A down\", \"Spin A up\"]\n",
    "\n",
    "phase = []\n",
    "fit_phase = []\n",
    "for k in range(2):\n",
    "    ###########################################################\n",
    "    data = full_data[:,:,k]\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "    pops = [p0,p1,p2,p3]\n",
    "    sigmoid_length = 5\n",
    "    x = 4e-6*np.array(CZ_durations)\n",
    "    labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "    ############################### Fit function #################################\n",
    "\n",
    "    def ramsey_fit(t,f,phi,A,B):\n",
    "        return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "    \n",
    "        ############################### subplot 1 ###############################    \n",
    "    fit_results = []\n",
    "    if len(readout_freqs)==4:\n",
    "        for i in range(len(readout_freqs)): \n",
    "            guess = [180*1e-3, np.pi*(i+1),0.45, 0.5]\n",
    "            try:\n",
    "                est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i], lb=[0,0,0,0])\n",
    "                if i == 3: fit_phase.append(est)\n",
    "                ax[k].plot(fine,data_fit, color = colors[i])\n",
    "                # ax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "            except:\n",
    "                print(\"fit failed\")\n",
    "                est = guess\n",
    "                std = guess\n",
    "            fit_results.append(est)\n",
    "            ax[k].plot(x,pops[i],\"o\", label = labels[i])\n",
    "    else:\n",
    "        ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data)), label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "    if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "    ax[k].set_ylabel(\"P\")\n",
    "    ax[k].set_xlabel('CZ duration (ms)')\n",
    "    ax[k].legend(loc = \"lower right\")\n",
    "    # plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "    plt_label = f\"{plot_labels[k]}: f1 = {fit_results[2][0]*1e3:.1f} Hz,  f2 = {fit_results[3][0]*1e3:.1f}Hz\"\n",
    "    ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "    ax[k].set_ylim(0,1)\n",
    "    phase.append(2*(p3-0.5))\n",
    "    \n",
    "\n",
    "phase_1 = (2*np.pi*x*fit_phase[0][0]+fit_phase[0][1])/(2*np.pi)\n",
    "phase_2 = (2*np.pi*x*fit_phase[1][0]+fit_phase[1][1])/(2*np.pi)\n",
    "phase_diff = (phase_1 - phase_2)\n",
    "ax[2].plot(x, phase_2,\"ro--\",label = \"A down phase\")\n",
    "ax[2].plot(x, phase_1,\"ko--\",label = \"A up phase\")\n",
    "ax[2].plot(x, phase_diff,\"go-\", label = \"Phase diff\")\n",
    "ax[2].hlines(0.5, x[0], x[-1], linestyle='--')\n",
    "ax[2].hlines(-0.5, x[0], x[-1], linestyle='--')\n",
    "ax[2].set_ylabel(\"Phase/2π\")\n",
    "ax[2].set_xlabel('CZ duration (ms)')\n",
    "ax[2].legend()\n",
    "# ax[2].set_xlim(4,4.25)\n",
    "# ax[2].set_ylim(-0.499,-0.501)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'CZ_durations': CZ_durations,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ae7dbc-6957-4862-9ca6-864d1a872b00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-21T13:34:08.480520Z",
     "iopub.status.busy": "2024-03-21T13:34:08.480520Z",
     "iopub.status.idle": "2024-03-21T13:34:08.673011Z",
     "shell.execute_reply": "2024-03-21T13:34:08.673011Z",
     "shell.execute_reply.started": "2024-03-21T13:34:08.480520Z"
    }
   },
   "source": [
    "## Frame rotations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a48eec-d325-4850-a174-6934f89fc140",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "71ec3bba-f8c5-4e9d-bbad-e0d40a141a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:14:56.369638Z",
     "iopub.status.busy": "2024-03-26T15:14:56.367528Z",
     "iopub.status.idle": "2024-03-26T15:15:00.843156Z",
     "shell.execute_reply": "2024-03-26T15:15:00.843156Z",
     "shell.execute_reply.started": "2024-03-26T15:14:56.369638Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights loaded\n"
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "amplitude_factor = 1.5\n",
    "correction_phase = 0.80\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase, phase_rotation):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "            Pauli_swept('aX',delta_freq)\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX90',delta_freq)\n",
    "\n",
    "            align()\n",
    "            CZ_gate(delta_freq, CZ_phase_correction_b = CZ_phase_correction_b_prep+phase)\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX-90',delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "2462792a-7f3d-4ea3-8c64-13d97497c4bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:20:06.795732Z",
     "iopub.status.busy": "2024-03-26T15:20:06.795732Z",
     "iopub.status.idle": "2024-03-26T15:20:07.639393Z",
     "shell.execute_reply": "2024-03-26T15:20:07.639393Z",
     "shell.execute_reply.started": "2024-03-26T15:20:06.795732Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 11, 4)\n",
      "Phase offset =-0.10292246202034445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.0)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin A up\", \"Spin A up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, 0, 0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 3: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "            #Eax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        fit_results.append(est)\n",
    "        ax[k].errorbar(x,pops[i],yerr=np.sqrt((1-pops[i])*pops[i]/data.shape[0]),fmt=\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"{plot_labels[k]}: f1 = {fit_results[1][0]*1e3:.1f} Hz,  f2 = {fit_results[3][0]*1e3:.1f}Hz\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "\n",
    "print(\"Phase offset =\"+ str(0-fit_phase[0][1]/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8734ab-1a64-4813-91a5-91c1493430c1",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "68b78608-298f-4843-aad9-46536951f51a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:44:43.909074Z",
     "iopub.status.busy": "2024-03-26T14:44:43.899132Z",
     "iopub.status.idle": "2024-03-26T14:44:48.474112Z",
     "shell.execute_reply": "2024-03-26T14:44:48.474112Z",
     "shell.execute_reply.started": "2024-03-26T14:44:43.909074Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase_set = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase_set, phase_rotation):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "            \n",
    "            Pauli_swept('bX', delta_freq)\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX90', delta_freq)\n",
    "\n",
    "            align()\n",
    "            CZ_gate(delta_freq, CZ_phase_correction_a = CZ_phase_correction_a_prep+phase_set)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "d1aeca1f-30c8-41d5-8899-11e1017f9465",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:55:01.808611Z",
     "iopub.status.busy": "2024-03-26T14:55:01.808611Z",
     "iopub.status.idle": "2024-03-26T14:55:02.643063Z",
     "shell.execute_reply": "2024-03-26T14:55:02.643063Z",
     "shell.execute_reply.started": "2024-03-26T14:55:01.808611Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 11, 4)\n",
      "Phase offset =0.055886239121992885\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "phase_append = []\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True,kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, 0, 0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 3: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "            #Eax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        fit_results.append(est)\n",
    "        ax[k].errorbar(x,pops[i],yerr=np.sqrt(pops[i]*(1-pops[i])/data.shape[0]),fmt=\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"{plot_labels[k]}: f1 = {fit_results[1][0]*1e3:.1f} Hz,  f2 = {fit_results[3][0]*1e3:.1f}Hz\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "phase_append.append(2*(p3-0.5))\n",
    "\n",
    "print(\"Phase offset =\"+ str(0-fit_phase[0][1]/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e48cfbe-35fc-4c48-9a53-85ecce747ddb",
   "metadata": {},
   "source": [
    "## CZ table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "d816b559-3456-4f15-a9f5-fe10cb9e88a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:06:45.772373Z",
     "iopub.status.busy": "2024-03-26T14:06:45.772373Z",
     "iopub.status.idle": "2024-03-26T14:06:49.676904Z",
     "shell.execute_reply": "2024-03-26T14:06:49.676904Z",
     "shell.execute_reply.started": "2024-03-26T14:06:45.772373Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "amplitude_factor = 1.5\n",
    "correction_phase = 0.80\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,21)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(k, [0,1,2,3]):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "            #with if_(k==1):\n",
    "            #    Pauli_swept('aX',delta_freq)\n",
    "            #with if_(k==2):\n",
    "            #    Pauli_swept('bX',delta_freq)\n",
    "            #with if_(k==0):\n",
    "            #    Pauli_swept('aX',delta_freq)\n",
    "            #    Pauli_swept('bX',delta_freq)\n",
    "                \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX90', delta_freq)\n",
    "\n",
    "            align()\n",
    "            CZ_gate(delta_freq)#, CZ_phase_correction_a = 0)\n",
    "\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX-90', delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(4).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "201c1f96-43fe-4348-a4c1-6646b67ae5d4",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CZ_phase_correction_a_prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "fd6005d6-2fb0-4ba5-a887-2a99d303f34c",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-03-26T14:43:48.504763Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17, 11, 4)\n",
      "Preparing ↑↑: 0.0 |↑↑> + 0.8 |↑↓> + 0.0 |↓↑> + 0.2 |↓↓>\n",
      "\n",
      "Preparing ↑↓: 0.0 |↑↑> + 0.8 |↑↓> + 0.0 |↓↑> + 0.2 |↓↓>\n",
      "\n",
      "Preparing ↓↑: 0.0 |↑↑> + 0.6 |↑↓> + 0.0 |↓↑> + 0.4 |↓↓>\n",
      "\n",
      "Preparing ↓↓: 0.0 |↑↑> + 0.4 |↑↓> + 0.0 |↓↑> + 0.6 |↓↓>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "phase_append = []\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(full_data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = np.array([p0,p1,p2,p3]).T\n",
    "\n",
    "labels_unicode = ['↑↑', '↑↓', '↓↑', '↓↓']\n",
    "for i in range(4):\n",
    "    string_cnot = ' + '.join([f'{p:.1f} |{labels_unicode[j]}>' for j, p in enumerate(pops[i])])\n",
    "    print(f'Preparing {labels_unicode[i]}: {string_cnot}\\n' )\n",
    "\n",
    "    \n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2250e7-ea2b-4874-81c1-ce7506958ec1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# RB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e300150b-73e4-4d02-930c-a9b6d63220c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gate_matrices = {\n",
    "    'aW90': np.eye(2),\n",
    "    'aX90': np.array([[1, -1j], [-1j, 1]]) / np.sqrt(2),\n",
    "    'aY90': np.array([[1, -1], [1, 1]]) / np.sqrt(2),\n",
    "    'aX': np.array([[0, 1], [1, 0]]),\n",
    "    'aY': np.array([[0, -1j], [1j, 0]]),\n",
    "    'aX-90': np.array([[1, 1j], [1j, 1]]) / np.sqrt(2), \n",
    "    'aY-90': np.array([[1, 1], [-1, 1]]) / np.sqrt(2),  \n",
    "}\n",
    "\n",
    "def generate_random_clifford_sequence(num_gates,clifford_gates = [ 'aX90', 'aY90','aX', 'aY', 'aX-90','aY-90']):\n",
    "    return [random.choice(clifford_gates) for _ in range(num_gates)]\n",
    "\n",
    "def apply_gate(state, gate):\n",
    "    return np.dot(gate_matrices[gate], state)\n",
    "\n",
    "def get_recovery_gate(clifford_sequence):\n",
    "\n",
    "    # Compose the recovery gate matrix by multiplying the Clifford gates in reverse order\n",
    "    gate_matrix=np.eye(2)\n",
    "    for gate in clifford_sequence:\n",
    "        gate_matrix = apply_gate(gate_matrix, gate)\n",
    "    \n",
    "    # Find the closest Clifford gate to the recovery gate matrix\n",
    "    min_distance = float('inf')\n",
    "    closest_gate = None\n",
    "    for gate in list(gate_matrices.keys()):\n",
    "        isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "        distance = np.linalg.norm(np.eye(2) - abs(isidentity_matrix))\n",
    "        #print(apply_gate(np.eye(2), gate))\n",
    "        #print(distance)\n",
    "        if distance < min_distance:\n",
    "            min_distance = distance\n",
    "            closest_gate = gate\n",
    "\n",
    "    return closest_gate\n",
    "\n",
    "# Example usage\n",
    "num_gates = 5\n",
    "clifford_sequence = generate_random_clifford_sequence(num_gates)\n",
    "recovery_gate = get_recovery_gate(clifford_sequence)\n",
    "\n",
    "print(\"Recovery gate:\", recovery_gate)\n",
    "\n",
    "\n",
    "RB_sequence=clifford_sequence + [recovery_gate]\n",
    "\n",
    "# Apply the Clifford gates and the recovery gate to the identity matrix\n",
    "total_matrix = np.eye(2)\n",
    "for gate in RB_sequence:\n",
    "    total_matrix = apply_gate(total_matrix, gate)\n",
    "\n",
    "print(\"Check for identity recovery:\", np.round(abs(total_matrix),3))\n",
    "\n",
    "\n",
    "print(\"Random Benchmarking sequence:\", RB_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdc2a41-5564-439a-9ee1-09549d2ddb56",
   "metadata": {},
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2086,
   "id": "bd290fc2-ad67-4ba6-9bf6-62781a8d02ac",
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     "shell.execute_reply": "2024-04-01T00:22:09.832804Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.421794Z"
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    "scrolled": true,
    "tags": []
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   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'apply_gate' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\2482842590.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[0mclosest_gate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mgate\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgate_matrices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m             \u001b[0misidentity_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapply_gate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgate_matrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m             \u001b[0mdistance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misidentity_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdistance\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mmin_distance\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'apply_gate' is not defined"
     ]
    }
   ],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'aW90': np.eye(2),\n",
    "    \n",
    "    'aX': x_gate,\n",
    "    'aY': y_gate,\n",
    "    \n",
    "    'aX90': sqrtm(x_gate),\n",
    "    'aY90': sqrtm(y_gate),\n",
    "\n",
    "    'aX-90': sqrtm(-x_gate),\n",
    "    'aY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3ee6174-a021-4fa6-bb42-fa29263ec6d3",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-04-01T00:22:09.837801Z"
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   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 10,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='RB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    RB_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            with for_(k, 0, k<n_gates, k+1):\n",
    "\n",
    "                assign(r, random_generator.rand_int(6)+1)\n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                save(r, RB_stream)\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c61ab8c-1a39-41ad-836e-1c9900bf0c40",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.839800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.839800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.839800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.839800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2)\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "\n",
    "est, std, fine, data_fit = fit_function(guess, p_decay, gate_numbers, p3, lb=[0.5,0,0.5-1e-9], ub=[1,1,0.5+1e-9])\n",
    "\n",
    "plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = f\"Reference  decay const ={np.round(popt[0],3)}\")\n",
    "plt.plot(fine, data_fit, 'k')\n",
    "\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3679606d-32ae-4d86-aa1c-3c09e32ad046",
   "metadata": {},
   "source": [
    "## iRB on A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8c7de5-2961-4f7f-8fb6-9918987541e9",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.841798Z",
     "iopub.status.idle": "2024-04-01T00:22:09.842800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.842800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.841798Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'aX'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 10,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    gate_to_test_yes = declare(bool)\n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    recovery_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                with else_():\n",
    "                    assign(r, gate_to_test_index)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    \n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ecc52e-0dd5-4541-807c-ea82797d2529",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.843800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.844800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.844800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.844800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2.0)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "guess = [0.9,0.5,0.5]\n",
    "\n",
    "# popt, pcov = sp.curve_fit(p_decay, gate_numbers, p3, guess)\n",
    "# plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'k')\n",
    "\n",
    "est, std, fine, data_fit = fit_function(guess, p_decay, gate_numbers, p3, lb=[0.5,0,0.5-1e-9], ub=[1,1,0.5+1e-9])\n",
    "plt.plot(fine, data_fit, 'k')\n",
    "plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = f\"Reference  decay const ={np.round(est[0],3)}\")\n",
    "\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f28683ad-c2f7-40ef-bafd-ac66779163c6",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.846800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.846800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.846800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.846800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'aX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 10,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    gate_to_test_yes = declare(bool)\n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    recovery_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                with else_():\n",
    "                    assign(r, gate_to_test_index)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    \n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfa57cd9-035a-4b81-8fcf-1c03deaf513e",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.848800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.848800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.848800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.848800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_numbers, p3, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = f\"Reference  decay const ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'k')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b5b0c0-8116-4d54-a551-4fc709115a2a",
   "metadata": {},
   "source": [
    "## B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fcf20fd-4b6b-46a6-8b45-f8ccbb6d1c3e",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.850800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.850800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.850800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.850800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'bW90': np.eye(2),\n",
    "    \n",
    "    'bX': x_gate,\n",
    "    'bY': y_gate,\n",
    "    \n",
    "    'bX90': sqrtm(x_gate),\n",
    "    'bY90': sqrtm(y_gate),\n",
    "\n",
    "    'bX-90': sqrtm(-x_gate),\n",
    "    'bY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f9acd0-2836-4e71-b332-4cd8136092c1",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.852800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.853798Z",
     "shell.execute_reply": "2024-04-01T00:22:09.852800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.852800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 5,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='RB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            with for_(k, 0, k<n_gates, k+1):\n",
    "\n",
    "                assign(r, random_generator.rand_int(6)+1)\n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                save(r, RB_stream)\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "            Pauli_swept('aX', delta_freq)\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1975116-562d-4153-ba9b-9d894ee26bf5",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.854800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.854800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.854800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.854800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2.0)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "p = [p0,p1,p2,p3]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "\n",
    "for i in range(3):\n",
    "    plt.errorbar(gate_numbers,p[i],yerr=np.sqrt(p[i]*(1-p[i])/data.shape[0]),fmt=\"o\",color=colors[i])\n",
    "    \n",
    "popt, pcov ,fine, data_fit = fit_function([0.9,0.5,0.5], p_decay, gate_numbers, p[3])\n",
    "plt.errorbar(gate_numbers,p[3],yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o\",label = f\"Reference  decay const ={np.round(popt[0],3)}\", color=colors[3])\n",
    "plt.plot(fine, data_fit, color=colors[3])\n",
    "print(popt)\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b2aa809-1159-4f90-874e-dd23297a3186",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Two_qubit_monster(n):\n",
    "    \"\"\"\n",
    "    Takes an input integer and plays one of the 101 possible 2 qubit gates\n",
    "    \"\"\"\n",
    "    if n > 100: \n",
    "        print(\"Not one of the allowed gates\")\n",
    "        return()\n",
    "    elif n == 100:\n",
    "        print(f\"Pulse sequence = CNOT\")\n",
    "    elif n < 100:\n",
    "        row = n//10\n",
    "        column = n%10\n",
    "        print(f\"Pulse sequence: A={Two_qubit_monster_matrices[row]}, B={Two_qubit_monster_matrices[column]}\")\n",
    "        \n",
    "Two_qubit_monster_matrices = {\n",
    "    0:'I',\n",
    "    1:'X',\n",
    "    2:'Y',\n",
    "    3:'Z',\n",
    "    4:'rX',\n",
    "    5:'rY',\n",
    "    6:'rZ',\n",
    "    7:'r-X',\n",
    "    8:'r-Y',\n",
    "    9:'r-Z',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be65988-7734-4383-ba9b-e952ef45e58b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Two_qubit_monster(92)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e995ba-3000-42ee-b244-36105e80df59",
   "metadata": {},
   "source": [
    "## iRB on B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9c5f9b0-7289-466d-9de8-943d76e34eb3",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.856800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.857800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.857800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.857800Z"
    }
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'bX'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 5,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    gate_to_test_yes = declare(bool)\n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    recovery_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                with else_():\n",
    "                    assign(r, gate_to_test_index)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    \n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af0e337-5a11-4a6c-80c1-7f145bbda429",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.858800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.859800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.859800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.859800Z"
    }
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "guess = [0.9,0.5,0.5]\n",
    "\n",
    "# popt, pcov = sp.curve_fit(p_decay, gate_numbers, p3, guess)\n",
    "# plt.plot(gate_numbers, p_decay(np.array(gate_numbers), *popt),'k')\n",
    "\n",
    "est, std, fine, data_fit = fit_function(guess, p_decay, gate_numbers, p3, lb=[0.5,0,0], ub=[1,1,1])\n",
    "plt.plot(fine, data_fit, 'k')\n",
    "plt.errorbar(gate_numbers,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = f\"Reference  decay const ={np.round(est[0],3)}\")\n",
    "\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c35efadf-d854-4208-b6d6-bc4e336c12fe",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.861799Z",
     "iopub.status.idle": "2024-04-01T00:22:09.861799Z",
     "shell.execute_reply": "2024-04-01T00:22:09.861799Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.861799Z"
    }
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'bX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "gate_numbers = [int(i) for i in sinhspace_asymm(1,110, 6,nonlinearity = 3)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    gate_to_test_yes = declare(bool)\n",
    "    n_gates = declare(int)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    recovery_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(n_gates, gate_numbers):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                with else_():\n",
    "                    assign(r, gate_to_test_index)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    \n",
    "                assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "            save(recovery, recovery_stream)\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(gate_numbers)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f739590-9e88-4294-8505-29f5e655b21d",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-04-01T00:22:09.863800Z",
     "iopub.status.idle": "2024-04-01T00:22:09.863800Z",
     "shell.execute_reply": "2024-04-01T00:22:09.863800Z",
     "shell.execute_reply.started": "2024-04-01T00:22:09.863800Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "better_sleep(3600*2)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "p = [p0,p1,p2,p3]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "guess = [0.9,0.5,0.5]\n",
    "\n",
    "for i in range(3):\n",
    "    plt.errorbar(gate_numbers,p[i],yerr=np.sqrt(p[i]*(1-p[i])/data.shape[0]),fmt=\"o\",color=colors[i])\n",
    "    \n",
    "popt, pcov ,fine, data_fit = fit_function([0.9,0.5,0.5], p_decay, gate_numbers, p[3])\n",
    "plt.errorbar(gate_numbers,p[3],yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"o\",label = f\"Reference  decay const ={np.round(popt[0],3)}\", color=colors[3])\n",
    "plt.plot(fine, data_fit, color=colors[3])\n",
    "print(popt)\n",
    "\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'gate_numbers': gate_numbers,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd11122-f861-40f1-8888-8f9b5e995774",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Sweeping duration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4a0a03-5e40-43a6-a9ac-f035628bbc90",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2f027bc-b6ea-4c5f-a660-dafb4646fa3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sideband_freq_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2859ebd5-e549-4d41-b6c9-f3afaaf5a558",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'aW90': np.eye(2),\n",
    "    \n",
    "    'aX': x_gate,\n",
    "    'aY': y_gate,\n",
    "    \n",
    "    'aX90': sqrtm(x_gate),\n",
    "    'aY90': sqrtm(y_gate),\n",
    "\n",
    "    'aX-90': sqrtm(-x_gate),\n",
    "    'aY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73a1266-ffb7-4ac9-9eb4-c25ead8a900f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'aX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "\n",
    "n_gates = 25\n",
    "duration_shift = [int(d//4) for d in np.linspace(-0.20e6, 0.20e6, 15)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    delta_duration_shift = declare(int)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(delta_duration_shift, duration_shift):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, pulse_duration_adj=delta_duration_shift)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(duration_shift)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd146cc-8a14-430d-b04b-2a032755fb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*4)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "plt.xlabel('$\\Delta$ duration (ms)')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "x = np.array(duration_shift)*4e-6\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",)\n",
    "plt.ylim([0.5, 0.8])\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9a02ef-8c04-4efa-bd7c-0f10e3fd065d",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b0e8ee2-7094-42fc-a4fd-2d039e6f5128",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'bW90': np.eye(2),\n",
    "    \n",
    "    'bX': x_gate,\n",
    "    'bY': y_gate,\n",
    "    \n",
    "    'bX90': sqrtm(x_gate),\n",
    "    'bY90': sqrtm(y_gate),\n",
    "\n",
    "    'bX-90': sqrtm(-x_gate),\n",
    "    'bY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fff8306-a04b-461a-9dcd-c2df7c6a3036",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'bX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "n_gates = 10\n",
    "duration_shift = [int(d//4) for d in np.linspace(-0.20e6, 0.20e6, 15)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    delta_duration_shift = declare(int)\n",
    "    r = declare(int)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(delta_duration_shift, duration_shift):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            \n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "\n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, pulse_duration_adj=delta_duration_shift, sideband_freq_adj=0)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(duration_shift)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331281b-912f-4e23-a61f-392a575baa5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*1)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "x=np.array(duration_shift)*4e-6\n",
    "quadratic_function = lambda x,*y: y[0]+y[1]*x+y[2]*x**2\n",
    "guess = [0.65, 0, -1]\n",
    "est,std,fine, data_fit = fit_function(guess, quadratic_function,x,p3 )\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = 'data')\n",
    "plt.plot(fine,data_fit, label = 'quadratic fit')\n",
    "maximum = -est[1]/(2*est[2])\n",
    "plt.annotate(r\"Max $F$ at $\\Delta_{\\tau} = %.3f$ ms\"%maximum, xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "\n",
    "\n",
    "plt.xlabel(r'$\\Delta_{\\tau}$ (ms)')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "plt.ylim([0.50, None])\n",
    "plt.legend()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f706d49-6d96-4c7d-a9ce-1d5af84532c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "25ada069-c90a-4d3c-8a09-9eb5648c8f8c",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Sweeping frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eaf8ed7-7e73-4631-a5ca-ae564687c2bc",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e025cb06-544c-451a-904a-c65c788d1893",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'aW90': np.eye(2),\n",
    "    \n",
    "    'aX': x_gate,\n",
    "    'aY': y_gate,\n",
    "    \n",
    "    'aX90': sqrtm(x_gate),\n",
    "    'aY90': sqrtm(y_gate),\n",
    "\n",
    "    'aX-90': sqrtm(-x_gate),\n",
    "    'aY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c7bf0fe-baae-4704-adb7-0eebe6f810ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'aX'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "\n",
    "n_gates = 15\n",
    "frequency_shift = [int(f) for f in np.linspace(-30, 30, 11)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    delta_frequency_shift = declare(int)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(delta_frequency_shift, frequency_shift):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, sideband_freq_adj=delta_frequency_shift)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(frequency_shift)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba93d5f-4dd2-4880-bd36-eeb1cbef1d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*2)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "plt.xlabel('$\\Delta$f (Hz)')\n",
    "# ax2.set_xlabel(r\"Modified x-axis: $1/(1+X)$\")\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "x = frequency_shift\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",)\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ab8a22a-f9c6-44d2-8284-5163db45408c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3,1, figsize=(7,7))\n",
    "\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].set_xlabel('$\\Delta$f (Hz)')\n",
    "    axs[i].set_ylabel(f'{labels[3]} population')\n",
    "    x = frequency_shift\n",
    "    p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data[len(data)//3*i:len(data)//3*(i+1)], frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "    axs[i].errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data[len(data)//3*i:len(data)//3*(i+1)].shape[0]),fmt=\"ko\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "171a9ef5-f51e-41a1-a34e-1edcb016a8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02a490d-061b-404b-aec1-fed0bbf4c081",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42127ae2-6cec-4797-a85f-96f099b3ddc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'bW90': np.eye(2),\n",
    "    \n",
    "    'bX': x_gate,\n",
    "    'bY': y_gate,\n",
    "    \n",
    "    'bX90': sqrtm(x_gate),\n",
    "    'bY90': sqrtm(y_gate),\n",
    "\n",
    "    'bX-90': sqrtm(-x_gate),\n",
    "    'bY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68f19ade-1ee1-418f-bd24-c478a863d20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'bX'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "n_gates = 10\n",
    "frequency_shift = [int(f) for f in np.linspace(-30, 30, 6)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    delta_frequency_shift = declare(int)\n",
    "    r = declare(int)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(delta_frequency_shift, frequency_shift):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            \n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "\n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, sideband_freq_adj=delta_frequency_shift)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90', delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',   delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',   delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90', delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90', delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(frequency_shift)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0855f4d6-4e78-46f8-9caa-843e634f225d",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(2400*0)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "quadratic_function = lambda x,*y: y[0]+y[1]*x+y[2]*x**2\n",
    "guess = [0.65, 0, -1]\n",
    "est,std,fine, data_fit = fit_function(guess, quadratic_function,x,p3 )\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = 'data')\n",
    "plt.plot(fine,data_fit, label = 'quadratic fit')\n",
    "maximum = -est[1]/(2*est[2])\n",
    "plt.annotate(\"Max $F$ at $\\Delta_f = %.3f$ Hz\"%maximum, xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "\n",
    "\n",
    "plt.xlabel('$\\Delta_f$ (Hz)')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "plt.ylim([0.4, None])\n",
    "plt.legend()\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bd1b0d-d07d-462c-8f5d-69f0cd012b0b",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Sweeping Z rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b0926b-ce15-4f84-975f-ca38c2172f63",
   "metadata": {},
   "source": [
    "### A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87c139d1-8608-43fb-af3d-a16226d5cbef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'aW90': np.eye(2),\n",
    "    \n",
    "    'aX': x_gate,\n",
    "    'aY': y_gate,\n",
    "    \n",
    "    'aX90': sqrtm(x_gate),\n",
    "    'aY90': sqrtm(y_gate),\n",
    "\n",
    "    'aX-90': sqrtm(-x_gate),\n",
    "    'aY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b198fb04-a7e3-45e2-a519-3247c6cab06b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'aX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "\n",
    "n_gates = 15\n",
    "z_rot_shifts = [float(f) for f in np.linspace(-0.1, 0.1, 11)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    z_rot_shift = declare(fixed)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(z_rot_shift, z_rot_shifts):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('aY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, phi_adj=z_rot_shift)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('aW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('aY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('aX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('aY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('aX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('aY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,1,0,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(z_rot_shifts)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4d832c3-0b9c-42a8-bbd1-b12f882991b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "plt.xlabel(r'$\\Delta_Z/2\\pi$')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "x = z_rot_shifts\n",
    "\n",
    "quadratic_function = lambda x,*y: y[0]+y[1]*x+y[2]*x**2\n",
    "guess = [0.65, 0, -1]\n",
    "est,std,fine, data_fit = fit_function(guess, quadratic_function,x,p3 )\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = 'data')\n",
    "plt.plot(fine,data_fit, label = 'quadratic fit')\n",
    "maximum = -est[1]/(2*est[2])\n",
    "plt.annotate(r\"Max $F$ at $\\Delta_Z = %.3f\\times2\\pi$\"%maximum, xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "\n",
    "plt.ylim([0.45, 0.9])\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4573f6bf-8165-47f1-bc76-60edb3b202f0",
   "metadata": {},
   "source": [
    "### B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fe4d910-03df-4c27-9d22-9440b99cb147",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import sqrtm\n",
    "\n",
    "x_gate = np.array([[0,   1], [ 1,  0]])\n",
    "y_gate = np.array([[0, -1j], [1j,  0]])\n",
    "gate_matrices = {\n",
    "    'bW90': np.eye(2),\n",
    "    \n",
    "    'bX': x_gate,\n",
    "    'bY': y_gate,\n",
    "    \n",
    "    'bX90': sqrtm(x_gate),\n",
    "    'bY90': sqrtm(y_gate),\n",
    "\n",
    "    'bX-90': sqrtm(-x_gate),\n",
    "    'bY-90': sqrtm(-y_gate),\n",
    "}\n",
    "\n",
    "gate_keys = {key: i for i, key in enumerate(gate_matrices.keys())}\n",
    "\n",
    "# Calculate the matrix that correlates what is the new recovery matrix given a previous recovery matrix and an applied gate\n",
    "N = len(gate_matrices)\n",
    "recovery_table = np.zeros([N,N],dtype='>U6')\n",
    "for ii, recovery_gate in enumerate(gate_matrices.values()):\n",
    "    for jj, applied_gate in enumerate(gate_matrices.values()):\n",
    "        # We calculate the state we are if the recovery matrix and the applied state are known\n",
    "        gate_matrix = applied_gate @ np.linalg.inv(recovery_gate) @ np.eye(2)\n",
    "\n",
    "        min_distance = float('inf')\n",
    "        closest_gate = None\n",
    "        for gate in list(gate_matrices.keys()):\n",
    "            isidentity_matrix = apply_gate(gate_matrix, gate)\n",
    "            distance = np.linalg.norm(np.eye(2) - np.abs(isidentity_matrix))\n",
    "            if distance < min_distance: \n",
    "                min_distance = distance\n",
    "                closest_gate = gate\n",
    "        recovery_table[ii, jj] = closest_gate\n",
    "\n",
    "recovery_flat=[]\n",
    "for i in range(N):\n",
    "    for j in range(N):\n",
    "        recovery_flat.append(gate_keys[recovery_table[i, j]])\n",
    "        \n",
    "recovery_table  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3794df13-9c8a-4840-b292-6149b9ffbcd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "gate_to_test = 'bX90'\n",
    "gate_to_test_index = gate_keys[gate_to_test]\n",
    "\n",
    "\n",
    "n_gates = 10\n",
    "z_rot_shifts = [float(f) for f in np.linspace(-0.1, 0.1, 5)]\n",
    "\n",
    "N_repetition = int(1e10)\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = []\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='iRB'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "    random_generator = Random()\n",
    "\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "    \n",
    "    z_rot_shift = declare(fixed)\n",
    "    gate_to_test_yes = declare(bool)\n",
    "    r = declare(int)\n",
    "    recovery = declare(int)\n",
    "    recovery_matrix_qua = declare(int, value=recovery_flat)\n",
    "\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    pulse = declare(int)\n",
    "\n",
    "\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_each_(z_rot_shift, z_rot_shifts):\n",
    "\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "            assign(recovery, 0)\n",
    "            assign(gate_to_test_yes, False)\n",
    "            with for_(k, 0, k<2*n_gates, k+1):\n",
    "                \n",
    "                with if_(gate_to_test_yes==False):\n",
    "                    assign(r, random_generator.rand_int(6)+1)\n",
    "                    assign(gate_to_test_yes, True)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + r])\n",
    "\n",
    "                with else_():\n",
    "                    assign(r, 7)\n",
    "                    assign(gate_to_test_yes, False)\n",
    "                    assign(recovery, recovery_matrix_qua[recovery*7 + gate_to_test_index])\n",
    "\n",
    "                with switch_(r):\n",
    "                    with case_(0):\n",
    "                        Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                    with case_(1):\n",
    "                        Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                    with case_(2):\n",
    "                        Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                    with case_(3):\n",
    "                        Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                    with case_(4):\n",
    "                        Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                    with case_(5):\n",
    "                        Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                    with case_(6):\n",
    "                        Pauli_swept('bY-90', delta_freq)\n",
    "                    \n",
    "                    with case_(7):\n",
    "                        Pauli_swept(gate_to_test, delta_freq, phi_adj=z_rot_shift)\n",
    "\n",
    "            # Apply a recovery gate\n",
    "            with switch_(recovery):\n",
    "                with case_(0):\n",
    "                    Pauli_swept('bW90',  delta_freq)\n",
    "\n",
    "                with case_(1):\n",
    "                    Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "                with case_(2):\n",
    "                    Pauli_swept('bY',    delta_freq)\n",
    "\n",
    "                with case_(3):\n",
    "                    Pauli_swept('bX90',  delta_freq)\n",
    "\n",
    "                with case_(4):\n",
    "                    Pauli_swept('bY90',  delta_freq)\n",
    "\n",
    "                with case_(5):\n",
    "                    Pauli_swept('bX-90', delta_freq)\n",
    "\n",
    "                with case_(6):\n",
    "                    Pauli_swept('bY-90', delta_freq)\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                  state_list=[0,0,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(z_rot_shifts)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55eb249e-e1be-4aa4-8e14-f51dff7cc24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_sleep(3600*0)\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "print(p3)\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "plt.xlabel(r'$\\Delta_Z/2\\pi$')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "x = z_rot_shifts\n",
    "\n",
    "quadratic_function = lambda x,*y: y[0]+y[1]*x+y[2]*x**2\n",
    "guess = [0.65, 0, -1]\n",
    "est,std,fine, data_fit = fit_function(guess, quadratic_function,x,p3 )\n",
    "plt.errorbar(x,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = 'data')\n",
    "plt.plot(fine,data_fit, label = 'quadratic fit')\n",
    "maximum = -est[1]/(2*est[2])\n",
    "plt.annotate(r\"Max $F$ at $\\Delta_Z = %.3f\\times2\\pi$\"%maximum, xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "\n",
    "plt.ylim([0.45, 0.7])\n",
    "plt.legend()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': data,\n",
    "        'p3': p3,\n",
    "        'readout_freqs': readout_freqs,\n",
    "        'x': x,\n",
    "        'gate_to_test': gate_to_test\n",
    "}\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecab3af-cb41-4f0b-8482-1b7a32bb8354",
   "metadata": {},
   "source": [
    "# Frame rotations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ac5e0c-a2ff-408b-9d27-ee2a97dd3c04",
   "metadata": {},
   "source": [
    "## effect of B pulse on A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1304,
   "id": "8947f3a3-192a-40ef-b300-b68dd89602d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T15:51:43.507013Z",
     "iopub.status.busy": "2024-03-27T15:51:43.505998Z",
     "iopub.status.idle": "2024-03-27T15:51:48.002226Z",
     "shell.execute_reply": "2024-03-27T15:51:48.001225Z",
     "shell.execute_reply.started": "2024-03-27T15:51:43.507013Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase, phase_rotation):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX90', delta_freq)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX', delta_freq, phi2_adj=phase)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX-90', delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1307,
   "id": "70a53fd8-7db8-49a3-9d74-5d2e5d1fa09f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T15:53:23.467515Z",
     "iopub.status.busy": "2024-03-27T15:53:23.467515Z",
     "iopub.status.idle": "2024-03-27T16:05:31.029547Z",
     "shell.execute_reply": "2024-03-27T16:05:31.028551Z",
     "shell.execute_reply.started": "2024-03-27T15:53:23.467515Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 720 s                                                        \n",
      "\n",
      "(44, 11, 4)\n",
      "fit failed\n",
      "fit failed\n",
      "Phase offset =-0.3301570718410097\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.2)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, 0.5, -0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 3: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "            #Eax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        fit_results.append(est)\n",
    "        ax[k].errorbar(x,pops[i],yerr = np.sqrt((pops[i]*(1-pops[i]))/data.shape[0]),fmt =\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"{plot_labels[k]}: f1 = {fit_results[1][0]*1e3:.1f} Hz,  f2 = {fit_results[3][0]*1e3:.1f}Hz\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "\n",
    "print(\"Phase offset =\"+ str((-fit_phase[0][1])/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48c185a1-68e9-45f9-aa68-46389d34d3f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4062ece2-b8a9-44b2-a8ca-debd6088a2e3",
   "metadata": {},
   "source": [
    "## effect of B/2 pulse on A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1308,
   "id": "d8caf560-b1a6-443b-9c40-b02fbaf1ba85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T16:05:31.032545Z",
     "iopub.status.busy": "2024-03-27T16:05:31.032545Z",
     "iopub.status.idle": "2024-03-27T16:05:35.403458Z",
     "shell.execute_reply": "2024-03-27T16:05:35.401457Z",
     "shell.execute_reply.started": "2024-03-27T16:05:31.032545Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase, phase_rotation):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX90', delta_freq)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX90', delta_freq, phi2_adj=phase)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX-90', delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1309,
   "id": "f1ace4ea-2980-4f4f-a0d6-f51caa8835af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T16:05:35.405463Z",
     "iopub.status.busy": "2024-03-27T16:05:35.405463Z",
     "iopub.status.idle": "2024-03-27T16:17:43.053170Z",
     "shell.execute_reply": "2024-03-27T16:17:43.052169Z",
     "shell.execute_reply.started": "2024-03-27T16:05:35.405463Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 720 s                                                        \n",
      "\n",
      "(39, 11, 4)\n",
      "fit failed\n",
      "fit failed\n",
      "Phase offset =0.5040303965573423\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.2)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, 0, 0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 3: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "            #Eax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        fit_results.append(est)\n",
    "        ax[k].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"{plot_labels[k]}: f1 = {fit_results[1][0]*1e3:.1f} Hz,  f2 = {fit_results[3][0]*1e3:.1f}Hz\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "\n",
    "print(\"Phase offset =\"+ str((np.pi-fit_phase[0][1])/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf1916b-91e1-4097-ab98-95f1e728e71a",
   "metadata": {},
   "source": [
    "## effect of A pulse on B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1341,
   "id": "710f4625-c602-4a5d-9853-750e37cacc0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T16:42:10.916233Z",
     "iopub.status.busy": "2024-03-27T16:42:10.915234Z",
     "iopub.status.idle": "2024-03-27T16:42:15.512151Z",
     "shell.execute_reply": "2024-03-27T16:42:15.511155Z",
     "shell.execute_reply.started": "2024-03-27T16:42:10.916233Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase, phase_rotation):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX90', delta_freq)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX', delta_freq, phi2_adj=phase)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX-90', delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1346,
   "id": "413cf4f4-3622-4a91-bd6f-4e9fddc24c27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T16:48:04.744838Z",
     "iopub.status.busy": "2024-03-27T16:48:04.743839Z",
     "iopub.status.idle": "2024-03-27T16:48:05.480425Z",
     "shell.execute_reply": "2024-03-27T16:48:05.479415Z",
     "shell.execute_reply.started": "2024-03-27T16:48:04.744838Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18, 11, 4)\n",
      "Phase offset =-0.022063981221361415\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*0.)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, 0, 0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 1: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "            #Eax[1].plot(fine,ramsey_fit(fine,*guess), color = colors[i],alpha = 0.5)\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "        fit_results.append(est)\n",
    "        ax[k].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"z_phase_rotation_a_on_b_prep: $\\Delta \\phi += ${(-fit_phase[0][1])/(2*np.pi):.3f} $\\cdot 2\\pi$\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "\n",
    "print(\"Phase offset =\"+ str((-fit_phase[0][1])/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "    \n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024523d1-aac6-4567-a7dd-991074a6ee93",
   "metadata": {},
   "source": [
    "## effect of A/2 pulse on B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2097,
   "id": "66641a69-5683-4e1e-9001-b411491e9356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T10:39:20.335059Z",
     "iopub.status.busy": "2024-04-01T10:39:20.334058Z",
     "iopub.status.idle": "2024-04-01T10:39:25.628451Z",
     "shell.execute_reply": "2024-04-01T10:39:25.627449Z",
     "shell.execute_reply.started": "2024-04-01T10:39:20.335059Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "###################### CZ params  #######################\n",
    "\n",
    "phase_rotation = [float(phase) for phase in np.linspace(-0.5,0.5,11)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='CZ'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    k = declare(int)           # Index for Ramsey sweep\n",
    "\n",
    "    phase = declare(fixed)\n",
    "    freq_set  = declare(int)\n",
    "\n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "\n",
    "    click_acc=declare(int)\n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        ################# Raman experiment #################\n",
    "        # Sweep Raman frequency detunning sideband interpulse delay\n",
    "\n",
    "        # with for_each_(detuning, freqs):\n",
    "        # with for_each_(amp_set, amps):\n",
    "        with for_each_(phase, phase_rotation):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            ################# Now play the Ramsey sequence #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "\n",
    "            align()\n",
    "\n",
    "            align()\n",
    "\n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX90', delta_freq)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('aX90', delta_freq, phi2_adj=phase)\n",
    "            \n",
    "            #Pi/2 A\n",
    "            Pauli_swept('bX-90', delta_freq)\n",
    "            \n",
    "            Pauli_swept('aX-90', delta_freq)\n",
    "            \n",
    "            save(0, timing_stream)\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False, state_list=[1,1,1,1])\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(len(phase_rotation)).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2098,
   "id": "a4558927-f942-462f-ac12-751da5309599",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-01T10:39:41.128531Z",
     "iopub.status.busy": "2024-04-01T10:39:41.128531Z",
     "iopub.status.idle": "2024-04-01T10:45:40.649674Z",
     "shell.execute_reply": "2024-04-01T10:45:40.647684Z",
     "shell.execute_reply.started": "2024-04-01T10:39:41.128531Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoying Manu for 355 s of 3600 s                                             \r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\2185243080.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m#     time.sleep(0.5)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mbetter_sleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3600\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mplot_guess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\4252601885.py\u001b[0m in \u001b[0;36mbetter_sleep\u001b[1;34m(t_sleep)\u001b[0m\n\u001b[0;32m     77\u001b[0m     \u001b[0mPrintStatic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Annoying Manu for %i s of %s s\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_sleep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_sleep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m         \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m         \u001b[0mPrintStatic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Annoying Manu for %i s of %s s\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_sleep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_sleep\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_sleep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# matrix = (np.random.random([15,40]) > 0.5)*1\n",
    "\n",
    "# while res.clicks.count_so_far()<100:\n",
    "#     matrix = conway(matrix)\n",
    "#     PrintStatic('be patient Jaime, '+str(res.clicks.count_so_far())+' averages out of 10\\n'+print_conway(matrix))\n",
    "#     time.sleep(0.5)\n",
    "\n",
    "better_sleep(3600*1)\n",
    "plot_guess = 0\n",
    "\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "full_data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print(full_data.shape)\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,5))\n",
    "plot_labels = [\"Spin B down\", \"Spin B up\"]\n",
    "\n",
    "ax=[ax]\n",
    "k=0\n",
    "\n",
    "fit_phase = []\n",
    "###########################################################\n",
    "data = full_data[:,:]\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True)\n",
    "pops = [p0,p1,p2,p3]\n",
    "sigmoid_length = 5\n",
    "x = np.array(phase_rotation)\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "############################### Fit function #################################\n",
    "\n",
    "def ramsey_fit(t,f,phi,A,B):\n",
    "    return A*np.cos(2*np.pi*f*t+phi)+B\n",
    "\n",
    "    ############################### subplot 1 ###############################    \n",
    "fit_results = []\n",
    "if len(readout_freqs)==4:\n",
    "    for i in range(len(readout_freqs)): \n",
    "        guess = [1, np.pi/4*k, 0.5,0.5]\n",
    "        try:\n",
    "            est,std,fine,data_fit = fit_function(guess, ramsey_fit, x, pops[i])\n",
    "            if i == 3: fit_phase.append(est)\n",
    "            ax[k].plot(fine,data_fit, color = colors[i])\n",
    "        except:\n",
    "            print(\"fit failed\")\n",
    "            est = guess\n",
    "            std = guess\n",
    "            fine=x\n",
    "        fit_results.append(est)\n",
    "        ax[k].plot(x,pops[i],\"o\", label = labels[i])\n",
    "else:\n",
    "    ax[k].errorbar(x, p_data.mean(0), p_data.std(0)/np.sqrt(len(data))     , label = r\"${|\\downarrow\\downarrow\\rangle}$\",fmt = \"o\") \n",
    "\n",
    "if plot_guess: ax[k].plot(fine,ramsey_fit(fine,*guess), color = \"black\",alpha = 0.5)\n",
    "ax[k].set_ylabel(\"P\")\n",
    "ax[k].set_xlabel('Phase (rad)')\n",
    "ax[k].legend(loc = \"lower right\")\n",
    "# plt_label = f'f = {est[0]:.4f}$\\pm${std[0]:.4f} kHz, $\\delta $f = {(est[0] - ramsey_detuning*1e6):.4f} kHz \\n T$_1$ = {est[1]:.2f}$\\pm${std[1]:.4f} ms'\n",
    "\n",
    "plt_label = f\"z_phase_rotation_a_half_on_b_prep: $\\Delta \\phi += ${(-fit_phase[0][1])/(2*np.pi):.3f} $\\cdot 2\\pi$\"\n",
    "ax[k].set_title(plt_label, fontsize = \"small\")\n",
    "ax[k].set_ylim(0,1)\n",
    "\n",
    "print(\"Phase offset =\"+ str((np.pi-fit_phase[0][1])/(2*np.pi)))\n",
    "\n",
    "save_fig_manustyle(directory+filename+'_'+'CZ_ramsey.pdf')\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "        'click_array': full_data,\n",
    "        'timing': timing,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d02355fc-5406-43e5-9692-7bcbd90eec76",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Tomography with CZ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a35f729-d40a-4a42-bb8e-86f46ad9c54b",
   "metadata": {},
   "source": [
    "## downdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1366,
   "id": "b772a5b7-7473-4379-b5c0-d777db01eff4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:45:19.393583Z",
     "iopub.status.busy": "2024-03-27T17:45:19.392584Z",
     "iopub.status.idle": "2024-03-27T17:45:27.238860Z",
     "shell.execute_reply": "2024-03-27T17:45:27.237862Z",
     "shell.execute_reply.started": "2024-03-27T17:45:19.393583Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1365,
   "id": "bae6d7a6-a868-4298-90b0-60342a30c0e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:42:20.700678Z",
     "iopub.status.busy": "2024-03-27T17:42:20.700678Z",
     "iopub.status.idle": "2024-03-27T17:42:22.809979Z",
     "shell.execute_reply": "2024-03-27T17:42:22.808977Z",
     "shell.execute_reply.started": "2024-03-27T17:42:20.700678Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (159, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240327180119_Tomography.hdf5'"
      ]
     },
     "execution_count": 1365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3600*5)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5c7365-69bd-47db-a881-ec54c1af72da",
   "metadata": {},
   "source": [
    "## updown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1367,
   "id": "6629ca9f-9b61-49e6-8ee4-4d898e95730b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:51:16.211309Z",
     "iopub.status.busy": "2024-03-27T17:51:16.209300Z",
     "iopub.status.idle": "2024-03-27T17:51:24.724362Z",
     "shell.execute_reply": "2024-03-27T17:51:24.722444Z",
     "shell.execute_reply.started": "2024-03-27T17:51:16.211309Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 A #################\n",
    "            Pauli_swept('aX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1368,
   "id": "9dacf773-532e-42b9-b054-27a607a9339b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:51:24.727353Z",
     "iopub.status.busy": "2024-03-27T17:51:24.727353Z",
     "iopub.status.idle": "2024-03-27T22:54:18.782240Z",
     "shell.execute_reply": "2024-03-27T22:54:18.781242Z",
     "shell.execute_reply.started": "2024-03-27T17:51:24.727353Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 18000 s                                                      \n",
      "\n",
      "Data shape: (1193, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240327185116_Tomography.hdf5'"
      ]
     },
     "execution_count": 1368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3600*5)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb1582a-2d1d-48bb-9159-3617047cb0a0",
   "metadata": {},
   "source": [
    "## downup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1369,
   "id": "eb81fae5-de69-44c1-be1c-b734d7fdf51b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T22:54:18.785241Z",
     "iopub.status.busy": "2024-03-27T22:54:18.785241Z",
     "iopub.status.idle": "2024-03-27T22:54:26.957226Z",
     "shell.execute_reply": "2024-03-27T22:54:26.956217Z",
     "shell.execute_reply.started": "2024-03-27T22:54:18.785241Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 B #################\n",
    "            Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1370,
   "id": "f1d39b80-b416-4c29-8546-c9c035be8c2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T22:54:26.959239Z",
     "iopub.status.busy": "2024-03-27T22:54:26.959239Z",
     "iopub.status.idle": "2024-03-28T03:57:20.941878Z",
     "shell.execute_reply": "2024-03-28T03:57:20.940878Z",
     "shell.execute_reply.started": "2024-03-27T22:54:26.959239Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 18000 s                                                      \n",
      "\n",
      "Data shape: (1192, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240327235419_Tomography.hdf5'"
      ]
     },
     "execution_count": 1370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3600*5)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65f9023-76f4-4a00-b230-20940b2e92bf",
   "metadata": {},
   "source": [
    "## upup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1371,
   "id": "1a7c07ff-8da2-495c-9e23-d83a3c29df1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T03:57:20.944880Z",
     "iopub.status.busy": "2024-03-28T03:57:20.944880Z",
     "iopub.status.idle": "2024-03-28T03:57:29.460816Z",
     "shell.execute_reply": "2024-03-28T03:57:29.459796Z",
     "shell.execute_reply.started": "2024-03-28T03:57:20.944880Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 A #################\n",
    "            Pauli_swept('aX',    delta_freq)\n",
    "            \n",
    "            ################# Pi/2 B #################\n",
    "            Pauli_swept('bX',    delta_freq)\n",
    "\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1372,
   "id": "267b97d8-cac9-47a5-8a9b-8e61b256e3e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T03:57:29.462811Z",
     "iopub.status.busy": "2024-03-28T03:57:29.461814Z",
     "iopub.status.idle": "2024-03-28T09:00:25.636066Z",
     "shell.execute_reply": "2024-03-28T09:00:25.635061Z",
     "shell.execute_reply.started": "2024-03-28T03:57:29.462811Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoyed Manu for 18000 s                                                      \n",
      "\n",
      "Data shape: (1188, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240328045721_Tomography.hdf5'"
      ]
     },
     "execution_count": 1372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(3600*5)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc7d0bc-f32e-4ef9-8609-6f3857691af9",
   "metadata": {},
   "source": [
    "## upup downdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1351,
   "id": "61f7a984-bffd-4ab6-9223-3884d2b6b18e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:01:18.917871Z",
     "iopub.status.busy": "2024-03-27T17:01:18.916870Z",
     "iopub.status.idle": "2024-03-27T17:01:27.798325Z",
     "shell.execute_reply": "2024-03-27T17:01:27.797312Z",
     "shell.execute_reply.started": "2024-03-27T17:01:18.917871Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Tomography'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(1e9)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "    n = declare(int)           # Index for repeated preparation pulses\n",
    "    m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "    j = declare(int)           # Index for Ramsey sweep\n",
    "    t = declare(int)           # Index for Ramsey times\n",
    "    k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "    l = declare(int)\n",
    "    \n",
    "    freq_set  = declare(int)\n",
    "    \n",
    "    rabi_stream  = declare_stream()\n",
    "    timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "    #### Frequency Tracking variables ####\n",
    "    delta_freq = declare(int) \n",
    "    assign(delta_freq,0)\n",
    "    delta_freq_stream = declare_stream()\n",
    "    Y=declare(fixed)\n",
    "    angle=declare(fixed)\n",
    "    delta_freq_acc = declare(int) \n",
    "    assign(Y,0)\n",
    "    assign(delta_freq_acc,0)\n",
    "\n",
    "    click_acc=declare(int)\n",
    "    \n",
    "\n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "        with for_(l, 0, l<9, l + 1):\n",
    "            ################# Preparation into down-down #################\n",
    "            # Sweep number of readout pulses\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "\n",
    "            save(0, timing_stream)\n",
    "\n",
    "            amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "            gaussian_pulse_length = 5000//4\n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "            delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "            save(delta_freq, delta_freq_stream)\n",
    "            save(0, timing_stream)\n",
    "            \n",
    "            align()\n",
    "            chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "            align()\n",
    "            wait(int(20e6/4))\n",
    "            align()\n",
    "            save(0, timing_stream)\n",
    "\n",
    "\n",
    "            ################# Now play the Bell-state preparations #################\n",
    "            reset_frame(spin_sticky_element)\n",
    "            reset_frame(spin_sticky_extra_element)\n",
    "            reset_frame(spin_sticky_extra2_element)\n",
    "            reset_frame(spin_sticky4_element)\n",
    "            \n",
    "            # Update pulses for spin b\n",
    "            update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase=True) # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase=True)                            # Detuned Electron frequency\n",
    "\n",
    "            # Update pulses for spin a\n",
    "            update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase=True)  # Detuned Sideband frequency\n",
    "            update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase=True)                             # Detuned Electron frequency\n",
    "\n",
    "            # FSV trigger\n",
    "            play('ON',fsv_trigger)\n",
    "\n",
    "            ################# Pi/2 A #################\n",
    "            Pauli_swept('aX90',    delta_freq)\n",
    "            \n",
    "            ################# Pi/2 B #################\n",
    "            Pauli_swept('bX90',    delta_freq)\n",
    "            \n",
    "            ################# CZ B #################\n",
    "            align()\n",
    "            CZ_gate(delta_freq)\n",
    "            \n",
    "            Pauli_swept('aX-90',    delta_freq)\n",
    "            \n",
    "            with switch_(l):\n",
    "                with case_(0): # XX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(1): # XY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(2): # XZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X A\n",
    "                    Pauli_swept('aX90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "                with case_(3): # YX\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "                    \n",
    "                with case_(4): # YY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(5): # YZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 Y A\n",
    "                    Pauli_swept('aY90',    delta_freq)\n",
    "\n",
    "                    # No pulse B\n",
    "\n",
    "\n",
    "                with case_(6): # ZX\n",
    "\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # Pi/2 X B\n",
    "                    Pauli_swept('bX90',    delta_freq)\n",
    "\n",
    "                with case_(7): # ZY\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "                    \n",
    "                    # Pi/2 Y B\n",
    "                    Pauli_swept('bY90',    delta_freq)\n",
    "                    \n",
    "                with case_(8): # ZZ\n",
    "                    # FSV trigger\n",
    "                    play('ON',fsv_trigger)\n",
    "                    # No pulse A\n",
    "                    align()\n",
    "\n",
    "                    # No pulse B\n",
    "                    align()\n",
    "\n",
    "                with default_():\n",
    "                    pass\n",
    "\n",
    "\n",
    "            ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "            save(0, timing_stream)\n",
    "            align()\n",
    "            wait(int(5e6//4))\n",
    "            align()\n",
    "\n",
    "            nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "            save(0, timing_stream)\n",
    "            ######################################\n",
    "\n",
    "    with stream_processing():\n",
    "        \n",
    "        delta_freq_stream.save_all('delta_freq')\n",
    "        rabi_stream.buffer(len(readout_freqs)).buffer(3).buffer(3).save_all('clicks')\n",
    "        timing_stream.timestamps().buffer(6).save_all('timing')\n",
    "        \n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1365,
   "id": "3708101b-c5a7-4624-92c9-c96e3a4e2c0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T17:42:20.700678Z",
     "iopub.status.busy": "2024-03-27T17:42:20.700678Z",
     "iopub.status.idle": "2024-03-27T17:42:22.809979Z",
     "shell.execute_reply": "2024-03-27T17:42:22.808977Z",
     "shell.execute_reply.started": "2024-03-27T17:42:20.700678Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (159, 3, 3, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Z:\\\\SMPD3-8\\\\SpinRun3-1\\\\Tomography\\\\20240327180119_Tomography.hdf5'"
      ]
     },
     "execution_count": 1365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_sleep(0)\n",
    "###########################################################\n",
    "res = job.result_handles\n",
    "timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "print('Data shape:', data.shape)\n",
    "\n",
    "\n",
    "p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "pops = [p0,p1,p2,p3]\n",
    "labels = [r\"${|\\uparrow\\uparrow\\rangle}$\",r\"${|\\uparrow\\downarrow\\rangle}$\",r\"${|\\downarrow\\uparrow\\rangle}$\",r\"${|\\downarrow\\downarrow\\rangle}$\"]\n",
    "\n",
    "cmaps = ['Blues', 'Oranges', 'Greens', 'Reds']\n",
    "fig, axs = plt.subplots(2,2,figsize=(10,10),tight_layout=True)\n",
    "axs = axs.flatten()\n",
    "for i in range(4):\n",
    "    plt.sca(axs[i])\n",
    "    plot_2d_sweep(\n",
    "        pops[i], \n",
    "        x = ['X', 'Y', 'Z'],\n",
    "        y = ['X', 'Y', 'Z'],\n",
    "        clabel = \"Population\",\n",
    "        cmap = cmaps[i],\n",
    "        horizontal_ticks=True,\n",
    "        vmax=0.5,\n",
    "        vmin=0,\n",
    "        annot=True\n",
    "    )\n",
    "    axs[i].set_title('Preparing '+labels[i], color=colors[i])\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': data,\n",
    "            'readout_freqs': readout_freqs,\n",
    "}\n",
    "\n",
    "\n",
    "save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "plt.show()\n",
    "fullpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f08259-b3d2-451e-9727-15dada81d57f",
   "metadata": {},
   "source": [
    "# 2qRB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "541f4afb-3159-49b4-8484-2d061ee40b22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:21:12.286463Z",
     "iopub.status.busy": "2024-03-26T15:21:12.286463Z",
     "iopub.status.idle": "2024-03-26T15:21:13.172267Z",
     "shell.execute_reply": "2024-03-26T15:21:13.172267Z",
     "shell.execute_reply.started": "2024-03-26T15:21:12.286463Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import cirq\n",
    "from cirq import circuits\n",
    "\n",
    "# The two qubits to be characterized in this example.\n",
    "q_0 = cirq.GridQubit(0, 0)\n",
    "q_1 = cirq.GridQubit(0, 1)\n",
    "\n",
    "cliffords = cirq.experiments.qubit_characterizations._single_qubit_cliffords()\n",
    "cfd_matrices = cirq.experiments.qubit_characterizations._two_qubit_clifford_matrices(q_0, q_1, cliffords)\n",
    "\n",
    "def gate_finder(op_):\n",
    "    if op_.gate==(cirq.Y**0.5):\n",
    "        gateq='Y90'\n",
    "    if op_.gate==(cirq.Y**-0.5):\n",
    "        gateq='Y-90'\n",
    "    if op_.gate==(cirq.Y):\n",
    "        gateq='Y'\n",
    "    if op_.gate==(cirq.X**0.5):\n",
    "        gateq='X90'\n",
    "    if op_.gate==(cirq.X**-0.5):\n",
    "        gateq='X-90'\n",
    "    if op_.gate==(cirq.X):\n",
    "        gateq='X'\n",
    "    if op_.gate==(cirq.X**0):\n",
    "        gateq='I'\n",
    "    if op_.gate==(cirq.Y**0):\n",
    "        gateq='I'\n",
    "    return gateq\n",
    "\n",
    "def _find_inv_matrix(mat: np.ndarray, mat_sequence: np.ndarray) -> int:\n",
    "    mat_prod = np.einsum('ij,...jk->...ik', mat, mat_sequence)\n",
    "    diag_sums = list(np.absolute(np.einsum('...ii->...', mat_prod)))\n",
    "    idx = diag_sums.index(max(diag_sums))\n",
    "    return idx\n",
    "\n",
    "def get_gate_sequence(n_cliffords,two_qubit=True):\n",
    "    \n",
    "    circuit = circuits.Circuit()\n",
    "\n",
    "    if two_qubit:\n",
    "        clifford_group_size = 11520\n",
    "        idx_list = list(np.random.choice(clifford_group_size, n_cliffords))\n",
    "        for idx in idx_list:\n",
    "            circuit.append(cirq.experiments.qubit_characterizations._two_qubit_clifford(q_0, q_1, idx, cliffords))\n",
    "            #circuit.append(cirq.CZ(cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)))\n",
    "\n",
    "        inv_idx = _find_inv_matrix(cirq.experiments.qubit_characterizations.protocols.unitary(circuit), cfd_matrices)\n",
    "        circuit.append(cirq.experiments.qubit_characterizations._two_qubit_clifford(q_0, q_1, inv_idx, cliffords))\n",
    "\n",
    "    \n",
    "    else:\n",
    "        circuit = circuits.Circuit()\n",
    "        for idx in range(n_cliffords):\n",
    "            circuit.append(\n",
    "                (\n",
    "                    np.random.choice(np.concatenate(cliffords.c1_in_xy),1)[0].on(cirq.GridQubit(0, 0)),\n",
    "                    np.random.choice(np.concatenate(cliffords.c1_in_xy),1)[0].on(cirq.GridQubit(0, 1))\n",
    "                )\n",
    "            )\n",
    "        inv_idx = _find_inv_matrix(cirq.experiments.qubit_characterizations.protocols.unitary(circuit), cfd_matrices)\n",
    "        circuit.append(cirq.experiments.qubit_characterizations._two_qubit_clifford(q_0, q_1, inv_idx, cliffords))\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    gate_list=[]\n",
    "    for moments_ in circuit.moments:\n",
    "        if len(moments_.operations)==1:\n",
    "\n",
    "            if moments_.operations[0]==cirq.CZ(cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)):\n",
    "                gate_list.append(['CZ'])\n",
    "            elif moments_.operations[0].qubits[0].col==0:\n",
    "                gate_list.append([gate_finder(moments_.operations[0]),'I'])\n",
    "            elif moments_.operations[0].qubits[0].col==1:\n",
    "                gate_list.append(['I',gate_finder(moments_.operations[0])])\n",
    "\n",
    "        else:\n",
    "            gateq12=[None,None]\n",
    "            for i,op_ in enumerate(moments_.operations):\n",
    "                gateq12[i]=gate_finder(op_)\n",
    "\n",
    "                if gateq12[0]==None:\n",
    "                    print(op_.gate)\n",
    "            gate_list.append(gateq12)\n",
    "            \n",
    "    RB_sequence=[]\n",
    "    for gate_ in gate_list:\n",
    "        #print(gate_)\n",
    "        if len(gate_)==2:\n",
    "            for qstring_,gate1Q_ in zip(['a','b'],gate_):\n",
    "                if gate1Q_!='I':\n",
    "                    RB_sequence.append(qstring_+gate1Q_)\n",
    "        else: RB_sequence.append('CZ')\n",
    "\n",
    "    return RB_sequence\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8cc95adb-120c-49e4-9d7f-e4f9f9c15f47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T11:11:15.042310Z",
     "iopub.status.busy": "2024-03-26T11:11:15.042310Z",
     "iopub.status.idle": "2024-03-26T11:11:15.336432Z",
     "shell.execute_reply": "2024-03-26T11:11:15.336432Z",
     "shell.execute_reply.started": "2024-03-26T11:11:15.042310Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aX-90',\n",
       " 'bY-90',\n",
       " 'aY90',\n",
       " 'bX-90',\n",
       " 'CZ',\n",
       " 'bY',\n",
       " 'aX90',\n",
       " 'bX',\n",
       " 'bY90',\n",
       " 'bX-90',\n",
       " 'CZ',\n",
       " 'aY90',\n",
       " 'bY',\n",
       " 'aX90',\n",
       " 'bX90']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gate_sequence(1, two_qubit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "b8fb0e63-1782-45b7-a18c-ff1601b30011",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:21:47.024169Z",
     "iopub.status.busy": "2024-03-26T15:21:47.024169Z",
     "iopub.status.idle": "2024-03-26T15:21:47.288010Z",
     "shell.execute_reply": "2024-03-26T15:21:47.288010Z",
     "shell.execute_reply.started": "2024-03-26T15:21:47.024169Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "RB_sequences = [['aX-90',\n",
    " 'bY-90',\n",
    " 'aY90',\n",
    " 'bX-90',\n",
    " 'CZ',\n",
    " 'bY',\n",
    " 'aX90',\n",
    " 'bX',\n",
    " 'bY90',\n",
    " 'bX-90',\n",
    " 'CZ',\n",
    " 'aY90',\n",
    " 'bY',\n",
    " 'aX90',\n",
    " 'bX90']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "a91418e4-1a6b-43e0-b013-a90952ec4234",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:21:48.390686Z",
     "iopub.status.busy": "2024-03-26T15:21:48.390686Z",
     "iopub.status.idle": "2024-03-26T15:23:24.228545Z",
     "shell.execute_reply": "2024-03-26T15:23:24.228545Z",
     "shell.execute_reply.started": "2024-03-26T15:21:48.390686Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (20, 4)\n",
      "0.2\n",
      "Data shape: (20, 4)\n",
      "0.35\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\2603860043.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult_handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m         \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait_for_all_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m         \u001b[0mtiming\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtiming\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\qm37\\lib\\site-packages\\qm\\_results_01.py\u001b[0m in \u001b[0;36mwait_for_all_values\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    742\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    743\u001b[0m                 \u001b[0mtime_remaining\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 744\u001b[1;33m             \u001b[0mall_done\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_done\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait_for_all_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime_remaining\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    745\u001b[0m             \u001b[0mkeys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    746\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mall_done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\qm37\\lib\\site-packages\\qm\\_results_01.py\u001b[0m in \u001b[0;36mwait_for_all_values\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    172\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m             \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"result {self.name} was not done in time\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%write_and_run temp.py\n",
    "%matplotlib inline\n",
    "from Config import *\n",
    "import time as time\n",
    "\n",
    "####################### Chirped prep params #######################\n",
    "chirped_pump_freq = Photon_IF - int(0.810e6)\n",
    "chirped_pump_amplitude = 0.1\n",
    "chirped_pump_duration = int(3.5e6//4)\n",
    "\n",
    "chirp_rate = calc_chirp_rate(0,50e3,chirped_pump_duration*4)\n",
    "####################### Define the readout frequencies #######################\n",
    "\n",
    "gate_dict = {\n",
    "    'aX90': 0,\n",
    "    'aY90': 1,\n",
    "    'aX': 2,\n",
    "    'aY': 3,\n",
    "    'aX-90': 4, \n",
    "    'aY-90': 5,\n",
    "    'aW90': 6,\n",
    "    \n",
    "    'bX90': 7,\n",
    "    'bY90': 8,\n",
    "    'bX': 9,\n",
    "    'bY': 10,\n",
    "    'bX-90': 11, \n",
    "    'bY-90': 12,\n",
    "    'bW90': 13,\n",
    "    \n",
    "    'CZ': 14\n",
    "}\n",
    "\n",
    "\n",
    "readout_freqs = Photon_IF + centre_freq*1e3 + 1e3*np.array([-spacing1-spacing2, -spacing1+spacing2,spacing1-spacing2,spacing1+spacing2])/2\n",
    "readout_freqs = [int(freq) for freq in readout_freqs]\n",
    "freq_electron = readout_freqs[-1]\n",
    "\n",
    "\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "N_repetition = int(20)\n",
    "\n",
    "####################### Run program #######################\n",
    "vals = [[],[],[],[],[]]\n",
    "while True:\n",
    "    \n",
    "    #RB_sequences=[]\n",
    "    #gate_nums = [1,2,5,10,30]\n",
    "    #for i in gate_nums:\n",
    "    #    RB_sequences.append(get_gate_sequence(i,two_qubit=True))\n",
    "    \n",
    "    for i, RB_sequence in enumerate(RB_sequences):\n",
    "\n",
    "        ####################### Save params #######################\n",
    "\n",
    "        experiment_name='RB'\n",
    "        time_stamp=get_timestamp()\n",
    "        filename=time_stamp+'%s'%(experiment_name)\n",
    "        directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "        shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "        shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "\n",
    "        pulse_list = [int(gate_dict[RB_sequence_]) for RB_sequence_ in RB_sequence]\n",
    "        with program() as Spin_detection_linewidth_angle:\n",
    "\n",
    "            n = declare(int)           # Index for repeated preparation pulses\n",
    "            m = declare(int)           # Index for repeated preparation pulses\n",
    "\n",
    "            j = declare(int)           # Index for Ramsey sweep\n",
    "            t = declare(int)           # Index for Ramsey times\n",
    "            k = declare(int)           # Index for Ramsey time long repeated wait\n",
    "\n",
    "\n",
    "            freq_set  = declare(int)\n",
    "\n",
    "            rabi_stream  = declare_stream()\n",
    "            timing_stream = declare_stream() # Stream for Ramsey index\n",
    "\n",
    "            #### Frequency Tracking variables ####\n",
    "            delta_freq = declare(int) \n",
    "            assign(delta_freq,0)\n",
    "            delta_freq_stream = declare_stream()\n",
    "            Y=declare(fixed)\n",
    "            angle=declare(fixed)\n",
    "            delta_freq_acc = declare(int) \n",
    "            assign(Y,0)\n",
    "            assign(delta_freq_acc,0)\n",
    "\n",
    "            click_acc=declare(int)\n",
    "            pulse = declare(int)\n",
    "\n",
    "            with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "                ################# Preparation into down-down #################\n",
    "                # Sweep number of readout pulses\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                amplitude_pulse = 0.073 # Pi pulse amplitude\n",
    "                gaussian_pulse_length = 5000//4\n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=25_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)            \n",
    "                delta_freq = track_frequency_2spins(delta_freq, delta_freq_acc, Y, centre_freq + (spacing1+spacing2)/2, amplitude_pulse, gaussian_pulse_length, interrogation_time=50_000//4)#, N_Freq_tracking=50, interrogation_time=50e3)\n",
    "                save(delta_freq, delta_freq_stream)\n",
    "\n",
    "                save(0, timing_stream)\n",
    "\n",
    "                align()\n",
    "                chirped_pumping(centre_freq, delta_freq, pump_steps = pump_steps_after_tracking_prep, enable_fsv_trigger=False)\n",
    "                align()\n",
    "                wait(int(20e6/4))\n",
    "                align()\n",
    "\n",
    "                ################# Now play the Bell-state preparations #################\n",
    "                reset_frame(spin_sticky_element)\n",
    "                reset_frame(spin_sticky_extra_element)\n",
    "                reset_frame(spin_sticky_extra2_element)\n",
    "                reset_frame(spin_sticky4_element)\n",
    "\n",
    "                # Update pulses for spin a\n",
    "                update_frequency(spin_sticky_element       , freq_electron + delta_freq + raman_detuning_a_prep + nuclear_spin_freq_a_prep, keep_phase = True)  # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky_extra_element , freq_electron + delta_freq + raman_detuning_a_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                align()\n",
    "\n",
    "                update_frequency(spin_sticky_extra2_element, freq_electron + delta_freq + raman_detuning_b_prep + nuclear_spin_freq_b_prep, keep_phase = True) # Detuned Sideband frequency\n",
    "                update_frequency(spin_sticky4_element      , freq_electron + delta_freq + raman_detuning_b_prep, keep_phase = True)                             # Detuned Electron frequency\n",
    "\n",
    "                # FSV trigger\n",
    "                play('ON',fsv_trigger)\n",
    "\n",
    "                with for_each_(pulse, pulse_list):\n",
    "                    with if_(pulse==0):\n",
    "                        Pauli_swept('aX90', delta_freq)\n",
    "                    with elif_(pulse==1):\n",
    "                        Pauli_swept('aY90', delta_freq)\n",
    "                    with elif_(pulse==2):\n",
    "                        Pauli_swept('aX', delta_freq)\n",
    "                    with elif_(pulse==3):\n",
    "                        Pauli_swept('aY',delta_freq)\n",
    "                    with if_(pulse==4):\n",
    "                        Pauli_swept('aX-90',delta_freq)\n",
    "                    with elif_(pulse==5):\n",
    "                        Pauli_swept('aY-90',delta_freq)\n",
    "                    with elif_(pulse==6):\n",
    "                        Pauli_swept('aW90',delta_freq)\n",
    "\n",
    "                    with elif_(pulse==7):\n",
    "                        Pauli_swept('bX90', delta_freq)\n",
    "                    with elif_(pulse==8):\n",
    "                        Pauli_swept('bY90', delta_freq)\n",
    "                    with elif_(pulse==9):\n",
    "                        Pauli_swept('bX', delta_freq)\n",
    "                    with elif_(pulse==10):\n",
    "                        Pauli_swept('bY',delta_freq)\n",
    "                    with if_(pulse==11):\n",
    "                        Pauli_swept('bX-90',delta_freq)\n",
    "                    with elif_(pulse==12):\n",
    "                        Pauli_swept('bY-90',delta_freq)\n",
    "                    with elif_(pulse==13):\n",
    "                        Pauli_swept('bW90',delta_freq)\n",
    "\n",
    "                    with elif_(pulse==14):\n",
    "                        CZ_gate(delta_freq)\n",
    "\n",
    "                ################# Nuclear spin readout (N_ROcycle x 2 spin excitations + SMPD acumulation) #################\n",
    "                save(0, timing_stream)\n",
    "                align()\n",
    "                wait(int(5e6//4))\n",
    "                align()\n",
    "\n",
    "                # nuclear_spin_RO_4freq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False)\n",
    "                nuclear_spin_RO_Nfreq_interleaved(rabi_stream, N_ROcycle_prep, readout_freqs, delta_freq, enable_fsv_trigger = False,\n",
    "                                                      state_list=[1,1,1,1])\n",
    "                save(0, timing_stream)\n",
    "                ######################################\n",
    "\n",
    "            with stream_processing():\n",
    "\n",
    "                delta_freq_stream.save_all('delta_freq')\n",
    "                rabi_stream.buffer(len(readout_freqs)).save_all('clicks')\n",
    "                timing_stream.timestamps().buffer(5).save_all('timing')\n",
    "\n",
    "        qmm = QuantumMachinesManager()\n",
    "        qm = qmm.open_qm(config)\n",
    "        job = qm.execute(Spin_detection_linewidth_angle, flags=['auto-element-thread'])\n",
    "\n",
    "\n",
    "        res = job.result_handles\n",
    "        res.wait_for_all_values()\n",
    "        timing= np.array([item[0] for item in res.timing.fetch_all()])\n",
    "        data = np.array([item[0] for item in res.clicks.fetch_all()])\n",
    "        print('Data shape:', data.shape)\n",
    "        p0,p1,p2,p3,delta_x,delta_y = extract_populations_4state(data, frequency_domain = True, accumulated=True, kmeans=kmeans_standard)\n",
    "        print(p3)\n",
    "        vals[i].append([p0,p1,p2,p3])\n",
    "        fullpath=directory+filename+'.hdf5'\n",
    "        datasets= {\n",
    "                'click_array': data,\n",
    "                'readout_freqs': readout_freqs,\n",
    "                'pulse_list': pulse_list,\n",
    "        }\n",
    "        save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "vals = np.array(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "316f444b-48c3-4864-9b3d-823cb65d2e18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T15:23:30.352890Z",
     "iopub.status.busy": "2024-03-26T15:23:30.352890Z",
     "iopub.status.idle": "2024-03-26T15:23:30.617160Z",
     "shell.execute_reply": "2024-03-26T15:23:30.617160Z",
     "shell.execute_reply.started": "2024-03-26T15:23:30.352890Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[0.4, 0.35, 0.05, 0.2], [0.25, 0.25, 0.15, 0.35]], [], [], [], []]"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "46e49d12-8886-48d9-b38b-1da1049d46f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T12:51:35.032068Z",
     "iopub.status.busy": "2024-03-26T12:51:35.031997Z",
     "iopub.status.idle": "2024-03-26T12:51:35.297876Z",
     "shell.execute_reply": "2024-03-26T12:51:35.297876Z",
     "shell.execute_reply.started": "2024-03-26T12:51:35.032068Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.07272727 0.01818182 0.90909091]\n",
      "[0.         0.09545455 0.00909091 0.89545455]\n",
      "[0.02272727 0.09090909 0.18181818 0.70454545]\n",
      "[0.08181818 0.13181818 0.26818182 0.51818182]\n",
      "[0.18  0.24  0.205 0.375]\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(np.mean(vals[i], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d79aaaf9-da12-4077-b1da-f3e72c2035b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel('Number of Cliffords')\n",
    "plt.ylabel(f'{labels[3]} population')\n",
    "\n",
    "popt, pcov = sp.curve_fit(p_decay, gate_nums, p3, [0.9,0.5,0.5])\n",
    "plt.errorbar(gate_nums,p3,yerr=np.sqrt(p3*(1-p3)/data.shape[0]),fmt=\"ko\",label = f\"Reference  decay const ={np.round(popt[0],3)}\")\n",
    "plt.plot(gate_nums, p_decay(np.array(gate_nums), *popt),'k')\n",
    "\n",
    "# popt, pcov = sp.curve_fit(exp_decay, gate_nums, vals, [50,0.5,0.5])\n",
    "# plt.errorbar(gate_nums,vals,yerr=np.sqrt(vals*(1-vals)/data.shape[0]),fmt=\"ro\", label = \"Pi X\")\n",
    "# plt.plot(gate_nums, exp_decay(np.array(gate_nums), *popt),'r')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "save_fig_manustyle(directory+filename+'_'+'population.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74126d4-4a70-43c6-85aa-84164c01ccba",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Echo Raman (Double Endor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cc1c65-6e5c-4136-b416-fce013ab765b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## sweep duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1150,
   "id": "f52eae45-c194-4614-b3b3-840c33bbe4e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:33:50.855843Z",
     "iopub.status.busy": "2024-03-27T14:33:50.854843Z",
     "iopub.status.idle": "2024-03-27T14:33:52.222903Z",
     "shell.execute_reply": "2024-03-27T14:33:52.220898Z",
     "shell.execute_reply.started": "2024-03-27T14:33:50.855843Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse_ramsey = 0.032*2 # Pi pulse amplitude\n",
    "gaussian_pulse_length_ramsey = 5000//4\n",
    "\n",
    "freq_electron = Photon_IF + centre_freq*1e3 \n",
    "###################### CZ params  #######################\n",
    "\n",
    "ramsey_detuning= 1e-6 #GHz 0.2e-4\n",
    "duration_range = [int(d*1e6//4) for d in np.linspace(1e-3,1,21)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Echo_Raman_Endor'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "\n",
    "with program() as Spin_detection_Ramsey:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    t = declare(int)\n",
    "    \n",
    "    duration_set = declare(int)\n",
    "    meas_angle = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, freq_electron)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        save(j, index_stream)\n",
    "\n",
    "        align()\n",
    "        wait(int(10e6)//4,spin_element)####Wait for ten millisecond after each sequence\n",
    "        align()\n",
    "\n",
    "        with for_each_(duration_set, duration_range):\n",
    "\n",
    "            with for_each_(meas_angle, [0, 0.5]):\n",
    "                \n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                reset_frame(spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                Raman_pulse_cos(\n",
    "                    nuclear_spin_freq_a_bare+10000, \n",
    "                    freq_electron, \n",
    "                    raman_detuning_a_prep-5000, \n",
    "                    detuned_electron_amplitude_a_prep*0.5, \n",
    "                    detuned_sideband_amplitude_a_prep*0.5, \n",
    "                    duration_set, \n",
    "                    int(10e3)\n",
    "                )\n",
    "\n",
    "                frame_rotation_2pi(0.25, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                Raman_pulse_cos(\n",
    "                    nuclear_spin_freq_a_bare, \n",
    "                    freq_electron, \n",
    "                    raman_detuning_a_prep, \n",
    "                    detuned_electron_amplitude_a_prep*0, \n",
    "                    detuned_sideband_amplitude_a_prep*0, \n",
    "                    duration_set, \n",
    "                    int(10e3)\n",
    "                )\n",
    "                \n",
    "                frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*duration_set)-0.25 + meas_angle, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                align()\n",
    "                wait(waiting_time_spin_prep, readout_element)\n",
    "                align()\n",
    "                \n",
    "                measure_SMPD(p_stream, N_readout_prep, waiting_time_SMPD_prep, accumulate=True)\n",
    "\n",
    "\n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.buffer(2).buffer(len(duration_range)).save_all('clicks')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "res = job.result_handles\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1159,
   "id": "7ba4c64e-3a03-4131-b887-f878d5b0e5ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:37:47.864167Z",
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     "iopub.status.idle": "2024-03-27T14:37:48.892200Z",
     "shell.execute_reply": "2024-03-27T14:37:48.891200Z",
     "shell.execute_reply.started": "2024-03-27T14:37:47.864167Z"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2860a681a88>"
      ]
     },
     "execution_count": 1159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "# Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "better_sleep(0)\n",
    "\n",
    "fig,ax0=plt.subplots(1,1, figsize=(5,4), tight_layout=True)\n",
    "colors = ['k', 'r']\n",
    "labels = ['Resonance', 'Detuned']\n",
    "fig,ax=plt.subplots(2,1, figsize=(5,7), tight_layout=True)\n",
    "\n",
    "i=0\n",
    "average_number=res.clicks.count_so_far()\n",
    "print(average_number)\n",
    "clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])\n",
    "excess = clicks.mean(0)\n",
    "error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "times_ramsey = np.array(duration_range)*4e-3\n",
    "y = excess[:,0] - excess[:,1]\n",
    "dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "ax[0].plot(times_ramsey*2, excess[:,0], '-o', color='black')\n",
    "ax[0].plot(times_ramsey*2, excess[:,1], '-o', color='red')\n",
    "ax[0].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "ax[0].set_ylabel('C')\n",
    "\n",
    "guess=[ramsey_detuning*1e3, 1e3, max(y)-min(y), +np.pi/2]\n",
    "est, std, fine, fine_y = fit_function(guess, osc_decay, times_ramsey*2, y)\n",
    "ax[1].errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color='blue')\n",
    "ax[1].plot(fine, osc_decay(fine, *est), label=f\"T$_2$ = {est[1]*1e-3:.2f} ms | f = {est[0]*1e3:.2f} kHz\")\n",
    "if plot_guess: ax[1].plot(times_ramsey*2, osc_decay(times_ramsey*2, *guess), 'grey', alpha=0.2)\n",
    "ax[1].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "ax[1].set_ylabel('$\\Delta$ C')\n",
    "ax[1].set_ylim([-0.3,0.3])\n",
    "ax[1].legend()\n",
    "\n",
    "ax0.errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color=colors[i])\n",
    "ax0.plot(fine, osc_decay(fine, *est), color=colors[i], label=labels[i])\n",
    "#plot_both.append()\n",
    "\n",
    "try:\n",
    "    plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "except:\n",
    "    print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "fullpath=directory+filename+'.hdf5'\n",
    "datasets= {\n",
    "            'click_array': excess,\n",
    "            'ramsey_detuning':ramsey_detuning,\n",
    "            'Ramsey_time':times_ramsey,\n",
    "            }\n",
    "save_h5(fullpath,datasets, group=str(0), overwrite=True)\n",
    "\n",
    "ax0.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "id": "ffd354ab-a695-477d-9369-debf7cf5a998",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T13:04:49.778282Z",
     "iopub.status.busy": "2024-03-27T13:04:49.777281Z",
     "iopub.status.idle": "2024-03-27T13:04:50.068305Z",
     "shell.execute_reply": "2024-03-27T13:04:50.067307Z",
     "shell.execute_reply.started": "2024-03-27T13:04:49.778282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "808787"
      ]
     },
     "execution_count": 1003,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nuclear_spin_freq_a_prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "id": "0d39634a-5837-4f45-bf9c-184400766852",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:08:45.334477Z",
     "iopub.status.busy": "2024-03-27T14:08:45.333479Z",
     "iopub.status.idle": "2024-03-27T14:08:46.688489Z",
     "shell.execute_reply": "2024-03-27T14:08:46.687496Z",
     "shell.execute_reply.started": "2024-03-27T14:08:45.334477Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse_ramsey = 0.032 # Pi pulse amplitude\n",
    "gaussian_pulse_length_ramsey = 10000//4\n",
    "\n",
    "freq_electron = Photon_IF + centre_freq*1e3\n",
    "\n",
    "###################### CZ params  #######################\n",
    "duration_range = [int(d*1e6//4) for d in np.linspace(1e-3,1,21)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Echo_Raman_Endor'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "\n",
    "with program() as Spin_detection_Ramsey:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    t = declare(int)\n",
    "    k = declare(int)\n",
    "    \n",
    "    duration_set = declare(int)\n",
    "    meas_angle = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, freq_electron)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        save(j, index_stream)\n",
    "\n",
    "        align()\n",
    "\n",
    "        with for_each_(duration_set, duration_range):\n",
    "            with for_each_(k, [0, 1]):\n",
    "                with for_each_(meas_angle, [0, 0.5]):\n",
    "\n",
    "                    play('ON',fsv_trigger)\n",
    "                    align()\n",
    "                    \n",
    "                    reset_frame(spin_element)\n",
    "                    reset_frame(spin_sticky_element)\n",
    "                    reset_frame(spin_sticky_extra_element)\n",
    "                    \n",
    "                    \n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "\n",
    "                    Raman_pulse_cos(\n",
    "                        nuclear_spin_freq_a_prep + k*5000, \n",
    "                        freq_electron, \n",
    "                        raman_detuning_a_prep, \n",
    "                        detuned_electron_amplitude_a_prep, \n",
    "                        detuned_sideband_amplitude_a_prep, \n",
    "                        duration_set, \n",
    "                        int(10e3)\n",
    "                    )\n",
    "\n",
    "                    #frame_rotation_2pi(0.25, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "                    \n",
    "                    frame_rotation_2pi(-0.5, spin_sticky_extra_element)\n",
    "                    Raman_pulse_cos(\n",
    "                        nuclear_spin_freq_a_prep, \n",
    "                        freq_electron, \n",
    "                        raman_detuning_a_prep, \n",
    "                        detuned_electron_amplitude_a_prep*0, \n",
    "                        detuned_sideband_amplitude_a_prep*0, \n",
    "                        duration_set, \n",
    "                        int(10e3)\n",
    "                    )\n",
    "                    \n",
    "                    frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*duration_set) -0.25+meas_angle, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "\n",
    "                    align()\n",
    "                    wait(waiting_time_spin_prep, readout_element)\n",
    "                    align()\n",
    "\n",
    "                    measure_SMPD(p_stream, N_readout_prep, waiting_time_SMPD_prep, accumulate=True)\n",
    "\n",
    "                    wait(int(3e6)//4,spin_element) #### Wait for ten millisecond after each sequence\n",
    "                    \n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.buffer(2).buffer(2).buffer(len(duration_range)).save_all('clicks')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "id": "c19edcb5-596c-427c-86df-976f6d9fd90e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:14:42.770380Z",
     "iopub.status.busy": "2024-03-27T14:14:42.770380Z",
     "iopub.status.idle": "2024-03-27T14:14:43.340420Z",
     "shell.execute_reply": "2024-03-27T14:14:43.339417Z",
     "shell.execute_reply.started": "2024-03-27T14:14:42.770380Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "304\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\2674507845.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mtimes_ramsey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mduration_range\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m4e-3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexcess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mexcess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[0mdy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_excess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0merror_excess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    },
    {
     "data": {
      "image/png": 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1AAAA7C9lhedTTz01NTU1Wbp0aRYtWrRbfcaMGUmScePGFfYaNWpUDj300Lz55ptZvHjxbvXmj2u3dj9pAAAA6EhlhecuXbrk2muvTZJcc801Ldc4J8mUKVNSX1+fMWPGZMSIES3rU6dOzXHHHZdJkybt0uuAAw7IjTfemFKplGuuuWaXb8mePXt2HnzwwVRVVeXv/u7v2nRgAAAAUCllXfOcJLfeemtmz56dZ599NoMHD87o0aPT0NCQ+fPnp3fv3nnggQd2ef6aNWuyePHirFq1ardeX/3qVzNnzpzMnj07Q4YMySmnnJI1a9aktrY2O3bsyF133ZWTTz657UcHAAAAFVD2raq6du2aOXPmZPLkyenWrVseffTRNDQ05Morr0xdXV0GDhy4170OPPDAPP744/n2t7+dww47LLNmzcqLL76YMWPGZObMmbn55pvLHQ8AAAAqrqpUKpX29xCVtGHDhtTU1KSxsTG9evXa3+MAAADQDjo6+5V95hkAAAA+aIRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUaHN43rp1a2677bYMGTIkXbt2TZ8+fTJhwoS8/vrr+zTQ7373u3zoQx9KVVVVzj777H3qBQAAAJXQpvD89ttv58wzz8w3v/nNbNq0KRdeeGH69euXf/qnf8rw4cPz+9//vs0DffGLX0xTU1ObXw8AAACV1qbwfOedd6a2tjYjR47Mq6++munTp2f+/Pm55557snr16kyYMKFNw9x///2ZO3duvvCFL7Tp9QAAANAeyg7P27Zty9SpU5MkP/zhD9OjR4+W2o033pihQ4fmqaeeyvPPP19W3z/84Q/56le/mnPOOSeXX355uWMBAABAuyk7PM+bNy+NjY0ZNGhQhg8fvlv9kksuSZLMnDmzrL4TJ07M1q1b86Mf/ajckQAAAKBdlR2eX3jhhSTJiSee2Gq9eb2+vn6vez7++OOZPn16br755nzkIx8pdyQAAABoV2WH5+XLlydJ+vbt22q9eb2hoWGv+m3evDlXX311jj322Hzta18rdxwAAABodweU+4JNmzYlSbp169ZqvXv37kmSjRs37lW/W2+9NQ0NDZkzZ066dOlS7jhpamra5du5N2zYUHYPAAAA2JM23+e5EhYuXJgf/OAH+exnP5szzjijTT3uvvvu1NTUtDz69etX2SEBAAD4wCs7PDd/u/aWLVtarW/evDlJ0rNnzz322b59e77whS/koIMOyne/+91yx2gxadKkNDY2tjxWrFjR5l4AAADQmrI/tt2/f/8kycqVK1utN68PGDBgj31WrlyZRYsW5cgjj8yll166S239+vVJkueff77ljPTcuXNb7VNdXZ3q6uq9nB4AAADKV3Z4HjZsWJKkrq6u1Xrz+tChQ/eq35tvvpk333yz1dr69evz1FNPlTsiAAAAVFTZH9s+9dRTU1NTk6VLl2bRokW71WfMmJEkGTdu3B77HH300SmVSq0+5syZkyQ566yzWtYAAABgfyk7PHfp0iXXXnttkuSaa65pucY5SaZMmZL6+vqMGTMmI0aMaFmfOnVqjjvuuEyaNKkCIwMAAEDHKvtj28mfbi81e/bsPPvssxk8eHBGjx6dhoaGzJ8/P717984DDzywy/PXrFmTxYsXZ9WqVRUZGgAAADpSm25V1bVr18yZMyeTJ09Ot27d8uijj6ahoSFXXnll6urqMnDgwErPCQAAAPtNVel9dkHxhg0bUlNTk8bGxvTq1Wt/jwMAAEA76Ojs16YzzwAAAPBBIjwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKBAm8Pz1q1bc9ttt2XIkCHp2rVr+vTpkwkTJuT111/f6x7r16/PtGnTcvnll+eYY45Jly5d0rNnz3zsYx/Lvffem3feeaet4wEAAEDFVJVKpVK5L3r77bczduzY1NbW5qijjsro0aOzbNmyLFiwIL17905tbW0GDhxY2OfWW2/NXXfdlaqqqpxwwgkZMmRIVq9enXnz5qWpqSmnnXZaZs2alW7duu31bBs2bEhNTU0aGxvTq1evcg8NAACAvwAdnf3adOb5zjvvTG1tbUaOHJlXX30106dPz/z583PPPfdk9erVmTBhwl716d69e2666aYsW7YsdXV1+fd///f813/9V1588cX0798///3f/50777yzLSMCAABAxZR95nnbtm05/PDD09jYmLq6ugwfPnyX+rBhw1JfX5+FCxdmxIgRbR7s3/7t3/LpT386Rx99dF577bW9fp0zzwAAAO9/7/kzz/PmzUtjY2MGDRq0W3BOkksuuSRJMnPmzH0abNiwYUmSN954Y5/6AAAAwL4qOzy/8MILSZITTzyx1Xrzen19/T6Mlfz+979Pkhx55JH71AcAAAD2Vdnhefny5UmSvn37tlpvXm9oaNiHsZJ77703SXLhhRfuUx8AAADYVweU+4JNmzYlybt+A3b37t2TJBs3bmzzUPfdd19mz56dgw46KF//+tf3+NympqY0NTW1/Lxhw4Y2vy8AAAC0ps33eW4vzzzzTCZOnJiqqqo88MAD6dOnzx6ff/fdd6empqbl0a9fvw6aFAAAgA+KssNzjx49kiRbtmxptb558+YkSc+ePcse5qWXXsqFF16Ybdu25d57783FF19c+JpJkyalsbGx5bFixYqy3xcAAAD2pOyPbffv3z9JsnLlylbrzesDBgwoq+9rr72Wc889N+vWrcvtt9+e6667bq9eV11dnerq6rLeCwAAAMpR9pnn5ltI1dXVtVpvXh86dOhe91y1alXOOeecrFq1KhMnTsw3vvGNcscCAACAdlN2eD711FNTU1OTpUuXZtGiRbvVZ8yYkSQZN27cXvVbt25dzjvvvCxdujRXXXVVvve975U7EgAAALSrssNzly5dcu211yZJrrnmmpZrnJNkypQpqa+vz5gxYzJixIiW9alTp+a4447LpEmTdum1ZcuWXHDBBXnxxRdz2WWX5R//8R9TVVXV1mMBAACAdlH2Nc9Jcuutt2b27Nl59tlnM3jw4IwePToNDQ2ZP39+evfunQceeGCX569ZsyaLFy/OqlWrdlm/5ZZb8txzz6Vz58454IAD8vnPf77V93vwwQfbMiYAAABURJvCc9euXTNnzpzcfffdmTZtWh599NEccsghufLKK/PNb34zffv23as+69atS5Ls2LEj06ZNe9fnCc8AAADsT1WlUqm0v4eopA0bNqSmpiaNjY3p1avX/h4HAACAdtDR2a/sa54BAADgg0Z4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAgTaH561bt+a2227LkCFD0rVr1/Tp0ycTJkzI66+/XnavdevWZeLEiRkwYECqq6szYMCA3HDDDVm/fn1bxwMAAICKqSqVSqVyX/T2229n7Nixqa2tzVFHHZXRo0dn2bJlWbBgQXr37p3a2toMHDhwr3qtWbMmI0eOzJIlSzJw4MCcdNJJefnll/Pyyy9nyJAhee6553LIIYfs9WwbNmxITU1NGhsb06tXr3IPDQAAgL8AHZ392nTm+c4770xtbW1GjhyZV199NdOnT8/8+fNzzz33ZPXq1ZkwYcJe97rhhhuyZMmSjB8/PosXL8706dPz0ksv5brrrsurr76aG2+8sS0jAgAAQMWUfeZ527ZtOfzww9PY2Ji6uroMHz58l/qwYcNSX1+fhQsXZsSIEXvstWrVqvTt2zcHHHBAli9fniOOOKKl1tTUlH79+mXt2rV54403cvjhh+/VfM48AwAAvP+95888z5s3L42NjRk0aNBuwTlJLrnkkiTJzJkzC3v98pe/zM6dOzN69OhdgnOSVFdXZ9y4cdmxY0cef/zxcscEAACAiik7PL/wwgtJkhNPPLHVevN6fX19h/YCAACA9lJ2eF6+fHmSpG/fvq3Wm9cbGho6tBcAAAC0lwPKfcGmTZuSJN26dWu13r179yTJxo0bO6RXU1NTmpqaWn5ubGxM8qfPvwMAAPD+1Jz52nADqTYpOzy/19x999254447dlvv16/ffpgGAACAjvTHP/4xNTU17f4+ZYfnHj16JEm2bNnSan3z5s1Jkp49e3ZIr0mTJu1yO6v169dnwIABWb58eYf8B4T2smHDhvTr1y8rVqzwzfH8RbOXeb+wl3m/sJd5v2hsbEz//v1zyCGHdMj7lR2e+/fvnyRZuXJlq/Xm9QEDBnRIr+rq6lRXV++2XlNT45cB7wu9evWyl3lfsJd5v7CXeb+wl3m/6NSp7K/yatv7lPuCYcOGJUnq6uparTevDx06tEN7AQAAQHspOzyfeuqpqampydKlS7No0aLd6jNmzEiSjBs3rrDXxz/+8XTq1CnPPPNM3nrrrV1qTU1NmTlzZjp37pzzzz+/3DEBAACgYsoOz126dMm1116bJLnmmmtarktOkilTpqS+vj5jxozJiBEjWtanTp2a4447LpMmTdql11FHHZXLL78827Zty9VXX53t27e31G666aasXr06V1xxRQ4//PC9nq+6ujrf+MY3Wv0oN/wlsZd5v7CXeb+wl3m/sJd5v+jovVxVasP3er/99ts544wzMn/+/Bx11FEZPXp0GhoaMn/+/PTu3Tu1tbUZOHBgy/Nvv/323HHHHfnc5z6XBx98cJdea9asySmnnJKlS5dm0KBBOemkk/Lyyy/npZdeyuDBg1NbW9thF4ADAABAa9p0ZXXXrl0zZ86cTJ48Od26dcujjz6ahoaGXHnllamrq9slOBc57LDDsmDBglx33XXZtm1bHnnkkTQ2Nub666/PggULBGcAAAD2uzadeQYAAIAPko75Tu99sHXr1tx2220ZMmRIunbtmj59+mTChAl5/fXXy+61bt26TJw4MQMGDEh1dXUGDBiQG264IevXr6/84PB/VGIvr1+/PtOmTcvll1+eY445Jl26dEnPnj3zsY99LPfee2/eeeeddjwC+JNK/l7+c7/73e/yoQ99KFVVVTn77LMrNC28u0rv5WXLluVLX/pSjjnmmFRXV+ewww7LyJEj853vfKfCk8OuKrmXn3zyyVxwwQXp3bt3DjzwwBx66KE599xz88gjj7TD5PC/nn/++XzrW9/K+PHj07dv31RVVaWqqqrN/dol+5Xew7Zu3Vo65ZRTSklKRx11VOmyyy4rnXzyyaUkpd69e5eWLl26171Wr15d+shHPlJKUho4cGDpsssuK/3VX/1VKUlpyJAhpT/+8Y/teCR80FVqL99yyy2lJKWqqqrS8OHDS5/85CdLZ555Zqm6urqUpHTaaaeVNm/e3M5HwwdZJX8v/19nnHFGqaqqqpSkdNZZZ1Vwathdpffy448/XurWrVupqqqqNGLEiNKnPvWp0jnnnFM68sgjS4MGDWqno4DK7uXvfe97LX9njBo1qvTJT36yNGrUqJbfzTfffHM7HgkfdBdeeGEpyW6Ptmiv7PeeDs/NQWHkyJGljRs3tqzfc889pSSlMWPG7HWvz3zmM6UkpfHjx5feeeedlvXrrruulKT0uc99roKTw64qtZf//u//vnTTTTeVGhoadll/9dVXS/379y8lKU2aNKmSo8MuKvl7+c/95Cc/KSUpffGLXxSe6RCV3Mv/8z//U+ratWupd+/epXnz5u1S27FjR+k3v/lNpcaG3VRqL7/11lul6urq0oEHHliaO3fuLrWnnnqqVF1dXaqqqtqnfySFPfnWt75Vmjx5cukXv/hFadWqVS0nh9qivbLfezY8NzU1lWpqakpJSnV1dbvVhw4dWkpSWrhwYWGvN954o9SpU6dSly5dSm+++eYutbfffrvUu3fvUufOnUt/+MMfKjY/NKvkXt6TadOmlZKUjj766H3qA++mvfbym2++WTr44INL55xzTmnOnDnCM+2u0nv5E5/4RClJ6bHHHqv0qLBHldzLM2fOLCUpnXfeea3W/+Zv/qaUpDR9+vR9nhv2RlvDc3tmv/fsNc/z5s1LY2NjBg0alOHDh+9Wv+SSS5IkM2fOLOz1y1/+Mjt37szo0aNzxBFH7FKrrq7OuHHjsmPHjjz++OOVGR7+TCX38p4MGzYsSfLGG2/sUx94N+21lydOnJitW7fmRz/6UUXmhCKV3MsrVqzIrFmzMnDgwJx//vkVnxX2pJJ7eW/vk3vooYeWNyR0sPbMfu/Z8PzCCy8kSU488cRW683r9fX1HdoLytVR++/3v/99kuTII4/cpz7wbtpjLz/++OOZPn16br755nzkIx/Z9yFhL1RyL8+dOzc7d+7MqFGjsn379vzHf/xHJk6cmGuvvTb33Xdf1q1bV7nB4f+o5F4++eSTc9BBB+XXv/51nnrqqV1qTz/9dGbNmpXBgwdn9OjR+zg1tK/2/Nv7gLaP1b6WL1+eJOnbt2+r9eb1hoaGDu0F5eqo/XfvvfcmSS688MJ96gPvptJ7efPmzbn66qtz7LHH5mtf+1plhoS9UMm9/Nvf/jZJ0qNHj4wePTq1tbW71G+55ZbMmDEjY8eO3ZeRoVWV3Ms1NTW5//778+lPfzpjx47NqFGj0rdv36xcuTLPPvtsTj311PzLv/xLunTpUrkDgHbQnn97v2fPPG/atClJ0q1bt1br3bt3T5Js3LixQ3tBuTpi/913332ZPXt2DjrooHz9619vcx/Yk0rv5VtvvTUNDQ257777/DFGh6rkXm4+s/yTn/wkr7zySqZNm5a1a9dm8eLFueKKK7J27dpcfPHF+3wrN2hNpX8vjx8/Pk888UQOPfTQzJs3L9OnT8+8efPSs2fPnHvuufnwhz9cmcGhHbXn397v2fAM7J1nnnkmEydOTFVVVR544IH06dNnf48EhRYuXJgf/OAH+exnP5szzjhjf48DbbZz584kyfbt2/PjH/84l19+eQ4++OAMGTIkDz30UD760Y+msbHRNf38Rbjnnnty9tln5/TTT099fX02bdqU+vr6nHnmmbntttsyfvz4/T0i7Ffv2fDco0ePJMmWLVtarW/evDlJ0rNnzw7tBeVqz/330ksv5cILL8y2bdty77335uKLL277oFCgUnt5+/bt+cIXvpCDDjoo3/3udys7JOyF9vgbo0ePHrn00kt3q1911VVJsts1pFAJldzLc+fOzVe+8pWccMIJefjhh3P88cene/fuOf744zNjxoyccMIJeeyxx/LEE09U7gCgHbTn397v2Wue+/fvnyRZuXJlq/Xm9QEDBnRoLyhXe+2/1157Leeee27WrVuX22+/Pdddd92+DQoFKrWXV65cmUWLFuXII4/cLWysX78+SfL888+3nJGeO3du24eGVlTy93Lzc/r375+qqqrd6kcffXSS5K233mrLqLBHldzLDz30UJLk4osvTqdOu55f69y5c8aPH59Fixbl6aefzic+8Yl9GRvaVXtmv/dseG6+7U5dXV2r9eb1oUOHdmgvKFd77L9Vq1blnHPOyapVqzJx4sR84xvf2PdBoUCl9/Kbb76ZN998s9Xa+vXrnamj3VRyLzffHujdvlV77dq1Sf73TAhUUiX3cnOgqKmpabXevO4b5Hmva8/s95792Papp56ampqaLF26NIsWLdqtPmPGjCTJuHHjCnt9/OMfT6dOnfLMM8/s9i+/TU1NmTlzZjp37uz+jLSLSu7l5E//0zrvvPOydOnSXHXVVfne975XyXHhXVVqLx999NEplUqtPubMmZMkOeuss1rWoNIq+Xt51KhROfTQQ/Pmm29m8eLFu9Wb/xGotXvwwr6q5F5uvtXlwoULW63/5je/SfK/n6aA96p2zX6l97BbbrmllKQ0atSo0qZNm1rW77nnnlKS0pgxY3Z5/j/8wz+Ujj322NLXv/713Xp95jOfKSUp/e3f/m3pnXfeaVm//vrrS0lKn/vc59rrMKBie3nz5s2lkSNHlpKULrvsstL27ds7YnxoUcnfy62ZM2dOKUnprLPOquTYsJtK7uW77rqrZd82Nja2rD/55JOlAw88sFRVVVWaP39+ux0LH2yV2ss/+9nPSklKnTt3Ls2cOXOX2qOPPlrq1KlTqVOnTqVXXnml3Y4F/lx1dXVpT3F1f2S/9+zHtpM/3cZk9uzZefbZZ1tuyt7Q0JD58+end+/eeeCBB3Z5/po1a7J48eKsWrVqt17f//73U1tbm5/+9Kc57rjjctJJJ+Xll1/OSy+9lMGDB2fKlCkddVh8AFVqL99yyy157rnn0rlz5xxwwAH5/Oc/3+r7Pfjgg+11KHzAVfL3MuxPldzLX/3qVzNnzpzMnj07Q4YMySmnnJI1a9aktrY2O3bsyF133ZWTTz65ow6ND5hK7eWLLrool156aR5++OGMGzcuJ510Uo455pi89tprLWej77rrrhx77LEddmx8sDz22GP55je/2fLztm3bkiSnnHJKy9rkyZNzwQUXJNk/2e89+7HtJOnatWvmzJmTyZMnp1u3bnn00UfT0NCQK6+8MnV1dRk4cOBe9zrssMOyYMGCXHfdddm2bVseeeSRNDY25vrrr8+CBQtyyCGHtOOR8EFXqb3cfJ3Rjh07Mm3atPzzP/9zqw9oL5X8vQz7UyX38oEHHpjHH3883/72t3PYYYdl1qxZefHFFzNmzJjMnDkzN998czseCR90ldrLVVVVmT59eu6///6cfvrpWbJkSR555JEsW7Ys559/fp544gl7mXa1evXqzJ8/v+VR+v8v3frztdWrV+9Vr/bKflWlkgvKAAAAYE/e02eeAQAA4L1AeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACZYfn559/Pt/61rcyfvz49O3bN1VVVamqqmrzAOvWrcvEiRMzYMCAVFdXZ8CAAbnhhhuyfv36NvcEAACASqoqlUqlcl5w0UUX5ec///lu62W2SZKsWbMmI0eOzJIlSzJw4MCcdNJJefnll/Pyyy9nyJAhee6553LIIYeU3RcAAAAqqewzzyNHjszkyZPzi1/8IqtWrUp1dXWb3/yGG27IkiVLMn78+CxevDjTp0/PSy+9lOuuuy6vvvpqbrzxxjb3BgAAgEop+8zz/9W1a9c0NTWVfeZ51apV6du3bw444IAsX748RxxxREutqakp/fr1y9q1a/PGG2/k8MMP35cRAQAAYJ/sty8M++Uvf5mdO3dm9OjRuwTnJKmurs64ceOyY8eOPP744/tpQgAAAPiT/RaeX3jhhSTJiSee2Gq9eb2+vr7DZgIAAIDW7LfwvHz58iRJ3759W603rzc0NHTYTAAAANCaA/bXG2/atClJ0q1bt1br3bt3T5Js3Lhxj32amprS1NTU8vPOnTuzdu3aHHrooft0Cy0AAADeu0qlUjZu3Jg+ffqkU6f2Py+838Jzpdx9992544479vcYAAAA7AcrVqx41080V9J+C889evRIkmzZsqXV+ubNm5MkPXv23GOfSZMm7XJLq8bGxvTv3z8rVqxIr169KjQtAAAA7yUbNmxIv379CjNjpey38Ny/f/8kycqVK1utN68PGDBgj32qq6tbvdd0r169hGcAAID3uY66XHe/fWHYsGHDkiR1dXWt1pvXhw4d2mEzAQAAQGv2W3j++Mc/nk6dOuWZZ57JW2+9tUutqakpM2fOTOfOnXP++efvpwkBAADgT9o9PE+dOjXHHXdcJk2atMv6UUcdlcsvvzzbtm3L1Vdfne3bt7fUbrrppqxevTpXXHFFDj/88PYeEQAAAPao7GueH3vssXzzm99s+Xnbtm1JklNOOaVlbfLkybnggguSJGvWrMnixYuzatWq3Xp9//vfT21tbX7605/muOOOy0knnZSXX345L730UgYPHpwpU6aUfUAAAABQaWWH59WrV2f+/Pm7rf/52urVq/eq12GHHZYFCxbk9ttvz6OPPppHHnkkRxxxRK6//vrccccdOeigg8odDwAAACquqlQqlfb3EJW0YcOG1NTUpLGx0bdtAwAAvE91dPbbb18YBgAAAH8phGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgAJtDs9bt27NbbfdliFDhqRr167p06dPJkyYkNdff73sXk8++WQuuOCC9O7dOwceeGAOPfTQnHvuuXnkkUfaOh4AAABUTFWpVCqV+6K33347Y8eOTW1tbY466qiMHj06y5Yty4IFC9K7d+/U1tZm4MCBe9Xr+9//fv7f//t/qaqqysiRI9OvX7+sWLEizz33XEqlUm6++ebcddddez3bhg0bUlNTk8bGxvTq1avcQwMAAOAvQEdnvzaF51tvvTV33XVXRo4cmV/96lfp0aNHkmTKlCn58pe/nDFjxmTu3LmFfVavXp1+/fpl586defLJJzNmzJiW2tNPP51zzz0327Zty5IlS/Y6jAvPAAAA738dnf3K/tj2tm3bMnXq1CTJD3/4w5bgnCQ33nhjhg4dmqeeeirPP/98Ya/58+enqakpZ5555i7BOUlOP/30nHfeeSmVSlm4cGG5YwIAAEDFlB2e582bl8bGxgwaNCjDhw/frX7JJZckSWbOnFnYq7q6eq/e89BDDy1vSAAAAKigssPzCy+8kCQ58cQTW603r9fX1xf2Ovnkk3PQQQfl17/+dZ566qldak8//XRmzZqVwYMHZ/To0eWOCQAAABVTdnhevnx5kqRv376t1pvXGxoaCnvV1NTk/vvvT6dOnTJ27Nicdtpp+dSnPpXTTjstZ5xxRj760Y9m1qxZ6dKlS7ljAgAAQMUcUO4LNm3alCTp1q1bq/Xu3bsnSTZu3LhX/caPH58nnngil112WebNm9ey3qtXr5x77rn58Ic/vMfXNzU1pampqeXnDRs27NX7AgAAwN5q832eK+Wee+7J2WefndNPPz319fXZtGlT6uvrc+aZZ+a2227L+PHj9/j6u+++OzU1NS2Pfv36ddDkAAAAfFCUHZ6bv117y5YtrdY3b96cJOnZs2dhr7lz5+YrX/lKTjjhhDz88MM5/vjj07179xx//PGZMWNGTjjhhDz22GN54okn3rXHpEmT0tjY2PJYsWJFuYcEAAAAe1R2eO7fv3+SZOXKla3Wm9cHDBhQ2Ouhhx5Kklx88cXp1GnXUTp37txy1vnpp59+1x7V1dXp1avXLg8AAACopLLD87Bhw5IkdXV1rdab14cOHVrYqzlo19TUtFpvXl+3bl25YwIAAEDFlB2eTz311NTU1GTp0qVZtGjRbvUZM2YkScaNG1fY68gjj0ySLFy4sNX6b37zmyTJ0UcfXe6YAAAAUDFlh+cuXbrk2muvTZJcc801Ldc4J8mUKVNSX1+fMWPGZMSIES3rU6dOzXHHHZdJkybt0uuiiy5Kkvzrv/5r/vM//3OX2s9//vNMmzYtnTp1ysUXX1zumAAAAFAxZd+qKkluvfXWzJ49O88++2wGDx6c0aNHp6GhIfPnz0/v3r3zwAMP7PL8NWvWZPHixVm1atUu6xdddFEuvfTSPPzwwxk3blxOOumkHHPMMXnttddazkbfddddOfbYY9t4eAAAALDv2nSrqq5du2bOnDmZPHlyunXrlkcffTQNDQ258sorU1dXl4EDB+5Vn6qqqkyfPj33339/Tj/99CxZsiSPPPJIli1blvPPPz9PPPFEbr755raMCAAAABVTVSqVSvt7iErasGFDampq0tjY6Ju3AQAA3qc6Ovu16cwzAAAAfJAIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKNCm8Lx169bcdtttGTJkSLp27Zo+ffpkwoQJef3119s0xLJly/KlL30pxxxzTKqrq3PYYYdl5MiR+c53vtOmfgAAAFBJVaVSqVTOC95+++2MHTs2tbW1OeqoozJ69OgsW7YsCxYsSO/evVNbW5uBAwfudb8nnngil1xySbZu3ZoTTzwxgwcPzh//+Me8+OKL6d69e5YsWVLWAW3YsCE1NTVpbGxMr169ynotAAAAfxk6OvsdUO4L7rzzztTW1mbkyJH51a9+lR49eiRJpkyZki9/+cuZMGFC5s6du1e9XnnllYwfPz49e/bMk08+mVGjRrXUdu7cmbq6unLHAwAAgIor68zztm3bcvjhh6exsTF1dXUZPnz4LvVhw4alvr4+CxcuzIgRIwr7nX/++XniiSfy2GOP5fzzzy9/+lY48wwAAPD+19HZr6xrnufNm5fGxsYMGjRot+CcJJdcckmSZObMmYW9VqxYkVmzZmXgwIEVC84AAADQHsr62PYLL7yQJDnxxBNbrTev19fXF/aaO3dudu7cmVGjRmX79u352c9+lnnz5mXHjh3567/+63zyk5/MwQcfXM54AAAA0C7KCs/Lly9PkvTt27fVevN6Q0NDYa/f/va3SZIePXpk9OjRqa2t3aV+yy23ZMaMGRk7dmw5IwIAAEDFlfWx7U2bNiVJunXr1mq9e/fuSZKNGzcW9lq3bl2S5Cc/+UleeeWVTJs2LWvXrs3ixYtzxRVXZO3atbn44osLb3/V1NSUDRs27PIAAACASmrTfZ4rYefOnUmS7du358c//nEuv/zyHHzwwRkyZEgeeuihfPSjH01jY2N+9KMf7bHP3XffnZqampZHv379OmJ8AAAAPkDKCs/Nt6XasmVLq/XNmzcnSXr27LnXvXr06JFLL710t/pVV12VJHnqqaf22GfSpElpbGxseaxYsaLwvQEAAKAcZV3z3L9//yTJypUrW603rw8YMKCwV/Nz+vfvn6qqqt3qRx99dJLkrbfe2mOf6urqVFdXF74fAAAAtFVZZ56HDRuWJKmrq2u13rw+dOjQwl7Nt7pqvvb5/1q7dm2S/z1DDQAAAPtLWeH51FNPTU1NTZYuXZpFixbtVp8xY0aSZNy4cYW9Ro0alUMPPTRvvvlmFi9evFu9+ePard1PGgAAADpSWeG5S5cuufbaa5Mk11xzTcs1zkkyZcqU1NfXZ8yYMRkxYkTL+tSpU3Pcccdl0qRJu/Q64IADcuONN6ZUKuWaa67Z5VuyZ8+enQcffDBVVVX5u7/7uzYdGAAAAFRKWdc8J8mtt96a2bNn59lnn83gwYMzevToNDQ0ZP78+endu3ceeOCBXZ6/Zs2aLF68OKtWrdqt11e/+tXMmTMns2fPzpAhQ3LKKadkzZo1qa2tzY4dO3LXXXfl5JNPbvvRAQAAQAWUfauqrl27Zs6cOZk8eXK6deuWRx99NA0NDbnyyitTV1eXgQMH7nWvAw88MI8//ni+/e1v57DDDsusWbPy4osvZsyYMZk5c2ZuvvnmcscDAACAiqsqlUql/T1EJW3YsCE1NTVpbGxMr1699vc4AAAAtIOOzn5ln3kGAACADxrhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABdocnrdu3ZrbbrstQ4YMSdeuXdOnT59MmDAhr7/++j4N9Lvf/S4f+tCHUlVVlbPPPnufegEAAEAltCk8v/322znzzDPzzW9+M5s2bcqFF16Yfv365Z/+6Z8yfPjw/P73v2/zQF/84hfT1NTU5tcDAABApbUpPN95552pra3NyJEj8+qrr2b69OmZP39+7rnnnqxevToTJkxo0zD3339/5s6dmy984Qttej0AAAC0h7LD87Zt2zJ16tQkyQ9/+MP06NGjpXbjjTdm6NCheeqpp/L888+X1fcPf/hDvvrVr+acc87J5ZdfXu5YAAAA0G7KDs/z5s1LY2NjBg0alOHDh+9Wv+SSS5IkM2fOLKvvxIkTs3Xr1vzoRz8qdyQAAABoV2WH5xdeeCFJcuKJJ7Zab16vr6/f656PP/54pk+fnptvvjkf+chHyh0JAAAA2lXZ4Xn58uVJkr59+7Zab15vaGjYq36bN2/O1VdfnWOPPTZf+9rXyh0HAAAA2t0B5b5g06ZNSZJu3bq1Wu/evXuSZOPGjXvV79Zbb01DQ0PmzJmTLl26lDtOmpqadvl27g0bNpTdAwAAAPakzfd5roSFCxfmBz/4QT772c/mjDPOaFOPu+++OzU1NS2Pfv36VXZIAAAAPvDKDs/N3669ZcuWVuubN29OkvTs2XOPfbZv354vfOELOeigg/Ld73633DFaTJo0KY2NjS2PFStWtLkXAAAAtKbsj233798/SbJy5cpW683rAwYM2GOflStXZtGiRTnyyCNz6aWX7lJbv359kuT5559vOSM9d+7cVvtUV1enurp6L6cHAACA8pUdnocNG5Ykqaura7XevD506NC96vfmm2/mzTffbLW2fv36PPXUU+WOCAAAABVV9se2Tz311NTU1GTp0qVZtGjRbvUZM2YkScaNG7fHPkcffXRKpVKrjzlz5iRJzjrrrJY1AAAA2F/KDs9dunTJtddemyS55pprWq5xTpIpU6akvr4+Y8aMyYgRI1rWp06dmuOOOy6TJk2qwMgAAADQscr+2Hbyp9tLzZ49O88++2wGDx6c0aNHp6GhIfPnz0/v3r3zwAMP7PL8NWvWZPHixVm1alVFhgYAAICO1KZbVXXt2jVz5szJ5MmT061btzz66KNpaGjIlVdembq6ugwcOLDScwIAAMB+U1V6n11QvGHDhtTU1KSxsTG9evXa3+MAAADQDjo6+7XpzDMAAAB8kAjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAo0ObwvHXr1tx2220ZMmRIunbtmj59+mTChAl5/fXX97rH+vXrM23atFx++eU55phj0qVLl/Ts2TMf+9jHcu+99+add95p63gAAABQMVWlUqlU7ovefvvtjB07NrW1tTnqqKMyevToLFu2LAsWLEjv3r1TW1ubgQMHFva59dZbc9ddd6WqqionnHBChgwZktWrV2fevHlpamrKaaedllmzZqVbt257PduGDRtSU1OTxsbG9OrVq9xDAwAA4C9AR2e/Np15vvPOO1NbW5uRI0fm1VdfzfTp0zN//vzcc889Wb16dSZMmLBXfbp3756bbropy5YtS11dXf793/89//Vf/5UXX3wx/fv3z3//93/nzjvvbMuIAAAAUDFln3netm1bDj/88DQ2Nqauri7Dhw/fpT5s2LDU19dn4cKFGTFiRJsH+7d/+7d8+tOfztFHH53XXnttr1/nzDMAAMD733v+zPO8efPS2NiYQYMG7Rack+SSSy5JksycOXOfBhs2bFiS5I033tinPgAAALCvyg7PL7zwQpLkxBNPbLXevF5fX78PYyW///3vkyRHHnnkPvUBAACAfVV2eF6+fHmSpG/fvq3Wm9cbGhr2Yazk3nvvTZJceOGF+9QHAAAA9tUB5b5g06ZNSfKu34DdvXv3JMnGjRvbPNR9992X2bNn56CDDsrXv/71PT63qakpTU1NLT9v2LChze8LAAAArWnzfZ7byzPPPJOJEyemqqoqDzzwQPr06bPH5999992pqalpefTr16+DJgUAAOCDouzw3KNHjyTJli1bWq1v3rw5SdKzZ8+yh3nppZdy4YUXZtu2bbn33ntz8cUXF75m0qRJaWxsbHmsWLGi7PcFAACAPSn7Y9v9+/dPkqxcubLVevP6gAEDyur72muv5dxzz826dety++2357rrrtur11VXV6e6urqs9wIAAIBylH3mufkWUnV1da3Wm9eHDh261z1XrVqVc845J6tWrcrEiRPzjW98o9yxAAAAoN2UHZ5PPfXU1NTUZOnSpVm0aNFu9RkzZiRJxo0bt1f91q1bl/POOy9Lly7NVVddle9973vljgQAAADtquzw3KVLl1x77bVJkmuuuablGuckmTJlSurr6zNmzJiMGDGiZX3q1Kk57rjjMmnSpF16bdmyJRdccEFefPHFXHbZZfnHf/zHVFVVtfVYAAAAoF2Ufc1zktx6662ZPXt2nn322QwePDijR49OQ0ND5s+fn969e+eBBx7Y5flr1qzJ4sWLs2rVql3Wb7nlljz33HPp3LlzDjjggHz+859v9f0efPDBtowJAAAAFdGm8Ny1a9fMmTMnd999d6ZNm5ZHH300hxxySK688sp885vfTN++ffeqz7p165IkO3bsyLRp0971ecIzAAAA+1NVqVQq7e8hKmnDhg2pqalJY2NjevXqtb/HAQAAoB10dPYr+5pnAAAA+KARngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACrQ5PG/dujW33XZbhgwZkq5du6ZPnz6ZMGFCXn/99bJ7rVu3LhMnTsyAAQNSXV2dAQMG5IYbbsj69evbOh4AAABUTFWpVCqV+6K33347Y8eOTW1tbY466qiMHj06y5Yty4IFC9K7d+/U1tZm4MCBe9VrzZo1GTlyZJYsWZKBAwfmpJNOyssvv5yXX345Q4YMyXPPPZdDDjlkr2fbsGFDampq0tjYmF69epV7aAAAAPwF6Ojs16Yzz3feeWdqa2szcuTIvPrqq5k+fXrmz5+fe+65J6tXr86ECRP2utcNN9yQJUuWZPz48Vm8eHGmT5+el156Kdddd11effXV3HjjjW0ZEQAAACqm7DPP27Zty+GHH57GxsbU1dVl+PDhu9SHDRuW+vr6LFy4MCNGjNhjr1WrVqVv37454IADsnz58hxxxBEttaampvTr1y9r167NG2+8kcMPP3yv5nPmGQAA4P3vPX/med68eWlsbMygQYN2C85JcskllyRJZs6cWdjrl7/8ZXbu3JnRo0fvEpyTpLq6OuPGjcuOHTvy+OOPlzsmAAAAVEzZ4fmFF15Ikpx44omt1pvX6+vrO7QXAAAAtJeyw/Py5cuTJH379m213rze0NDQob0AAACgvRxQ7gs2bdqUJOnWrVur9e7duydJNm7c2CG9mpqa0tTU1PJzY2Njkj99/h0AAID3p+bM14YbSLVJ2eH5vebuu+/OHXfcsdt6v3799sM0AAAAdKQ//vGPqampaff3KTs89+jRI0myZcuWVuubN29OkvTs2bNDek2aNGmX21mtX78+AwYMyPLlyzvkPyC0lw0bNqRfv35ZsWKFb47nL5q9zPuFvcz7hb3M+0VjY2P69++fQw45pEPer+zw3L9//yTJypUrW603rw8YMKBDelVXV6e6unq39ZqaGr8MeF/o1auXvcz7gr3M+4W9zPuFvcz7RadOZX+VV9vep9wXDBs2LElSV1fXar15fejQoR3aCwAAANpL2eH51FNPTU1NTZYuXZpFixbtVp8xY0aSZNy4cYW9Pv7xj6dTp0555pln8tZbb+1Sa2pqysyZM9O5c+ecf/755Y4JAAAAFVN2eO7SpUuuvfbaJMk111zTcl1ykkyZMiX19fUZM2ZMRowY0bI+derUHHfccZk0adIuvY466qhcfvnl2bZtW66++ups3769pXbTTTdl9erVueKKK3L44Yfv9XzV1dX5xje+0epHueEvib3M+4W9zPuFvcz7hb3M+0VH7+WqUhu+1/vtt9/OGWeckfnz5+eoo47K6NGj09DQkPnz56d3796pra3NwIEDW55/++2354477sjnPve5PPjgg7v0WrNmTU455ZQsXbo0gwYNykknnZSXX345L730UgYPHpza2toOuwAcAAAAWtOmK6u7du2aOXPmZPLkyenWrVseffTRNDQ05Morr0xdXd0uwbnIYYcdlgULFuS6667Ltm3b8sgjj6SxsTHXX399FixYIDgDAACw37XpzDMAAAB8kHTMd3oDAADAX7D3fHjeunVrbrvttgwZMiRdu3ZNnz59MmHChLz++utl91q3bl0mTpyYAQMGpLq6OgMGDMgNN9yQ9evXV35w+D8qsZfXr1+fadOm5fLLL88xxxyTLl26pGfPnvnYxz6We++9N++88047HgH8SSV/L/+53/3ud/nQhz6UqqqqnH322RWaFt5dpffysmXL8qUvfSnHHHNMqqurc9hhh2XkyJH5zne+U+HJYVeV3MtPPvlkLrjggvTu3TsHHnhgDj300Jx77rl55JFH2mFy+F/PP/98vvWtb2X8+PHp27dvqqqqUlVV1eZ+7ZL9Su9hW7duLZ1yyimlJKWjjjqqdNlll5VOPvnkUpJS7969S0uXLt3rXqtXry595CMfKSUpDRw4sHTZZZeV/uqv/qqUpDRkyJDSH//4x3Y8Ej7oKrWXb7nlllKSUlVVVWn48OGlT37yk6UzzzyzVF1dXUpSOu2000qbN29u56Phg6ySv5f/rzPOOKNUVVVVSlI666yzKjg17K7Se/nxxx8vdevWrVRVVVUaMWJE6VOf+lTpnHPOKR155JGlQYMGtdNRQGX38ve+972WvzNGjRpV+uQnP1kaNWpUy+/mm2++uR2PhA+6Cy+8sJRkt0dbtFf2e0+H5+agMHLkyNLGjRtb1u+5555SktKYMWP2utdnPvOZUpLS+PHjS++8807L+nXXXVdKUvrc5z5XwclhV5Xay3//939fuummm0oNDQ27rL/66qul/v37l5KUJk2aVMnRYReV/L38537yk5+UkpS++MUvCs90iEru5f/5n/8pde3atdS7d+/SvHnzdqnt2LGj9Jvf/KZSY8NuKrWX33rrrVJ1dXXpwAMPLM2dO3eX2lNPPVWqrq4uVVVV7dM/ksKefOtb3ypNnjy59Itf/KK0atWqlpNDbdFe2e89G56bmppKNTU1pSSlurq63epDhw4tJSktXLiwsNcbb7xR6tSpU6lLly6lN998c5fa22+/Xerdu3epc+fOpT/84Q8Vmx+aVXIv78m0adNKSUpHH330PvWBd9Nee/nNN98sHXzwwaVzzjmnNGfOHOGZdlfpvfyJT3yilKT02GOPVXpU2KNK7uWZM2eWkpTOO++8Vut/8zd/U0pSmj59+j7PDXujreG5PbPfe/aa53nz5qWxsTGDBg3K8OHDd6tfcsklSZKZM2cW9vrlL3+ZnTt3ZvTo0TniiCN2qVVXV2fcuHHZsWNHHn/88coMD3+mknt5T4YNG5YkeeONN/apD7yb9trLEydOzNatW/OjH/2oInNCkUru5RUrVmTWrFkZOHBgzj///IrPCntSyb1cXV29V+956KGHljckdLD2zH7v2fD8wgsvJElOPPHEVuvN6/X19R3aC8rVUfvv97//fZLkyCOP3Kc+8G7aYy8//vjjmT59em6++eZ85CMf2fchYS9Uci/PnTs3O3fuzKhRo7J9+/b8x3/8RyZOnJhrr7029913X9atW1e5weH/qORePvnkk3PQQQfl17/+dZ566qldak8//XRmzZqVwYMHZ/To0fs4NbSv9vzb+4C2j9W+li9fniTp27dvq/Xm9YaGhg7tBeXqqP137733JkkuvPDCfeoD76bSe3nz5s25+uqrc+yxx+ZrX/taZYaEvVDJvfzb3/42SdKjR4+MHj06tbW1u9RvueWWzJgxI2PHjt2XkaFVldzLNTU1uf/++/PpT386Y8eOzahRo9K3b9+sXLkyzz77bE499dT8y7/8S7p06VK5A4B20J5/e79nzzxv2rQpSdKtW7dW6927d0+SbNy4sUN7Qbk6Yv/dd999mT17dg466KB8/etfb3Mf2JNK7+Vbb701DQ0Nue+++/wxRoeq5F5uPrP8k5/8JK+88kqmTZuWtWvXZvHixbniiiuydu3aXHzxxft8KzdoTaV/L48fPz5PPPFEDj300MybNy/Tp0/PvHnz0rNnz5x77rn58Ic/XJnBoR2159/e79nwDOydZ555JhMnTkxVVVUeeOCB9OnTZ3+PBIUWLlyYH/zgB/nsZz+bM844Y3+PA222c+fOJMn27dvz4x//OJdffnkOPvjgDBkyJA899FA++tGPprGx0TX9/EW45557cvbZZ+f0009PfX19Nm3alPr6+px55pm57bbbMn78+P09IuxX79nw3KNHjyTJli1bWq1v3rw5SdKzZ88O7QXlas/999JLL+XCCy/Mtm3bcu+99+biiy9u+6BQoFJ7efv27fnCF76Qgw46KN/97ncrOyTshfb4G6NHjx659NJLd6tfddVVSbLbNaRQCZXcy3Pnzs1XvvKVnHDCCXn44Ydz/PHHp3v37jn++OMzY8aMnHDCCXnsscfyxBNPVO4AoB2059/e79lrnvv3758kWblyZav15vUBAwZ0aC8oV3vtv9deey3nnntu1q1bl9tvvz3XXXfdvg0KBSq1l1euXJlFixblyCOP3C1srF+/Pkny/PPPt5yRnjt3btuHhlZU8vdy83P69++fqqqq3epHH310kuStt95qy6iwR5Xcyw899FCS5OKLL06nTrueX+vcuXPGjx+fRYsW5emnn84nPvGJfRkb2lV7Zr/3bHhuvu1OXV1dq/Xm9aFDh3ZoLyhXe+y/VatW5ZxzzsmqVasyceLEfOMb39j3QaFApffym2++mTfffLPV2vr1652po91Uci833x7o3b5Ve+3atUn+90wIVFIl93JzoKipqWm13rzuG+R5r2vP7Pee/dj2qaeempqamixdujSLFi3arT5jxowkybhx4wp7ffzjH0+nTp3yzDPP7PYvv01NTZk5c2Y6d+7s/oy0i0ru5eRP/9M677zzsnTp0lx11VX53ve+V8lx4V1Vai8fffTRKZVKrT7mzJmTJDnrrLNa1qDSKvl7edSoUTn00EPz5ptvZvHixbvVm/8RqLV78MK+quRebr7V5cKFC1ut/+Y3v0nyv5+mgPeqds1+pfewW265pZSkNGrUqNKmTZta1u+5555SktKYMWN2ef4//MM/lI499tjS17/+9d16feYznyklKf3t3/5t6Z133mlZv/7660tJSp/73Ofa6zCgYnt58+bNpZEjR5aSlC677LLS9u3bO2J8aFHJ38utmTNnTilJ6ayzzqrk2LCbSu7lu+66q2XfNjY2tqw/+eSTpQMPPLBUVVVVmj9/frsdCx9sldrLP/vZz0pJSp07dy7NnDlzl9qjjz5a6tSpU6lTp06lV155pd2OBf5cdXV1aU9xdX9kv/fsx7aTP93GZPbs2Xn22Wdbbsre0NCQ+fPnp3fv3nnggQd2ef6aNWuyePHirFq1arde3//+91NbW5uf/vSnOe6443LSSSfl5ZdfzksvvZTBgwdnypQpHXVYfABVai/fcsstee6559K5c+cccMAB+fznP9/q+z344IPtdSh8wFXy9zLsT5Xcy1/96lczZ86czJ49O0OGDMkpp5ySNWvWpLa2Njt27Mhdd92Vk08+uaMOjQ+YSu3liy66KJdeemkefvjhjBs3LieddFKOOeaYvPbaay1no++6664ce+yxHXZsfLA89thj+eY3v9ny87Zt25Ikp5xySsva5MmTc8EFFyTZP9nvPfux7STp2rVr5syZk8mTJ6dbt2559NFH09DQkCuvvDJ1dXUZOHDgXvc67LDDsmDBglx33XXZtm1bHnnkkTQ2Nub666/PggULcsghh7TjkfBBV6m93Hyd0Y4dOzJt2rT88z//c6sPaC+V/L0M+1Ml9/KBBx6Yxx9/PN/+9rdz2GGHZdasWXnxxRczZsyYzJw5MzfffHM7HgkfdJXay1VVVZk+fXruv//+nH766VmyZEkeeeSRLFu2LOeff36eeOIJe5l2tXr16syfP7/lUfr/L93687XVq1fvVa/2yn5VpZILygAAAGBP3tNnngEAAOC9QHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAX+P8rtWmCZX9e0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "# Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "better_sleep(0)\n",
    "\n",
    "for i in range(2):\n",
    "    fig,ax=plt.subplots(2,1, figsize=(10,7), tight_layout=True)\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])[:,:,i]\n",
    "    excess = clicks.mean(0)\n",
    "    error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "    times_ramsey = np.array(duration_range)*4e-3\n",
    "    y = excess[:,0] - excess[:,1]\n",
    "    dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "    ax[0].plot(times_ramsey*2, excess[:,0], '-o', color='black')\n",
    "    ax[0].plot(times_ramsey*2, excess[:,1], '-o', color='red')\n",
    "    ax[0].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[0].set_ylabel('C')\n",
    "\n",
    "    guess=[ramsey_detuning*1e3, 1e3, max(y)-min(y), +np.pi/2]\n",
    "    est, std, fine, fine_y = fit_function(guess, osc_decay, times_ramsey*2, y)\n",
    "    ax[1].errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color='blue')\n",
    "    ax[1].plot(fine, osc_decay(fine, *est), label=f\"T$_2$ = {est[1]*1e-3:.2f} ms | f = {est[0]*1e3:.2f} kHz\")\n",
    "    if plot_guess: ax[1].plot(times_ramsey*2, osc_decay(times_ramsey*2, *guess), 'grey', alpha=0.2)\n",
    "    ax[1].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[1].set_ylabel('$\\Delta$ C')\n",
    "    ax[1].set_ylim([-0.2,0.2])\n",
    "    ax[1].legend()\n",
    "\n",
    "    try:\n",
    "        plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': excess,\n",
    "\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "id": "d2f37ea3-d786-4dc1-95e3-ea4afb1cdd1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:10:55.045489Z",
     "iopub.status.busy": "2024-03-27T14:10:55.044490Z",
     "iopub.status.idle": "2024-03-27T14:10:56.765577Z",
     "shell.execute_reply": "2024-03-27T14:10:56.764578Z",
     "shell.execute_reply.started": "2024-03-27T14:10:55.045489Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "275\n",
      "276\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2864c13b2c8>"
      ]
     },
     "execution_count": 1086,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "# Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "better_sleep(0)\n",
    "\n",
    "fig,ax0=plt.subplots(1,1, figsize=(10,4), tight_layout=True)\n",
    "colors = ['k', 'r']\n",
    "labels = ['Resonance', 'Detuned']\n",
    "\n",
    "for i in range(2):\n",
    "    fig,ax=plt.subplots(2,1, figsize=(10,7), tight_layout=True)\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])[:,:,i]\n",
    "    excess = clicks.mean(0)\n",
    "    error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "    times_ramsey = np.array(duration_range)*4e-3\n",
    "    y = excess[:,0] - excess[:,1]\n",
    "    dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "    ax[0].plot(times_ramsey*2, excess[:,0], '-o', color='black')\n",
    "    ax[0].plot(times_ramsey*2, excess[:,1], '-o', color='red')\n",
    "    ax[0].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[0].set_ylabel('C')\n",
    "\n",
    "    guess=[ramsey_detuning*1e3, 1e3, max(y)-min(y), +np.pi/2]\n",
    "    est, std, fine, fine_y = fit_function(guess, osc_decay, times_ramsey*2, y)\n",
    "    ax[1].errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color='blue')\n",
    "    ax[1].plot(fine, osc_decay(fine, *est), label=f\"T$_2$ = {est[1]*1e-3:.2f} ms | f = {est[0]*1e3:.2f} kHz\")\n",
    "    if plot_guess: ax[1].plot(times_ramsey*2, osc_decay(times_ramsey*2, *guess), 'grey', alpha=0.2)\n",
    "    ax[1].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[1].set_ylabel('$\\Delta$ C')\n",
    "    ax[1].set_ylim([-0.3,0.3])\n",
    "    ax[1].legend()\n",
    "    \n",
    "    ax0.errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color=colors[i])\n",
    "    ax0.plot(fine, osc_decay(fine, *est), color=colors[i], label=labels[i])\n",
    "    #plot_both.append()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': excess,\n",
    "\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "ax0.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f553081-f503-4f57-bcbf-da56d9c68356",
   "metadata": {},
   "source": [
    "## pi/2 pi pi pi/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c3c230d-e1ce-46a1-a976-fc1d63d472ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "id": "26d1e31a-075e-43d3-a005-7add704972e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T13:26:48.260205Z",
     "iopub.status.busy": "2024-03-27T13:26:48.260205Z",
     "iopub.status.idle": "2024-03-27T13:26:49.518608Z",
     "shell.execute_reply": "2024-03-27T13:26:49.516591Z",
     "shell.execute_reply.started": "2024-03-27T13:26:48.260205Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse_ramsey = 0.032 # Pi pulse amplitude\n",
    "gaussian_pulse_length_ramsey = 10000//4\n",
    "\n",
    "freq_electron = Photon_IF + centre_freq*1e3\n",
    "\n",
    "###################### CZ params  #######################\n",
    "duration_range = [int(d*1e6//4) for d in (np.linspace(0,0.5,21)+0.001)]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Echo_Raman_Endor'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "\n",
    "with program() as Spin_detection_Ramsey:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    t = declare(int)\n",
    "    k = declare(fixed)\n",
    "    \n",
    "    duration_set = declare(int)\n",
    "    meas_angle = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, freq_electron)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        save(j, index_stream)\n",
    "\n",
    "        align()\n",
    "\n",
    "        with for_each_(duration_set, duration_range):\n",
    "            with for_each_(k, [0., 1.]):\n",
    "                with for_each_(meas_angle, [0, 0.5]):\n",
    "\n",
    "                    play('ON',fsv_trigger)\n",
    "                    align()\n",
    "                    \n",
    "                    reset_frame(spin_element)\n",
    "                    reset_frame(spin_sticky_element)\n",
    "                    reset_frame(spin_sticky_extra_element)\n",
    "                    \n",
    "                    \n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2)\n",
    "                    \n",
    "                    wait(duration_set+int(10e3))\n",
    "                    \n",
    "                    frame_rotation_2pi(0.25, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                    Raman_pulse_cos(\n",
    "                        nuclear_spin_freq_a_prep, \n",
    "                        freq_electron, \n",
    "                        raman_detuning_a_prep, \n",
    "                        detuned_electron_amplitude_a_prep*k, \n",
    "                        detuned_sideband_amplitude_a_prep*k, \n",
    "                        2*duration_set, \n",
    "                        int(10e3)\n",
    "                    )\n",
    "\n",
    "                    #frame_rotation_2pi(0.25, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "                    frame_rotation_2pi(-0.25, spin_element)\n",
    "                    \n",
    "                    wait(duration_set)\n",
    "                    \n",
    "                    frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*duration_set) -0.25+meas_angle, spin_element)\n",
    "                    play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length//2) \n",
    "\n",
    "                    align()\n",
    "                    wait(waiting_time_spin_prep+int(10e3), readout_element)\n",
    "                    align()\n",
    "\n",
    "                    measure_SMPD(p_stream, N_readout_prep, waiting_time_SMPD_prep, accumulate=True)\n",
    "\n",
    "                    wait(int(3e6)//4,spin_element) #### Wait for ten millisecond after each sequence\n",
    "                    \n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.buffer(2).buffer(2).buffer(len(duration_range)).save_all('clicks')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "res = job.result_handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "id": "4ae5f71c-02c1-4dbd-89ce-ef2f8b9eddb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T13:28:23.857073Z",
     "iopub.status.busy": "2024-03-27T13:28:23.857073Z",
     "iopub.status.idle": "2024-03-27T13:28:24.659222Z",
     "shell.execute_reply": "2024-03-27T13:28:24.658222Z",
     "shell.execute_reply.started": "2024-03-27T13:28:23.857073Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Optimal parameters not found: Number of calls to function has reached maxfev = 1000.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\2006204442.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[0mguess\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mramsey_detuning\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m     \u001b[0mest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfine_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mguess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mosc_decay\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimes_ramsey\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimes_ramsey\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mosc_decay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mf\"T$_2$ = {est[1]*1e-3:.2f} ms | f = {est[0]*1e3:.2f} kHz\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9480\\3206686619.py\u001b[0m in \u001b[0;36mfit_function\u001b[1;34m(guess, func, xdata, ydata, lb, ub, extend)\u001b[0m\n\u001b[0;32m     20\u001b[0m     returns est,std,fine,data_fit\"\"\"\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m     \u001b[0mest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurve_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mydata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mguess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mub\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m     \u001b[0mstd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcov\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m#print (est,std)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\qm37\\lib\\site-packages\\scipy\\optimize\\minpack.py\u001b[0m in \u001b[0;36mcurve_fit\u001b[1;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[0;32m    792\u001b[0m         \u001b[0mcost\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfodict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fvec'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mier\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Optimal parameters not found: \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    795\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m         \u001b[1;31m# Rename maxfev (leastsq) to max_nfev (least_squares), if specified.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Optimal parameters not found: Number of calls to function has reached maxfev = 1000."
     ]
    },
    {
     "data": {
      "image/png": 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L3xsAAADKUdY1z4MGDUqSrFixos16y/rgwYMLe7U8Z9CgQamqqtqpfsQRRyRJ3nrrrV32qa6uTnV1deH7AQAAQHuVdeZ5xIgRSZL6+vo26y3rw4cPL+zVcqurlmuf/681a9Yk+d8z1AAAALC3lBWeTzrppNTU1GTJkiVZtGjRTvUZM2YkScaPH1/Ya+zYsTn44IPz5ptvZvHixTvVWz6u3db9pAEAAKAzlRWeu3XrlquvvjpJctVVV7Ve45wkU6dOTUNDQ8aNG5fRo0e3rt9zzz055phjMnny5B167bfffrn++utTKpVy1VVX7fAt2bNnz84DDzyQqqqq/N3f/V27DgwAAAAqpaxrnpPk5ptvzuzZs/PMM89k6NChqa2tTWNjY+bPn5++ffvm/vvv3+H5q1evzuLFi7Ny5cqden31q1/NnDlzMnv27AwbNiwnnnhiVq9enbq6umzbti133HFHTjjhhPYfHQAAAFRA2beq6t69e+bMmZMpU6akR48eeeSRR9LY2JjLL7889fX1GTJkyG732n///fPYY4/l29/+dg455JDMmjUrL7zwQsaNG5eZM2fmxhtvLHc8AAAAqLiqUqlU2ttDVNL69etTU1OTpqam9OnTZ2+PAwAAQAfo7OxX9plnAAAA+KARngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACrQ7PG/evDm33HJLhg0blu7du6d///6ZOHFiXn/99T0a6He/+10+9KEPpaqqKmeeeeYe9QIAAIBKaFd4fuedd3L66afnm9/8ZjZu3Jjzzz8/AwcOzD/90z9l5MiR+f3vf9/ugb74xS+mubm53a8HAACASmtXeL799ttTV1eXMWPG5JVXXsn06dMzf/783HXXXVm1alUmTpzYrmHuu+++zJ07N1/4whfa9XoAAADoCGWH5y1btuSee+5Jkvzwhz9Mr169WmvXX399hg8fnieffDLPPfdcWX3/8Ic/5Ktf/WrOOuusXHrppeWOBQAAAB2m7PA8b968NDU15aijjsrIkSN3ql900UVJkpkzZ5bVd9KkSdm8eXN+9KMflTsSAAAAdKiyw/Pzzz+fJBk1alSb9Zb1hoaG3e752GOPZfr06bnxxhvzkY98pNyRAAAAoEOVHZ6XLVuWJBkwYECb9Zb1xsbG3eq3adOmXHnllTn66KPzta99rdxxAAAAoMPtV+4LNm7cmCTp0aNHm/WePXsmSTZs2LBb/W6++eY0NjZmzpw56datW7njpLm5eYdv516/fn3ZPQAAAGBX2n2f50pYuHBhfvCDH+Szn/1sTj311Hb1uPPOO1NTU9P6GDhwYGWHBAAA4AOv7PDc8u3ab7/9dpv1TZs2JUl69+69yz5bt27NF77whRxwwAH57ne/W+4YrSZPnpympqbWx/Lly9vdCwAAANpS9se2Bw0alCRZsWJFm/WW9cGDB++yz4oVK7Jo0aIcfvjhufjii3eorVu3Lkny3HPPtZ6Rnjt3bpt9qqurU11dvZvTAwAAQPnKDs8jRoxIktTX17dZb1kfPnz4bvV788038+abb7ZZW7duXZ588slyRwQAAICKKvtj2yeddFJqamqyZMmSLFq0aKf6jBkzkiTjx4/fZZ8jjjgipVKpzcecOXOSJGeccUbrGgAAAOwtZYfnbt265eqrr06SXHXVVa3XOCfJ1KlT09DQkHHjxmX06NGt6/fcc0+OOeaYTJ48uQIjAwAAQOcq+2PbyZ9uLzV79uw888wzGTp0aGpra9PY2Jj58+enb9++uf/++3d4/urVq7N48eKsXLmyIkMDAABAZ2rXraq6d++eOXPmZMqUKenRo0ceeeSRNDY25vLLL099fX2GDBlS6TkBAABgr6kqvc8uKF6/fn1qamrS1NSUPn367O1xAAAA6ACdnf3adeYZAAAAPkiEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFGh3eN68eXNuueWWDBs2LN27d0///v0zceLEvP7667vdY926dZk2bVouvfTSHHnkkenWrVt69+6dj33sY7n77rvz7rvvtnc8AAAAqJiqUqlUKvdF77zzTk477bTU1dWlX79+qa2tzdKlS7NgwYL07ds3dXV1GTJkSGGfm2++OXfccUeqqqpy3HHHZdiwYVm1alXmzZuX5ubmnHzyyZk1a1Z69Oix27OtX78+NTU1aWpqSp8+fco9NAAAAP4CdHb2a9eZ59tvvz11dXUZM2ZMXnnllUyfPj3z58/PXXfdlVWrVmXixIm71adnz5654YYbsnTp0tTX1+ff//3f81//9V954YUXMmjQoPz3f/93br/99vaMCAAAABVT9pnnLVu25NBDD01TU1Pq6+szcuTIHeojRoxIQ0NDFi5cmNGjR7d7sH/7t3/Lpz/96RxxxBF57bXXdvt1zjwDAAC8/+3zZ57nzZuXpqamHHXUUTsF5yS56KKLkiQzZ87co8FGjBiRJHnjjTf2qA8AAADsqbLD8/PPP58kGTVqVJv1lvWGhoY9GCv5/e9/nyQ5/PDD96gPAAAA7Kmyw/OyZcuSJAMGDGiz3rLe2Ni4B2Mld999d5Lk/PPP36M+AAAAsKf2K/cFGzduTJL3/Absnj17Jkk2bNjQ7qHuvffezJ49OwcccEC+/vWv7/K5zc3NaW5ubv15/fr17X5fAAAAaEu77/PcUZ5++ulMmjQpVVVVuf/++9O/f/9dPv/OO+9MTU1N62PgwIGdNCkAAAAfFGWH5169eiVJ3n777TbrmzZtSpL07t277GFefPHFnH/++dmyZUvuvvvuXHjhhYWvmTx5cpqamlofy5cvL/t9AQAAYFfK/tj2oEGDkiQrVqxos96yPnjw4LL6vvbaazn77LOzdu3a3Hrrrbnmmmt263XV1dWprq4u670AAACgHGWfeW65hVR9fX2b9Zb14cOH73bPlStX5qyzzsrKlSszadKkfOMb3yh3LAAAAOgwZYfnk046KTU1NVmyZEkWLVq0U33GjBlJkvHjx+9Wv7Vr1+acc87JkiVLcsUVV+R73/teuSMBAABAhyo7PHfr1i1XX311kuSqq65qvcY5SaZOnZqGhoaMGzcuo0ePbl2/5557cswxx2Ty5Mk79Hr77bdz3nnn5YUXXsgll1ySf/zHf0xVVVV7jwUAAAA6RNnXPCfJzTffnNmzZ+eZZ57J0KFDU1tbm8bGxsyfPz99+/bN/fffv8PzV69encWLF2flypU7rN9000159tln07Vr1+y33375/Oc/3+b7PfDAA+0ZEwAAACqiXeG5e/fumTNnTu68885MmzYtjzzySA466KBcfvnl+eY3v5kBAwbsVp+1a9cmSbZt25Zp06a95/OEZwAAAPamqlKpVNrbQ1TS+vXrU1NTk6ampvTp02dvjwMAAEAH6OzsV/Y1zwAAAPBBIzwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8AwAAAAFhGcAAAAoIDwDAABAAeEZAAAACgjPAAAAUEB4BgAAgALCMwAAABRod3jevHlzbrnllgwbNizdu3dP//79M3HixLz++utl91q7dm0mTZqUwYMHp7q6OoMHD851112XdevWtXc8AAAAqJiqUqlUKvdF77zzTk477bTU1dWlX79+qa2tzdKlS7NgwYL07ds3dXV1GTJkyG71Wr16dcaMGZNXX301Q4YMyfHHH5+XXnopL730UoYNG5Znn302Bx100G7Ptn79+tTU1KSpqSl9+vQp99AAAAD4C9DZ2a9dZ55vv/321NXVZcyYMXnllVcyffr0zJ8/P3fddVdWrVqViRMn7nav6667Lq+++momTJiQxYsXZ/r06XnxxRdzzTXX5JVXXsn111/fnhEBAACgYso+87xly5YceuihaWpqSn19fUaOHLlDfcSIEWloaMjChQszevToXfZauXJlBgwYkP322y/Lli3LYYcd1lprbm7OwIEDs2bNmrzxxhs59NBDd2s+Z54BAADe//b5M8/z5s1LU1NTjjrqqJ2Cc5JcdNFFSZKZM2cW9vrlL3+Z7du3p7a2dofgnCTV1dUZP358tm3blscee6zcMQEAAKBiyg7Pzz//fJJk1KhRbdZb1hsaGjq1FwAAAHSUssPzsmXLkiQDBgxos96y3tjY2Km9AAAAoKPsV+4LNm7cmCTp0aNHm/WePXsmSTZs2NApvZqbm9Pc3Nz6c1NTU5I/ff4dAACA96eWzNeOG0i1S9nheV9z55135rbbbttpfeDAgXthGgAAADrTH//4x9TU1HT4+5Qdnnv16pUkefvtt9usb9q0KUnSu3fvTuk1efLkHW5ntW7dugwePDjLli3rlP+A0FHWr1+fgQMHZvny5b45nr9o9jLvF/Yy7xf2Mu8XTU1NGTRoUA466KBOeb+yw/OgQYOSJCtWrGiz3rI+ePDgTulVXV2d6urqndZramr8MuB9oU+fPvYy7wv2Mu8X9jLvF/Yy7xddupT9VV7te59yXzBixIgkSX19fZv1lvXhw4d3ai8AAADoKGWH55NOOik1NTVZsmRJFi1atFN9xowZSZLx48cX9vr4xz+eLl265Omnn85bb721Q625uTkzZ85M165dc+6555Y7JgAAAFRM2eG5W7duufrqq5MkV111Vet1yUkyderUNDQ0ZNy4cRk9enTr+j333JNjjjkmkydP3qFXv379cumll2bLli258sors3Xr1tbaDTfckFWrVuWyyy7LoYceutvzVVdX5xvf+EabH+WGvyT2Mu8X9jLvF/Yy7xf2Mu8Xnb2Xq0rt+F7vd955J6eeemrmz5+ffv36pba2No2NjZk/f3769u2burq6DBkypPX5t956a2677bZ87nOfywMPPLBDr9WrV+fEE0/MkiVLctRRR+X444/PSy+9lBdffDFDhw5NXV1dp10ADgAAAG1p15XV3bt3z5w5czJlypT06NEjjzzySBobG3P55Zenvr5+h+Bc5JBDDsmCBQtyzTXXZMuWLXn44YfT1NSUa6+9NgsWLBCcAQAA2OvadeYZAAAAPkg65zu9AQAA4C/YPh+eN2/enFtuuSXDhg1L9+7d079//0ycODGvv/562b3Wrl2bSZMmZfDgwamurs7gwYNz3XXXZd26dZUfHP6PSuzldevWZdq0abn00ktz5JFHplu3bundu3c+9rGP5e677867777bgUcAf1LJ38t/7ne/+10+9KEPpaqqKmeeeWaFpoX3Vum9vHTp0nzpS1/KkUcemerq6hxyyCEZM2ZMvvOd71R4cthRJffyE088kfPOOy99+/bN/vvvn4MPPjhnn312Hn744Q6YHP7Xc889l29961uZMGFCBgwYkKqqqlRVVbW7X4dkv9I+bPPmzaUTTzyxlKTUr1+/0iWXXFI64YQTSklKffv2LS1ZsmS3e61atar0kY98pJSkNGTIkNIll1xS+qu/+qtSktKwYcNKf/zjHzvwSPigq9Revummm0pJSlVVVaWRI0eWPvnJT5ZOP/30UnV1dSlJ6eSTTy5t2rSpg4+GD7JK/l7+v0499dRSVVVVKUnpjDPOqODUsLNK7+XHHnus1KNHj1JVVVVp9OjRpU996lOls846q3T44YeXjjrqqA46CqjsXv7e977X+nfG2LFjS5/85CdLY8eObf3dfOONN3bgkfBBd/7555eS7PRoj47Kfvt0eG4JCmPGjClt2LChdf2uu+4qJSmNGzdut3t95jOfKSUpTZgwofTuu++2rl9zzTWlJKXPfe5zFZwcdlSpvfz3f//3pRtuuKHU2Ni4w/orr7xSGjRoUClJafLkyZUcHXZQyd/Lf+4nP/lJKUnpi1/8ovBMp6jkXv6f//mfUvfu3Ut9+/YtzZs3b4fatm3bSr/5zW8qNTbspFJ7+a233ipVV1eX9t9//9LcuXN3qD355JOl6urqUlVV1R79Iynsyre+9a3SlClTSr/4xS9KK1eubD051B4dlf322fDc3NxcqqmpKSUp1dfX71QfPnx4KUlp4cKFhb3eeOONUpcuXUrdunUrvfnmmzvU3nnnnVLfvn1LXbt2Lf3hD3+o2PzQopJ7eVemTZtWSlI64ogj9qgPvJeO2stvvvlm6cADDyydddZZpTlz5gjPdLhK7+VPfOITpSSlRx99tNKjwi5Vci/PnDmzlKR0zjnntFn/m7/5m1KS0vTp0/d4btgd7Q3PHZn99tlrnufNm5empqYcddRRGTly5E71iy66KEkyc+bMwl6//OUvs3379tTW1uawww7boVZdXZ3x48dn27ZteeyxxyozPPyZSu7lXRkxYkSS5I033tijPvBeOmovT5o0KZs3b86PfvSjiswJRSq5l5cvX55Zs2ZlyJAhOffccys+K+xKJfdydXX1br3nwQcfXN6Q0Mk6Mvvts+H5+eefT5KMGjWqzXrLekNDQ6f2gnJ11v77/e9/nyQ5/PDD96gPvJeO2MuPPfZYpk+fnhtvvDEf+chH9nxI2A2V3Mtz587N9u3bM3bs2GzdujX/8R//kUmTJuXqq6/Ovffem7Vr11ZucPg/KrmXTzjhhBxwwAH59a9/nSeffHKH2lNPPZVZs2Zl6NChqa2t3cOpoWN15N/e+7V/rI61bNmyJMmAAQParLesNzY2dmovKFdn7b+77747SXL++efvUR94L5Xey5s2bcqVV16Zo48+Ol/72tcqMyTshkru5d/+9rdJkl69eqW2tjZ1dXU71G+66abMmDEjp5122p6MDG2q5F6uqanJfffdl09/+tM57bTTMnbs2AwYMCArVqzIM888k5NOOin/8i//km7dulXuAKADdOTf3vvsmeeNGzcmSXr06NFmvWfPnkmSDRs2dGovKFdn7L977703s2fPzgEHHJCvf/3r7e4Du1LpvXzzzTensbEx9957rz/G6FSV3MstZ5Z/8pOf5OWXX860adOyZs2aLF68OJdddlnWrFmTCy+8cI9v5QZtqfTv5QkTJuTxxx/PwQcfnHnz5mX69OmZN29eevfunbPPPjsf/vCHKzM4dKCO/Nt7nw3PwO55+umnM2nSpFRVVeX+++9P//799/ZIUGjhwoX5wQ9+kM9+9rM59dRT9/Y40G7bt29PkmzdujU//vGPc+mll+bAAw/MsGHD8uCDD+ajH/1ompqaXNPPX4S77rorZ555Zk455ZQ0NDRk48aNaWhoyOmnn55bbrklEyZM2Nsjwl61z4bnXr16JUnefvvtNuubNm1KkvTu3btTe0G5OnL/vfjiizn//POzZcuW3H333bnwwgvbPygUqNRe3rp1a77whS/kgAMOyHe/+93KDgm7oSP+xujVq1cuvvjinepXXHFFkux0DSlUQiX38ty5c/OVr3wlxx13XB566KEce+yx6dmzZ4499tjMmDEjxx13XB599NE8/vjjlTsA6AAd+bf3PnvN86BBg5IkK1asaLPesj548OBO7QXl6qj999prr+Xss8/O2rVrc+utt+aaa67Zs0GhQKX28ooVK7Jo0aIcfvjhO4WNdevWJUmee+651jPSc+fObf/Q0IZK/l5uec6gQYNSVVW1U/2II45Ikrz11lvtGRV2qZJ7+cEHH0ySXHjhhenSZcfza127ds2ECROyaNGiPPXUU/nEJz6xJ2NDh+rI7LfPhueW2+7U19e3WW9ZHz58eKf2gnJ1xP5buXJlzjrrrKxcuTKTJk3KN77xjT0fFApUei+/+eabefPNN9usrVu3zpk6Okwl93LL7YHe61u116xZk+R/z4RAJVVyL7cEipqamjbrLeu+QZ59XUdmv332Y9snnXRSampqsmTJkixatGin+owZM5Ik48ePL+z18Y9/PF26dMnTTz+907/8Njc3Z+bMmenatav7M9IhKrmXkz/9T+ucc87JkiVLcsUVV+R73/teJceF91SpvXzEEUekVCq1+ZgzZ06S5Iwzzmhdg0qr5O/lsWPH5uCDD86bb76ZxYsX71Rv+Uegtu7BC3uqknu55VaXCxcubLP+m9/8Jsn/fpoC9lUdmv1K+7CbbrqplKQ0duzY0saNG1vX77rrrlKS0rhx43Z4/j/8wz+Ujj766NLXv/71nXp95jOfKSUp/e3f/m3p3XffbV2/9tprS0lKn/vc5zrqMKBie3nTpk2lMWPGlJKULrnkktLWrVs7Y3xoVcnfy22ZM2dOKUnpjDPOqOTYsJNK7uU77rijdd82NTW1rj/xxBOl/fffv1RVVVWaP39+hx0LH2yV2ss/+9nPSklKXbt2Lc2cOXOH2iOPPFLq0qVLqUuXLqWXX365w44F/lx1dXVpV3F1b2S/ffZj28mfbmMye/bsPPPMM603ZW9sbMz8+fPTt2/f3H///Ts8f/Xq1Vm8eHFWrly5U6/vf//7qaury09/+tMcc8wxOf744/PSSy/lxRdfzNChQzN16tTOOiw+gCq1l2+66aY8++yz6dq1a/bbb798/vOfb/P9HnjggY46FD7gKvl7GfamSu7lr371q5kzZ05mz56dYcOG5cQTT8zq1atTV1eXbdu25Y477sgJJ5zQWYfGB0yl9vIFF1yQiy++OA899FDGjx+f448/PkceeWRee+211rPRd9xxR44++uhOOzY+WB599NF885vfbP15y5YtSZITTzyxdW3KlCk577zzkuyd7LfPfmw7Sbp37545c+ZkypQp6dGjRx555JE0Njbm8ssvT319fYYMGbLbvQ455JAsWLAg11xzTbZs2ZKHH344TU1Nufbaa7NgwYIcdNBBHXgkfNBVai+3XGe0bdu2TJs2Lf/8z//c5gM6SiV/L8PeVMm9vP/+++exxx7Lt7/97RxyyCGZNWtWXnjhhYwbNy4zZ87MjTfe2IFHwgddpfZyVVVVpk+fnvvuuy+nnHJKXn311Tz88MNZunRpzj333Dz++OP2Mh1q1apVmT9/fuuj9P9fuvXna6tWrdqtXh2V/apKJReUAQAAwK7s02eeAQAAYF8gPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAv8foPQveZA9JnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "# Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "better_sleep(0)\n",
    "\n",
    "fig,ax0=plt.subplots(1,1, figsize=(10,4), tight_layout=True)\n",
    "colors = ['k', 'r']\n",
    "labels = ['Resonance', 'Detuned']\n",
    "\n",
    "for i in range(2):\n",
    "    fig,ax=plt.subplots(2,1, figsize=(10,7), tight_layout=True)\n",
    "\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])[:,:,i]\n",
    "    excess = clicks.mean(0)\n",
    "    error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "    times_ramsey = np.array(duration_range)*8e-3\n",
    "    y = excess[:,0] - excess[:,1]\n",
    "    dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "    ax[0].plot(times_ramsey*2, excess[:,0], '-o', color='black')\n",
    "    ax[0].plot(times_ramsey*2, excess[:,1], '-o', color='red')\n",
    "    ax[0].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[0].set_ylabel('C')\n",
    "\n",
    "    guess=[ramsey_detuning*1e3, 1e3, max(y)-min(y), +np.pi/2]\n",
    "    est, std, fine, fine_y = fit_function(guess, osc_decay, times_ramsey*2, y)\n",
    "    ax[1].errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color='blue')\n",
    "    ax[1].plot(fine, osc_decay(fine, *est), label=f\"T$_2$ = {est[1]*1e-3:.2f} ms | f = {est[0]*1e3:.2f} kHz\")\n",
    "    if plot_guess: ax[1].plot(times_ramsey*2, osc_decay(times_ramsey*2, *guess), 'grey', alpha=0.2)\n",
    "    ax[1].set_xlabel(r'2$\\tau$ ($\\mu$s)')\n",
    "    ax[1].set_ylabel('$\\Delta$ C')\n",
    "    ax[1].set_ylim([-0.3,0.3])\n",
    "    ax[1].legend()\n",
    "    \n",
    "    ax0.errorbar(times_ramsey*2, y, yerr=dy, fmt='o', color=colors[i])\n",
    "    ax0.plot(fine, osc_decay(fine, *est), color=colors[i], label=labels[i])\n",
    "    #plot_both.append()\n",
    "    \n",
    "    try:\n",
    "        plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': excess,\n",
    "\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "ax0.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a287f7c3-ca75-468b-b70d-83d74c92c5c8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## sweep freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1292,
   "id": "89b82862-9706-436d-a776-0e7b5ba5eb19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T15:34:22.427855Z",
     "iopub.status.busy": "2024-03-27T15:34:22.426856Z",
     "iopub.status.idle": "2024-03-27T15:34:23.725870Z",
     "shell.execute_reply": "2024-03-27T15:34:23.724865Z",
     "shell.execute_reply.started": "2024-03-27T15:34:22.427855Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from Config import *\n",
    "\n",
    "\n",
    "### Experiments\n",
    "amplitude_pulse_ramsey = 0.032*2 # Pi pulse amplitude\n",
    "gaussian_pulse_length_ramsey = 5000//4\n",
    "\n",
    "freq_electron = Photon_IF + centre_freq*1e3\n",
    "duration = int(1e6//4)\n",
    "\n",
    "###################### CZ params  #######################\n",
    "\n",
    "ramsey_detuning= 1e-6 # GHz\n",
    "freq_range = [int(f) for f in np.linspace(-2,2,21)*1e3]\n",
    "\n",
    "####################### Save params #######################\n",
    "\n",
    "experiment_name='Echo_Raman_Endor'\n",
    "time_stamp=get_timestamp()\n",
    "filename=time_stamp+'%s'%(experiment_name)\n",
    "directory = make_exp_directory(path,experiment_name) \n",
    "\n",
    "shutil.copy('temp.py',directory+filename+'_QUA.py')\n",
    "shutil.copy('config.py',directory+filename+'_config.py')\n",
    "\n",
    "####################### Measurement time estimate #######################\n",
    "\n",
    "N_repetition = int(1e6)\n",
    "\n",
    "####################### Run program #######################\n",
    "\n",
    "\n",
    "with program() as Spin_detection_Ramsey:\n",
    "    I = declare(fixed)\n",
    "    I1 = declare(fixed)\n",
    "    Q2 = declare(fixed)\n",
    "    click=declare(bool)\n",
    "\n",
    "    i = declare(int)\n",
    "    j = declare(int)\n",
    "    t = declare(int)\n",
    "    k = declare(fixed)\n",
    "    \n",
    "    freq_set = declare(int)\n",
    "    meas_angle = declare(fixed)\n",
    "\n",
    "    p_stream = declare_stream()\n",
    "    index_stream = declare_stream()\n",
    "\n",
    "    update_frequency(spin_element, freq_electron)\n",
    "    \n",
    "    with for_(j, 0, j < N_repetition, j + 1):\n",
    "\n",
    "        save(j, index_stream)\n",
    "\n",
    "        align()\n",
    "\n",
    "        with for_each_(freq_set, freq_range):\n",
    "            with for_each_(meas_angle, [0, 0.5]):\n",
    "\n",
    "                play('ON',fsv_trigger)\n",
    "                align()\n",
    "                reset_frame(spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                Raman_pulse_cos(\n",
    "                    nuclear_spin_freq_a_prep+2*freq_set, \n",
    "                    freq_electron, \n",
    "                    raman_detuning_a_prep-freq_set, \n",
    "                    detuned_electron_amplitude_a_prep*0.5, \n",
    "                    detuned_sideband_amplitude_a_prep*0.5, \n",
    "                    duration, \n",
    "                    int(10e3)\n",
    "                )\n",
    "\n",
    "                frame_rotation_2pi(0.25, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                Raman_pulse_cos(\n",
    "                    nuclear_spin_freq_a_bare, \n",
    "                    freq_electron, \n",
    "                    raman_detuning_a_prep, \n",
    "                    detuned_electron_amplitude_a_prep*0, \n",
    "                    detuned_sideband_amplitude_a_prep*0, \n",
    "                    duration, \n",
    "                    int(10e3)\n",
    "                )\n",
    "                \n",
    "                frame_rotation_2pi(-0.25 + meas_angle, spin_element)\n",
    "\n",
    "                #frame_rotation_2pi(Cast.mul_fixed_by_int(ramsey_detuning*4, 2*duration_set)-0.25 + meas_angle, spin_element)\n",
    "                play(spin_gauss_pulse*amp(amplitude_pulse*0.5), spin_element, duration=gaussian_pulse_length) \n",
    "\n",
    "                align()\n",
    "                wait(waiting_time_spin_prep)\n",
    "                align()\n",
    "                \n",
    "                measure_SMPD(p_stream, N_readout_prep, waiting_time_SMPD_prep, accumulate=True)\n",
    "                align()\n",
    "                wait(int(5e6)//4)\n",
    "                align()\n",
    "                \n",
    "    with stream_processing():\n",
    "\n",
    "        p_stream.buffer(2).buffer(len(freq_range)).save_all('clicks')\n",
    "        index_stream.save('interation')\n",
    "\n",
    "qmm = QuantumMachinesManager()\n",
    "qm = qmm.open_qm(config)\n",
    "job = qm.execute(Spin_detection_Ramsey, flags=['auto-element-thread'])\n",
    "\n",
    "res = job.result_handles\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1299,
   "id": "eadcf935-ba32-4b8b-bc61-b8abe1f26a27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T15:40:08.246261Z",
     "iopub.status.busy": "2024-03-27T15:40:08.245260Z",
     "iopub.status.idle": "2024-03-27T15:40:08.923295Z",
     "shell.execute_reply": "2024-03-27T15:40:08.922293Z",
     "shell.execute_reply.started": "2024-03-27T15:40:08.246261Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "957\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(360*0)\n",
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "ys = []\n",
    "for i in range(1):\n",
    "    # Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "    fig,ax=plt.subplots(2,1, figsize=(4,7), tight_layout=True)\n",
    "    fig.suptitle(['Echo', 'Echo Ramsey'][i])\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])\n",
    "    excess = clicks.mean(0)\n",
    "    error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "    x = (nuclear_spin_freq_a_bare+2*np.array(freq_range))*1e-3\n",
    "    y = (excess[:,0] - excess[:,1])\n",
    "    dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "    ax[0].plot(x, excess[:,0], '-o', color='black')\n",
    "    ax[0].plot(x, excess[:,1], '-o', color='red')\n",
    "    ax[0].set_xlabel(r'f (kHz)')\n",
    "    ax[0].set_ylabel('C')\n",
    "\n",
    "    ax[1].errorbar(x, y, yerr=dy, fmt='-o', color='blue')\n",
    "    ax[1].set_xlabel(r'f (kHz)')\n",
    "    ax[1].set_ylabel('$\\Delta$ C')\n",
    "    # ax[1].vlines([nuclear_spin_freq_a_prep*1e-3], min(y), max(y), color='k', linestyle='--')\n",
    "\n",
    "    ys.append(y)\n",
    "    try:\n",
    "        plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': excess,\n",
    "\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1257,
   "id": "684bc429-75f7-466f-a414-bfbafc38d050",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T15:21:21.051016Z",
     "iopub.status.busy": "2024-03-27T15:21:21.050017Z",
     "iopub.status.idle": "2024-03-27T15:21:21.473021Z",
     "shell.execute_reply": "2024-03-27T15:21:21.472019Z",
     "shell.execute_reply.started": "2024-03-27T15:21:21.051016Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4., 16., 28., 44., 69., 41., 42., 20.,  6.,  2.]),\n",
       " array([0.317 , 0.3297, 0.3424, 0.3551, 0.3678, 0.3805, 0.3932, 0.4059,\n",
       "        0.4186, 0.4313, 0.444 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 1257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "flatclick=clicks.flatten()\n",
    "N=1000\n",
    "plt.hist(flatclick[:len(flatclick)//N*N].reshape(len(flatclick)//N,N).mean(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0050711-a10a-46ba-b293-3d16fea4236c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4be4d2a5-4a74-406d-b472-28adf4c3f161",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d1beb05-70c5-47b8-b76f-10a36630f061",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07f6bd8-207a-4f20-abe4-99a4ca88b657",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "id": "57e8f0ee-b2f0-4433-854a-5a0efaed7b15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T14:00:08.121619Z",
     "iopub.status.busy": "2024-03-27T14:00:08.121619Z",
     "iopub.status.idle": "2024-03-27T14:00:09.566665Z",
     "shell.execute_reply": "2024-03-27T14:00:09.565665Z",
     "shell.execute_reply.started": "2024-03-27T14:00:08.121619Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6034\n",
      "6035\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x2860592b808>"
      ]
     },
     "execution_count": 1081,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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btkX//v1x9epVjBs3DtOnT69xe9988w0GDx6MQ4cOoWfPnujbty+OHTuGgQMHYuHChVUen5KSgmnTptWZkrFERERERFJbeSQWEbezUM/GAtMf9ZU6HMnINvFLTU3FxIkTodFoEBYWhj179iAsLAwRERHw8fHB/PnzsWfPnmq3FxkZienTp8PS0hL79u3Dli1bsG7dOpw5cwYuLi6YOnUqoqKi7tvG1KlTkZOTgzFjxjzg1RERERERUVWSswrw1T9XAABvPuYHJxu1xBFJR7aJ36JFi5CZmYkhQ4Zg+PDhZdvd3Nwwd+5cAMD8+fOr3d6CBQug0WgwefJkdO/evWx7y5Yt8d5776G4uBgLFiyo9Pjt27dj1apVeO+99+Dt7V2LKyIiIiIiopqYuzUCWQXFaNPEEU92cZc6HEnJNvErnXcXGhpa7rWQkBBYWVlhx44dyM/Pf+D2Srdt3LixwmNzc3MxefJktGrVCm+++Wa1zkdERESkLweuppT9nF+kkTASIuM5FZeGv04mAAA+HNIayjpY0OVusk38zp49CwDo2LFjudfUajUCAgKQn5+PK1euVNlWeno64uLiAAAdOnQo97q7uztcXV0RGxuLzMzMcq/Pnj0b169fx08//QS1uu52LxMREZHxiaKIBSVrlwHA1D/OokijlTAiIsPTaEXMWq8r6DKyU1N09KgncUTSk2Xil5mZiYyMDABA06ZNK9yndHtsbGyV7ZUmffXq1YOtrW2N2jtz5gy+/vprTJgwAQ899FD1LoCIiIhIT3ZHJuHizX+/mN57JRnT/jwLjVaUMCoiw/rjeDzO38iAvZUKb/X3kzockyDLdfyys7PLfraxsalwn9IELisrq9rtVdZWZe1pNBo899xzcHR0xLx586oOvAIFBQUoKCgo+72iHkUiIjI/lpaW+PPPP8t+JjIEURSxYMfVe7apFAI2nr0JeysVPhkawGrjJDtpOYWYuy0CADDtkZaob8/3WMCEE79hw4bh8uXLNTpmxYoVCAwMNFBENbdgwQKcOHECS5YsgYuLS63a+Oyzz/Dhhx/qOTIiIpKaSqXCyJEjpQ6DZG7vlWScTciAtYUSeSVz+74Y0QbTw87h16NxcLS2wNvsDSGZ+fKfSKTnFsGvoT3GdvOUOhyTYbKJX3R0NCIjI2t0TG5uLgDAzs7unm0ODg7l9s3JyQEA2NvbV9luaXul7Vfkv+3FxsZi5syZeOihhzB+/PjqXUAFZsyYgWnTppX9npmZCXf3ul2RiIiIiKp299y+J7u4Y9mhGADA420aoVAjYsba8/hxzzXYW6nwUrCPhJES6c+FGxn49ZhumtaHg1tDpZTlzLZaMdnE78yZM7U+1sHBAY6OjsjIyEBCQgL8/f3L7ZOQoKvw4+lZ9bcAHh4eAIC0tDTk5ORUOM/vv+3t3r0bOTk5SEpKQp8+fe7ZNyYmBgCwePFi7NixA+3bt8c333xT4bktLS05BIiISIaKi4vx999/A9CNclGpqv+RnFtYDP+Z2wAAlz56DDZqk/04Jwntv5qC03HpsFQpMLGXV1niBwBPB3ogK78In26OwNytkXCwssAY9oyQmdNqRcxcfwGiCAxp3xhdvWs34k6uZPtJ0a5dO+zbtw+nTp0ql/gVFRXhwoULsLKyQsuWLatsy8nJCR4eHoiLi8Pp06fRq1eve16Pj49HSkoKPD09y/UuRkREICIiosJ2Y2JiypJAIiKqWwoKCvDEE08A0M0lr0niR1SVu3v7Rnf1hKtd+S+Rn3+oOTLyivD97mv4YP0F2FupMKR9E2OHSqQ3a0/fwKm4dNiqlXh3QCupwzE5su37DAkJAQCEhYWVe23Tpk3Iz89Hv379YGVl9cDtlW4bNGhQ2bbx48dDFMUK/8yaNQsA8PHHH0MURezZs6dG10ZERER0P4eu3cHJ2DRYqhSYHORd6X7TH/XF2G6eEEXgjT/PYuflRCNGKb3cwmJ4vRMOr3fCkVtYLHU49AAy84vw+RZdfZDXHm4BN4fqPePXJbJN/CZNmgQHBwesX78ea9euLduelJSEt956CwDwxhtvlDvOz88Pfn5+uHHjxj3bp0yZAqVSiZ9++glHjhwp23716lV88sknUKlUmDJlioGuhoiIiKh67q7k+XSgBxrc5wFYEAR8OLg1hnVogmKtiJdWn8KR63eMFSqR3nyz/SpSsgvhXd8WE3o2kzockyTbxM/Z2RlLliyBQqFAaGgo+vbti5EjR8LX1xdRUVGYNm0agoODyx0XGRmJyMhIFBUV3bPd19cX8+bNQ0FBAXr37o0BAwZg6NChaNeuHe7cuYOvvvoKPj6cGE1ERETSOnz9Do7FpEKtVGByUPMq91coBMwNbYt+rRqgoFiLSctP4FxCuuEDJdKTiNuZWH44BoCuoItaJdsU54HI+v/KiBEjsG/fPjz22GM4ffo0Nm/eDB8fHyxbtgzz58+vcXtTp07Fhg0b0L17d+zfvx87d+5E586dsXHjRrz66qsGuAIiIiKimvm2ZG7fU4HuaOhYveFuFkoFFo7qiO7eLsguKMa4JcdwNbHqtY6JpCaKImatvwiNVsTjAQ3Ru0V9qUMyWbKfSd6zZ09s2bKl2vuLonjf1wcNGnTPXL7amD17NmbPnv1AbRARERH919Hrd3Dkuq6378Xgqnv77mZlocQv4zpj9C9HcDYhA2MWH0XY5B5wd7YxULRED27juVs4Gp0KKwsF3gthQZf7kXWPHxEREVFdUlrJc2TnpmjkaF3j4+0sVVg2IRAt3eyQmFmAMYuPIikzX99hEulFTkExPgm/BAB4OdgHTevxS4r7YeJHREQkAbVajaVLl2Lp0qVQq9VSh0MycDwmFYeu3YGFUsBLfWpfd6CerRorn+0Kd2drxN7JxdjFx5CeW6jHSIn047tdUUjMLICHsw2ee6jy6rWkw8SPiEhiVQ0xJ3mysLDA+PHjMX78eFhYWEgdDslA6dy+0E7uaOJU896+u7k5WGH1s93QwN4SkYlZGL/0OHIKuNwBmY5rydlYfOA6AGDWIH9YWSgljsj0MfEjIpLY1gu3y37mgxUR1cbJ2DTsv5oClULASzWc21cZDxcbrJrUFU42FjgTn47nV55AfpFGL20TPQhRFDF7w0UUaUT09WuAh1u5SR2SWWDiR0QkoeyCYnyxNbLs979P37jP3iQnxcXFCA8PR3h4OIqLmfDTgynt7RvRsalei7G0dLPHsgmBsFUrcTDqDl797TSKNVq9tU+my5QXt992MRH7r6ZArVRg5kB/qcMxG0z8iIgktGDHFSRlFZT9vvpoHLRaDv2sCwoKCjBw4EAMHDgQBQUFVR9AVIkz8enYeyUZSoWAlx9gbl9l2rs74ZdxnaFWKbD9UiLeCjvH9ymSTF6hBh9v0hV0eSHIG16uthJHZD6Y+FGtHb1+R+oQiMxa5O0sLDkYc8+22Du52HslWZqAiMgsLdhxBQAwvEMTeLgYpqphj+au+GFURygVAtaevoGPNl3i/GSSxI97r+FGeh6aOFnjpWD9f9EhZ0z8qNb+PJFQ9nNcaq6EkRCZH1EU8cH6C9BoRfRr1eCe15YcjJYoKiIyN+cS0rE70nC9fXfr5++G+SPbQRCAZYdi8PX2KwY9H9F/xd3JxU97rwEA3g9pBWs1C7rUBBM/qrU5QwPKfn5x1Slk5BZJGA2ReVl35gaOlSw4+/bjfmXbFQKw/2oKopKyJIyOiMxF6dy+Ie0bG2XI29AOTfDR4Na6c++KwqL91w1+TqJSH226hMJiLXr5uKJ/QEOpwzE7TPyo1u7+liU6JQeTV51EYTEnfBNVJSOvCJ+ERwAAXu3b4p6y6338dL1/yw7FSBEaEZmRCzcysONyEhQC8IqBe/vuNra7F958zBcAMCf8Mv44Hme0c1PdtTsiCTsuJ0KlEDB7sD8EQZA6JLPDxI/0wkatxOHrd/DBugsc809Uha+3X0FKdgG8XW0xqXeze14b09UDALDm5A32ohPRfS0o6e0b3K4xvOvbGfXcLwU3xwslC2bPWHse4eduGfX8VLcUFGvw4caLAICJvZrBp4G9xBGZJyZ+pBfzR7aDQgD+OBGPn/dx2AdRZS7ezMCKwzEAgI+GBMBSde/8hMBmzvBraI+8Ig3+PBEvQYREZA4u3szA9kuJEATglb4tjH5+QRDwzuN+eDrQHVoReP2P09gTmWT0OKhuWLQ/GjF3ctHA3hKv9mVBl9pi4kd6EeRbHx+UrKPy+ZYIbL3Ab/6I/kurFTFz/UVoRSCkbSP0auFabh9BEDC+hxcAYPnhGGhYMl221Go1Fi5ciIULF0KtVksdDpmZ73ZGAQAGtW0MnwbG7e0rJQgC5gxtg4FtG6FII2LyqpM4HpMqSSwkXzfS8/DdLl3v9rsDWsHeykLiiMwXEz/Sm/E9vPBMd08AwOt/nMG5hHRpAyIyMWGnEnAyNg02aiXeD2lV6X5DOzRBPRsLJKTlYcflRCNGSMZkYWGBl19+GS+//DIsLPggQ9V3+VYmtl68DUGA5L0fSoWAr55oj2Df+sgv0mLi0uO4cCND0phIXj4Nv4z8Ii0CvZwxpH1jqcMxa0z8SG8EQcDMgf4Iaql78392+QncTM+TOiwik5CeW4jPt+gKurzerwUaOVpXuq+VhRJPB+rm+i3l0g5E9B8Ld+l6+wa0aYQWbtLPdVKrFPhxdCcEejkjq6AY45Ycw7XkbKnDIhk4GJWC8PO3oBCAD4e0ZkGXB8TEj/RKpVRg4agO8HWzR3JWASYuO47sgmKpwyKS3Jf/RCI1pxAtGthhQs9mVe4/ppsnlAoBR66n4vKtTCNESMam0WiwZ88e7NmzBxqNRupwyExcSczC5pLpFK9JMLevMtZqJRaN74yAJg64k1OIsYuO4ga//KUHUKTRYtYGXUGXZ7p7oVUjB4kjMn9M/Ejv7K0ssHh8Z7jaWSLidhZe/fUUijVc5oHqrnMJ6Vh9VFfu/KMhAbBQVv3W29jJumyNomUHYwwZHkkkPz8fffr0QZ8+fZCfny91OGQmvt15FaIIPB7QEL4Npe/tu5uDlQWWTwhE8/q2uJmRj7GLjiI5q0DqsMhMLT8Ug6ikbLjYqjH1kZZShyMLTPzIIJrWs8GicZ1hqVJgd2Qy5oRfljokIklotWLJMifA0PaN0b25S7WPnVBS5GXdmRtIzSk0UIREZC6ikrIQfr6kt+9h0+ntu5uLnSVWTeqKJk7WuJ6Sg2eWHENGHpemoZpJyszHNzt0BV3e7u8HR2vOg9YHJn5kMO3dnfDVE+0B6BajLi1hT1SX/H48HmcTMmBvqcK7Ayov6FKRTp710KaJIwqKtfjtGBdIJqrrvtsVBVEEHmvtZtLD3ho5WmPVpK5wtbPE5VuZeHbZceQWctoHVd9nWyKQXVCMdu5OCO3UVOpwZIOJHxlUSNtGePMxXwDA7A0XucYP1SmpOYWYu01X0GXqIy3RwMGqRscLgoAJPb0AACsPx6KIQ6aJ6qxrydnYePYmAOBVE5rbV5lmrrZY+WwgHKxUOBGbhhdWnkRBMeeyUtWORafi79M3IAjAx0NaQ6FgQRd9YeJHBvdScHOEdmoKrQi88utpRNxmoQqqG+ZujUB6bhH8GtqXLXVSUyFtG8HVzhK3M/Ox9cJtPUdIRObi+11R0IpAv1ZuCGjiKHU41dKqkQOWTgiEtYUS+6+mYOofZ7g2Kd1XsUaLmesvAACe6uKBtk2dpA2oAjZqFWI+D0HM5yGwUaukDqdGmPiRwQmCgE+HtUHXZs7ILijGs8tOICmLhQxI3k7FpeH34/EAgDlDA6CqRkGXiliqlBjdVbe0w7JDMfoKj4jMSHRKDtaduQEAmGKic/sq08mzHv73TCeolQpsPn8bM9aegygy+aOK/XosDhG3s+BobVE2Yoz0h4kfGYVapcDPYzuhmastbqTn4bkVJ5FXyCEfJE+akoIuABDaqSk6ezk/UHuju3nAQingZGwaziWk6yFCIjInC0t6+/r6NUCbpubR23e33i3q49un20MhAH+eSMCc8MtM/qicO9kF+HJbJABg+mO+cLZVSxyR/DDxI6NxslFjyfgucLKxwNn4dLzx1xloZTjkI7ewGF7vhMPrnXBOZq+jVh+NxcWbmXCwUuGdx/0euL0G9lYY2LYxAC7tICcWFhaYO3cu5s6dCwsLVqyjisXeMd/evrv1D2iEL0a0BQAsPhCN70oWoScqNW9bJDLzi+HfyAGjAj2kDkeWmPiRUTVztcXPYzrBQilg8/nb+PKfSKlDItKrlOwCzCv5xvLNx3zhamepl3bHlyztsPHcTQ6Vlgm1Wo0333wTb775JtRqfrNNFft+dxQ0WhHBvvXRzt1J6nAeyMjO7pg50B8A8NX2K1h2MFriiMhUnIlPxx8ndNMjPhrSGkoWdDEIJn5kdF29XfD5cN23fj/suYY/S/6hE8nBZ5sjkJVfjIAmDhjVtXYFXSrSzt0JHT2cUKQRsfoIl3YgqgviU3Ox9pSut89U1+2rqYm9muH1frprmb3xEtacTJA4IpKaViti1nrderfDOzZ54OkRVDkmfiSJEZ2a4pU+PgCA9/4+j8PX7kgcEdGDOx6TijWnEkpKUAfo/RvLCT2bAQBWH41jWXQZ0Gg0OH78OI4fPw6Nhn+fVN73u6NQrBXRu4UrOnrUkzocvZnycAtMLHk/e2vNOWy7yIrFddmfJ/5d71Yf0yOockz8SDLTHmmJkLaNUKQRMXnVSVxPzpY6JKJaK9Zoywq6PNXFHR0M8JDWP6AhGjpYISW7AOHnbum9fTKu/Px8BAYGIjAwEPn5HL5L90pIy0VYSW9YaQ+ZXAiCgPdDWiG0U1NotCJe/fU0DkalSB0WSSA9txBfbNWtd/v6Iy3RwL5m691SzTDxI8koFALmj2yH9u5OyMgrwsRlx5GWUyh1WES1svxwLCJuZ8HJxgJvPmaYbywtlAqMLVkPcOnBGFbFI5KxH/ZcQ7FWRC8fV3TylN/QN4VCwOfD26B/64Yo1Gjx3IoTOBWXJnVYZGRfbb+CtNwitHSzq/V6t1R9TPxIUlYWSvzyTGc0cbJGzJ1cvLDyJIewkdlJyszH19uvAADe7u9n0BLUT3Vxh1qlwPkbGXxIIpKpG+l5+Ktk/rtc5vZVRKVUYMHT7dG7hStyCzUYv+QYIm5nSh0WGcnFmxlYdSQWADB7cGtY1HK9W6o+/h8mydW3t8TSCV1gb6nCsZhUzFh7nj0ZZFY+2XwZ2QXFaOfuhCc7uxv0XC52lhjaXre0wxIu7UAkSz/tuYYijYju3i4IbCa/3r67WaqU+HlsJ3T0cEJmfjHGLj6GmJQcqcMiAxNFEbPWX4RWBAa2bYQezV2lDqlOYOJHJqGlmz0Wju4IpULA2lM38P1uru9D5uHwtTtYf+YmBAGYMyQACiOUoB7fQ1cUYeuF27iVkWfw8xGR8dzKyMMfx3W9fVNkNrevMjZqFZaOD4RfQ3skZxVg9KKjfG+Tub9P38CJ2DRYWyjxXkgrqcOpM5j4kckIalkfswe3BgB8+c8VbDp3U+KIiO6vSKPFzPW6gi6ju3qgTVNHo5zXv7EDujZzhkYrYuXhWKOck4iM46c911Co0SKwmTO6ebtIHY7RONpYYOWzXeHlYoMb6XkYu/gYUjnvX5ay8ovw6WZdQZdXH/ZBI0driSOqO5j4kUkZ282zrMTztD/Pcg4TmbQlB6JxNSkbLrZqvPmocUtQly7t8NuxOOQXcV4skRwkZubjt5LevtdlPLevMvXtLbFqUlc0crRCVFI2xi05hqz8IqnDIj1bsOMqUrIL0MzVFs/2aiZ1OHUKEz8yOe+FtMLDfg1QWKzF8ytOID41V+qQiMq5lZGHBTuvAgDeedwPjjYWRj3/I/5uaOJkjbTcIqw/c8Oo5yb9sLCwwKxZszBr1ixYWBj3/iHT9NPeaygs1qKLVz10b153evvu1rSeDVY+2xXOtmqcv5GBZ5ef4JdbMnIlMQtLD8UAAGYN8oelSiltQHUMEz8yOUqFgG+f7gD/Rg5IyS7Es8uPI5Pf+JGJmbPpMnILNejkWQ8jOjY1+vmVCgHjenBpB3OmVqsxe/ZszJ49G2q14SrBknlIyszHr0fjAOgqeQqC4ecLmyqfBnZYMTFQV/QtOhUvrT6FIo1W6rDoAYmiiNkbLkKjFfGovxuCfRtIHVKdw8SPTJKtpQqLx3dGA3tLXEnMxsurT6GYb/pkIvZfTUb4+VtQCMDHRiroUpEnO3vA2kKJiNtZOHI9VZIYiEg//rfvOgqKtejo4YRePqxwGNDEEYvHd4GlSoFdEUmY9udZaLT8gsucbT5/G4eu3YGlSoEPBvpLHU6dVO3Eb9euXVixYgUuXbpU5b6XLl3CihUrsHv37gcKjuq2Ro7WWDyuC6wtlNh/NQWzNlxkrwZJrqBYg1nrLwIAnunuBf/GDpLF4mhjgeEdmwAAlh6MliwOqh2tVouLFy/i4sWL0Gr5xVZdlpxVgFVHdYWapvRrWad7++4W2MwZP43tBJVCwMazN/HB+gt8DjBTuYXFmBOuyyFeDG4Od2cbiSOqm6qV+MXHxyMkJARz5syBu3vVa1S5u7vjk08+wcCBA3HzJiszUu21aeqIb55qD0EAVh+N47plJLlF+6NxPSUH9e0tMe3RllKHg/E9vAAAOy4ncj6smcnLy0NAQAACAgKQl8fS9XXZL/uvI79Ii3buTnioBXv77tbHt0HZc8CvR+Mwd1uk1CFRLSzcFYVbGfloWs8ak4OaSx1OnVWtxG/RokUoLCzE3LlzYW9vX+X+9vb2mDdvHvLy8rB48eIHDpLqtsdaN8S7j+vWeJkTfgk7LiVKHBHVVQlpufhul66gy3sDWsHBSvqCHC3c7NG7hSu0IrDicIzU4RBRDaVkF5Qty/J6HZ/bV5mBbRvj02FtAAA/7rmGH/ZwrV9zcj05G7/svw4AmDnQH1YWLOgilWolftu3b0f9+vUxdOjQajc8ePBguLm5YcuWLbWNjajMpN7N8HSgO0QReO3307h4M0PqkKgO+mjjJeQXadG1mTOGtG8sdThlJvT0AgD8fjweOQXF0gZDRDWyaH808oo0aNvUEcG+9aUOx2Q9HeiBdwfols2ZuzUSq45wDVNzIIoiPtx4CUUaEUEt6+MRfzepQ6rTqpX4RUREoEuXLjVuvHPnzoiMZJc8PThBEPDRkAD09HFBbqEGzy47gcTMfKnDojpkd0QS/rmUCKVCwMdDA0zqW/nglg3g5WKDrPxirD3NpR2IzEVqTmFZT/1rfdnbV5XnH2qOV/r4AAA+WH+BS9mYgR2Xk7D3SjIslAJmDfLnPS6xaiV+OTk5cHR0rHHjjo6OyM7OrvFxRBWxUCrww+hOaF7fFrcz8/Hs8uPILWTvBhlefpEGszfqCrpM7OmFlm5VD3k3JoVCwLiSuX7LDkZDy8p3RGZh0f7ryC3UIKCJAx5uxdL21fHGoy3xTHdPiCLwxp9nsfMyp3+YqvwiDT7apPvsfK63N7zr20kcEVUr8atXrx4SE2v+DysxMRH16tWr8XFElXG0tsDS8YFwtlXjwo1MTPn9DMs7k8H9vPc6Yu/kws3BElP6SV/QpSKhnZrCzlKFa8k5OBCVInU4RFSF9NxCLC9ZyJq9fdUnCAJmD2qNYR2aoFgr4qXVp3Dk+h2pw6IK/Lz3OuJT89DI0Qqv9PWROhxCNRM/f39/HDlypEZVx3Jzc3H48GH4+3OdDtIvDxcb/PJMJ6hVCmy/lIgvtkZIHRLJWNyd3LJCAh8M9IedpUriiCpmb2WB0E66heS5tAOR6Vt8IBo5hRq0auTAeU81pFAImBvaFv1auaGgWItJy0/gXEK61GHRXeJT//3sfC+kFWzUpvnZWddUK/EbOHAgcnJyMGfOnGo3PGfOHOTl5WHQoEG1Do6oMp08nTEvtC0A3aK3vx2LkzgikiNRFDF740UUFGvRy8cVIW0aSR3SfY3v4QVBAHZHJuN6MofZmzoLCwtMnz4d06dPh4WF9BViyXgycouwrGR5oikP+7C3rxYslAosHNUB3b1dkF1QjHFLjuFqYpbUYVGJjzddQkGxFt29XUz+s7MuqVbiN3nyZLi5ueHzzz/HnDlz7rvQrFarxccff4zPP/8cbm5ueOGFF/QWLNHdhrRvgqklw+4+WHcBB65yeBvp147LSdgVkQQLpYDZg1ub/MOZl6st+vrq5gmtOMyKd6ZOrVZj3rx5mDdvHtRqtdThkBEtORiNrIJi+DW0x6P+DaUOx2xZWSjxy7jOaNfUEWm5RRiz+CjXMzUBeyL/LYb24RDT/+ysS6qV+NnY2GDNmjWwtLTErFmz0KxZM0yfPh2//vortm/fju3bt+PXX3/F9OnT0axZM8yePRtWVlZYs2YNbGxsDH0NVIe99rBP2Tj/F1efRFQSv+0j/cgr1GD2Bt2k9Em9veHTwDwmpY8vWdrhrxPxyMwvkjYYIionI68IS0qGY7/2cAsoFHwofhB2liosmxCIlm52SMwswJjFR5HEqt+SKSjW4MONlwDoRqGYWjG0uq7aA2579OiBQ4cOYezYsbh48SK+/vrrcvuIoq7IRuvWrbFq1Sq0a9dOf5ESVUAQBHw+og3iU3NxIjYNE5Ydx7qXesLFzlLq0MjM/bAnCjfS89DEyRqvmtGk9F4+rvBpYIeopGyEnUjAxF7NpA6JKqHVahEXpxum7uHhAYWiWt/FkplbdjAGWfnFaOlmh/6t2dunD/Vs1Vj5bFeM/OkwYu/kYuziY/jjhW5wsmFPurEtORCD6JQcuNpZYkq/FlKHQ/9Ro0+Z9u3b4/z589i8eTNeeukl9OjRA76+vvD19UWPHj3w8ssvY/PmzTh//jyTPjIaS5USP4/tBA9nG8Sn5uH5lSeRX6SROiwyY9EpOfh573UAuoIu5jQpXRAEjC9Z2mH54RhWvTVheXl5aNasGZo1a1aj4mlkvrLyi7D4gO695dW+7O3TJzcHK6ye1BUN7C0RmZiF8UuPI6eASz4Z062MPHy36yoA4N0BfnCw4txlU1Orp5n+/fujf//++o6FqNZc7CyxZHwXDPvhIE7GpuGtsHNY8FR7jiunGhNFETPXX0ChRouglvXxWGvzq7Y3vGMTzN0agdg7udgTmYSHW5nfNRDJ0fJDMcjML4ZPAzsMYMELvXN3tsGqSV3xxM+HcSY+Hc+vPIHF47rAykIpdWh1wqebI5BbqEFnz3oY1qGJ1OFQBTiuhGTDp4EdfhrTCSqFgA1nb+KbHVelDonM0NYLt7H/agrUSgU+NIOCLhWxUavwdKAHAGBpSeVAIpJWdkExFh3Qze17ta8PlOztM4iWbvZYPiEQtmolDkbdwau/nUaxpvKihKQfh6/dwcazN6EQwIIuJoyJH8lKTx9XzBkaAABYsPMq1p2+IXFEZE5yCorx0SbdpPTJQd7wcrWVOKLaG9vdEwoBOBCVgisscU4kuRWHY5CeWwTv+rYY2Lax1OHIWjt3Jywa16Vsvd+3ws5By2HvBlOk0WLWhgsAgNFdPdG6saPEEVFlmPiR7DwV6IEXgrwBAG+FncOJmFSJIyIAyC0shtc74fB6Jxy5haY57+K7XVG4lZEPd2drvNTHfAq6VKRpPZuyMvHLDsVIGwxRHZdTUIxf9pXO7WNvnzF0b+6CH0Z1hFIhYO3pG/ho06WyIoSkXysOx+JKYjbq2VjgjUdbSh0O3QcTP5Kltx/zQ//WDVGo0eL5lScReydH6pDIxEUlZWHRft2D2exBrWUxJ6R0aYe1pxKQnlsobTBEddjKI7FIyy2Cl4sNBrG3z2j6+bvhqyfaQRB0X4B9vf2K1CHJTlJWPr4p+f/6Vn8/VlI1cUz8SJYUCgFfP9kebZs6IjWnEBOXHUdGLtc0o4rpCrpcRLFWRL9WDWRTDKVrM2e0auSA/CIt/jgeL3U4RHVSbuG/vX2v9G0BlVKaRy8btQoxn4cg5vMQs6pU/KCGtG+Cj4bopoB8uyuq7As+0o8vtkQiq6AYbZs64onO7lKHQ1Vg4keyZa1WYtEzndHI0QrXknPw4uqTKOIEb6rAxnO3cOjaHViqFJg1qLXU4eiNIAiYULK0w4rDsSxwYGJUKhVeeuklvPTSS1Cp6s6DeF2z+kgc7uQUwsPZBkPbs7dPCmO7eeLNx3wBAHPCL+OP43ESRyQPJ2NTseZUAgDgw8GtOYTZDBg88dNq+aBB0mngYIXF47rAVq3EoWt38MG6CxzjT/fIyi/CnJKCLi/38YG7s43EEenX4PaN4Wyrxo30PGy/lCh1OHQXS0tLfP/99/j+++9haWkpdThkAHmFGvy87xoA4JU+PpL19hHwUnDzsvn/M9aeR/i5WxJHZN40Wt1IGQB4srM7OnjUkzgiqg6DvQOdPn0a06ZNQ9OmTQ11CqJq8W/sgO9GdYBCAH4/Ho9fOMyD7rJgx1UkZRXAy8UGzz/kLXU4emdlocSo0qUdWOSFyKhWH41FSnYh3J2tMawj1zWTkiAIeKe/H54O9IBWBF7/4zT2RCZJHZbZ+u1YHC7ezISDlQpv9feVOhyqJr0mfvHx8fj8888REBCAzp07Y8GCBUhMlPYb5oMHD2LAgAFwdnaGnZ0dAgMDsWLFilq3t3HjRgQFBcHBwQEODg4IDg5GeHh4lcft3bsXI0aMQMOGDWFpaYnGjRvj8ccfx4YNG2odC1VfXz83fDDQHwDw2ZYIbL1wW+KIyBRE3M4sS4ZmD5ZHQZeKjOnmCZVCwLHoVFy8mSF1OFRCFEUkJycjOTm5xiMROHLB9OUXafBzydy+l4N9YMHePskJgoA5QwMwsG0jFGlETF51Eidj06QOy+yk5hRi3rZIAMAbj/rCxY4jFszFA78LZWVlYcmSJejbty+aNWuG9957D5cuXYKLiwvq1ZO223fNmjUICgrC1q1b0bZtW/Tv3x9Xr17FuHHjMH369Bq3980332Dw4ME4dOgQevbsib59++LYsWMYOHAgFi5cWOlxs2fPLksQW7dujeHDh8PHxwf79+9n4mdE43t44ZnunhBLvuk7n8AH4LpMFEXMXHcRGq2I/q0bIti3gdQhGUxDRys83qYRAGAZF3Q3Gbm5uWjQoAEaNGiA3NzcGh17d7GeM3Hpeo6M9OG3Y3FIzipAEydrDO/I0U+mQqkQ8NUT7dHHtz7yi7R4cdUpqUMyO/O2RSIjrwh+De0xuquH1OFQDdQq8dNoNAgPD8dTTz2Fhg0b4rnnnsOePXtgZWWFp556Cps2bcKtW7fQpk0bfcdbbampqZg4cSI0Gg3CwsKwZ88ehIWFISIiAj4+Ppg/fz727NlT7fYiIyMxffp0WFpaYt++fdiyZQvWrVuHM2fOwMXFBVOnTkVUVFS545YtW4YPP/wQXbt2RVRUFHbu3InffvsN+/btQ1JSEl5//XX9XTTdlyAImDnQH0EtdW/2zy4/jpvpeVKHRRL5+/QNHItJhbWFEh8M8pc6HIMbX1LkZf3Zm7iTXSBtMPRA4lNzMe+ff8vST1pxguuVmpj8Ig1+2qub2/dSn+ZQq9jbZ0rUKgV+GN0JgV7OyC4wzXVlTdW5hHT8XlIc56MhAZy3amZq9Ld1/PhxvPbaa2jcuDEGDx6MP//8E4WFhXjsscewcuVKJCYmYvXq1RgwYACUSmmHTC1atAiZmZkYMmQIhg8fXrbdzc0Nc+fOBQDMnz+/2u0tWLAAGo0GkydPRvfu3cu2t2zZEu+99x6Ki4uxYMGCe47Jy8vD9OnTYW9vj3Xr1pWb72hjY4OAgIDaXB7VkkqpwMJRHeDrZo+krAI8u/wE3/TroIy8Iny6+TIA4NWHfdDEyVriiAyvo4cT2jV1RGGxFr8eZUU7c6XVinh7zTnkFWrKtuUWavDMkmM4Fs3kz1T8eSIeiZkFaOxohZGdWOLeFFmrlVg0vjP8GzmUbRv502FMWn4C7687j+93RyHsZAIOXE1BVFI2nxWge/+Zuf4iRBEY2r4xAps5Sx0S1VC16kfPmTMHq1evxpUrV8rmFXTt2hWjR4/Gk08+ifr16xs0yNoonXcXGhpa7rWQkBBYWVlhx44dyM/Ph5WV1QO1FxoaimnTpmHjxo347rvvyravXbsWd+7cwcSJE9GwYcPaXgrpmb2VBRaP74yh3x/C5VuZeO230/jlmc4sQ1yHfL39ClKyC+Fd3xaTesmvoEtFBEHAhJ7N8PofZ7DySCwmBzfnnCMz9OuxOBy6dgdWFgrkF+mqZnf3dsHh63cwfukxLBnfBd28XSSOUjq5hcXwn7kNAHDpo8ckWa+uoFiDH/foevte7OPD3j4T5mBlgf890wm9vtgNALh4MxMXb2ZWur+dpQoNHa3Q0MEKbg5WaORoBbeS3xs56ra52KqhkOnzRNipBJyJT4etWokZA1pJHQ7VQrXeEWfOnAlBENCwYUO8+OKLePrpp9G8eXNDx/ZAzp49CwDo2LFjudfUajUCAgJw4sQJXLlyBW3btr1vW+np6YiL031D3qFDh3Kvu7u7w9XVFbGxscjMzISDg+7bo127dgEAevTogfT0dKxevRoXLlyAtbU1evTogaFDh3LtJok0rWeDX57phKf+dwS7IpIwJ/ySrNZvo8pduJGBFYdjAAAfDwmoUw9lA9o0wiebLyMpqwCbz9/CkPasMmhO4lNz8VlJT/XUfi3x2ZYIAMD3oztgyu9nsP9qCiYsPY7F4zujR3NXKUOt0/48kYBbGflo6GCFJzpzbp+pc7ZVl/38/agOSMstwu2MfNzOzEdiZj5uZeQjMSMfWQXFyC4oRlRSNqKSsittz0IpoIG9VVmCWJYoOuqSw4YOVmjgYAlLlXkVE8vMK8IXJe85r/drCTeHqjtNyPRUO+sQRRG3b9/Gtm3bUL9+fdSrVw/OzqbZxZuZmYmMDF3hjsqWk2jatClOnDiB2NjYKhO/0qSvXr16sLW1rbS9lJQUxMbGls1tvHRJtzZYcnIy/P39cevWv2vGfP3112jTpg3Cw8Ph7l75MJCCggIUFPw7Hyczs/JvoqhmOnjUw1dPtMfLv57C0oMx8Ha1xdjuXlKHRQakG6ZyAVoRGNi2EXr61K2HY7VKgTFdPfH1jitYdiiGiZ8ZEUUR76w9h5xCDbp41cPorh5liZ+VhRK/PNMZL6w8ib1XkjFx2XEsHtelzt3fpqCwWIsfd+vm+78Y3NzsHu7ruj5+DSrtJc4uKMbtDF0yWJoY/ve/KdkFKNKIuJGehxtV1BBwsVXDrTQxLE0S7/rdzcEKDlYqCIJp9B4u3B2FOzmF8Glgh/E9vaQOh2qpWonf0aNHsWLFCvzxxx84ePAgDh06hNdeew39+/fH6NGjMXjw4GoNlzSW7Ox/v4mxsal4MebSBC4rK6va7VXWVmXtpaXpSgR/8MEH8PX1xZ9//ol27drh8uXLePHFF3Hq1CmMGDECR48erfQf9meffYYPP/ywyhipdkLaNkLMHV/M2xaJ2Rsvwd3ZRtbVHeu6sJMJOBWnG6byfoj8C7pUZFRXD3y/Owqn49JxJj4d7d2dpA6JquG3Y/E4GKUb4jk3tF25oWRWFkr8PLYTXlx1ErsjdcnfL890xkMtTW8qhpyFnUzAzYx8NLC3xJNdOLdPTuwsVfBpYAefBnaV7lOk0SIpq6AsQbx1d6JYmiRm5qOwWIs7OYW4k1OIS7cq/0LfRq2scFjp3b2JrnaWRpmqUjo3fPag1pwmYMaqlfh16dIFXbp0wddff40tW7Zg1apV2LhxIzZu3IhNmzbBzs4Ow4YNw6hRo9CvXz8oFA9+QwwbNgyXL1+u0TErVqxAYGDgA59bX7Ra3fwLpVKJLVu2lPXsBQYGYsuWLfD29sbx48exY8cOPPLIIxW2MWPGDEybNq3s98zMzPv2EFLNvRTcHNEpOQg7mYBXfj2NNS/2gG9De6nDIj1Lzy3E51v/HabS0NF0vqwypvr2lhjYrhHWnrqBpQejseCp8sPXyThUKhXGjRtX9nNlEtJy8Um4bgTJm4/5oZmrLXILyxeasLJQ4qexnfDy6lPYcTkJk1acwP/GduKXWUZSWKzF9yW9fZODmst2XVCqnIVSgSZO1vctGCaKYtlw0tLkUNdrmIfbmQVIzMjHrYw8ZOYXI7dQg+spObieklNpe0qFgAb2lv8mhyUJYdnPJb8/6P2oFYEBbRqiVwuOJDBnNZpgplKpMGjQIAwaNAiZmZn466+/sHLlSuzfvx8rVqzAypUr0aBBAzz11FMYNWrUAwUWHR2NyMjIGh1Tug6SnZ3dPdtK59zdLSdH94/I3r7qB/zS9u63zlJF7ZUe9/DDD5dL1ho0aICQkBD8+eef2Lt3b6WJn6WlJSwtuTCmIQmCgE+HtUF8ai6ORqdi4rLj+PvlHmhgXzcTA7maty0SqTmFaOnGYSoTejTD2lM3EH7uFt4d0IpzNSRiaWmJZcuW3XcfURQxY+155BRq0NmzXtmyHJW2qVLih9Gd8PKvp7D9UiKeX3ESP4/thD5+TP4M7e/TCbiRnof69pYYxbXNqBKCIMDZVg1nWzX8G5d/Pi2VV6jB7UxdEqjrNSwoSQ7zcTtT93NyVgE0WhG3MnQJ5Jn4SpuDk43FvXMOKyhO42htUekINCsLBd6royNl5KTWlUUcHBzw7LPP4tlnn0V8fDxWrVqFlStXIiIiAgsWLMC33377QIGdOXOm1sc6ODjA0dERGRkZSEhIgL9/+Rs1ISEBAODp6Vllex4eujfwtLQ05OTkVDjPr6L2PD09cfr0aXh5eVXYbun2pKSkKmMgw1KrFPh5bCcM++EQolNy8NyKk/jj+W78xlYmzsan49dj/647VNeHqbRp6ojOnvVwIjYNq4/EYtqjvlKHRJX4/Xg89l9NgaVKgbmhbas1pEutUuD7UR3x6m+nsO1iIl5YeRI/jumIh1u5GSHiuqlIo8XCkt6+Fx7y5mcHPTBrtRLNXG3RzLXi2hIAUKzRIiW78K7kMB+3MnXFaO6ee5hfpEV6bhHSc4sQcbvyKU6WKsU9w0hd7ip88/xD3nVi6SO500tJSXd3d8yYMQMzZszAqVOnsHLlSvz+++9ITEyUbFJqu3btsG/fPpw6dapc4ldUVIQLFy7AysoKLVu2rLItJycneHh4IC4uDqdPn0avXr3ueT0+Ph4pKSnw9PS8p3exQ4cOWLduXdlcv/9KTdWtuXR3DyVJx8lGjSXju2DYDwdxNj4db/x5Ft893UG2ZZnrCo1WxAfrL0AUgWEdmtTpUvd3m9CzmS7xOxqHl/v6sAiFBERRLBtJYmNjU+7z8kZ6Hj4J1015ePMxX3jXr/5nhVqlwMJRHTHl99PYfP42Jq86ie9HdcSjrbm0kCH8ffoG4lPz4GqnxuiuVX+hTKQPKqWirBhMZURRRGZeMW5l5lU497D057TcIhQUaxF7Jxexd8qPcJtQxWgDMg96X0ugY8eO6NixI+bPn49t27Zh1apV+j5FtYSEhGDfvn0ICwvDmDFj7nlt06ZNyM/Px8CBA6tdlCYkJAQ//vgjwsLCyiV+YWFhAIBBgwbds33w4MGYNWsWDh06hKKiIlhYWJS9ptVqceDAAQAVLxFB0mjmaoufx3TCmMVHEX7+Fpq52mL6Y+wNMWe/H4/DuYQM2FuqMGOAn9ThVMhGrULM5yFGPeejrd3QyNEKtzLysfHsLYR2Ytl5Y8vNzS374i87O/ue0SSiKOKdNeeQXVCMjh5OmNCzWY3bt1AqsOCpDhCEMwg/dwsvrT6FhaM6on8Akz99Ktb8O7fv+Ye8Ya3mlyhkOgRBgKONBRxtLODXsPKhpflFmnIVSxPScrHyiG60jCV7sWWhWuOd9u7di08//RSJiYn3bM/MzMS6deuwePFirF+/HjExMf82rFDg8ccfx+rVq/UacHVNmjQJDg4OWL9+PdauXVu2PSkpCW+99RYA4I033ih3nJ+fH/z8/HDjxo17tk+ZMgVKpRI//fQTjhw5Urb96tWr+OSTT6BSqTBlypR7jmnfvj0eeeQRxMbG4v3334coimWvzZkzBxEREWjQoAGGDx+ul2sm/ejq7YLPhuuW+Fi4OwphJxMkjohqKzWnEHO36uYKT3u0Jedt3sVCqcDY7rqeiaUHo+95fyLp/Xni3yGe80a2q3XVPgulAguebI/B7RqjWCvilV9PYcv5W1UfSNW2/sxNxN7JhbOtGmO6sbePzJOVhRKeLrbo6u2CIe2b4IWg5lykXYaqlfh9+umn+Oqrr+Di8u8QqfDwcDRr1gwjRozA888/j+HDh6N58+Zo06YNFi9ebLCAq8vZ2RlLliyBQqFAaGgo+vbti5EjR8LX1xdRUVGYNm0agoODyx0XGRmJyMhIFBUV3bPd19cX8+bNQ0FBAXr37o0BAwZg6NChaNeuHe7cuYOvvvoKPj4+5dpbvHgxmjRpgrlz58LPzw+hoaFo3bo1Zs2aBWtra6xevbrStQFJOqGdmuKVPrq/zxlrz+HI9TsSR0S18cWWCGTkFaFVIweM5QNZOU938YClSoGLNzNxIrbiIelkfDfT8zBnk26I5/RHfdG8BkM8K6JSKvDVE+0wtH1J8vfbaWw6d1MfodZ5xXfN7Xuut3ela8AREZmCaiV+p06dQqdOncrKTd+6dQtPP/000tLS0LZtW4wZMwaDBg1CgwYNcPHiRTz//PMIDg5Genq6IWOv0ogRI7Bv3z489thjOH36NDZv3gwfHx8sW7YM8+fPr3F7U6dOxYYNG9C9e3fs378fO3fuROfOnbFx40a8+uqrFR7j7u6O06dP4+WXX0Z+fj42bNiAlJQUPPXUUzh27Bj69ev3oJcpmdLhaTGfh8jyw27aIy0R0rYRijQiXlh5EteTs6s+iEzGydg0/HFCV+JsztDWUNXxgi4VqWerxrAOukXclx6MljgaAv6t4plVUIwOHk6Y2KvmQzwrolIqMP+J9hjesQk0WhFTfj+DDWeZ/D2ojeduIjolB/VsLPBMd365RESmrVpP6xkZGWjQ4N9S0EuXLkV2djZmz56NmTNnlm0XRREHDhzA9OnTsW/fPgwePBi7d++GUinduOCePXtiy5Yt1d6/quFOpctZ1ET9+vWxcOFCLFy4sEbHkbQUCgHzR7bDjbQ8nIlPx7PLT2Dtiz1Q764qV2SaNFoRH6y7AAAY2akpOnk6SxyR6Rrf0wu/H4/HtouJuJGex6ptEvvrZAL2XkmGWqXAvNDaD/GsiFIhYF5oOygEAWEnE/D676eh1YoYWpL8U81otCK+26Xr7ZvU2xu2lvL7ApSI5KVaX4E7ODiguPjfxWL/+ecfeHh43JP0AboJpL1798bhw4cxadIkHDx4EEuXLtVvxERGZGWhxC/PdEYTJ2tEp+TghVUnUVislTosqsKqI7G4dCsTDlYqvPO4aRZ0MRV+DR3Q3dsFGq2IlYdjpQ6nTruVkYePN+oWan/jkZbwaaD/is9KhYC5I9riyc7u0IrAtD/PYO0pzmOujU3nbuJ6cg4crdnbR0TmoVqJX+PGjXHt2jUAuoXKqxqiqFAosHDhQjRs2BDLly/XT6REEqlvb4mlE7rA3lKFY9GpmLH2PAthmLDkrAJ8+Y+uoMub/f3gYmcpcUSmb0LJgva/HYtDXqFG2mDqqLuHeLZ3d8Kk3t4GO5dCIeCz4W3wdKAHtCLwxl9n8deJ+6z8TOXc09vXqxnsrSyqOIKISHrVSvz69++PU6dOYenSpfjggw+Qn58Prfb+vR5qtRq9e/fG+fPn9RIokZRautlj4eiOUCoErDmVgB/2XJM6JKrEZ1suIyu/GG2aOGJUoIfU4ZiFh1u5wd3ZGhl5RVh35kbVB5BeKJVKhIaGIjQ0FBvOJ2FPpG6I55cjq7dQ+4NQKAR8MjQAY7p5QBSBt9acwx/H4wx6TjnZcuEWopKy4WClwriSL06IiExdtRK/V199Fa6urpg0aRIWLFgAtVqNbdu24bfffrvvcenp6eWqYxKZq6CW9TF7cGsAwLxtkQg/x5LopuZYdCrWnroBQQA+Hhpg8IdnuVAqBIzr7gWASzsYk5WVFf766y98t2glPtt2FQAwtV9L+DSwN8r5FQoBHw8JwLjunhBF4O015/HrUSZ/VdFqRXy7U/f3NbFXMziwt4+IzES1Ej93d3ccOHAAzz33HPr164e1a9diwYIFGD16NMaPH49Lly6VO2bHjh3YtWsXWrRoofegiaQytpsnJpYspDztzzM4HccS+KaiWKPFzPW6gi5PdfFAe3cnaQMyMyM7u8NGrcSVxGwcvsblS4xFFEW8+/d5ZOUXo527E57rrZ8qntUlCAJmD25dNtz33b/PY+URzvW8n60Xb+NKYjbsrVSY0NO4f19ERA+i2iWofHx88NNPP92zbeHChZgyZQpWrlwJLy8vdOrUCXZ2doiKisKhQ4cgiiJeeOEFvQdNJKX3Qloh9k4OdkYk4bkVJ/D3Sz3h7mwjdVh13rJDMYi4nYV6NhZ46zFfqcMxO47WFhjRsSlWHonFkoMx6OHjKnVIdcLaUzewKyIJaqUCX4a2lWTZEUEQMHOgP5SCgEUHovHBugsQRRHPlPQC07/u7u2b0LMZHK3Z20dE5uOBPmFeeuklHDp0CEFBQYiOjkZYWBiWLVuGAwcOQKFQ4M0338SLL76or1iJTIJSIeDbpzvAv5EDUrIL8ezy48jM55BmKSVm5uObHbqHsbf7+3HJjVoaX9LrszMiEbF3cqQNpg6Ivp2K11ceBAC8FOSJFm7GGeJZEUEQ8F5IK7zwkK6ozMz1F7m2YwX+uZSIiNtZsLNU4Vn29hGRmXngrxa7dOmCXbt24datWwgPD8fSpUsRFhaG2NhYfP755/qIkcjk2FqqsHh8ZzSwt8SVxGy88utpFGu4zINUPgm/jOySaohPdHaXOhyz1by+HYJa1ocoAiu4tINBiaKI2eFXoLSyQ8GtK5jQXfr7VhAEvPO4H14Mbg4A+HDjJSzaf13iqEyHKP7b2ze+hxccbdjbR0TmRW9jStzc3PD4449j3LhxGD58OBo1aqSvpolMUiNHaywe1wXWFkrsu5KMDzdeYlEMCRy6loINZ29CIQBzhgZAwYIuD6S01+/P4/HILii+/85Ua3+fvoG9V+9ALC7Cnc0LoFIYf4hnRQRBwFuP+eKVPj4AgDnhl/G/faxiDADbLyXi0q1M2KqVeLYXe/uIyPyYxicNkZlq09QR3zzVHoIArDwSi2WHYqQOqU4pLNZi5vqLAIAx3TwR0MRR4ojMX1CL+vB2tUVWQTEX9jaQpMx8zN6gu2/TD/6KohTT6l0VBAFvPNoSrz2sK8726eYI/FjHl7ARRRHf7tL19o3r4cXh5ERklpj4ET2gx1o3xIzH/QAAH2+6hD2RSRJHVHcsORiNqKRsuNiq8cYjLOiiDwqFgHE9vAAAyw7GQKtlL7Y+lVbxzMwvRutGdsg8ukbqkCokCAKmPdISr/fTJX9fbI3A97ujJI5KOrsiknDhRiZs1EpM6u0tdThERLXCxI9ID57r7Y2nA92hFYE3/jondTh1ws30vLL5NjMGtOJ8Gz0a0akp7C1VuJ6Sg71Xk6UOR1bWn7mJHZeTYKEU8MlgP0A07bnBr/driTceaQlAt35p6b+5ukQURSwoue6x3T3hzN4+IjJTTPyI9EAQBHw0JAA9fVyQV6iROpw6YU74JeQWatDFqx5GdGwidTiyYmepwsiSIjnLDsZIG4yMJGXlY1bJEM8pD7dAiwZ2EkdUPa8+3AJvliyR8tX2K/hmxxWJIzKuPVeScS4hA9YWSjzH3j4iMmNM/Ij0xEKpwA+jO8Hb1bZs2x/H45FbyAIZ+rbvSjI2n78NpUKXcAsCC7ro2/geXhAEYO+VZEQlZUsdjtkTRRHv/X0BGXlFCGjigBeCmkOpVGLAgAEYMGAAlEql1CHe18t9fPBOyZD2b3ZcxVf/RNaJYlaiKGJByVIxY7p5wNXOUuKIiIhqj4kfkR45WlvgxzEdy37/cOMldPt0Jz4Jv4T41FwJI5OPgmJNWa/JuO5eaNXIQeKI5MnDxQYP+7kBAFYcjpE2GBnYcPYmtl9KhIVSwLzQdrBQKmBlZYXw8HCEh4fDyspK6hCrNDmoOd4b0AoA8O2uKHxZB5K/fVdTcCY+HVYWCjz/UHOpwyEieiBM/Ij0zN3Z5q6frZGZX4xf9kfjoXm7MWn5CRyMSpH9w5IhLdofjeiUHNS3t8Trj7SQOhxZm1CytEPYyQRk5BVJG4wZu3uI56t9W5j1lxXPPeSN90N0yd/3u6/hi63yTf50vX26Ya2ju3qivj17+4jIvDHxIzKgLa/1xpLxnfFQyaLYOy4nYvSio3j0631YdSSWw0BrKD41F9+VlFR/P6QVHKxY0MWQejR3QUs3O+QWavDXiXipwzFLoiji/b8vID23CP6NHMoWRzdnk3p7Y9YgfwDAT3uv4bMtEbJM/g5G3cGpuHRYqhR44SHO7SMi88fEj8iAFAoBff3csGJiIHa+EYRx3T1hq1bialI23l93AV0/3Yk5my4h7g6HgVbHR5suIb9Ii27ezhjcrrHU4cieIAgY30O3UPXywzHQcGmHGtt47hb+uZQIlULAlyN1QzxL5eTkwNbWFra2tsjJyZEwypqb0LMZPhrSGgDwv33XMSf8sqySP10lT11v39OBHmjgYPpDcYmIqsLEj8hImte3w4dDAnDk3Ycxa5A/mrnaIiu/GIsORCPoy914dtlx7L+aLKuHJ33aFZGI7SUP0B+zoIvRDOvQBE42FohPzcPOy4lSh2NWkrMKMGv9BQC6IZ7+jcsP8czNzUVurnl+8fNMdy/MGRoAAFh8IBofbbokm/evw9fv4HhMGtQqhSx6aYmIACZ+REZnb2WBCT2bYee0ICyd0AXBvrphoDsjkjB28TH0+2ovVh6OQU4Bh4GWyi/SYPaGSwCAZ3s1Qws3e4kjqjus1Uo81cUDALDsUIy0wZgRURTxwboLSCsZ4vlSH3kmD2O6eeLTYW0AAEsPxmD2houySP5KK3k+1cUdbuztIyKZYOJHJBGFQkAf3wZYNiEQu94IwvgeXrCzVOFacg4+WH8R3T7diQ83XkRMinkNATOEn/ZeQ1xqLho6WOG1h1nQxdjGdveEUiHg0LU7iLidKXU4ZiH8/C1svXgbKoWAeSPb3jPEU25GdfXAFyPaQBCA5Ydj8cH6C9Ca8bDgI9fv4Gh0KtRK9vYRkbzI95OIyIx417fD7MGtcXhGX3w4uDW8XW2RVVCMpQdj0Gf+HkxYegx7rySb9cNUbcXeycEPe64BAD4Y6A9bS5XEEdU9TZys8Vhr3dIOXNC9ainZBZi5XlfF8+U+Pmjd2FHiiAzvyS4emDuiLQQBWHUkDu+tM9/k79udut6+J7o0RSNHa4mjISLSHyZ+RCbE3soC43p4Yce0ICyfGIg+JcNAd0cmY9wS3TDQ5YdikF1HhoGKoojZGy6isFiL3i1cMaBNQ6lDqrNKi7z8ffoG0nIKJY7GtM1cfwGpOYXwa2iPl/v4SB2O0Yzs7I4vQ9tBEIDfjsXh3b/Pm13ydzwmFYeu3YGFUsCLwXXn746I6gYmfkQmSKEQENSyPpZOCMSe6cGY2LMZ7C1VuJ6Sg1kbdMNAZ2+4iOvJ2VKHalDbLyVid2QyLJQCZg9uzYIuEuriVQ+tGzugoFiL347HGeWcuYXF8HonHF7vhJvN0ifh525h8/nbUJZU8VSr6tbH7IhOTfH1E+2hEIDfj8fj7TXnzKoabOncvtBO7mjixN4+IpKXuvWJRGSGvFxtMXOQPw6/+zA+HtIazevbIrugGMsOxaDv/L0Yt+QYdkcmmd0361XJK9Tgw426gi7PP+SN5vXtJI6obhMEARN66nr9Vh6ORbFGK3FEpudOdgE+KKni+XJwcwQ0uf8QT4VCgaCgIAQFBUGhkM/H8dAOTfD1k7rk76+TCXgz7KxZJH8nY1NxICoFKoWAlzi3j4hkiJNliMyEnaUKY7t7YUw3TxyISsGygzHYFZmEvVeSsfdKMpq52mJsN0+Edm4qi4XNv98dhRvpeWjiZF2nhsuZsoFtG+GzzZdxKyMf2y4mIqRtI6lDMikzN1wsG+L5St+qixBZW1tjz549hg9MAkPaN4FSIWDK72ew9tQNiCLw5ch2UCpMt9d+wc4oAEBop6Zwd7aROBoiIv2Tz1eMRHWEIAjo3aI+Fo/vgj3Tg/Fsr2awt1IhOiUHH226hO6f7sTM9RcQlWS+w0CvJ2fjf/uuAwBmDvKHjZrfUZkCKwslRnfVLe2w9GC0xNGYls3nbyH83K06O8SzIgPbNsZ3T3eASiHg79M3MPWPMybbU3w6Lg37riRDqRDwEuf2EZFM8ZOJyIx5utjig4H+ODLjYXw8NAA+DeyQU6jBisOx6PfVXoxdfBS7IhLNahioKIqYteEiCjVa9PGtj0f93aQOie4yupsnVAoBJ2LTcD4hQ+pwTEJqTiE+WKcb4vlSNYZ41iUD2jTCwlEdoVII2HD2Jl430eRvQUklz+EdmsDDhb19RCRPTPyIZMDWUoWx3TyxfepDWD2pK/q1coMgAPuvpmDishPoM38PFh+IRmZ+kdShVmnLhdvYfzUFapWCBV1MkJuDVdkQz6WH2OsHALM2XMSdnEL4utnjlb7V7y3KyclB/fr1Ub9+feTkyHe9zv4BDfHjmE6wUArYdO4WXvv9NIpMKPk7G5+OPZG63r6a/P0REZkbJn5EMiIIAnr6uGLRuM7YO70PnuvdDA5WKsTeycXHmy6h26c78f6684hKypI61ArlFBTjo5KCLi8GNYeni63EEVFFSou8bDp7C8lZBRJHI62tF25h49mbUJYs1G6pUtbo+JSUFKSkpBgoOtPxiL8bfhrTCWqlApvP38arv55GYbFpJH+l6/YNad+Y7zlEJGtM/IhkysPFBu+F+OPIuw/j02Ft0NLNDrmFGqw6Eod+X+3DmEVHseNSoklV2/t211XczsyHu7M1XmRVPZPV3t0J7d2dUKjR4tejxlnawRSl5hTi/ZIhnpODvNG2qZO0AZm4h1u54eexnaBWKbD14m28/OspyZO/8wkZ2BmRBIUAvFqNgjxEROaMiR+RzNmoVRjV1QPbXn8Iv07qikf93aAQgANRKZi04gSCv9yNRfuvIyNP2mGgVxOzsHi/bujgh4Nbw8qiZj0nZFwTenoBAFYdjZX84V0qszdcREp2IVq62eG1h5k0VEcfvwb45ZnOUKsU2H4pES+tPomCYo1k8Xy7q7S3rwmaubK3j4jkjYkfUR0hCAJ6+Ljif890xt43++CFh7zhaG2B+NQ8zAm/jG6f7sS7f5/HlUTjDwMVRREz119EsVZEv1Zu6OvHgi6m7vGARmhgb4nkrAJsPn9L6nCMbuuF29hQMsTzy5HtajzEsy4Lalkfi8d1hqVKgR2Xk/DiqlPILzJ+8nfxZga2X0qEIIBLxhBRncDEj6gOcne2wYwBrXBkxsP4bHgb+DW0R16RBr8ejcOjX+/DqF+O4J+Lt402DHTD2Zs4fP0OrCwUmDXI3yjnpAejVikwtpsnAN3SDqJoOkOGDS3triGeLzzEIZ610btFfSwZ3wVWFgrsikjCCytPGj35K53bN6htY/g0sDPquYmIpMDEj6gOs1Yr8XSgB7ZM6Y3fn++G/q0bQiEAh67dwfMrTyJo3m78vPca0nMLDRZDVn4RPgm/DAB4pY8PF042I6O6ekCtUuBsQgZOx6dLHY7RfLjxIlKyC9CigR2m9OMQz9rq6eOKJeO7wNpCib1XkvHcihNGS/4u38rEtou63r5XWcmTiOoIJn5EBEEQ0M3bBT+N7YR9b/XB5KDmcLKxQEJaHj7bEoFun+3EjLXnEHE7U+/n/mbHVSRlFaCZqy2ee8hb7+2T4bjYWWJwu8YAgKUHY6QNxkj+uXgb687chEIA5j3gEE+FQoHOnTujc+fOUCjq5sdxj+auWDqhC2zUSuy/moJnlx9HXqHhk7/vSub2DWjTCC3c7A1+PiIiU1A3P2mIqFJN69ngncf9cGTGw/hiRBu0auSA/CItfjsWj/7f7MdT/zuMrRdu6WUR5ojbmVh2KAYAMHtwa86TMkPje3gBALacv4XbGfnSBmNg6bmFeK9kiOfzDzVHe3enB2rP2toax48fx/Hjx2Ftba2HCM1TN28XLJsQCFu1Egej7mDisuPILSw22Pkib2dh8/nbAIDXWMmTiOoQJn5EVCErCyWe7OKBza/1wh/Pd8OANg2hVAg4cj0Vk1edQtC8Pfhp7zWk5dRuGKgoivhg3QVotCIeD2iIoJb19XwFZAwBTRwR6OWMYq2IVUdipQ7HoD7ceAnJWQVoXt8Wr3OIp14FNnPG8omBsLNU4fD1O5iw9DhyCgyT/P3b29cQvg3Z20dEdQcTPyK6L0EQ0NXbBT+M7oT9b/XBS8HNUc/GAjfS8/B5yTDQt8PO4dLNmg0DXXvqBo7HpMFGrcQHA1nQxZyVLu3w67E4SaozGsP2S4n4+/QNKATgy5HtuNyIAXT2+jf5OxqdiglLjyNbz8nf1cQshJdUoeW6fURU1zDxI6Jqa+xkjbf6++HwjIcxN7Qt/Bs5oKBYiz9OxGPAt/vxxM+HseV81cNAM/OK8NkWXUGX1x5ugcZOdXeYmxw84u+GJk7WSM0pxIazN6UOR+/Scwvx7t/nAQDPPeSNDh719NJubm4uvLy84OXlhdzcXL20ae46edbDymcDYW+pwrGYVIxfcgxZ+fpbY/S7XVEQReCx1m5o1chBb+0SEZkDJn5EVGNWFko80dkd4a/1wl+TuyOkbSMoFQKORafixdWn8NDc3fhhTxRSKxkG+u2uKKRkF8KngR0m9mxm5OhJ31RKBcZ2L13aIUZ2Szt8tOnfIZ5T+7XUW7uiKCI2NhaxsbGy+3/2IDp41MOqSV3hYKXCidg0jFtyDJl6SP6ikrKx8Zzui4nXHmZvHxHVPUz8iKjWBEFAFy9nfD+qIw683Qev9PGBs60aNzPyMXdrJLp9thNv/nUWF25k3HPc78fiAAAfDW4NtYpvQ3LwVBd3WFkocPlWJo5Gp0odjt7svJyItadulFXx5BBP42jn7oTVk7rB0doCp+LS8cziB0/+vt+t6+17xN8NrRs76ilSIiLzwScuItKLRo7WmP6YLw690xdfjmyHgCYOKCzW4q+TCRj43QGMWXS0bF+tCAxq1xg9fFwljJj0yclGjWEdmgIAlslkaYeM3KKyIZ6Tenujo56GeFL1tGnqiNWTusLJxgJn4tMxdtFRZOTVLvm7npyN9WduAGAlTyKqu5j4EZFeWVkoEdqpKTa+0gtrXuyOgW0bQaUQcCouvWwfW0sl3g9pJV2QZBClRV7+uXQb8anmP2ft4/BLSMwsgLerLaY9or8hnlR9AU0c8eukbqhnY4GzCRkYs+go0nNrXkl44e4oaEXgYb8GaNOUvX1EVDcx8SMigxAEAZ08nbFwVEcceLsvJgf9uzj76/1awM3BSsLoyBBautmjp48LtCLMfmmHXRGJCDuZAEEA5o1syyGeEvJv7IBfn+sGZ1s1zt/IwOhFR2u0jExMSg7Wn+HcPiIiJn5EZHANHa3ueeAa3dVTwmjIkCb00BXr+e1YnEEX4TakjLwizFirG+L5bM9m6OTpLHFE1KqRA357rhtcbNW4eDMToxYdrbR41H99vzsKGq2IYN/6aOfuZNhAiYhMGBM/IiLSm75+DeDpYoPM/GL8ffqG1OHUypxN/w7xnP6Yr8HOIwgC/P394e/vD0EQDHYeufBtaI/fn+8GVztLXL6ViVG/HMGd7IL7HhN3JxdrS+7DKeztI6I6jokfERHpjUIh4JnuXgB0RV7MbZmC3ZFJ+KtkiOfcUMMO8bSxscHFixdx8eJF2NjYGOw8ctLCTZf81be3RMTtLIz65ShS7pP8/bBH19v3UMv6elt/kYjIXDHxIyIivRrZuSls1UpcTcrGgagUqcOptsz8IsxYoxviObFnM3T24hBPU+TTwA6/P98NDewtEZmYhaf/dwTJWeWTvxtpeQg7mQAAmPKwj7HDJCIyOUz8iIhIrxysLBDayfyWdvhk02XczsyHl4sNpj9quCGe9OCa17fDHy90R0MHK1xNysZT/zuMpMz8e/b53/7rKNaK6OXjynmaRERg4kdERAYwrocXAGBXZBJiUnKkDaYa9l5Jxh8n4kuGeLaDtdrwVTxzc3PRunVrtG7dGrm55r/8hbE1c7XF7893QyNHK1xLzsFT/ztyT/JXOsd0Sj/O7SMiApj4ERGRAXjXt0Mf3/oQRWD54Ripw7mvzPwivLPmHABgfA8vBDYzTu+QKIq4dOkSLl26ZHZzIU2Fl6st/ni+O5o4WeN6Sg7GLT1e9lqxRkR3bxd04ZBdIiIATPyIiMhAxvfULe3w14kEZOUXSRxN5T4Nv4xbGfnwdLHBmwas4qlPNmoVYj4PQcznIbBRq6QOR1IeLjb4/fluaFrPGrF37u05ZW8fEdG/mPgREZFBPNTCFc3r2yK7oLisyIap2XclGb8f1w3xnBfars4nUebK3fnf5K9UF6966ObtImFURESmhYkfEREZhCAIGF8y12/5oRhotaY1nDHrriGe47obb4gnGUbTejZYPrFL2e+v9GElTyKiuzHxIyIigxnesSnsrVSIuZOLPVeSpA7nHp9ujsDNjHx4ONvgrf7mMcST7q+R4109fkzkqRo4bJrqEiZ+RERkMLaWKjzVxR0AsNSElnY4cDUFvx2LA6BbqJ0PfEREJHdM/IiIyKCe6e4FhQDsv5qCqKQsqcNBdkEx3i4b4ukp2TwwQRDg6ekJT09PCIIgSQxERFR3yD7xO3jwIAYMGABnZ2fY2dkhMDAQK1asqHV7GzduRFBQEBwcHODg4IDg4GCEh4dXur9Wq8XPP/+M7t27w8HBAWq1Gk2bNsWoUaNw5syZWsdBRGQu3J1t0K+VGwDT6PX7bPNl3EjPg7uzNd7q7ydZHDY2NoiJiUFMTAxsbGwki4OIiOoGWSd+a9asQVBQELZu3Yq2bduif//+uHr1KsaNG4fp06fXuL1vvvkGgwcPxqFDh9CzZ0/07dsXx44dw8CBA7Fw4cJy+4uiiNDQUEyePBlnz55F9+7dMXToUDg4OOC3335DYGAgNm/erI9LJSIyaeN7egEA1p66gYxc6ZZ2OBiVgtVHS4Z4jmgHW0sO8SQiorpBtolfamoqJk6cCI1Gg7CwMOzZswdhYWGIiIiAj48P5s+fjz179lS7vcjISEyfPh2WlpbYt28ftmzZgnXr1uHMmTNwcXHB1KlTERUVdc8xGzduxN9//w0vLy9ERUVh27Zt+PPPP3Hp0iV88cUXKCoqwssvv6znKyciMj3dvV3g19AeeUUa/HEiTpIYsguK8VaYbojnM9090b05S/0TEVHdIdvEb9GiRcjMzMSQIUMwfPjwsu1ubm6YO3cuAGD+/PnVbm/BggXQaDSYPHkyunfvXra9ZcuWeO+991BcXIwFCxbcc8y+ffsAAC+88AIaN258z2tvvvkmHB0dERMTg6Qk06p0R0Skb4IgYEJJr9/yQ7HQSLC0w+dbdEM8m9azxtsSDvEslZeXhy5duqBLly7Iy8uTOhwiIpI52SZ+pfPuQkNDy70WEhICKysr7NixA/n5+Q/cXum2jRs33rPd0tKy0vYEQYAgCFAqlXB0dKxWDERE5mxI+yaoZ2OBG+l52H4p0ajnPhSVglVHSod4tjWJIZ5arRYnTpzAiRMnoNVqpQ6HiOgeXOpCfmSb+J09exYA0LFjx3KvqdVqBAQEID8/H1euXKmyrfT0dMTF6R4YOnToUO51d3d3uLq6IjY2FpmZmWXbH330UQDAzz//jJs3b95zzNy5c5Geno4xY8bcN0EkIpILKwslng70AAAsPRhttPPmFBTjrZIqnmO6eaCHj6vRzk1ERGQqZJn4ZWZmIiMjAwDQtGnTCvcp3R4bG1tle6VJX7169WBra1vt9oKCgvDmm28iJiYGPj4+6N+/P5544gn4+/vj/fffx/jx4/Hjjz9W/8KIiMzcmG6eUCoEHI1OxaWbmVUfoAdfbI1AQloemjhZ453HWxnlnERERKZGlolfdnZ22c+VlcguTeCysqpeU6q0vfuV266svblz5+Kbb75BUVERtm3bhr/++guXL1+Gp6cnHnnkEVhbW9/33AUFBcjMzLznDxGRuWrsZI3+AQ0BAMsOGb7X7/C1O1hxWPeF3NzQtrAzgSGeREREUjDZT8Bhw4bh8uXLNTpmxYoVCAwMNFBENVdQUIBnnnkGa9aswXvvvYcJEybAxcUFJ06cwGuvvYbRo0fjxo0bePPNNytt47PPPsOHH35oxKiJiAxrYk8vhJ+7hXVnbuKdx1vB2VZtkPPohnjqhv2P6uqBnhziSUREdZjJJn7R0dGIjIys0TG5ubkAADs7u3u2OTg4lNs3JycHAGBvb19lu6XtlbZfkYra++yzz/Dnn39iypQp9yRvffr0QXh4OPz9/TF79mxMmDABrq4VP5DMmDED06ZNK/s9MzMT7u7uVcZMRGSqOnrUQ5smjjh/IwO/HYvDy318DHKeuVsjEJ+qG+L57gAO8SQiorrNZId6njlzBqIo1uhPcHAwAMDBwaGsUmZCQkKF7Zdu9/T0rDIWDw9dMYK0tLSyBK867a1cuRJAxZVAPTw80LVrV+Tm5uLkyZOVntvS0hIODg73/CEiMmd3L+2w8nAsijT6r2h55PodLC8Z4vnFCNMd4unq6lrpF39ERET6ZLKJ34Nq164dAODUqVPlXisqKsKFCxdgZWWFli1bVtmWk5NTWfJ3+vTpcq/Hx8cjJSUFnp6e9yRmpclgZcs1lG5PS0urMgYiIjkJadsIrnaWuJ2Zj60Xbuu17dzCfxdqfzrQA71amGZiZWtri+TkZCQnJ1daOIyIiEhfZJv4hYSEAADCwsLKvbZp0ybk5+ejX79+sLKyeuD2SrcNGjTonu0NG+oKGJw4caLcMRqNpiyJ9PLyqlYMRERyYalSYnRXwyztMHdrJOJSc9HY0QrvDpB+oXYiIiJTINvEb9KkSXBwcMD69euxdu3asu1JSUl46623AABvvPFGueP8/Pzg5+eHGzdu3LN9ypQpUCqV+Omnn3DkyJGy7VevXsUnn3wClUqFKVOm3HPM0KFDAQAzZ868Z71AjUaDd999FzExMfD09ETnzp0f+HqJiMzN6G4esFAKOBWXjrPx6Xpp8+j1O1h2KAYA8PmItrC3stBLu0REROZOtomfs7MzlixZAoVCgdDQUPTt2xcjR46Er68voqKiMG3atLI5gXeLjIxEZGQkioqK7tnu6+uLefPmoaCgAL1798aAAQMwdOhQtGvXDnfu3MFXX30FH597CxTMnDkTvr6+SEhIQNu2bdG3b1+EhoaiZcuWmDt3LqytrbFkyRKoVKY594SIyJAa2FthYNvGAFCWrD2IvEJN2ULtT3Vxx0Mt6z9wm4aUl5eH4OBgBAcHIy8vT+pwiIhI5mSb+AHAiBEjsG/fPjz22GM4ffo0Nm/eDB8fHyxbtgzz58+vcXtTp07Fhg0b0L17d+zfvx87d+5E586dsXHjRrz66qvl9ndxccHx48cxa9Ys+Pr64tixY9iwYQOKi4sxbtw4nDx5En379tXHpRIRmaXxPbwAAJvO3URSZv4DtTVvWyRi7+SikaMV3g0x/SqeWq0We/fuxd69e6HV6r/ADRER0d1k39XUs2dPbNmypdr7i6J439cHDRpUbi7f/djb22P27NmYPXt2tY8hIqor2rk7oaOHE07FpWP10ThMfaTqglsVORadiqUlC8J/PqItHDjEk4iI6B6y7vEjIiLTN6FnMwDA6qOxKCjW1Pj4vEIN3go7C1EEnuzsjiATH+JJREQkBSZ+REQkqf4BDdHQwQop2YUIP3erxsd/+U8kYkqGeL430PSHeBIREUmBiR8REUnKQqnA2O6eAIClB2OqHHJ/txMxqVhSshzEp8PbcIgnERFRJZj4ERGR5J4O9IBapcD5Gxk4GZtWrWPyizR4M+wcRBEY2akp+vg2MHCURERE5ouJHxERSc7ZVo2h7XVLOyyt5tIO8/+JRHRKDtwcLPH+QH8DRmc4NjY2sLGxkToMIiKqA5j4ERGRSRjfQ1fkZeuF27iZfv917U7GpmLRgZIqnsPbwtHa/IZ42traIicnBzk5ObC1tZU6HCIikjkmfkR6ZqNWIebzEMR8HgIbtexXTCHSG//GDujazBkarYhVR2Ir3S+/SIM3/9IN8Qzt1BR9/DjEk4iIqCpM/IiIyGSULu3w27E45BdVvLTDV9uv4HrJEM8PQsxziCcREZGxMfEjIiKT8Yi/G5o4WSMttwibKlja4WRsGhbtvw4A+Gx4GzjamN8Qz1L5+fkICQlBSEgI8vPzpQ6HiIhkjokfERGZDKVCwLgeuqUd/jvcU1fF8yy0IjC8YxP09XOTIkS90Wg02Lx5MzZv3gyNpuYL1xMREdUEEz8iIjIpT3b2gLWFElcSs+/Z/vWOK7ienIMG9paYNbC1RNERERGZJyZ+RERkUhxtLDC8Y5N7tp2NT8cv+3RDPD8dZt5DPImIiKTAxI+IiEzOhJ5e9/z+3roL0IrAsA5N0M/fvId4EhERSYGJHxERmRyfBvbo2dyl7PfryTmob2+JWYNYxZOIiKg2mPgREZFJGtPN857fPx3WBk42aomiISIiMm9M/IiIyCT1buFa9vPAto3wCId4EhER1ZpK6gCIiIgqolAIZT9/MLCVhJEYhq2tLURRlDoMIiKqI9jjR0REJs/eilU8iYiIHgQTPyIiIiIiIplj4kdERERERCRzTPyIiIiIiIhkjokfERERERGRzDHxIyIiIiIikjkmfkRERERERDLHxI+IiIiIiEjmmPgRERERERHJHBM/IiIiIiIimWPiR0REREREJHNM/IiIiIiIiGSOiR8REREREZHMMfEjIiIiIiKSOSZ+REREREREMsfEj4iIiIiISOaY+BEREREREckcEz8iIiIiIiKZY+JHREREREQkc0z8iIiIiIiIZE4ldQBEVDfYqFWI+TxE6jCIiIiI6iT2+BEREREREckcEz8iIiIiIiKZY+JHREREREQkc0z8iIiIiIiIZI6JHxERERERkcwx8SMiIiIiIpI5Jn5EREREREQyx8SPiIiIiIhI5riAOxEREcmCjVqFmM9DpA6DiMgkscePiIiIiIhI5pj4ERERERERyRwTPyIiIiIiIplj4kdERERERCRzTPyIiIiIiIhkjokfERERERGRzDHxIyIiIiIikjkmfkRERERERDLHxI+IiIiIiEjmmPgRERERERHJHBM/IiIiIiIimWPiR0REREREJHOyT/wOHjyIAQMGwNnZGXZ2dggMDMSKFStq3E5KSgoWL16M559/Hu3bt4dKpYIgCFi2bFmVx168eBEjR45E/fr1YW1tjTZt2uCbb76BVqutxRURERERERHVjErqAAxpzZo1ePLJJ6HVavHQQw/B1dUVO3fuxLhx43Du3Dl8+eWX1W7rwIEDmDRpUo1jOHz4MB5++GHk5eUhMDAQXl5e2LdvH6ZOnYpDhw7hjz/+gCAINW6XiIiIiIioumTb45eamoqJEydCo9EgLCwMe/bsQVhYGCIiIuDj44P58+djz5491W7Pzc0NL730EpYsWYLz58/jueeeq/KYoqIijB49Gnl5efjqq69w9OhR/PHHH7h69Sq6d++Ov/76C8uXL3+AqyQiIiIiIqqabBO/RYsWITMzE0OGDMHw4cPLtru5uWHu3LkAgPnz51e7ve7du+P777/HhAkTEBAQAIWi6v91f//9N6Kjo9GuXTtMnTq1bLudnR0WLlxY4xiIiIiIiIhqQ7aJX3h4OAAgNDS03GshISGwsrLCjh07kJ+fL0kMHTt2hLe3Ny5cuICYmBiDxUBERERERCTbxO/s2bMAdAnWf6nVagQEBCA/Px9XrlyRJIa7t587d85gMRAREREREcky8cvMzERGRgYAoGnTphXuU7o9NjbWYHHExcVJHgMREREREZEsq3pmZ2eX/WxjY1PhPra2tgCArKwsg8fxIDEUFBSgoKCg7PfMzEw9RkhERERERHWBySZ+w4YNw+XLl2t0zIoVKxAYGGigiKTx2Wef4cMPP5Q6DCIiIiIiMmMmm/hFR0cjMjKyRsfk5uYC0FXNvHubg4NDuX1zcnIAAPb29g8Q5f3Z2dkhLS2tLK7axDBjxgxMmzat7PfMzEy4u7vrN1AiIiIiIpI1k038zpw5U+tjHRwc4OjoiIyMDCQkJMDf37/cPgkJCQAAT0/PWp+nKh4eHkhLS0NCQgLatm1bqxgsLS1haWlpsBiJiIiIiEj+ZFncBQDatWsHADh16lS514qKinDhwgVYWVmhZcuWksRw9/aKkkIiIiIiIiJ9kW3iFxISAgAICwsr99qmTZuQn5+Pfv36wcrKSpIYTp8+jevXryMgIABeXl4Gi4GIiIiIiEi2id+kSZPg4OCA9evXY+3atWXbk5KS8NZbbwEA3njjjXLH+fn5wc/PDzdu3HjgGIYNG4ZmzZrh7Nmz+Prrr8u25+Tk4OWXX640BiIiIiIiIn0SRFEUpQ7CUNasWYMnnngCoigiODgYLi4u2LFjB9LT0zFt2jTMnz+/3DGCIADQFZf5b09ct27dyn6Ojo5GUlISvL29Ub9+fQC6Bdl/+OGHe445dOgQ+vXrh7y8PHTt2hWenp7Yv38/bt26hdDQUPz5559l56yOzMzMsvmLFRWtISKSi9zCYvjP3AYAuPTRY7BRm+y0dCIiIknUJDeQ9afoiBEjsG/fPsyZMwdHjhxBYWEh/P398corr2DcuHE1bu/o0aPltl2/fh3Xr18HgAqHjfbo0QPHjx/HrFmzsGfPHpw9exbNmzfHm2++iSlTptQo6SMiIiIiIqoNWSd+ANCzZ09s2bKl2vvfrwO0tp2jrVu3rnCeHxERERERkTHIdo4fERERERER6TDxIyIiIiIikjkmfkRERERERDLHxI+IiIiIiEjmmPgRERERERHJHBM/IiIiIiIimWPiR0REREREJHOyX8ePiIjMk41ahZjPQ6QOg4iISBbY40dERERERCRzTPyIiIiIiIhkjokfERERERGRzDHxIyIiIiIikjkmfkRERERERDLHxI+IiIiIiEjmmPgRERERERHJHBM/IiIiIiIimWPiR0REREREJHNM/IiIiIiIiGSOiR8REREREZHMMfEjIiIiIiKSOSZ+REREREREMsfEj4iIiIiISOaY+BEREREREckcEz8iIiIiIiKZU0kdANWMKIoAgMzMTIkjISIiIiIiKZXmBKU5wv0w8TMzWVlZAAB3d3eJIyEiIiIiIlOQlZUFR0fH++4jiNVJD8lkaLVa3Lx5E/b29hAEQepwkJmZCXd3d8THx8PBwUHqcMjE8X6hmuI9QzXFe4ZqivcM1ZQp3TOiKCIrKwuNGzeGQnH/WXzs8TMzCoUCTZs2lTqMchwcHCS/8cl88H6hmuI9QzXFe4ZqivcM1ZSp3DNV9fSVYnEXIiIiIiIimWPiR0REREREJHNM/OiBWFpaYtasWbC0tJQ6FDIDvF+opnjPUE3xnqGa4j1DNWWu9wyLuxAREREREckce/yIiIiIiIhkjokfERERERGRzDHxIyIiIiIikjkmfnXc8ePH8cQTT6Bx48awsLCAk5MTevfujaVLl6Ki6Z8ajQZff/012rRpA2tra9SvXx9PPPEELl++XO1zPvvssxAEAYIg4MCBA/q8HDICY90zoihi2bJleOihh+Ds7Axra2t4e3tj1KhRuHjxoqEujwzAGPdMRkYG3n33XbRu3Ro2NjawsrKCr68vpk6diqSkJENeHhlATe6ZyMhIfP3113j66afRvHnzss+XmJiYKs+zceNGBAUFla3FFRwcjPDwcANdFRmSoe+ZkydPYvbs2ejRowecnJygVqvh7u6OMWPG4Ny5cwa+OjIEY73P3E3yZ2CR6qywsDBRqVSKAMSOHTuKTzzxhNinTx9RpVKJAMRRo0bds79GoxGHDRsmAhCdnJzEESNGiEFBQaIgCKKNjY149OjRKs+5a9cuEYAoCIIIQNy/f7+hLo8MwFj3TF5enti/f38RgOjs7CwOHDhQHDlypNipUydRqVSKK1euNMblkh4Y455JTk4WW7RoIQIQGzZsKA4ePFgcPHiw2LBhQxGA2KhRIzEmJsZYl0wPqKb3zJQpU0QA5f5ER0ff9zxff/21CEBUqVRi//79xSFDhojW1tYiAPG7774z4BWSvhn6nikqKirbx9nZWXz88cfF0NBQsXnz5iIAUa1Wi3/99ZcRrpT0xVjvM3czhWdgJn51VFFRkdigQQMRgLh69ep7Xrt06ZLo7OwsAhB37dpVtv2XX34RAYgtWrQQb9++XbY9LCxMBCD6+PiIRUVFlZ4zLy9PbNGihdi6dWuxR48eTPzMjDHvmXHjxokAxOeee07Mzc2957WbN2+KsbGxer46MgRj3TNTp04VAYiDBw8W8/Lyyrbn5eWVJZHPPPOMga6S9Kk298yiRYvEt99+WwwLCxNjYmJEX1/fKh/IIiIiRKVSKVpaWoqHDh0q2x4ZGSm6uLiIKpVKvHr1qt6vj/TPGPdMUVGR2KVLF3HdunVicXFx2XaNRiO+9957IgDR3t5eTE5ONsg1kn4Z633mbqbyDMzEr446f/68CED09fWt8PXXXntNBCB+8cUXZdtatWolAhD//vvvcvsPHjxYBCCGhYVVes53331XFARB3L9/vxgUFMTEz8wY6545evSoCEAMDAwUtVqtXq+BjMtY90ynTp1EAOLhw4fLHXPq1CkRgNiqVasHuxgyitrcM/9VnQeyF198UQQgTpkypdxrX331lQhAfOWVV2oaPknAWPdMZbRabdnxy5Ytq/HxZHxS3DOm8gzMOX51VHUXnHRxcQEAREdH4/Lly7C2tkZISEi5/UJDQwHo5ktU5Pz585g3bx4mTpyIXr161TJqkpKx7plffvkFAPDKK69AEIQHCZkkZqx7pjrnKT0Hmbaa3jO1VTqPr/SeultVn2dkWox1z1RGEAS0bdsWAHDz5k2DnIP0y9j3jCk9AzPxq6O8vb3RvHlzREZG4tdff73ntcuXL2PVqlWoV68ehg0bBgA4e/YsACAgIAAWFhbl2uvYsSMAVDjBWavV4vnnn4eTkxPmzp2r70shIzHWPbNr1y4AQI8ePXDt2jXMmTMHL7zwAt5//30WAzIzxrpnHn30UQDA559/jvz8/LLt+fn5+PjjjwHoJtST6avpPVMb6enpiIuLAwB06NCh3Ovu7u5wdXVFbGwsMjMza30eMg5j3DNVuX79OgCgYcOGBjsH6Y8x7xmTewY2eh8jmYwDBw6ITk5OZRNbn3zyybKJrW3bthVPnTpVtu+CBQtEAOKwYcMqbCs9Pb1s0vN/ffvttyIAcfny5WXbONTTPBn6nsnLyyubMP2///1PtLS0LDeR+sknnxQLCgoMfq2kH8Z4n8nOzhb79OlTVtxlyJAh4pAhQ8SGDRuKTk5O4pdffmnQayT9qsk9U5GqhmCdPXtWBCDWq1ev0jbat28vAhDPnTv3IJdCRmLoe+Z+9u/fX1bg5ebNm7W8AjI2Y90zpvYMzMSvjjt79qzo7e19z4O1Wq0W33jjDTE9Pb1sv08++UQEII4ePbrCdkorXllYWNyzPT4+XrS3txeDg4Pv2c7Ez3wZ8p65detWWZsqlUocPny4ePnyZTE9PV1cu3at6OrqKgIQp0+fbvDrJP0x9PuMKIpibm6uOGbMmHJfFPTp00c8cuSIwa6NDKO690xFqnogO3jwoAhAbNKkSaVt9OzZUwQgHjx48EEug4zIkPdMZTIyMsoqCr/99tsPED1JwdD3jCk+A3OoZx3222+/ITAwEO7u7jh69Ciys7Nx5coVjB8/HvPnz0ffvn1RUFDwQOd4+eWXUVBQgB9//FFPUZOUDH3PaLXasp/9/Pzw119/wc/PD46Ojhg2bBiWL18OAFi4cCGHYJkJY7zPxMXFITAwEFu2bMGKFSuQlJSEpKQkLF++HGfPnkVwcDD279+vpysiQzPGPUPyIsU9o9FoMHr0aFy9ehWBgYH46KOP9No+GVadfQY2eqpJJuHKlSuihYWF2KRJEzErK6vc6wMHDhQBiD/88IMoirUbglVafv2DDz4otz97/MyPMe6ZjIyMsm/d5s6dW+FxpSWYt2/froerIkMyxj0jiqIYHBxcaSXQNWvWlFWJJdNX03umIhzqWbcY456pyHPPPVdWGZLLOJgXY9wzpvoMzB6/Our3339HUVER+vfvDzs7u3KvP/HEEwCAffv2AQA8PDwAAAkJCRW2V7rd09OzbFtpRbTt27cjODj4nj9nzpwBALz66qsIDg7GsmXL9HJdZDjGuGccHBxQr149AICXl1eFx5VuT0pKqvlFkFEZ456Jj4/Hnj17YGlpiUGDBpU7ZsiQIVCr1Th+/Pg9hV/INNX0nqmN0vssLS0NOTk5Fe5T0b1GpskY98x/vfPOO/jll1/g7u6O7du3w9XVVW9tk+EZ454x1WdgldHORCal9EPN0dGxwtdLt6elpQEA2rVrBwC4cOECioqKylXcO3XqFACUlTS+25EjRyqNo/TmDw4Orn7wJAlj3TPt27fH7t27y9r5r9TUVACo8M2aTIsx7pnSc9ja2kKpVJY7h1KphK2tLdLS0pCens6qeyaupvdMbTg5OcHDwwNxcXE4ffp0ufLq8fHxSElJgaenJxwcHGp9HjIOY9wzd5s7dy6++OILNGjQANu3b4e7u7te2iXjMeY9Y2rPwOzxq6NKH35OnDhR4evHjx8H8G/vSrNmzdCqVSvk5eWVrX90t7CwMAC45xv3ZcuWQdQVECr3JygoCACwf/9+iKKI2bNn6+vSyECMcc8AwODBgwEAe/bsKXdMXFwcYmJiAFRchp1MizHumdJzpKamIjo6utwx165dQ1paGmxtbfmtvBmo6T1TW6XrRJbeU3er7L2JTJOx7hlAt87s22+/DScnJ2zbtg2+vr4P3CYZnzHuGZN9Bjb64FIyCSdPniybS/XfMcyHDx8WbW1ty82j+uWXX0QAYosWLcTExMSy7aVzaHx8fMSioqJqnZ9z/MyPse6ZjIwM0dXVVVQoFOL69evLtufk5IghISEiAHHAgAEGukrSJ2PdM23bthUBiI888sg9ldjS0tLEhx9++L6VQsm01Oae+a/qzNeKiIgQlUqlaGlpKR4+fLhs+5UrV0QXFxdRpVKJV69efeDrIcMz1j3z119/iQqFQrSzsxMPHTqkr/BJAsa6ZyrD5RxIEtOnTy+78Vu3bi2OHDlS7Nmzp6hQKEQA4vPPP3/P/hqNRhw2bFjZpPjQ0FAxODhYFARBtLa2rlHJdCZ+5slY98zWrVtFCwsLURAEsVu3buKwYcPExo0biwBELy8vMSEhwRiXS3pgjHvmyJEjop2dnQhAdHV1FUNCQsSQkBDRxcWl7J65ceOGsS6ZHlBN75mTJ0+KXbt2LftjZWUlAhDbt29ftu2XX34pd56vvvpKRMnSMY8//rg4ZMgQ0draWgQgfvvtt8a6XNIDQ98ziYmJolqtFgGIbdq0EceNG1fhn4oKTJFpMtb7TEWY+JFk1q5dKz766KNl33DWq1dP7NOnj/jrr79WuH9xcbE4f/58sXXr1qKVlZXo4uIihoaGihcvXqzReZn4mS9j3TNnzpwRhw8fLrq6uooWFhait7e3OHXqVFZPM0PGuGeioqLE5557TvT29hYtLS1Fa2tr0d/fX3znnXfEO3fuGOrSyEBqcs/s3r37nnW4Kvoza9asCs+zYcMGsXfv3qKdnZ1oZ2cn9u7dW9y4caOBr44MwZD3THR0dJX73+8+I9NkrPeZ/5LyGVgQRVGsYjQoERERERERmTEWdyEiIiIiIpI5Jn5EREREREQyx8SPiIiIiIhI5pj4ERERERERyRwTPyIiIiIiIplj4kdERERERCRzTPyIiIiIiIhkjokfERERERGRzDHxIyIiqqVvv/0WrVu3hqWlJQRBQHBwcLWP/eijj6BQKHD+/Pl7tguCAC8vr2q34+XlBUEQEBMTU+k+e/bsqXF8//XNN99AEAQcO3as1m0QEZF0mPgRERHVwtq1azFlyhTcunULgwcPxrhx49C/f/9qHZuYmIh58+YhNDQUbdq0MXCk+vHCCy/Azc0N06dPlzoUIiKqBZXUARAREZmjdevWAQDCwsLQt2/fGh376aefIjs7GzNmzDBAZIZhbW2N119/HTNmzMCWLVvw+OOPSx0SERHVAHv8iIiIaiEhIQEA4O3tXaPjcnNzsXz5cgQEBKBDhw6GCM1gRo8eDUEQ8OOPP0odChER1RATPyIiohqYPXs2BEHA7t27AQDNmjWDIAgQBAF79uyp8vi//voLGRkZePrpp2t03gsXLqBJkyawsLDA6tWraxP6PWJiYsriruzPf+cEuru7o1evXti8eTNu3rz5wDEQEZHxcKgnERFRDbRv3x7jxo3D1q1bkfj/9u4npOk/juP46+d0ZE1domN0EfPfbh4ELyUzApspMhA6KGIIHcKDBgpDb146tIPHOohalExHUP6peWklEYrRJKiTl/LgJpiW6A7++R2iH5W6aerX3O/5OH6/78/22vG174fPNxxWbW2tLBaLJMlut8ddPzIyIkn7OmjlzZs3qqqqUjQa1ZMnT3T16tU/yv4zi8WixsbGHe+FQiHNzMzIZDJtu1deXq6JiQk9f/5cTU1NB84BADAGxQ8AgH1wu91yu90qLy9XOByW1+vd1ymcExMTSk5O3vM2z0AgoNraWqWkpGh8fFwXL178w+S/ysrKUl9f37brs7OzKi0tldlsVldX17b7paWlkqSXL19S/ADgBKH4AQBgkEgkonA4rNzcXKWmpsad9/l8amhoUGZmpgKBgIqLi3edzc3NPXC+r1+/qqamRouLi+rp6dGFCxe2zTgcDknfnwoCAE4Oih8AAAaJRCKSpLNnz8advXv3rpqbm5WTk6Px8XHl5+fHnP95y+nv5ufnFQgEYq7f3NxUXV2dPnz4oNbW1l2f5mVmZkqSFhYW4v4GAMDfg+IHAIBBlpeXJUlpaWkx5+bm5nTz5k2dOnVKL168UE5OTtzPjrXlNBgMxi1+Ho9Ho6OjqqiokNfr3XUuPT1dkrS0tBQ3EwDg78GpngAAGCQjI0OS9O3bt5hzNptNly9fVjQaVVtbm9bX148014MHD3Tnzh0VFhbK5/PteKjLDz/Kq9VqPdJMAIDDRfEDAMAgNptNkrS4uBhzzmw2a3h4WJcuXZLf71d9fb02NjaOJNPk5KRu3Lghq9Wqp0+fxi10X758kSRlZ2cfSR4AwNGg+AEAYBCbzSa73a7Pnz9rdXU15mxqaqpGRkbkdDo1ODiohoaGQy9/c3NzcrvdWl9fl8/nU1FRUdw1Hz9+lPT9tRYAgJOD4gcAgIHKysq0sbGhd+/exZ09ffq0RkdHVVZWpoGBATU2Nmpzc/NQcqytrcntdmt+fl5er1cVFRV7Wjc1NSVJcjqdh5IDAGAMDncBAMBAVVVVGhoaUjAY3PF1Cb87c+aMxsbG5HK59PDhQ5lMJvX29iop6WD/3fr9fr19+1YWi0WhUEjXr1/fNuNwOOTxeH65FgwGZTKZ5HK5DvT9AABjUfwAADDQtWvX1NLSokePHqmzs3NPaywWi549eyaXy6X79+8rKSlJPT09Byp/P7aNrqysqL+/f8cZp9P5S/H79OmTXr9+rerqap07d+6PvxsAYLx/tra2to47BAAA/ye3bt1Sd3e3pqenVVJSctxx9uz27dvq6OjQ2NiYKisrjzsOAGAfKH4AABgsEokoLy9PV65ckd/vP+44e7K2tqbz58+roKBAr169Ou44AIB94nAXAAAMZrPZ1N7ersePH+v9+/fHHWdP7t27999BMACAk4cnfgAAAACQ4HjiBwAAAAAJjuIHAAAAAAmO4gcAAAAACY7iBwAAAAAJjuIHAAAAAAmO4gcAAAAACY7iBwAAAAAJjuIHAAAAAAmO4gcAAAAACY7iBwAAAAAJ7l/Fcpgp9Cc+9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "better_sleep(360*0)\n",
    "plot_guess = 0\n",
    "\n",
    "def osc_decay(t,f,T,a,phi):\n",
    "    return a*np.cos(2*np.pi*f*t+phi)*np.exp(-t/T)\n",
    "\n",
    "ys = []\n",
    "for i in range(2):\n",
    "    # Sleep 30 mins then save and overwrite repeatedly. Reduces risk of crash and lost data\n",
    "    fig,ax=plt.subplots(2,1, figsize=(10,7), tight_layout=True)\n",
    "    fig.suptitle(['Echo', 'Echo Ramsey'][i])\n",
    "    average_number=res.clicks.count_so_far()\n",
    "    print(average_number)\n",
    "    clicks=np.array([sublist[0] for sublist in res.clicks.fetch_all()])[:,:,i]\n",
    "    excess = clicks.mean(0)\n",
    "    error_excess = clicks.std(0) / np.sqrt(clicks.shape[0])\n",
    "\n",
    "    x = np.array(freq_range)*1e-3\n",
    "    y = (excess[:,0] - excess[:,1])\n",
    "    dy = error_excess[:,0] + error_excess[:,1]\n",
    "\n",
    "    ax[0].plot(x, excess[:,0], '-o', color='black')\n",
    "    ax[0].plot(x, excess[:,1], '-o', color='red')\n",
    "    ax[0].set_xlabel(r'f (kHz)')\n",
    "    ax[0].set_ylabel('C')\n",
    "\n",
    "    ax[1].errorbar(x, y, yerr=dy, fmt='-o', color='blue')\n",
    "    ax[1].set_xlabel(r'f (kHz)')\n",
    "    ax[1].set_ylabel('$\\Delta$ C')\n",
    "    ax[1].vlines([nuclear_spin_freq_a_prep*1e-3], min(y), max(y), color='k', linestyle='--')\n",
    "\n",
    "    ys.append(y)\n",
    "    try:\n",
    "        plt.savefig(directory+filename+f'_Ramsey{i}.pdf')\n",
    "    except:\n",
    "        print(\"couldnt save cause you're looking at the figure, motherfucker\")\n",
    "\n",
    "    fullpath=directory+filename+'.hdf5'\n",
    "    datasets= {\n",
    "                'click_array': excess,\n",
    "\n",
    "                'ramsey_detuning':ramsey_detuning,\n",
    "                'Ramsey_time':times_ramsey,\n",
    "                }\n",
    "\n",
    "    save_h5(fullpath,datasets, group=str(0),overwrite=True)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.errorbar(x, ys[1]-ys[0], yerr=dy)\n",
    "plt.xlabel(r'f (kHz)')\n",
    "plt.ylabel('$\\delta \\Delta$ C')\n",
    "plt.vlines([nuclear_spin_freq_a_prep*1e-3], min(ys[1]-ys[0]), max(ys[1]-ys[0]), color='k', linestyle='--')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37589e6c-a7fe-47c2-9b90-5cc0460c2faf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "autoscrollcelloutput": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "scenes_data": {
   "active_scene": "Initialise",
   "init_scene": "",
   "scenes": [
    "Initialise"
   ]
  },
  "toc-autonumbering": true,
  "toc-showcode": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
